A FRAMEWORK FOR OPERATIONAL EXCELLENCE IN ...

A FRAMEWORK FOR

OPERATIONAL EXCELLENCE IN

HOSPITAL LOGISTICS

Application of Industrial Engineering Techniques in

Healthcare Supply Chain Management

Karen MOONS

August 2020

Examination committee:

Prof. dr. ir. B. Demoen, chairman

Prof. dr. ir. L. Pintelon, supervisor

Prof. dr. D. De Ridder, co-supervisor

Prof. dr. ir. G. Waeyenbergh, co-supervisor

Prof. dr. ir. P. Chemweno

Prof. dr. W. Sermeus

Prof. dr. ir. P. Timmermans

Prof. dr. ir. P. Vansteenwegen

Dissertation presented in

partial fulfilment of the

requirements for the

degree of Doctor of

Engineering Science

© 2020 KU Leuven, Faculty of Engineering Science

Uitgegeven in eigen beheer, Karen Moons, Celestijnenlaan 300A box 2422, B-3001 Heverlee

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i

DANKWOORD

Het verhaal achter dit manuscript nam zijn indrukwekkende start vijf jaar geleden. Het

begon op module 13 van KU Leuven Campus Groep T en eindigt in lokaal 04.51 bij

Centrum voor Industrieel Beleid (CIB), beter gekend als ‘de vierde verdieping boven

Fablab’ voor mijn vriendenkring. Graag neem ik jullie mee op mijn

doctoraatsavontuur. Ik richt me hierbij tot een aantal personen, zonder wiens steun het

me nooit gelukt was om de verschillende mijlpalen succesvol te behalen.

2015 – “Let the new adventures begin”

In dat eerste jaar kreeg ik de kans om studenten te onderwijzen, te begeleiden, te

coachen, én op zoek te gaan naar een onderzoeksproject waar ik mezelf ten volle mee

kon bezighouden de komende vier jaar. Hiervoor wil ik mijn co-promotor, Geert

Waeyenbergh, mijn uiterste dank betuigen. Je liet me kennis maken met je

academische netwerk en je nam me mee op werk-gerelateerde (sommigen noemden

het ook plezier) uitstapjes zoals onze twee-jaarlijkse conferentie in Innsbruck (met de

téléférique naar de Nordkette en Patscherkofel, schnaps drinken), een boottocht op de

Damse Vaart voor de Eco-Race Challenge of een lekker etentje/drankje. Ik prijs mezelf

gelukkig met een begeleider zoals jij, iemand die me steunt, motiveert en me wegwijs

gemaakt heeft in deze academische wereld met bijzondere aandacht voor het ‘fun’

gedeelte.

2016 – “Work hard, play harder”

Zo kwamen we in contact met UZ Leuven, waar het logistiek team van het

operatiekwartier een reorganisatie van de logistieke processen wou doorvoeren. De

ideale opportuniteit voor mij om een kijkje achter de schermen van het

operatiegebeuren te kunnen nemen. Al snel bleek dat de logistieke principes nog in

zijn kinderschoenen stonden. Een reden te meer om dit logistieke vraagstuk op te

lossen, waarbij ik ten zeerste apprecieer dat mijn doctoraatsonderzoek niet enkel een

puur academische bijdrage zou worden, maar ook voor het ziekenhuis een impact zou

kunnen hebben.

Ik zou heel graag mijn promotor, Liliane Pintelon, bedanken voor deze opportuniteit.

Je rotsvaste vertrouwen en geloof in mijn kunnen doen me sterker in mijn schoenen

ii

staan. Je deur stond altijd open voor kleine en grote problemen, en ook andersom

apprecieerde ik ten zeerste dat je bij mij kwam aankloppen om mijn mening te vragen.

Je kritische geest, zeer brede kennis en de vrijheid die je me steeds gaf zijn de

belangrijkste aspecten waardoor ik nu met trots dit manuscript kan voorstellen.

Bedankt voor de fijne samenwerking, Liliane!

2017 – “YES you can do it, NO it won’t be easy, but it’ll be worth it”

De eerste jaren van mijn doctoraat voelden aan als het beleven van een rollercoaster.

Met verschillende ‘Ups’ zoals het publiceren van mijn eerste artikels en deelname aan

conferenties, waren er toch ook emotionele ‘Downs’. Momenten waarop je beslist om

te stoppen wanneer je door de bomen het bos niet meer ziet. Een grote dankjewel,

Geert en Liliane, om me opnieuw de weg te wijzen en in mezelf te laten geloven. Als

beginnend doctoraatstudent weet je echt niet wat er op je pad zal komen, maar deze

ervaring heeft me sterker gemaakt op persoonlijk vlak met als bewijs mijn

doorzettingsvermogen, discipline, zelfstandigheid en positivisme.

2018 – “A little progress each day adds up to big results”

Dit onderzoek is tot stand gekomen in nauwe samenwerking met UZ Leuven. In het

bijzonder richt ik mijn dank tot Jo Artoos, de logistiek manager van het

operatiekwartier, bij wie ik steeds terecht kon voor het verzamelen van data en

oplossen van hands-on problemen. Ondanks zijn druk werkschema, maakte hij altijd

tijd vrij voor interessante discussies en liet hij me de ruimte om out-of-the-box te

denken over innovatiemogelijkheden. Zijn praktische ervaring vormt een verrijking

voor dit doctoraatsonderzoek om een goede balans te vinden tussen

wetenschappelijkheid en de implementatie op de werkvloer.

Daarnaast wens ik alle leden van mijn examencommissie te bedanken: Dirk De Ridder,

Paul Timmermans, Pieter Vansteenwegen, Walter Sermeus en Peter Chemweno. Dirk

en Paul, jullie jarenlange ervaring in het reilen en zeilen van het ziekenhuis, jullie

onbetaalbare inzichten en de ideeën die jullie hadden vormden een waardevolle basis

voor dit onderzoek. Dankzij jullie bleef het onderzoek steeds op het juiste spoor. Pieter

en Walter, bedankt voor jullie constructieve feedback op mijn tekst. Dit heeft ertoe

geleid dat mijn werk naar een hoger niveau getild kon worden. Also many thanks to

Peter, for your time and critical reflection on my work.

iii

2019 – “Make it simple but significant”

Doorheen mijn avontuur heb ik ook geleerd dat eenvoud siert. Het zoeken naar

eenvoudige en efficiënte oplossingen leidt tot meer begrip en betrokkenheid van

werknemers die dagelijks geconfronteerd worden met de logistieke problemen.

Samenwerking is de drijfkracht om een beter geheel te creëren. Daarom wil ik graag

alle personen bedanken die ik heb mogen interviewen om hun beeld te schetsen over

zorglogistiek en welke uitdagingen zij in de nabije toekomst zien. Ook een oprechte

merci voor alle thesisstudenten die ik heb mogen begeleiden: Ernest, Bram, Karishma,

Pieterjan, Ruben, Wim, Axelle, Lien en Goele. Jullie hebben telkens alles naar boven

gehaald om een stukje in deze puzzel te leggen en samen een impact te kunnen hebben

op de logistieke werking in het ziekenhuis. Jo, bedankt voor jouw onmisbare hulp en

toewijding bij deze thesissen.

2020 – “It always seems impossible until it’s done”

Een woordje van dank naar mijn naaste collega’s van module 13 en CIB kan natuurlijk

niet ontbreken. Bedankt Evy en Kristel, voor altijd paraat te staan, de leuke babbels en

de conferentie-tripjes tot in de puntjes te regelen. Most of the time I shared with my

office-mate, James. Thank you for your prayers and support that everything will be

fine. Although you liked to heaten up our office, I’m really thankful for the insights

we shared, the spiders/bees you killed for me and the nice working atmosphere you

created! Ook alle andere collega’s, bedankt voor de fijne koffiepauzes, pizza tijdens

de doctoral seminar days, en lekkere attenties bij verjaardagen, sinterklaas, paashaas.

Dit werk zou haast onmogelijk geweest zijn zonder ontspanning na de werkuren.

Basket was mijn uitlaatklep waarbij ik de PhD obstakels uit mijn hoofd kon zetten met

gezonde sport-agressie. Ik zou graag mijn twee ‘basketfamilies’ willen bedanken.

Enerzijds, de Dames A en B van Basket Lummen, voor het delen van hoogtepunten en

moeilijke momenten. Zowel op als naast het veld vormen wij een hecht team. Hoewel

ik soms niet met volle goesting ging trainen, maakten jullie elke training weer plezant

met de nodige humor en werden levenslange vriendschappen gesmeed! Anderzijds

maakte ik ook deel uit van de uniefploeg. Hier stond ‘fun’ en ‘teamspirit’ voorop wat

ook geleid heeft tot Belgisch Kampioen worden.

iv

De onvoorwaardelijke steun van mijn familie heeft er ook voor gezorgd dat ik dit

avontuur succesvol kan beëindigen. Een super grote dankjewel mama en papa voor

jullie liefde, de wijze raad, aanmoedigingen, het vertrouwen in mij en er altijd voor mij

te zijn! Jullie bewijzen steeds weer hoe trots ik mag zijn op wat ik al bereikt heb,

waarbij jullie misschien nog wel trotser zijn dan ikzelf. Ook Maarten en Shana,

bedankt voor jullie steun en de nodige afleiding. Ook bomma en bompa (Herk/Kermt),

bedankt voor het branden van de vele kaarsjes. Ze hebben geluk gebracht!

Tot slot, mijn liefste Jappie, zonder jou was me dit helemaal niet gelukt. We zijn dit

avontuur samen gestart, vijf jaar geleden, en altijd kon ik op jou rekenen. Een deel van

mijn werk mag ook op jouw naam geschreven worden, vb. Matlab codes schrijven.

Ook naast het werk bracht jij de beste afleiding die ik maar kon wensen en was je

steeds dichtbij! Je gaf me telkens weer de kracht om een nieuwe week te starten.

Blijkbaar waren mijn verhalen toch niet zo angstaanjagend, want zelf begon je ook aan

een doctoraat. Ik hoop dat ik voor jou dezelfde steun kan zijn als dat jij voor mij bent

geweest. Samen staan we sterk en maken we elkaar trots. I love you!

Het behalen van deze laatste mijlpaal geeft een gevoel van voldaanheid, vreugde en

dankbaarheid. Ik kijk uit naar waar de toekomst te bieden heeft. But for now, I am

#Ph.inisheD.

Karen Moons

Augustus 2020

v

ABSTRACT

The paradigm shift from volume-based to value-based care drives healthcare

organisations towards aligning logistics and medical processes. The Institute for

Healthcare Improvement (IHI) suggests health policy makers to follow the Quadruple

Aim strategy as a guide for reforming the health system. The overall population health

and the individual patients’ experience of care are at the centre of this strategy, while

simultaneously pursuing lower costs and improving staff satisfaction. Altogether,

optimizing the Quadruple Aim provides a measure for value in healthcare. Value is

defined as the ratio of quality of care over cost. In this dissertation, we call for action

to control the costs by streamlining the internal hospital supply chain processes.

Though often overlooked in the past, healthcare logistics, also referred to as healthcare

Supply Chain Management (SCM), is put forward as a crucial strategic target for

efficiency improvements in hospitals. Supply chain concepts are designed to reduce

costs, enhance visibility and streamline processes to the benefit of value-based care.

Moreover, SCM enhances integration among departments and therefore positively

impacts hospital performance through the effective use of resources. Today, hospitals

face multiple operational challenges, such as poor inventory control, redundant

distribution channels, standardization issues, lack of data, etc. which hinder the

material and information flows and cause misalignment between patient care and

supporting logistics services. The literature rarely addresses how logistics contribute

to value creation in healthcare by pursuing operational excellence. As a first step to

efficiency improvement, hospitals must be able to measure the performance of the

supply chain to identify the main source of waste and inefficiency. However, lack of

performance management systems as well as lack of expertise in Operations Research

and Operations Management (OR/OM) ask for a rigorous methodology that takes into

account the complex nature of the health system.

A data-driven approach is needed to evaluate logistics processes, monitor performance

and gain actionable insights to control the increasing healthcare expenses. Therefore,

this dissertation presents a healthcare logistics performance management framework.

On the one hand, we focus on internal hospital supply chain practices, including

storage and distribution of disposable medical supplies. Performance management, on

the other hand, allows to improve system understanding, identify efficiency gains,

ABSTRACT

vi

implement continuous improvement programs and enhance decision-making

capabilities by monitoring the relevant Key Performance Indicators (KPIs). The

framework developed in this dissertation will serve as proof-of-concept to show how

logistics contribute to healthcare by adopting SCM practices and addressing the unique

challenges inherent to the hospital supply chain. OR/OM tools from industrial

engineering applications (e.g. simulation, multi-criteria decision making, etc.) play a

key role in bringing objectivity in decision making and promoting data-driven process

improvement. Moreover, this reference framework incorporates stakeholder feedback

to stimulate more informed decision making, to reduce supply chain fragmentation and

to provide a common vocabulary and negotiation power. Hence, the framework is an

orchestrator for supply chain integration in healthcare by enabling uniform

performance measurement and increasing data transparency.

From both a theoretical and practical point of view, contributions are made when

developing the healthcare logistics performance management framework: (i) The

Analytic Network Process (ANP) allows to prioritize KPIs, which constitute the

elements of the “operational excellence” definition in healthcare. The ANP-based

prototype is presented to translate strategic/tactical objectives into operational KPIs

according to the studied application. (ii) ANP and Discrete-Event Simulation (DES)

are combined into a hybrid tool to quantify the logistics impact for value-based

healthcare. The Internal Logistics Efficiency Performance (ILEP) index is introduced

as a multi-dimensional evaluation tool for adopting SCM practices and identifying

potential efficiency gains. Besides the well-known trade-off between service level and

cost, we show that standardization is an important factor to streamline inventory and

distribution processes. (iii) Possibly conflicting stakeholder perspectives are integrated

in the framework. In contrast to early-participation of the stakeholders, our approach

aims to first increase understanding in order to create awareness of SCM. As a result,

the framework promotes stakeholder commitment to strive towards value

improvement, which is considered to be a shared goal that unites the interests of all

stakeholders. (iv) From a practical point of view, the framework aims to bridge the gap

between theory and practice-based SCM. The applicability of the framework is

demonstrated using real-life case studies focusing on storing and distributing surgical

disposables throughout the operating theatre. In addition, we present an

implementation roadmap acting as a guideline for implementing various logistics

strategies in different contexts using the framework as a blueprint. This dissertation

concludes with a note on how the digitalization trend will impact healthcare SCM.

vii

BEKNOPTE SAMENVATTING

In de transitie van volume-based naar value-based healthcare richten

gezondheidsinstellingen zich op het leveren van waardevolle zorg door kwaliteit en

kosten met elkaar te verbinden. In deze hervorming van het gezondheidszorgsysteem

dient de Quadruple Aim strategie als een kompas om de prestaties te optimaliseren.

Deze strategie heeft een vierledig doel, namelijk het verbeteren van de algemene

bevolkingsgezondheid, het verbeteren van de patiëntervaring, het verlagen van de

kosten en het verbeteren van het personeelswelzijn. Meer bepaald biedt de Quadruple

Aim een maatstaf voor waarde in de zorg, wat gedefinieerd wordt als de verhouding

tussen kwaliteit en kost. Dit doctoraatsonderzoek buigt zich voornamelijk over het

tweede aspect, en roept daarmee op tot actie om de toenemende kosten in de

gezondheidszorg te controleren door het stroomlijnen van de logistieke keten binnen

de ziekenhuismuren.

De rol van logistiek in de gezondheidszorg wordt alsmaar belangrijker om kwalitatieve

zorgverlening te garanderen aan een zo laag mogelijke kost. Hierbij wordt

zorglogistiek gezien als een strategisch wapen om efficiëntiewinsten te realiseren. Het

inburgeren van logistieke principes die leiden tot noodzakelijke kostenreducties,

verhoogde transparantie en op elkaar afgestemde processen zal uiteindelijk bijdragen

tot efficiënte zorg. Zorginstellingen zullen met minder middelen steeds meer moeten

bereiken. Daarom is het effectief beheer van deze middelen en een gecoördineerde

samenwerking in de logistieke keten essentieel om de prestaties van ziekenhuizen te

optimaliseren. Echter, vandaag de dag zien we verschillende logistieke knelpunten,

zoals intuïtief voorraadbeheer, parallelle materiaalstromen, gebrek aan logistieke data,

beperkte standaardisatie, enz. die leiden tot een belemmerde afstemming tussen de

zorgvraag en de logistieke ondersteuning. Ook in de literatuur is nog niet duidelijk

omschreven welke bijdrage logistiek heeft tot waardevolle zorg door het streven naar

operationele excellentie. Potentiële efficiëntiewinsten kunnen enkel geïdentificeerd

worden als men de huidige toestand van de logistieke keten kan opvolgen volgens het

principe ‘meten is weten’. Door de complexiteit van de zorgsector, het gebrek aan een

effectief prestatie-meting systeem en de beperkte training van personeel in logistieke

grondbeginselen is er nood aan een systematische aanpak om de impact van logistiek

te meten in zorgprocessen.


viii

Het in kaart brengen en meten van de logistieke dienstverlening vraagt het gebruik van

instrumenten om processen te evalueren, prestaties op te volgen en inzichten te

verwerven die tot kostenbesparing leiden. In dit onderzoek integreren we relevante

operationele onderzoekstechnieken in een performance management framework voor

zorglogistiek. Enerzijds focust het framework op de interne logistieke processen in

ziekenhuizen, zoals voorraadbeheer en distributie van materialen, om operationele

excellentie te bereiken. Daarnaast benadrukt het framework de nood aan effectieve

prestatie-meting om een systeem beter te begrijpen, efficiëntiewinsten te identificeren,

continue verbeteringsinitiatieven te implementeren en beslissingsprocessen

transparanter te maken. Het framework biedt dus een proof-of-concept om logistiek te

meten in de zorgsector door het adopteren van logistieke concepten en deze aan te

passen aan de unieke eigenschappen van de interne zorglogistiek keten. Het arsenaal

van operationele onderzoekstechnieken (vb. simulatie, multi-criteria

beslissingsanalyse, enz.), die reeds succesvol toegepast zijn in andere industriële

sectoren, spelen een belangrijke rol om objectiviteit te verkrijgen in

beslissingsprocessen door evidence-based procesverbetering te promoten. Het

framework speelt ook een belangrijke rol als beslissingsondersteuning door het

betrekken van verschillende betrokken partijen. Op basis van de stakeholder feedback

kunnen er beter geïnformeerde beslissingen genomen worden, vermindert het

versnipperde karakter van de logistieke keten en vergemakkelijkt de communicatie en

de onderhandelingsmacht. Dit leidt tot uniforme prestatie-meting en meer

transparantie, en daarom bevordert het framework integratie in de logistieke keten.

De ontwikkeling van het performance management framework voor zorglogistiek

biedt perspectieven, zowel voor academici als professionele zorgverleners: (i) het

Analytisch Netwerk Proces (ANP) laat toe om prestatie-indicatoren te selecteren en

prioriteren, die de componenten vormen van de operationele excellentie definitie in

zorglogistiek. De netwerkstructuur biedt hulp in het vertalen van strategische

doelstellingen naar operationele indicatoren die relevant zijn voor de specifieke

context. (ii) ANP en Discrete-Event Simulatie (DES) zijn gecombineerd in een hybride

ANP-DES instrument om de logistieke impact te meten. Het resultaat hiervan wordt

geïntegreerd in een Interne Logistieke Efficiëntie Prestatie (ILEP) index, die dienst

doet als evaluatie-maatstaf voor het implementeren van logistieke initiatieven en

identificeren van potentiële efficiëntiewinsten. We tonen aan dat, naast de bekende

afweging tussen serviceniveaus en kosten, standaardisatie een cruciale factor is om

logistieke processen te stroomlijnen. (iii) De mogelijk conflicterende belangen van


ix

verschillende stakeholders worden verwerkt in de laatste stap richting een multi-level,

multi-stakeholder framework. In tegenstelling tot stakeholder participatie in de eerste

ontwerpfase van het framework, beoogt onze aanpak eerst een toegenomen begrip van

logistieke principes, wat vervolgens bewustzijn zal verhogen van de logistieke impact.

Dit bewustzijn moedigt stakeholders aan zich in te zetten, tot actie over te gaan en te

streven naar waardevolle zorg, als een gedeelde doelstelling die elke belanghebbende

viseert, in plaats van eigen belangen voorop te stellen. (iv) De praktische kant van het

onderzoek streeft ernaar om de kloof te dichten tussen theorie en implementatie van

het framework. De toepasbaarheid van het framework wordt gedemonstreerd via case

studies, die verschillende scenario’s evalueren voor voorraadbeheer en

distributiesystemen van wegwerpbare materialen gebruikt in het operatiekwartier. Een

stappenplan is voorgesteld om alternatieve logistieke processen te implementeren in

verschillende toepassingen, waarbij het framework dienst doet als blauwdruk. Dit

onderzoek eindigt met een slotrede over de digitalisatietrend en hoe deze nieuwe

technologieën een impact hebben op zorglogistiek.

x

TABLE OF CONTENTS

ABSTRACT V

BEKNOPTE SAMENVATTING VII

TABLE OF CONTENTS X

LIST OF ABBREVIATIONS XIV

1 INTRODUCTION TO PHD DISSERTATION 1

1.1 Exploratory research: SCM in the healthcare sector 4 1.1.1 Current state of SCM in Belgian hospitals 4 1.1.2 Optimization of operations by simulation: a case study at the Red Cross Flanders 6 1.1.3 Need for training in logistics principles: Healthcare Logistics Education and Learning

Pathways 8 1.1.4 Similarities between manufacturing, maintenance and healthcare logistics 9 1.2 Problem statement 11 1.2.1 Research motivation 12 1.2.2 Research questions 14 1.3 Structure of the dissertation 17

2 METHODOLOGY 19

2.1 Framework development procedure 19 2.2 Healthcare logistics toolbox 22 2.2.1 Case study research design 22 2.2.2 Overview of OR/OM tools for healthcare SCM 23 2.2.3 Data collection 31

3 HEALTHCARE LOGISTICS AND PERFORMANCE MANAGEMENT 33

3.1 Defining logistics in the healthcare sector 33 3.1.1 Specificities in healthcare logistics 36 3.2 Performance management in healthcare logistics 37 3.2.1 Literature review: Measuring the logistics performance of internal hospital supply

chains 38 3.3 Conclusion 45 3.3.1 Research gaps 45

TABLE OF CONTENTS

xi

3.3.2 Research contributions 47

4 PRIORITIZATION OF PERFORMANCE INDICATORS USING AN ANP-

BASED PROTOTYPE FOR THE INTERNAL HOSPITAL SUPPLY CHAIN 51

4.1 Multi-Criteria Decision Making in healthcare 52 4.1.1 MCDM classification 53 4.1.2 Analytic Hierarchy/Network Process 55 4.2 Performance indicator selection and prioritization: an ANP-based

prototype for the operating theatre supply chain 58 4.2.1 Introduction 58 4.2.2 Stakeholder analysis: selecting logistics performance metrics for the OT 59 64 4.2.3 Performance indicator selection and prioritization using ANP 68 4.2.4 Results 73 4.2.5 Discussion 76 4.2.6 Sensitivity analysis 78 4.3 Challenges of AHP/ANP 80 4.3.1 Medical decision making for value-based healthcare: challenges in AHP 82 4.4 Conclusion 93

5 EMPIRICAL RESEARCH AT THE OPERATING THEATRE: POLICY

DECISION MAKING, PARAMETER SETTING AND PERFORMANCE

MONITORING 95

5.1 Inventory management and distribution systems in healthcare: State-of-

the-art 96 5.1.1 Overview of inventory classification techniques 96 5.1.2 Overview of inventory models in healthcare 99 5.1.3 Overview of healthcare distribution and scheduling systems 104 5.2 Empirical research at the operating theatre 108 5.2.1 Discrete-Event Simulation in healthcare logistics 109 5.2.2 Classification of inventory items in healthcare – A case study 117 5.2.3 Optimization of a two-echelon inventory system at the operating theatre – A case

study 133 5.2.4 Evaluating replenishment systems for disposable supplies at the operating theatre – A

case study 152 5.2.5 Evaluating case cart distribution systems in the operating theatre – A case study 161 5.3 Integrating inventory and distribution systems using surgical procedure

preference lists 181 5.3.1 Methodology 181 5.3.2 Results 182 5.3.3 Discussion 189 5.4 Conclusion 191

TABLE OF CONTENTS

xii

6 MULTI-LEVEL MULTI-STAKEHOLDER FRAMEWORK VALIDATION 195

6.1 Introduction 195 6.2 Literature 197 6.2.1 Stakeholder conflicts: how much do their preferences matter to decision making? 197 6.3 Methodology 198 6.3.1 Stakeholder analysis 200 6.3.2 Data collection and analysis 201 6.3.3 PROMETHEE methodology 203 6.4 Results 205 6.4.1 Multi-stakeholder KPI ranking 205 6.4.2 Robustness of the framework 210 6.4.3 Benchmarking 219 6.5 Discussion 224 6.5.1 Feedback loop 1: individual versus shared stakeholder ranking using ANP 224 6.5.2 Feedback loop 2: robustness in policy decision making 226 6.5.3 Benchmarking 230 6.6 Conclusion 232

7 ROADMAP FOR IMPLEMENTATION OF THE FRAMEWORK 235

7.1 Introduction 235 7.2 Healthcare logistics performance management framework 236 7.3 Working principle of the framework 238 7.3.1 Selecting indicators 239 7.3.2 Prioritizing indicators 239 7.3.3 Evaluating logistics policies and monitoring performance 240 7.3.4 Incorporating stakeholder feedback 241 7.4 Practice-based application of the framework 243 7.4.1 Testing the framework for internal distribution at the operating theatre 243 7.5 Level of replicability 247 7.5.1 Implementation roadmap 248 7.5.2 Implications for hospital-wide applications of the framework 252 7.5.3 Future directions 254 7.6 Conclusion 256

8 FUTURE OUTLOOK: DIGITAL TRENDS IN HEALTHCARE LOGISTICS 259

8.1 Introduction 259 8.2 Hospital of the future 260 8.3 Global healthcare trends 263 8.4 Implications for healthcare SCM 265 8.4.1 Fragmentation 266

TABLE OF CONTENTS

xiii

8.4.2 Stakeholders 267 8.4.3 Unpredictability 268 8.4.4 Expertise in OR/OM 268 8.4.5 Standardization 269 8.4.6 Automation 270 8.4.7 Other challenges 271 8.5 Conclusion 272

9 GENERAL CONCLUSION 275

APPENDIX 283

LIST OF REFERENCES 295

LIST OF PUBLICATIONS 321

xiv

LIST OF ABBREVIATIONS

ABS Agent-Based Simulation

ADM Automated Dispensing Machines

AGV Autonomous Guided Vehicles

AHP Analytic Hierarchy Process

AI Artificial Intelligence

ANP Analytic Network Process

ASHRM American Society for Healthcare Risk Management

CHUM Centre Hospitalier de l’Université de Montréal

CI Consistency Index

CR Consistency Ratio

CSA Central Sterilization Department (in Dutch: Centrale

Sterilisatie Afdeling)

DEMATEL Decision Making Trial and Evaluation Laboratory

DES Discrete-Event Simulation

DSN Digital Supply Networks

EDI Electronic Data Interchange

EHR Electronic Health Records

ELECTRE Elimination and Choice Translating Reality

EOQ Economic Order Quantity

FNS Fast-, Normal-, Slow-moving

GAIA Geometrical Analysis for Interactive Aid

GDP Gross Domestic Product

GDPR General Data Protection Regulation

GDSN Global Data Synchronisation Network

GPO Group Purchasing Organisation

GS1 Global Standards 1

GTIN Global Trade Item Number

HELP Healthcare logistics Education and Learning Pathways

HTA Health Technology Assessment


xv

ICHOM International Consortium for Health Outcomes Measurement

IDN Integrated Delivery Networks

IHI Institute for Healthcare Improvement

ILEP Internal Logistics Efficiency Performance

IOM Institute of Medicine

IoT Internet-of-Things

IRCC Interventional Radiological and Cardiovascular Centre

IT Information Technology

JIT Just-in-Time

KPI Key Performance Indicator

LabVIEW Laboratory Virtual Instrumentation Engineering Workbench

MACBETH Measuring Attractiveness by a Categorical Based Evaluation

Technique

MASTA Multi-Attribute Spare Tree Analysis

MAU(V)T Multi-Attribute Utility (Value) Theory

MC Monte Carlo

MCDM Multi-Criteria Decision Making

ML Machine Learning

MRP Material Requirements Planning

OECD Organisation for Economic Cooperation and Development

OR/OM Operations Research and Operations Management

OT Operating Theatre

OT1/2 Operating Theatre 1 (elaborative surgery) / 2 (day care surgery)

PAPRIKA Potentially All Pairwise RanKings of all possible Alternatives

PAR Periodic Automatic Replenishment

PDCA Plan, Do, Check, Act

POU Point-Of-Use

PREMs Patient Reported Experience Measures

PROMETHEE Preference Ranking Organisation Method for Enrichment

Evaluation

PROMs Patient Reported Outcome Measures

PSA Prostate-Specific Antigen

QR Quick Response


xvi

RFID Radio-Frequency Identification

RI Random Index

RQ Research Question

SCM Supply Chain Management

SCOR Supply Chain Operation Reference

SD System Dynamics

SKU Stock Keeping Unit

SLA Service Level Agreement

SPP Shortest Path Problem

TOPSIS Technique for Order Preference by Similarity to Ideal Solutions

TSP Travelling Salesman Problem

VED Vital, Essential, Desirable

VMI Vendor-Managed Inventory

WAM Weighted Arithmetic Mean

WGM Weighted Geometric Mean

Inventory-related indicators

Distribution-related indicators

ICo Inventory Cost ITu Inventory Turnover

IV Inventory Visibility IU Inventory Usage

IA Inventory Accuracy PS Product Standardization

ICr Inventory Criticality VoI Value of Inventory

ISL Inventory Service Level

CCE Case Cart Efficiency DSL Distribution Service Level

CI Centralization Impact PCo Personnel Cost

CSI Clinical Staff Involvement PM Personnel Management

DA Delivery accuracy RLT Replenishment Lead Time

DCo Distribution Cost RT Response Time

DF Delivery Frequency S Process Standardization


xvii

Prostate-cancer related indicators

AT Additional Therapy PSA Prostate Specific Antigen

BT Blood Transfusion SE Surgeon Experience

C Complications SM Surgical Margin

CS Clinical Stage TOR Total time in Operating Room

ED Erectile Dysfunction TTRF Time To Return to normal

Functioning

GS Gleason Score UI Urinary Incontinence

LOS Length of Stay

1

CHAPTER 1

1 Introduction to PhD Dissertation1

The healthcare sector is targeting its core activity to provide high-quality care to

patients. However, aging population, an increasing number of chronically ill patients,

reduced reimbursements and clinical innovations drive healthcare management to

deliver efficient and effective care services in order to reduce total costs of care and

simultaneously improve patient care (Deloitte 2019). Moreover, the reform from

volume-based to value-based care models forces healthcare organisations to balance

qualitative patient outcomes and healthcare expenses by encouraging coordination

between logistical and care processes across the healthcare continuum. According to

the Triple Aim initiative, as suggested by the Institute for Healthcare Improvement

(IHI), three overarching goals need to be simultaneously pursued in order to redesign

health systems and optimize overall healthcare performance (Berwick, Nolan, and

Whittington 2008):

Delivering the best treatment to patients in terms of quality and safety

Improving the health of the overall population

Reducing healthcare expenses per capita.

In addition, the critical role of physicians, nurses and other employees involved in the

care delivery cannot be underestimated in this healthcare reform journey. Therefore,

the Triple Aim is expanded to consider workforce engagement and safety into a

Quadruple Aim strategy by creating meaningful and joyful work, and thus improving

1 This chapter is partially based on the following paper/supervised Master’s Thesis:

Moons, K., Pintelon, L., Waeyenbergh, G. (2016). Optimization of operations by simulation – A case study at the

Red Cross Flanders. American Journal of Industrial and Business Management, 6(10), 1001-1017.

Vanpee, G. (2019). An insight view in the logistics processes of materials management of different health care

institutions: A multi-organisational, exploratory, cross-sectional study. KU Leuven.

CHAPTER 1

2

the experience of providing care (Sikka, Morath, and Leape 2015). The ultimate aim

in any healthcare organisation is protecting and improving patient population health,

while the other goals are considered to be of secondary importance to achieve the

primary goal (Bodenheimer and Sinsky 2014). In this dissertation, we aim to ensure

cost containment and coordination between logistical and care processes, and hence

achieve a more sustainable healthcare system.

In Belgium, the healthcare expenses amount 10.5% of GDP in 2015, which is greater

than the OECD average of 9.9% (OECD 2015). Nearly 40% of health expenditures is

assigned to hospitals (OECD 2017). Cost containment is thus a major target for

improvement in hospitals (OECD 2015). A financial analysis by the Belfius bank

agency shows that in 2017, 42% of the hospitals in Belgium make a loss (D’Hoore

2018). The cost of logistics operations (e.g. handling, moving and processing of

materials) ranges between 20% and 45% of the total hospital operating budget (Aptel,

Pomberg, and Pourjalali 2009; Landry and Beaulieu 2011; Pouline 2003; Who 2010),

constituting the second largest expenditure in hospitals after personnel cost (Volland

et al. 2017). This large share is mainly due to high service level requirements in

hospitals, criticality of medical supplies, independent clinician preferences for specific

brands or supplies and a considerable amount of waste in the healthcare supply chain

processes (Landry, Beaulieu, and Roy 2016). According to Pouline (2003), half of the

logistics-related costs in hospitals can be eliminated by implementing efficient

logistics management. Hence, the role of logistics services in healthcare can no longer

be neglected. On a global scale, there is increasing awareness of the benefits of

effective logistics support, such as significant impact on the hospital’s bottom line and

improved service levels in care delivery processes (Rosales, Magazine, and Rao 2019).

Surprisingly, little action has been taken to improve efficiency in the hospital supply

chain.

The global healthcare supply sector is faced with several operational challenges:

missed contract compliances, lack of inventory visibility, stock-outs resulting in

expensive emergency deliveries, highly diverse array of stakeholders, increased

involvement of clinical personnel in logistics tasks, lack of information technology

systems, etc. (Landry et al. 2016; Vanpee 2019). Sticking to traditional and intuitive-

based methods may come to the detriment of quality of care (Landry et al. 2016),

whereas tackling these challenges allows for pursuing operational excellence, cutting

costs and improving patient care by implementing well-coordinated healthcare Supply

Chain Management (SCM). Other sectors, such as manufacturing and retail, have

INTRODUCTION

3

significantly benefited from optimizing SCM practices (Chopra and Meindl 2010).

Examples are lean, six sigma, total quality management, benchmarking or business

process reengineering (Feibert 2017). Recently, healthcare providers start to recognize

the importance of SCM from operational, financial and clinical perspectives as a means

to realize efficiency targets by cutting back on costs and waste, streamlining operations

and improving productivity. However, transferring and adapting techniques or best

practices developed in industrial settings to the healthcare sector is not straightforward

due to the multi-dimensional complexity inherent to the healthcare supply chain. The

HIPS project is one of the few initiatives aligning and optimizing supporting processes

in hospitals by combining automation and supply chain integration. The supply chain

savings are estimated to be 30% (Devis and Van Ooteghem 2016).

While patient flows have been widely studied in literature, the discussion of healthcare

logistics processes is limited. Kumar et al. (2008) and de Vries and Huijsman (2011)

investigate the holistic supply chain in the healthcare context. Recently, Volland et al.

(2017) provide an overview of relevant quantitative methods useful to hospital

materials logistics. They distinguish between four streams of literature across the

supply chain: supply and procurement, inventory management, distribution and

scheduling, and holistic SCM. Rossetti et al. (2012) analyse the medical supply chain

from a materials management perspective and illustrate its complexity and uniqueness.

They state that medical supply logistics manage the flow of supplies and resources to

enable patient care, covering many stages such as purchasing, inventory control,

material handling, scheduling and distribution of medical supplies to point-of-use

locations. Healthcare supply chain operations, however, are often inefficient and

fragmented due to independent goals of multiple stakeholders, preventing the supply

chain from operating as a system (Belliveau 2016). The unique characteristics in a

healthcare setting complicate the adoption of a chain-oriented approach and hinder the

appropriate measurement of performance of logistics flow in order to reach operational

excellence while guaranteeing high service levels (Aronsson, Abrahamsson, and Spens

2011).

“Operational excellence is achieved through the use of best inventory

management and distribution systems, combined with continuous supply

chain process improvements and better integration with the patient care

process” (Landry and Beaulieu 2013).

CHAPTER 1

4

1.1 Exploratory research: SCM in the healthcare sector

Nowadays, a transition from volume-based to value-based healthcare requires

balancing costs with patient outcomes. This trade-off largely depends on the extent to

which logistics provide support to care processes. Unnecessary or inefficient

operations that do not contribute to value of patient care lead to wasteful processes.

Lean management, commonly associated with the Toyota Production System in the

manufacturing sector, is a relatively new concept to healthcare. The Institute for

Healthcare Improvement advocates the implementation of lean principles as it can

realize significant benefits in terms of maximizing value and eliminating waste

(Womack et al. 2005). In addition, total quality management, business process

reengineering, benchmarking, and information technology are useful tools to

implement an efficient supply chain. In other industrial sectors, SCM is widely applied

together with performance measurement techniques to achieve operational excellence.

Although the healthcare sector recognizes the importance of adopting these industrial

engineering techniques, the healthcare supply chain is yet at a very low level of

maturity, lagging behind the industrial supply chain. Rakovska and Stratieva (2018)

divide hospitals into three categories based on their level of adopting SCM techniques.

In general, hospitals classified as ‘leading institutions’ in terms of supply chain

integration have better overall performance and show improved inventory parameters

and quality of care compared to ‘developing hospitals’ or ‘underdeveloped hospitals’.

As an introduction to this dissertation, Section 1.1.1 discusses the current state of

adopting SCM in Belgian hospitals and analyses gaps between theory and practice. In

Section 1.1.2, an exploratory case study at the Red Cross Flanders shows the feasibility

of applying industrial engineering techniques, such as simulation, to identify efficiency

gain opportunities in care delivery systems. Section 1.1.3 emphasizes the importance

of stakeholder education and training to implement SCM programs in healthcare.

Finally, in Section 1.1.4, we compare the healthcare sector to industrial sectors to

explore parallels and differences in adopting logistics concepts.

1.1.1 Current state of SCM in Belgian hospitals

An exploratory study in four Flemish hospitals describes the current state of adopting

SCM (Vanpee 2019). The hospitals vary in size, infrastructure as well as organisation.

Typically, hospitals distinguish between a purchasing and internal logistics

department, where the latter is further divided into functions for transportation (e.g.

INTRODUCTION

5

internal/external transport, goods receipt, etc.), supply provision (e.g. last-mile

distribution, point-of-care warehouses) and access control.

Table 1-1 shows the current hospital practices, focusing on purchasing, inventory

management, information technology, performance measurement and staff education.

As suggested in literature, most purchasing policies strive for efficient management by

participating in Group Purchasing Organisations (GPOs) and thus leveraging

economies of scale and enhancing price transparency (Burns and Lee 2008).

Recently, Kritchanchai et al. (2018) recommend to focus on the fields of inventory

management and information technology to improve operational processes in

hospitals. Although the central warehouse function is often outsourced to a third party

logistics provider or distribution centre, the internal supply is organised by the

hospitals. Regardless the size or organisation of the hospital logistics department, the

inventory parameters are similar, namely determining a minimum stock level and a

fixed order quantity. Most point-of-care locations control stock levels using a two-bin

system, where parameters are set based on gut feeling, such as holding two times four

days of stock to cover the longest possible weekend. As a consequence, hospitals suffer

from stock-outs due to unpredictability in materials consumption as well as lack of

storage space due to overstocking. Moreover, the type of care provided in each hospital

facility determines the supply complexity, consumption rate and diversity. In the

operating theatre for example, Vendor-Managed Inventory (VMI) is applied to

expensive materials such as stents and prostheses (Epstein and Dexter 2000). VMI is

an effective inventory management technique, where the vendor or supplier is

responsible for stock control and replenishment decisions, while the consignment

items are usually stored in the hospital wards under supplier ownership until the items

are consumed (Machado Guimarães, Crespo de Carvalho, and Maia 2013). In general,

outsourcing the central warehouse function to a logistics provider results in simplified

material flows, limited number of suppliers and decreased stock levels and costs.

Performance measurement allows hospital facilities to define benchmarks, increase

visibility of material flows and reduce costs by eliminating waste. The external

logistics provider monitors performance in the Service Level Agreement (SLA) in

terms of quality of their suppliers (e.g. supplier reliability, delivery time, backorders,

etc.), inventory goods (e.g. order lines, price transparency) and internal quality (e.g.

picking errors, cross-dock lines), whereas internal hospital practices lack Key

Performance Indicators (KPIs) to measure internal logistics efficiency. In contrast, in-

house inventory management verifies the stock once a year through manual counting.

CHAPTER 1

6

Furthermore, information technology is a potential enabler to upgrade SCM by

improving integration. However, multiple information systems are used, containing

disparate elements and inconsistent input information throughout the hospital supply

chain. Finally, education is an important contributor to integrating logistics processes

along the supply chain. However, operational tasks rarely require a competence-based

diploma of logistics employees. This lack of education and training is an important

barrier to the internal logistics evolution in hospitals (McKone-Sweet, Hamilton, and

Willis 2005).

Table 1-1. Overview of current logistics practices in four Flemish hospitals (W =

Warehouse; POU = Point-of-Use).

Hospital 1 Hospital 2 Hospital 3 Hospital 4

Size 1995 beds 596 beds 811 beds 287 beds

Purchasing GPO GPO GPO GPO

Inventory

management

W: Outsourced

POU: 2-bin

W: Outsourced

POU: 2-bin

W: In-house

POU: 2-bin

W: In-house

POU: 2-bin

Information

technology

Barcode

scanning

Barcode

scanning

Barcode

scanning

Barcode

scanning

Performance

measurement

External: SLA

Internal: /

External: SLA

Internal: /

Manual

counting (1/year)

Manual

counting (1/year)

Logistics staff Executives: no education or training

Although this exploratory study provides a holistic view on logistics-related aspects in

the Flemish healthcare sector, it promotes further research to build a robust decision-

support methodology for adopting logistics strategies and identifying efficiency

improvement opportunities.

1.1.2 Optimization of operations by simulation: a case study at

the Red Cross Flanders

There is a need to develop a comprehensive approach for adapting industrial

engineering methods to the unique healthcare features in order to identify opportunities

for efficiency gains. Moreover, a structured methodology allows for rational and

transparent decision making in order to determine how logistics processes contribute

INTRODUCTION

7

to value-based healthcare. A preliminary case study at the Red Cross Flanders is

conducted to show the relevance of applying industrial engineering techniques, such

as lean, operations analysis or simulation, to achieve operational excellence while

guaranteeing high service levels to patients. At the department Blood Service, the main

goal is to ensure availability of safe, high-quality blood products to satisfy the highly

critical public health need in the majority of hospitals in Flanders. Blood products are

collected across 11 fixed donor centres in Flanders. As the supply of these blood

products relies on voluntary donations by Belgian citizens, it is important to maximize

their comfort and satisfaction during the donation process. Satisfied donors will return

and guarantee continuous supply of blood products. Therefore, the Red Cross is

interested in optimizing the donor flow in donor centres by focusing on minimal

waiting times and optimal organisation of the donor centre.

By analysing the current operations at a donor centre, bottlenecks and non-value-

adding activities are identified. Moreover, external factors and unpredictability

complicate the organisation of donors and resources at donor centres. Discrete-event

simulation is used to model the donor flow. The best performance of the donor flow is

found through scenario analysis by changing input parameters, such as donor arrival

patterns, bed capacity or number of doctors. Visualizing the donor flow is a strong

advantage in communicating the relevant findings to the medical personnel and

stakeholders as it supports effective, transparent decision-making processes based on

the simulation model. On the other hand, the drawback of developing a simulation

model is the effort required to verify and validate the modelled flow. Validation in

healthcare presents a challenge to model developers due to absence of accurate data.

A gap analysis is conducted to identify additional data needs to build a more detailed

simulation model. Please see Moons et al. (2016) for more information on the findings.

This exploratory case study shows the feasibility and motivation for applying industrial

engineering techniques in a healthcare setting in order to improve efficiency without

compromising on patient care service levels. However, adaptation of logistics

techniques to the unique characteristics of the healthcare context is required to

overcome the barriers to achieve efficient, value-based care delivery processes.

Typical healthcare challenges are unpredictability, multi-stakeholder conflicting

perspectives, fragmentation, and lack of data, lack of standardization and lack of

automation.

CHAPTER 1

8

1.1.3 Need for training in logistics principles: Healthcare

Logistics Education and Learning Pathways

As a response to the need for improving internal logistics in the healthcare sector, the

HELP project is introduced. HELP stands for Healthcare Logistics Education and

Learning Pathways. In this Erasmus+ project, international universities and colleges in

Estonia, Finland, Spain, The Netherlands and Belgium are collaborating to develop a

continuous educational curriculum from vocational level up to PhD level. The main

goal is to create a healthcare logistician education pathway to ensure the right skills,

competencies, training and knowledge are available for different logistics functions in

a healthcare institution. Figure 1-1 displays the overlapping responsibility of a

healthcare logistician who understands the concepts of logistics and speaks the same

language as the medical staff (Kotonen et al. 2016; Kotonen and Tuominen 2014).

This, in turn, will enhance alignment between patient care and logistics processes and

thus benefits overall hospital performance.

The implementation of supply chain fundamentals and innovative best practices to

improve logistics in healthcare is often counteracted by healthcare professionals,

including nurses, doctors and other employees involved in the care delivery system.

One of the greatest challenges to efficient hospital logistics management relates to lack

of logistics education. Gowen and Tallon (2003) show that supply chain training at

both the executive and managerial level provides a greater competitive advantage.

Current hospital practices lead to interrupted workflows and clinical personnel

involvement in logistics tasks, wasting valuable time to be spent with patients to ensure

high quality care (Landry et al. 2016). Inefficient organisation of logistics functions

and vague descriptions of those functions make logistics one of the biggest sources of

waste in hospitals. Introducing a new profession of healthcare logisticians in the HELP

project will release the medical professionals from logistics tasks so they can focus on

their core activity of nursing or caring (Kotonen et al. 2016). At the same time, efficient

material handling as well as effective inventory control can be achieved supporting

patient care services. In addition, the human factor contributes to the delivery of high-

quality logistics services; and therefore, measuring several aspects of personnel

management or employee engagement is a relevant performance indicator for

increasing efficiency in internal hospital logistics. Finally, the HELP project provides

new perspectives on detecting bottlenecks, implementing efficient healthcare logistics

operations and change management.

INTRODUCTION

9

1.1.4 Similarities between manufacturing, maintenance and

healthcare logistics

Driven by globalization, the industrial sector acknowledges ongoing improvements in

the field of SCM, involving a management shift from individual processes to a

network-based orientation. Concepts of OR/OM providing advanced analytical models

gain popularity for making planning and controlling decisions in terms of purchasing,

inventory management, distribution and scheduling, partnerships with suppliers, lean

management (e.g. Just-In-Time (JIT)), etc. (De Vries and Huijsman 2011). The

healthcare sector, on the other hand, prioritizes patient care and safety while cost

drivers related to logistics processes are often forgotten.

The healthcare logistics sector can reap similar benefits, provided that the OR/OM

tools are adapted to meet the specific conditions in a hospital. The hospital supply

chain resembles most to manufacturing or other industrial facilities when compared to

complex patient flows, and thus is most likely to adopt SCM (Thorwarth and Arisha

2009). Zhong et al. (2017) explore the parallels between production systems and care

delivery systems. Common features in terms of system modelling, performance

management, continuous improvement, data standards, product selection, etc. make it

interesting to transfer the methodologies and philosophies from manufacturing to

healthcare, fostering multi-disciplinary research to improve overall healthcare

performance. De Vries and Huijsman (2011) also investigate the similarities between

the industrial and healthcare sector. Like in the manufacturing industry, the focus of

OR/OM in healthcare originates from optimizing individual processes such as demand,

Figure 1-1. Consolidation of logistics flows and patient care as competences of a healthcare

logistician (Kotonen and Tuominen, 2014).

CHAPTER 1

10

order, supplier or inventory management. Information technology acts as an important

enabler for successfully integrating supply chain processes (Rossetti et al. 2012).

Inventory management has been widely discussed in manufacturing, while recently it

is also identified as a key lever to realize efficiency targets in a healthcare setting. To

some extent, hospital inventory management is comparable to industrial inventory

management. For example, actual use inventory management can be inspired on retail

management (Varghese et al. 2012). Kwon et al. (2016) compare healthcare and

commercial supply chains and emphasize the similarity between both. The authors

especially highlight logistics operational tools and process improvement as key

strategic areas to fully benefit from effective supply chain operations in healthcare.

Finally, controlling spare parts inventory in maintenance operations as mentioned by

Danas et al. (2006) shows most similarities to hospital logistics, where the overall goal

is to achieve a high service level while controlling inventory levels to reduce inventory

costs (De Vries 2011). Hu et al. (2018) describe special characteristics related to spare

parts logistics. Demand is unpredictable as equipment breakdowns can occur at any

instant (Huiskonen 2001). Moreover, the cost of spare parts varies from cheap to

extremely expensive and there is a great variety of items. Furthermore, equipment

downtime causing production losses must be minimized, and thus requiring high

availability of spare parts (Braglia, Grassi, and Montanari 2004; Danas et al. 2006).

The healthcare sector is struggling to effectively manage inventory. Since spare parts

management has similar characteristics, it might be useful to look at these best

practices. A good modelling approach (e.g. mathematical models, simulation, multi-

attribute classification) enables effective decision making for planning, scheduling or

inventory control (Braglia et al. 2004; Danas et al. 2006; Huiskonen 2001).

Waeyenbergh and Pintelon (2009) and Van Horenbeek and Pintelon (2014) develop a

framework enabling performance monitoring to measure outcomes and identify

improvement opportunities for maintenance purposes in a manufacturing company.

Transferring and adapting this concept to the healthcare sector allows to measure the

performance of internal supply chain processes and to develop a rational, systematic

and transparent decision-support tool.

Although similarities exist between healthcare delivery and maintenance systems,

there are some barriers or constraints which prevent a simple ‘copy-paste’ of SCM

solutions that prove successful in production-oriented settings. Discovering and

examining these challenges can turn them into opportunities to achieve efficient

healthcare logistics by using rigorous quantitative methods.

INTRODUCTION

11

According to Zhong et al. (2017), many factors drive up the level of complexity in the

healthcare supply chain, making them unique. These unique features are the

unpredictable nature of its processes, the central role of patients, fragmented

responsibilities, etc. which will be discussed further in Section 3.1.1. Jarrett (1998) and

McKone-Sweet et al. (2005) investigate barriers that hinder the implementation of

supply chain initiatives in hospitals, such as poor reliability of data, lack of decision-

making models, lack of common standards on product identification (i.e. Global Data

Synchronisation Network or GDSN), lack of cross-functional integration, high degree

of resistance to change, etc. Nachtmann and Pohl (2009) indicate lack of integration

due to lack of a collaborative framework among supply chain partners as the major

obstacle in achieving cost-effective, standardized processes in hospitals. Melo (2012)

states lack of decisions support tools, underestimation of logistics impact, divergent

stakeholder interests and lack of coordination between hospital departments to be the

major issues when implementing effective SCM. Finally, multi-disciplinary research

efforts are required to improve healthcare systems. Participation of researchers or

experts from various areas, whether they are from commercial, manufacturing or

healthcare sectors, allows for knowledge and experience transfer of supply chain

fundamentals and concepts to the healthcare delivery system (Kwon et al. 2016; Zhong

et al. 2017).

1.2 Problem statement

Value-based healthcare is a hot topic in the healthcare sector, driving healthcare

organisations towards increasing efficiency and thus decreasing costs, without

sacrificing high-quality patient outcomes. Accordingly, the Triple Aim strategy

introduced by IHI “unites the pursuit of lower cost with the pursuit of better health and

care, which is totally consistent with the modern definitions of ‘quality’ in most sectors

of the economy” (Whittington et al. 2015). Globally, healthcare definitely needs to do

more with less, which provides opportunities for classical OR/OM topics. Especially

SCM becomes a primary target for efficiency improvements in hospitals. A well-

coordinated supply chain adopts quantitative modelling techniques and advanced

information technologies to reduce the ever-increasing healthcare expenditures

(Feibert 2017). In this dissertation, the internal supply chain is of interest, as it is stated

to be the weak link in supply chain integration compared to external SCM, with VMI

or Electronic Data Interchange (EDI) as popular research topics (Landry and Philippe

2004).

CHAPTER 1

12

High-quality patient care can only be delivered together with effectively managed

logistics processes to ensure supply availability in the most efficient way. This

interrelationship between primary patient care and secondary logistics services will

influence the overall performance of the hospital (Feibert 2017; De Vries 2011).

However, literature rarely addresses to what extent logistics activities contribute to the

healthcare delivery system (Nachtmann and Pohl 2009). The increasing importance of

SCM in healthcare also raises the need for suitable decision support. A decision-

support model is a simplified representation of reality, allowing to capture the essence

of problems in well-specified models. Given the complexity of hospital logistics

processes, a decision-support tool provides a systematic approach to translate objective

and subjective data into insights to drive decision making and implementation, where

information technology acts as an important enabler. However, most decision making

is based on intuition of a particular decision-maker without using any support system.

One important objective relates to evaluating logistics improvement initiatives, which

requires the ability to measure the performance of internal supply chain processes. The

literature review by Volland et al. (2017) points to investigating performance metrics

in hospital logistics as an interesting research opportunity. However, lack of

coordinated SCM and multiple definitions for ‘quality of care’ due to conflicting goals

among stakeholders are two major obstacles to performance measurement in a

healthcare setting (De Vries and Huijsman 2011), which indicate the need for

developing a rigorous healthcare logistics performance management framework.

1.2.1 Research motivation

SCM in the healthcare sector is less explored and less mature compared to

manufacturing. Hence, it is a great opportunity to fill gaps in literature and practice by

analysing logistics operations in healthcare. Ever-increasing costs drive hospitals to

reengineer their processes from a logistics perspective and rationalize expenses while

improving the patient experience of care and the health of the overall population as a

balancing exercise in the Triple Aim initiative. Addressing these goals on a global

scale requires a systematic methodology that allows to structure and understand

problem settings, defines operational measures to support decision making on all

organisational levels, while it enables flexibility in order to customize to context-

specific conditions (Berwick et al. 2008). However, logistics has long been overlooked

which results in a lack of understanding of the impact of logistics on value-based

healthcare. Measuring the performance of the supply chain is fundamental to identify

INTRODUCTION

13

and address deficiencies in the logistics flow, and it serves as a good input for

managerial decision making. Furthermore, coordinating a process reengineering

project in a hospital requires bringing together all stakeholders involved from various

departments, and thus results in solving a multi-level multi-actor performance

management problem. Stakeholders often work independently in silos, striving

towards different objectives due to different backgrounds and thus complicate the

definition of efficiency management. The workforce element, considered as the fourth

goal in the Quadruple Aim strategy, needs to be taken into account by improving

stakeholder education and training to improve more informed decision making, which

promotes stakeholder commitment in order to have an impact on overall health system

performance. The main motivation for this research is therefore related to solving the

challenge of fragmentation in the internal hospital supply chain when adopting

logistics concepts from other industrial sectors.

The framework is tested at the Operating Theatre (OT). The OT is the financial hub of

any hospital, making cost awareness and maximizing efficiency especially important

(Rothstein and Raval 2018). According to Weiss et al. (2016), operating rooms account

for about 60% of total hospital costs, and the logistics costs are responsible for 30% of

total hospital expenditures (Feibert, Andersen, and Jacobsen 2017). However, the OT

has never been held accountable for supply costs and hence, medical-surgical supplies

are duplicated in many stocking areas and high supply availability rates are maintained

through overstocking supplies according to a “nice-to-have” principle, rather than

“need-to-have” (Camp et al. 2014). Gitelis et al. (2015) investigate surgeon education

and show a 10% decrease in costs of disposable supplies for laparoscopic

cholecystectomy with annual savings of $27,000. Disposable supplies represent the

majority of inventory value and volume compared to other hospital inventories, and

they require the highest workload from logistics personnel as they are stocked in

multiple locations (Ahmadi, Masel, and Hostetler 2019; Melson and Schultz 1989).

Therefore, disposables are selected as the subject of this study. Moreover, at the OT,

the quality of surgical procedures heavily relies on processes that align patients,

hospital resources (e.g. infrastructure, supplies, and personnel planning) and related

information flows. The risk of non-availability of supplies must be minimized, while

inventory managers aim to minimize costs at the same time. Solving this main trade-

off between service level and inventory cost is crucial to improve efficiency and

eliminate waste. Finally, implementation of advanced technologies, such as RFID or

barcoding, and standardization efforts (i.e. GS1 standards) can increase the efficiency

CHAPTER 1

14

of the supply chain through productivity gains for logistics processes, inventory

shrinkage and cost savings (Bendavid and Boeck 2011). The ultimate goal is to achieve

“a well-coordinated system that delivers care with great efficiency and quality, at

reasonable cost, matching the resources for care to where (and when) they are needed

most” (Hall 2012).

1.2.2 Research questions

The overall aim of this dissertation is to develop a rigorous decision-support

framework for healthcare SCM decision making and to identify the value of logistics

and potential efficiency gains, especially when multiple stakeholders are involved.

Simply stated, logistics can be described as moving stock, or in other words, efficiently

handling and coordinating flows of materials among different supply chain entities

(Rossetti et al. 2012). The five rights of logistics call for the availability of the right

goods and services to the right place at the right time in the right quantity and at the

right price (Melo 2012).

In particular, the framework drives internal hospital supply chain practices to better

understand the system, to explore several logistics policies, to evaluate the impact of

logistics policies on overall performance and to have a more integrated healthcare

supply chain by accounting for multiple stakeholder’s perspectives and thus

facilitating group decision making. “Decision aids do not guarantee perfect decisions

but when appropriately used they will yield better decisions on average than intuition”

(Hogarth 1980).

Based on the problem situation and the goal statement, we investigate the following

overall research question (RQ):

To answer the overall RQ, different work modules need to be solved according to the

steps for creating a hospital logistics decision-support framework. The framework

development consists of four main modules, which are addressed throughout the

different chapters in this dissertation. Four research questions and underlying sub-

questions have been formulated while considering the modular logic. The first module

initiates the procedure for developing the framework. Measuring the supply chain

“How to develop a decision-support framework to guide hospitals in adopting

SCM practices to improve performance of the internal hospital supply chain

according to the stakeholders’ preferences?”

f

INTRODUCTION

15

performance is critical to identify and address inefficiencies in logistics operations

which drive up costs. Moreover, it serves as a valuable input for transparent,

managerial decision making. Therefore, an extensive literature search is conducted

together with expert knowledge in order to find relevant indicators for measuring

performance in a healthcare logistics context. As our focus is on logistics processes

within the boundaries of the hospital, the indicators reflecting the performance of

inventory management and internal distribution processes are considered. Hence, the

first RQ1 is formulated as:

RQ 1 - “What is the state-of-the-art to measure performance in internal healthcare

logistics?”

With the literature review, we aim to provide a list of indicators for healthcare SCM

within the scope of this dissertation. This list forms the foundation for building the

logistics performance management framework. The framework provides guidelines for

translating logistics objectives into relevant indicators in order to select appropriate

inventory or distribution policies according to the overall hospital strategy. Multi-

Criteria Decision Making (MCDM) is applied as a useful OR/OM tool to select KPIs

and prioritize among improvement initiatives (Danas et al. 2006). The main advantage

of MCDM is its ability to evaluate both quantitative and qualitative criteria by

considering stakeholders’ judgments. However, only few studies consider the

usefulness of multi-criteria approaches in healthcare SCM. This module aims at

delivering new insights into the field of healthcare logistics decision making by

creating an initial prototype of the framework. Solving the second module will provide

an answer to the second RQ2:

RQ 2 - “How to prioritize among logistics objectives and KPIs to measure the impact

of logistics processes on the internal hospital supply chain?”

A major issue in hospital SCM relates to implementing the appropriate inventory and

distribution policy by answering where items should be stored, and when and how

much should be replenished. Current logistics parameters are missing or tend to be set

based on experience rather than evidence-based (Nicholson, Vakharia, and Selcuk

Erenguc 2004; Rappold et al. 2011). Typically, hospitals carry large amounts of

inventory, resulting in a relatively small number of deliveries and high inventory

holding costs (Rossetti 2008). Therefore, the third module provide empirical evidence

for parameter setting, policy decision making and performance monitoring in terms of

CHAPTER 1

16

inventory management and distribution. The OT in the university hospital UZ Leuven

serves as a study design to apply the prototype of the framework. The third RQ3

summarizes the objectives of this work module:

RQ 3 - “How to improve the performance of hospital logistics processes in terms of

inventory and distribution systems?”

This research question can be further divided into two sub questions:

3a) “What are optimal inventory parameters and the appropriate policy for

holding items, depending on different item characteristics?

3b) “What is the best distribution strategy for moving disposable supplies to

point-of-care locations?”

By going through modules 1 to 3, hospital managers gain deeper insights in how

logistics contribute to value-based healthcare by quantifying the impact of inventory

and distributions systems. The policies are evaluated from a logistics point of view, as

only one logistics-minded stakeholder is involved in the KPI prioritization process.

However, stakeholders at different stages of the supply chain have misaligned

incentives, pursuing different objectives and thus complicate priority setting and policy

decision making. The initial prototype of the framework increases awareness of the

significant share of logistics when improving overall performance. The final module

extends this prototype to a general logistics performance management framework by

including multi-level, multi-stakeholder validation. The prototype enables

stakeholders to make informed decisions to define what is important for efficiency

management and how different logistics policies impact the performance by

quantifying trade-offs and thus reducing uncertainty in the decision-making process.

Moreover, this module addresses the possibility of bias that comes with a single

decision-maker’s attitude and generalizes the findings to a wider healthcare context.

Finally, the current state of performance measurement is explored, generating insights

into benchmarking opportunities and advice for improving efficiency with a focus on

materials management. The final RQ4 is formulated as:

RQ 4 – “How do conflicting stakeholders’ perspectives impact healthcare logistics

performance?”

INTRODUCTION

17

1.3 Structure of the dissertation

This dissertation continues by explaining the methodology for developing the

healthcare logistics framework in Chapter 2, together with an overview of the relevant

industrial engineering techniques used in this work and a note on data collection. The

remainder is organised following the modular build-up of the framework (see Figure

1-2):

Chapter 3 initiates the development procedure by introducing the unique

challenges of SCM in healthcare and providing a state-of-the-art overview of

performance indicators relevant to healthcare logistics. The chapter concludes

with identifying research opportunities and contributions from both an

academic and practical point of view.

In Chapter 4, we describe the framework foundation. MCDM is introduced

as a suitable decision-support technique for selecting and prioritizing among

performance indicators. A prototype has been developed and tested for one

hospital department. Finally, we propose different methods to deal with

challenges inherent in any MCDM study.

Chapter 5 represents the third module of the framework. The prototype, as

developed in Chapter 4, is tested using several case studies. The case studies

differ with respect to the nature of the logistics processes, and therefore we

prove the generalizability of the findings of the prototype to specific contexts

addressing multiple logistics needs. In this dissertation, the prototype is

applied to support policy decision making and determine operational

parameter values for inventory and distribution systems in the operating

theatre. In addition, physician preference card management is discussed as an

interesting opportunity to strive towards standardization and increased cost

awareness among hospital stakeholders.

Chapter 6 introduces two feedback loops to extend the prototype into a

general healthcare logistics performance management framework by

integrating multi-level, multi-stakeholder perspectives. The framework is

checked for robustness and validity, and potential benchmarking

opportunities are identified. The chapter concludes with practical

implications to enhance implementation of the findings.

CHAPTER 1

18

A critical reflection on the framework is given in Chapter 7. We present the

framework, how the modules relate to each other as well as an example of a real-world

application of the framework at the OT. In addition, a roadmap to implement the

framework in different healthcare logistics contexts is provided, where the modules

are generic in nature but the content of the modules can be customized to the studied

application.

Finally, Chapter 8 provides future implications of the digitalization trend on

healthcare SCM and Chapter 9 states the major conclusions.

Figure 1-2. Outline of PhD dissertation.

19

CHAPTER 2

2 Methodology

This chapter describes the methodology followed to design the

healthcare logistics performance management framework. A case study

research design is chosen to empirically test the framework. Thereafter,

a comprehensive overview is provided of the industrial engineering tools

relevant for this dissertation. We end with a note on data collection in

healthcare logistics.

2.1 Framework development procedure

In this dissertation, we use a modular approach to develop a healthcare logistics

performance management framework following an iterative procedure similar to the

PDCA-cycle – Plan, Do, Check and Act. The purpose is to design a decision-support

tool that evaluates various logistics policies to find efficiency improvement

opportunities for the specific problem context. An overview of the methodology is

presented in Figure 2-1. The PDCA cycle is a tool to monitor the quality of

improvements within an organisation. Due to its cyclical feature, it supports the idea

of continuous improvement. Planning is the first step, focusing on improving processes

by defining objectives, indicators and the available resources. Translated to our

framework, the planning step corresponds to the first and second module and thus has

a two-fold objective:

A state-of-the-art overview is requested to select logistics objectives and

indicators to measure the performance of the internal hospital supply chain.

CHAPTER 2

20

Relationships between the performance indicators are identified and priorities

are established using MCDM techniques.

In this initialization stage, a prototype is tested for one hospital department, namely

the OT, and only involves one stakeholder – the OT logistics manager. Hence, the KPI

prioritization in the prototype is determined from a logistics viewpoint as this

stakeholder is most impactful and knowledgeable when defining the role of logistics

services, which has often been overlooked in healthcare.

Next, module 3 is represented by the Do and internal Check stage, which aim to collect

relevant information, test improvement initiatives and compare the results to the ideal

situation. The prototype is applied to several case studies in order to test different

logistics policies and analyse parameter values for the relevant KPIs. This dissertation

mainly focuses on management areas such as inventory and distribution to point-of-

care locations in order to find efficiency opportunity gains. Case studies are chosen as

research design to explore and provide a proof-of-concept of how simulation and other

industrial engineering techniques are relevant to a healthcare setting. In particular, the

prototype is applied to UZ Leuven and uses the OT as a primary study setting because

it is a critical department to any hospital with regard to resource utilization and cost-

effectiveness. Simulation is the recommended tool to understand the behaviour of the

current system, visualize the inventory and distribution systems and perform a scenario

analysis to find the most efficient supply chain organisation.

In addition to the internal check, three external feedback loops are incorporated to

extend the prototype to a general framework, considering a broader perspective (i.e.

module 4):

The first loop is related to the sample size of the prototype. Since the attitude

of a single decision-maker may be biased, multiple stakeholders need to be

incorporated to validate the impact of logistics on value-based healthcare. The

dispersed logistics responsibility among several departments asks for a multi-

level, multi-stakeholder framework validation by iterating between the

indicators and solving challenges inherent in a MCDM study, such as

conflicting priorities between stakeholders.

A second feedback loop enables iteration in the policy decision-making

process. The prototype proves that importance of logistics in healthcare can

no longer be neglected and stakeholders gain deeper insights into operational

METHODOLOGY

21

processes resulting in adapted policies and quantification of trade-offs from

different stakeholder viewpoints based on the framework.

Third, a benchmarking iteration is added to gain insights in how the current

way of monitoring performance deviates from optimal performance

management. Overall, the framework provides a good foundation for

evaluating performance between different hospitals or departments by

ensuring uniform performance measurement. Moreover, the framework is

extended to other logistics processes to enhance generalizability of the

findings and striving towards an integrated hospital supply chain.

Finally, the acting step involves making adjustments to solve deviations from the

desired output. Actions based on the framework involve procedures for implementing

efficient inventory and distribution policies. Identifying SCM implementation paths

based on the framework by combining performance measures and stakeholder

information enhances trust building in the findings.

Altogether, the well-known PDCA-cycle has been extended to a P²DC²A-cycle, with

a two-fold function for the planning and checking step, in order to construct a

healthcare logistics performance management framework that stimulates continuous

improvement.

Figure 2-1. Overview of the P²DC²A methodology.

CHAPTER 2

22

2.2 Healthcare logistics toolbox

This PhD research contributes to the field of OR/OM to develop a healthcare logistics

performance management framework that supports decision making. A comprehensive

overview of tools included in this framework is given in this section. We focus on the

logistics processes within the hospital’s boundaries, reflecting the internal hospital

supply chain; more specifically several case studies are selected to test different

logistics policies for storing and distributing the materials from central storage rooms

to point-of-use locations.

2.2.1 Case study research design

The applicability of the healthcare logistics performance management framework is

demonstrated using case studies for evaluating inventory control and distribution

systems at the OT department. Case studies are chosen as research design to investigate

the problem in a real-life setting and to bridge the gap between theory and practice by

adopting effective SCM in healthcare which is still lacking maturity compared to other

industrial sectors. Therefore, we aim to explore and provide a proof-of-concept of how

industrial engineering tools are relevant to a healthcare logistics problem.

The case findings provide empirical evidence to support existing knowledge, gain new

insights for healthcare SCM and enhance evidence-based decision making. Moreover,

we prove the generalizability of the framework by addressing multiple logistics needs.

Although the case studies differ with respect to the nature of the logistics process, a

systematic approach and reference platform are developed to reproduce the framework

and generate fruitful insights to healthcare policy makers, managers and executives to

expand the framework to a hospital-wide level. We provide a roadmap to implement

the framework in different settings:

First, case studies are selected to structure the problem (e.g. relationships

between criteria, data requirements for modelling), improve understanding

and increase awareness of SCM in healthcare. In this work, we use the

inventory and distribution processes as an example based on a discussion with

the logistics manager at the OT and the potential for providing efficiency gain

opportunities.

METHODOLOGY

23

Second, the cases are evaluated from multiple logistics perspectives to

enhance stakeholder commitment and ensure more informed decision making

in terms of SCM.

Finally, a reference framework is presented that delivers a blueprint to

identify process improvements across case studies and can be customized to

multiple departments, processes or hospitals as an orchestrator for supply

chain integration in healthcare.

The case studies are inspired on existing logistics concepts described in literature and

successfully applied in many industrial sectors. However, these tools are not ready-

for-use in practice-based applications and thus need adaptation to the unique

characteristics of the internal hospital supply chain (Melo 2012; Volland et al. 2017).

The OR/OM tools are not applied as routinely in healthcare because they are not part

of the management toolbox. In this dissertation, we identify the need for a

methodological approach and provide a comprehensive overview of the tools and data

requirements to adopt SCM in healthcare.

2.2.2 Overview of OR/OM tools for healthcare SCM

OR/OM is a broad collection of analytical tools and plays a key role in bringing

objectivity in decision making and promoting data-driven process improvement. In

this dissertation, we aim to develop a decision-support tool to analyse how logistics

contributes to value-based healthcare and address the barriers to effective SCM by

integrating a toolbox into a healthcare logistics performance management framework.

The included tools originate from industrial engineering applications, such as

performance management, MCDM methods (e.g. ANP, PROMETHEE), simulation

modelling (e.g. DES) and SCM concepts. Figure 2-2 displays how the tools are

connected to develop the framework. More information on the tools can be found

below the figure.

CHAPTER 2

24

2.2.2.1 Performance management

Developing an effective framework for performance management and gathering

logistics data to show its impact on overall hospital performance is an interesting

research opportunity. Volland et al. (2017) points to identifying performance metrics

in healthcare logistics and to transferring OR/OM tools from industrial to healthcare

problems. However, the different expectations and possibly conflicting goals between

multiple stakeholders involved in the hospital supply chain complicate the

performance definition. As no single measure can represent every aspect of operational

excellence, we develop a multi-dimensional performance management framework that

can assist healthcare organisations in translating the strategic or tactical goals into

operational indicators to ensure perfect alignment between strategy and execution,

while striving towards maximal value creation (Melnyk, Stewart, and Swink 2004).

Moreover, an effective performance management framework allows to incorporate

feedback relationships between indicators, make adjustments to solve deviations from

the desired outcome and encourage continuous improvement, and therefore our

framework is considered to be dynamic when making the healthcare supply chain

future-proof (Yadav, Sushil, and Sagar 2013). In addition, the framework aims to fill

the literature gap by proposing an approach for selecting and prioritizing performance

metrics as well as addressing the unique obstacles to effective SCM in healthcare.

Figure 2-2. Overview of the healthcare logistics toolbox.

METHODOLOGY

25

2.2.2.2 MCDM

MCDM encompasses a wide range of quantitative methods to solve selection and

prioritization problems because it utilizes quantitative and qualitative information,

accounts for multiple objectives and makes the decision process more explicit, rational

and transparent (Adunlin, Diaby, and Xiao 2015; Beck and Hofmann 2014).

Table 2-1 provides an overview of the most frequently applied MCDM methods in

healthcare (Adunlin et al. 2015; Velasquez and Hester 2013), and for each method the

benefits and drawbacks are discussed (Jorissen 2018). Choosing the appropriate

method requires a careful selection process such that it is suitable to the problem type,

the desired output and decision-makers’ preferences. Moreover, the robustness,

consistency and transparency of the method should be guaranteed. In this work, the

desired outcome of applying MCDM is a prioritization of KPIs, which constitute the

core elements when defining operational excellence in healthcare SCM. The nature of

the problem does neither recommend the application of TOPSIS as we are interested

in identifying the ideal result rather than using it as input, nor the application of

outranking methods, such as PROMETHEE and ELECTRE, as precise values for

indicator weights are requested. MAU(V)T and AHP/ANP are the remaining methods

which enable to synthesize preferences across indicators into a quantitative score. The

former method, however, is data-intensive to create utility/value functions, which is

difficult to obtain in a healthcare setting. The latter methods structure the problem into

respectively a hierarchy or network form and use pairwise comparisons to select

relevant performance indicators, assign weights by eliciting stakeholder preferences

and assess alternatives by trading off KPIs to create an overall value score (Marsh et

al., 2014). Moreover, AHP/ANP allows for group decision making by aggregating

individual preferences which improves the robustness of the framework. Since the

decision needs to account for interdependency relations between indicators, AHP

would oversimplify the problem. ANP is a generalization of AHP and is the most

suitable approach to the formulated research problem as it can deal with complexity

inherent in the hospital environment by taking into account interdependencies.

The methods discussed above are the most commonly used MCDM methods found in

healthcare literature. However, other methods using pairwise comparisons are

available, such as MACBETH (Bana E Costa, De Corte, and Vansnick 2012) or

PAPRIKA (Hansen and Ombler 2008), which are both additive multi-attribute value

measurement models.

CHAPTER 2

26

Tab

le 2

-1. C

om

par

ison

of

MC

DM

met

hod

s u

sed i

n h

ealt

hca

re (

Rep

rinte

d f

rom

Jori

ssen

(2018))

.

Meth

od

W

ork

ing

prin

cip

le

Pro

ble

m

Ad

van

tag

es

Dis

ad

van

tag

es

Refe

ren

ces

Anal

yti

c H

iera

rchy

Pro

cess

(AH

P)

Hie

rarc

hy

Pai

rwis

e co

mpar

iso

ns

(1-9

sca

le)

Pri

nci

pal

eig

env

ecto

r m

eth

od

Ran

kin

g

Qu

alit

ativ

e an

d

qu

anti

tati

ve

Eas

y-t

o-u

se

Tra

de-o

ff b

etw

een

con

flic

ting

obje

ctiv

es

Inco

nsi

sten

cy r

atio

Ran

k r

ever

sal

Mar

sh e

t al

.,

20

14

Saa

ty,

19

90

Saa

ty,

19

96

Anal

yti

c N

etw

ork

Pro

cess

(AN

P)

Sim

ilar

to

AH

P,

bu

t net

wo

rk i

s co

nst

ruct

ed

rath

er t

han

hie

rarc

hy

Acc

ou

nt

for

inte

rdep

end

ency

rel

atio

ns

Ran

kin

g

Sim

ilar

to

AH

P

Inte

rdep

end

enci

es

Sim

ilar

to

AH

P

Co

mp

lex

cal

cula

tio

ns

Saa

ty a

nd

Var

gas

, 2

00

6

Mar

sh e

t al

.,

20

14

Mu

lti

Att

ribu

te U

tili

ty

(Val

ue)

Th

eory

(MA

U(V

)T)

Sin

gle

uti

lity

or

val

ue

fun

ctio

n t

o d

eriv

e

per

form

an

ce o

f al

tern

ativ

es f

or

each

cri

teri

on

Ass

ign w

eigh

ts a

ccord

ing

to r

elat

ive

imp

ort

ance

Co

mb

ine

into

ov

eral

l u

tili

ty o

r val

ue

funct

ion

Ran

kin

g

Ch

oic

e

Eas

y t

o u

nd

erst

an

d

Rel

ativ

e im

port

an

ce o

f

each

att

ribu

te

Un

cert

ain

ty i

s ta

ken

into

acc

ou

nt

Dat

a-i

nte

nsi

ve

and

tim

e-c

ost

ly t

o d

eriv

e

uti

lity

or

val

ue

fun

ctio

ns

Mar

sh e

t al

.,

20

16

Pre

fere

nce

Ran

kin

g

Org

anis

atio

n M

eth

od

for

Enri

chm

ent

Eval

uat

ion

(PR

OM

ET

HE

E)

Pre

fere

nce

fu

nct

ion

s base

d o

n p

airw

ise

com

par

ison

s o

f al

tern

ativ

es a

gai

nst

cri

teri

a

Wei

ghts

are

ass

ign

ed b

y d

ecis

ion

mak

er

Ran

kin

g

No s

cali

ng

eff

ects

Red

uce

d

inco

mpar

abil

ity

Use

r fr

ien

dly

Sim

ple

im

ple

men

tati

on

No c

lear

str

uct

ure

for

dec

isio

n p

rob

lem

Su

bje

ctiv

ity w

hen

assi

gn

ing w

eig

hts

Ran

k r

ever

sal

Pir

das

hti

et

al., 2

01

1

Beh

zadia

n e

t

al., 2

01

0

Eli

min

atio

n a

nd C

hoic

e

Tra

nsl

atin

g R

eali

ty

(EL

EC

TR

E)

Pai

rwis

e co

mpar

iso

ns

to c

on

stru

ct o

utr

ank

ing

rela

tio

ns

Ex

plo

itat

ion p

roce

du

re t

o e

lab

ora

te

reco

mm

endat

ion

s fr

om

th

e in

itia

l o

bta

ined

resu

lts,

dep

end

ent

on

the

pro

ble

m t

ype

(ran

kin

g,

choic

e or

sort

ing p

rob

lem

)

Ran

kin

g

Ch

oic

e

Sort

ing

Qu

anti

tati

ve

and

qu

alit

ativ

e

Sim

ple

log

ic

Sy

stem

atic

com

pu

tati

onal

pro

cedu

re

No a

xio

mat

ic

assu

mpti

on

s

All

oca

tio

n o

f w

eig

hts

by

dec

isio

n m

aker

No c

om

ple

te r

ank

ing

of

alte

rnat

ives

Bla

ck b

ox

Pir

das

hti

et

al., 2

01

1

Tec

hn

iqu

e fo

r O

rder

Pre

fere

nce

by

Sim

ilar

ity

to I

dea

l

Solu

tio

ns

(TO

PS

IS)

Ch

oo

se a

lter

nat

ive

as

clo

se a

s p

oss

ible

to i

dea

l

solu

tion

(m

axim

um

sco

re o

n a

ll c

rite

ria)

and

as

far

as

po

ssib

le f

rom

neg

ativ

e-i

dea

l so

luti

on

(min

imu

m s

core

)

Ch

oic

e

No r

ank

rev

ersa

l

Ver

y q

uic

k

Def

ine

bes

t an

d w

ors

t

case

fo

r al

l cr

iter

ia

Les

s acc

ura

te

Pir

das

hti

et

al., 2

01

1

27

2.2.2.3 Simulation modelling

Simulation has become an indispensable tool for various sectors, ranging from

automobile industry to construction, in order to achieve streamlined operations and

reduce costs. In the healthcare sector, however, we see limited application of SCM

simulation due to the unique challenges, such as inaccurate data collection and logistics

fragmentation. Technological enablers (e.g. RFID scanning or Artificial Intelligence)

will play an important role in future research to increase visibility throughout the

supply chain and capture data to improve decision making.

In general, simulation models are classified along three dimensions (Kelton, Sadowski,

and Zupick 2015): static or dynamic, continuous or discrete, and deterministic or

stochastic.

Most operational models are dynamic, as time plays a natural role in decision

making.

The system’s state can only change on certain times when events take place

in discrete models, whereas it changes every moment in continuous models.

Models operate with random or fixed inputs in respectively stochastic or

deterministic models. Stochastic models incorporate probability distributions

to account for random input.

Different types of simulation, which are applicable in a healthcare setting, are briefly

described and an overview is provided in Table 2-2.

Discrete-Event Simulation (DES) is preferred for operational and tactical studies to

model individual occurrences at particular time intervals, changing the state of the

system. The model is developed by using several components, such as entities,

attributes, global variables, resources, queues, etc. (Karnon et al. 2012). The internal

simulation clock jumps from event to event, and in-between events the system remains

stationary. The main advantages of DES are its flexibility, modularity in model

building, user-friendly animation of modelled flows, incorporation of stochastic

factors, individual patient or item focus, etc. (Gunal 2012).

Second, Monte Carlo (MC) simulation is a stochastic modelling type, though it

typically has a static character in contrast to the dynamic nature of DES (Wasserstein

and Fishman 2006). The models use random generators instead of a time dimension.

MC simulation is also called spreadsheet simulation as it is a numerical method in

spreadsheet form (e.g. MS Excel) which shows only input and output streams based

CHAPTER 2

28

on discrete events (Mielczarek & Uziałko-Mydlikowska, 2012). Typical examples of

this approach are evaluating the cost-effectiveness of medical treatments or assessing

health risks (Katsaliaki and Mustafee 2011).

In contrast to DES, System Dynamics (SD) is generally a deterministic and continuous

simulation approach, where time and system state are constantly being tracked. As a

consequence, the required computing power and model complexity increase. SD is

specifically designed to incorporate the relationship between many system elements,

using feedback loops, to examine how strategic decisions impact the dynamic

behaviour of the system and to reveal future trends. The models require a holistic view

and interactions between elements, rather than focusing on individual objects

(Brailsford et al. 2004). Therefore, these models are designed for macro-level

healthcare modelling (e.g. capacity decisions, healthcare infrastructure) and evaluating

cause-and-effect relationships in a system (Gunal 2012; Rohleder, Cooke, et al. 2013).

Finally, Agent-Based Simulation (ABS) is the most recent technique for modelling the

actions and interactions of autonomous individuals, also called agents, in a network to

assess the effects of these agents on the system as a whole (Gunal 2012; Katsaliaki and

Mustafee 2011). “Unlike entities in traditional simulation techniques, agents are

capable of making independent decisions and showing active and social behaviours”

(Abukhousa et al. 2014). However, the use of ABS in healthcare and other sectors is

not yet widespread. Typical examples of ABS in healthcare are devoted to problems

such as natural disasters (e.g. infectious disease outbreaks) (Katsaliaki and Mustafee

2011; Marshall et al. 2015).

Table 2-2. Comparison of simulation methods.

DES SD ABS

Nature Stochastic Generally deterministic Stochastic

Focus Individual level (process) Aggregate level Individual level (agent)

Complexity Detailed complexity

No interactions

Holistic view

Interactions

Interactions

Level of

modelling

Micro Macro Macro and micro

Purpose Decision support:

optimization and

comparison

Policy making: gaining

understanding

Human behaviour

modelling

Healthcare

applications

Planning of healthcare

services

Health economic models

Public health policy

evaluation

Modelling healthcare

systems and infrastructure

Spread of epidemics

Examples of

software

packages

Arena, Simulink, etc. AnyLogic, Simulink, etc. AnyLogic, Simulink, etc.

METHODOLOGY

29

Choosing the appropriate simulation model depends on the system as well as the

purpose of the model. Marshall et al. (2015), Mielczarek and Uziałko-Mydlikowska

(2012), Brailsford et al. (2019), Brailsford and Hilton (2001), Tako and Robinson

(2009) and Majid et al. (2016) compare different simulation methods. Since the

problem under study requires a dynamic character, MC simulation is excluded from

the comparison in Table 2-2. DES has been suggested as a powerful approach for

supply chain managers and is widely used for inventory and distribution modelling in

spare parts management (Hu et al. 2018). It also has great potential to be accepted in

healthcare facilities due to its flexible approach (Kuljis, Paul, and Stergioulas 2007;

Rego and Sousa 2009; Rytile and Spens 2006; Thorwarth and Arisha 2009; Zhang

2018). Literature suggests that SD and DES are the most suited techniques for

modelling logistics processes (Babulak and Wang 2012). According to Tako and

Robinson (2012), SD is better suited for solving problems at a strategic level, whereas

DES is more appropriate for solving operational-level problems. Because the subject

of this study is determining parameter values and measuring performance of inventory

and distribution systems, the simulation model should support the decision-making

process at a more detailed level to gain operational insights, which makes the use of

SD modelling approach overly complex. Moreover, the importance of tracking

individual items is necessary to collect information on consumption, stock-outs,

inventory accuracy, etc., and variability in specific attributes of entities is requested.

Since there is no need for modelling active objects nor interaction among items, ABS

is inappropriate in this case. Therefore, DES is chosen in this dissertation to study the

integration of healthcare logistics processes on a micro-level.

2.2.2.4 SCM interventions

Healthcare SCM consists of a variety of logistics tools for inventory control,

distribution, purchasing, demand planning, information systems, etc. In this work, we

use case studies to apply the framework to the processes of storing and distributing

medical materials in order to ensure that the right items are available at the right time

and right place, as efficiently as possible. Different scenarios can be evaluated by

changing logistics policy parameters, such as maximum inventory level, reorder point,

reorder quantity, inventory location, replenishment time, etc. In consultation with the

OT logistics manager, alternative logistics policies are formulated to test potential

improvement initiatives without the need for physically implementing the changes. In

future work, the framework can be extended to include other managerial areas, such as

CHAPTER 2

30

exploring how logistics processes, stakeholders and information technology systems

can be connected to achieve standardization and integration.

By combining ANP and DES, we provide a sound basis for performance management

in healthcare. We introduce the hybrid ANP-DES tool as a way of empirically testing

the framework by using case studies to experiment with SCM concepts and gain

insights into real-life problems and potential efficiency gains. DES helps to define

operations, map processes and analyse available logistics data in a structured way to

understand the current situation, identify bottlenecks and perform scenario analysis.

ANP provides a priority ranking of the KPIs while considering power and interest

relationships among multiple stakeholders. By combining both tools, we address some

unique features in healthcare logistics that complicate SCM adoption. Multiple KPIs

are evaluated simultaneously and synthesized into a multi-dimensional evaluation

score, which we call the Internal Logistics Efficiency Performance (ILEP) index. The

ILEP index is thus the overall outcome of the hybrid ANP-DES tool and allows to

quantify how logistics contributes to healthcare, where we strive towards operational

excellence by trading-off multiple objectives and identifying efficiency gains.

2.2.2.5 Robustness check

In the final module of the framework development procedure, two MCDM methods

are combined into a hybrid ANP-PROMETHEE tool to ensure the robustness of the

framework and guarantee transparent decision support. PROMETHEE as a standalone

method is not recommended in this research because it lacks a weighting procedure.

In the hybrid tool, PROMETHEE complements ANP which analyses the structure of

the performance management problem and establishes KPI weights, whereas

PROMETHEE is a user-friendly outranking method that provides a complete ranking

of various SCM policies by identifying the characteristics of the best policy for the

studied application. Outranking methods utilize the concept of dominance between

preference functions of alternatives and have a non-compensatory character (i.e.

alternatives are downgraded if they perform poorly on one indicator) (Thokala et al.

2016). In Chapter 6, we will examine similarities and differences between the ILEP

index (i.e. only taking into account the ANP weights) and the hybrid ANP-

PROMETHEE tool to find a consistent preference order of the logistics policies.

METHODOLOGY

31

2.2.3 Data collection

A well-known challenge in addressing any healthcare SCM problem relates to the

scarcity of accurate logistics data. The output is unlikely to be informative if incorrect

data is inserted in the framework. Therefore, data requirements are critical for selecting

the appropriate OR/OM tools. Simulation tools allow to develop a proof-of-concept by

constructing a more abstract model using a limited amount of input data, whereas

mathematical modelling is a powerful tool that heavily relies on algorithms, data and

statistics to solve optimization problems. Moreover, the goal is to find a satisfactory,

practice-oriented solution by analysing the effect of changing parameters on system

performance, rather than obtaining a non-intuitive optimal policy.

In this dissertation, both quantitative and qualitative data is collected to improve

understanding of SCM and stimulate stakeholder engagement. The quantitative data is

obtained from pairwise comparisons in the ANP method, time studies and internal

hospital logistics databases (e.g. consumption data based on scanning), whereas the

qualitative data is collected by conducting semi-structured interviews with the

involved stakeholders.

The framework development procedure starts with input from a logistics-oriented

stakeholder at the tactical level based on his background, expertise and accessibility to

logistics data in order to increase understanding of SCM in healthcare and avoid

conflicts due to biased stakeholder preferences. Next, several logistics policies are

evaluated based on scanning data, providing a first glimpse of how logistics can service

healthcare. The final step in the development procedure aims to validate the framework

by incorporating multiple stakeholder perspectives. Building on the initial logistics

prototype, viewpoints from executives and managers, differing in background (e.g.

medical or logistical) are added to share findings and operational insights from the case

studies. Moreover, materials managers in three departments – OT, hospital pharmacy

and IRCC – are selected to test the framework for different material types and other

department characteristics (e.g. complexity, predictability, etc.), which possibly strive

towards different objectives. In addition, a hospital policy maker in the logistics

department is involved to extrapolate the knowledge to strategic decision making from

a holistic SCM perspective.

Altogether, the interviewees in this work represent eight stakeholders working in three

departments and representing all organisational control levels. From this qualitative

perspective, the framework stresses the importance of stakeholder education to

CHAPTER 2

32

increase logistics awareness and enhance stakeholder commitment. The findings are

relevant input to managerial and more informed decision making by facilitating the

dialogue between all stakeholders to coordinate SCM and maximize contribution to

value-based healthcare.

33

CHAPTER 3

3 Healthcare Logistics and Performance

Management2

This chapter starts by defining logistics and SCM concepts relevant for this

dissertation, as well as by describing unique features in the healthcare

supply chain. Next, performance management is introduced to initiate the

framework development procedure, followed by a state-of-the-art overview

of performance indicators for healthcare logistics. The chapter concludes

with identifying the gaps in literature and the contributions of this

dissertation.

3.1 Defining logistics in the healthcare sector

Logistics is the key enabler for reducing costs in both industrial and service

organisations. In their definition, the Council of Supply Chain Management

Professionals (2016) indicate that logistics is part of SCM:

“Logistics management is that part of SCM that plans, implements and

controls the efficient, effective forward and reverses flow and storage of

goods, services and related information between the point of origin and the

point of consumption in order to meet customers’ requirements.”

2 This chapter partially corresponds to the following paper:

Moons, K., Waeyenbergh, G., Pintelon, L. (2019). Measuring the logistics performance of internal hospital

supply chains – A literature study. Omega, 82, 205-217.

CHAPTER 3

34

This definition can be translated to healthcare logistics as:

The activities of design, planning, implementation and control of coordination

mechanisms between supplies, equipment, services and information from

suppliers to point-of-care units in order to enhance clinical outcomes while

controlling costs.

Simply stated, Abukhousa et al. (2014) define SCM in healthcare, also referred to as

healthcare logistics, as:

“The process of delivering the right products in the right quantities to the

right patient care locations and at the right time with satisfying service levels

and minimized system-wide costs.”

However, the healthcare supply chain is very fragmented with many parties involved

at various stages of the supply chain without close management control. Three key

players are identified, typically operating in silos independently from one another,

which results in badly coordinated supply chain processes (Rossetti et al. 2012):

producers (e.g. manufacturers of medical supplies, medical devices, etc.)

purchasers (e.g. wholesale distributors and GPOs)

healthcare providers (e.g. hospitals, physicians).

This dissertation focuses on the healthcare providers, where the hospital constitutes

the main entity in the healthcare supply chain. A distinction is made between internal

and external supply chain practices. The latter includes the first tier or further upstream

in the supply chain (e.g. manufacturers, distributors and GPOs), while the former

focuses on a typical healthcare provider with many internal point-of-care locations

(e.g. nursing units, operating room wards), requiring processes for receiving, put away,

storage, picking and replenishment (Rossetti et al. 2012). The missing link in supply

chain integration, as stated by Rivard‐Royer et al. (2002), promotes focusing on the

internal supply chain in the remainder of this work. The internal supply chain plays a

key role in linking logistics processes and patient care services within the hospital.

Figure 3-1 gives an overview of the external and internal supply chain for the operating

theatre department.

HEALTHCARE LOGISTICS AND PERFORMANCE MANAGEMENT

35

Hospital logistics cover a diversity of activities for both primary and secondary

processes. First, it enables patient care by coordinating care pathways and second, it

optimizes supporting processes including drug distribution, patient transportation,

materials handling, etc. These activities are managed at three levels of control:

strategic, tactical and operational level (Di Martinelly 2008). The interaction between

different control levels and management areas is summarized in a planning and control

framework as proposed by Hans et al. (2012). Strategic decisions cover long-term

hospital strategies to reach the overall hospital’s objectives and mission, such as

determining resource capacity, implementing new information technology, improving

collaboration with vendors or outsourcing of certain activities. Hospitals implementing

holistic SCM and focusing on strategic problems may realize significant benefits, such

as improved employee satisfaction, patient safety and outcomes (Lee 2011). Next, the

tactical level involves decisions concerning resource planning based on capacity,

which is determined at the strategic level, to meet demand. The tactical planning

addresses questions such as what, where, how, when and who for organising hospital

operations. Uncertainty should be reduced as much as possible in order to build a

robust planning. At the lowest level, operational decisions are made every day related

to executing processes. Examples of operational planning decisions are appointment

scheduling, inventory replenishment ordering or nurse rostering (Hall 2012). At this

level, the performance of the processes is monitored. Performance management

provides feedback by linking the strategic, tactical and operational level KPIs and aims

at shaping the efficiency and effectiveness of health service delivery (Mettler and

Figure 3-1. External and internal supply chain for the operating theatre department in hospitals.

CHAPTER 3

36

Rohner 2009). However, defining performance in healthcare logistics is complex

because different stakeholders – patients, nursing staff, doctors, management staff, and

payers – are involved with these activities. The different expectations and perceptions

of the stakeholders complicate the performance definition in healthcare.

3.1.1 Specificities in healthcare logistics

The complex character of a healthcare setting is related to its unique characteristics

and operational challenges (Melo 2012; Yanamandra 2018; Zhong et al. 2017). Figure

3-2 gives an overview of the specificities in healthcare logistics. One of the most

influential factors is unpredictability or uncertainty in hospitals. The essence of SCM

in healthcare is matching supply and demand, though accurately forecasting the

demand for supplies is a difficult task due to unforeseen epidemic conditions and lack

of accurate consumption data. In addition, the great amount and variety of items

complicate inventory management. Especially in the operating theatre, physician

preference cards increase the spectrum of items held in stock and thus hinder

standardization. Lack of item/location identification standards and automation reduce

transparency and visibility throughout the entire supply chain. Another challenge

relates to the absence of coordination between different entities of the supply chain.

The logistics responsibility is dispersed among different departments, working in silos

and thus resulting in a very fragmented healthcare supply chain. Moreover, it is

challenging to engage different stakeholders to cooperate and to embrace changes due

to their conflicting goals for efficiency management. Hospital executives and payers

strive to get more value for less costs by purchasing affordable quality items and

minimizing operational costs, whereas the doctor’s primary concern is to optimize

service quality by maximizing material availability (Belliveau 2016). Therefore, no

single measure can represent every aspect of efficiency when different stakeholders

have individual takes on performance management. Finally, the impact of logistics in

hospitals has long been underestimated due to poor understanding of healthcare SCM.

Although patient care is the core process in hospitals, logistics-related activities have

a significant impact on the quality of service as well as on hospital costs. However, the

lack of expertise in the field of OR/OM results in inefficient operation of healthcare

processes characterized by stock-outs, expired products or hoarding behaviour.

Overcoming these challenges is essential to obtain effective SCM in healthcare.


37

3.2 Performance management in healthcare logistics

Many industrial sectors have implemented performance management to understand

operational performance and hence achieve their strategic goals (Mettler and Rohner

2009). The current state of performance management in healthcare logistics, however,

is still at a low level of maturity due to the inherent complex nature of this setting.

Measuring performance enables piloting the hospitals towards enhancing strategic,

tactical and operational planning and it is beneficial to quality of care. Although

hospitals recognize the importance of defining logistics goals and KPIs in terms of

efficiency and effectiveness of health service delivery, the crucial link between multi-

level indicators is often ignored (Mettler and Rohner 2009). Therefore, we investigate

in this dissertation how a performance management framework guides healthcare

organisations in identifying efficiency opportunity gains while not compromising on

quality of patient care.

Section 3.2.1 presents a literature review discussing the indicators relevant for

measuring performance of internal supply chain practices in healthcare, focusing on

inventory and distribution of medical supplies to point-of-care locations. Selecting and

monitoring relevant performance metrics provides valuable input for managerial

decision making. Logistics managers need to identify opportunities to improve the

logistics processes in order to lower costs and to improve patient care quality.

Figure 3-2. Unique challenges within the healthcare supply chain.

CHAPTER 3

38

However, in order to improve the logistics processes, you must understand how the

internal supply chain is currently performing in order to identify and address

inefficiencies in the logistics activities. Performance indicators will help monitor

management policies such that the logistics department can take evidence-based

decisions that can optimize the inventory and distribution system. Currently, there is a

lack of clear and measurable cost and quality metrics for measuring hospital logistics

performance (Nachtmann and Pohl 2009). “If you don’t have data, it’s hard to get

physicians on board and aware of the expenses associated with stocking many similar

products” (Cardinal Health 2015). Volland et al. (2017) points to investigating

performance metrics in healthcare logistics as an interesting research opportunity.

With this literature review, we initiate the procedure for developing the healthcare

logistics performance management framework.

3.2.1 Literature review: Measuring the logistics performance of

internal hospital supply chains

3.2.1.1 Performance indicators for inventory management

The majority of literature is devoted to inventory management systems that reduce

stock levels in order to achieve the main objective of cost reduction. Sole cost cutting

objectives, however, do not suffice since patient satisfaction is influenced by the

perceived quality of care and prices. Hospital managers need to balance inventory

levels by trading-off between quality metrics and costs (De Vries 2011). Nachtmann

and Pohl (2009), Aronovich et al. (2010) and Hoeur and Kritchanchai (2015) develop

a set of performance measures to monitor the quality of its inventory activities,

including inventory visibility, inventory availability, stock-out rates, inventory

accuracy rate, stock wastage due to expiration or damage, etc. Supply availability is a

crucial factor in a healthcare logistics system’s ability to support patient care processes

(Vila-Parrish and Simmons 2013). An interesting indicator to measure the performance

of an operating room is the percentage of unplanned operating room closures due to

non-availability of supplies (Fixler and Wright 2013). Understanding the relationship

between logistics processes and clinical care processes is important to manage the

utilization of supplies and services. For hospital logistics managers, knowing what is

used in a surgical case and how often is more effective than knowing the supply cost

per case (Nachtmann and Pohl 2009). On the other hand, the financial performance

indicators (e.g. inventory holding cost, value of stock, average response cost, etc.)


39

identify the supply chain cost drivers and help move toward a more efficiently

managed supply chain (Aronovich et al. 2010). Although the operating theatre

generates about 42% of a hospital’s revenue (Cardoen, Beliën, and Vanhoucke 2015),

supply costs can make up to 40% of total hospital expenses in surgery-intensive

hospitals (Abdulsalam and Schneller 2019). Moving toward product standardization in

clinical practices is one way to cut costs. The physician preference cards typically

contain 20% to 40% more items than what is actually consumed, which increases costs

in terms of waste, returned supplies and unnecessary labour (Camp et al. 2014). Epstein

and Dexter (2000) categorize the inventory costs into five types of costs: ordering,

shipping, purchasing, storage and opportunity costs. In addition, one should also

consider the cost of not having the items in stock when needed (i.e. stock-out cost).

Stock-out costs are difficult to measure given its high variability. Therefore, prevention

of stock-out is often handled via a service level constraint that is dependent on the item

criticality, which is a measure of the consequences of a stock-out (Gebicki et al. 2014;

Guerrero, Yeung, and Guéret 2013).

Supeekit et al. (2016, 2015) explore four supply chain performance criteria of cost,

time, reliability and productivity to evaluate internal hospital supply chain

performance. Typical performance criteria to evaluate inventory management include

value of buffer stock, inventory days of supply, stock-out at point-of-care, preparation

time, picking accuracy, etc. Bijvank and Vis (2012) list the characteristics of relevant

literature on inventory management for point-of-care locations in hospitals, such as

demand process, lead time, number of items, capacity restriction, etc. They redirect the

focus of controlling inventory from a cost objective function (Dellaert and Van De

Poel 1996; Lapierre and Ruiz 2007; Nicholson et al. 2004) to a service level objective

(Bijvank and Vis 2012; Little and Coughlan 2008). The service level is defined as the

fraction of demand that can be satisfied directly from inventory on-hand (i.e. item fill

rate) (Bijvank and Vis 2012). They introduce a capacity model and a service model in

order to determine the reorder level that maximizes the fill rate and minimizes the

required capacity, respectively. Furthermore, Gebicki et al. (2014) propose medication

inventory policies that combine three drug characteristics – drug availability (i.e.

probability that a drug is available from the supplier when ordered), criticality (i.e.

based on the criticality of the treatment for which the drugs is used) and expiration

dates – that influence the performance of the system with respect to total cost and total

number of stock-outs with the aim to minimize the trade-off between costs and patient

safety. Kelle et al. (2012) propose reorder point policies and discuss the KPIs for

CHAPTER 3

40

inventory management in the pharmaceutical supply chain, including the expected

number of daily refills, the service level and storage space utilization. In the operating

room environment, Beaulieu et al. (2013) describes four objectives to effectively

manage supplies: supply availability for scheduled surgical procedures, up-to-date

inventory management information, minimal clinical personnel involvement and

automating inventory management. An optimized inventory management system will

result in an improved workflow of all employees, improved control of both inventory

and waste, significant cost savings, increased traceability, data collection and accurate

case costing. In this work, we present an overview of four types of performance

indicators – Quality, Time, Financial and Productivity – as adapted from Aronovich et

al. (2010) who focus on holistic SCM, including product procurement, inventory

management, distribution, etc. (see Table 3-1).

3.2.1.2 Performance indicators for distribution systems

Coordination of the healthcare resources, effective delivery strategies and

measurement of the delivery services are crucial for creating high-value outcomes

(Rohleder, Bailey, et al. 2013). Therefore, hospital SCM is faced with developing

distribution strategies (e.g. direct shipment, cross-docking, etc.) to deal with

movements of goods in the supply chain, while minimizing storage and transportation

costs. Variables such as costs, resources, the capacity restriction in warehouses and the

lay-out of the hospital are taken into account when searching for optimized

transportation or routing solutions. Typically, distribution problems aim at determining

the routing, the number and type of carriers, and the working schedules of the

transporters (Di Martinelly 2008). Little and Coughlan (2008) propose to expand their

inventory model to incorporate delivery resources and routing information to combine

distribution and inventory decisions. The hospital material managers are responsible

for having medicines and other medical supplies available at the health facility, while

being aware of the distribution costs (including the handling and transportation costs,

e.g. in terms of frequency of delivery) (Little and Coughlan 2008).


41

Tab

le 3

-1. O

ver

vie

w o

f p

erfo

rman

ce i

nd

icat

ors

for

inven

tory

man

agem

ent.

Qu

ali

ty

Tim

e

Fin

an

cia

l P

ro

du

cti

vit

y

A

vai

labil

i

ty

(ser

vic

e

level

)

Inven

tory

vis

ibil

ity

Cri

tica

lity

of

invento

ry

item

s

Pat

ient

safe

ty

(del

ays,

erro

rs)

Rep

lenis

h

men

t ti

me

Cli

nic

al

staf

f

inv

olv

e-

men

t

Inven

tory

cost

Val

ue

of

sto

ck,

sto

ck

was

tage

Inven

tory

turn

over

Uti

liza

-

tion

rat

e

Sta

nd

ardi

zati

on

Fo

ng e

t al

. (2

01

6)

X

X

X

X

X

Su

pee

kit

et

al. (2

01

6)

X

X

X

X

X

X

X

Car

rus

et a

l. (

20

15

)

X

X

X

X

X

Ho

eur

et a

l. (

20

15

) X

X

X

X

Ro

sale

s et

al.

(2

01

5)

X

X

Su

pee

kit

et

al. (2

01

5)

X

X

X

X

X

Abu

khou

sa e

t al

. (2

01

4)

X

X

X

X

Cam

p e

t al

. (2

01

4)

X

X

X

Geb

ick

i et

al.

(2

01

4)

X

X

X

X

X

X

Sar

no (

20

14

) X

X

X

X

X

Bea

uli

eu e

t al

. (2

01

3)

X

X

X

Lan

dry

et

al.

(20

13

)

X

X

X

X

Bij

vank

et

al. (2

01

2)

X

X

X

X

Kel

le e

t al

. (2

01

2)

X

X

X

Ro

sset

ti e

t al

. (2

01

2)

X

X

X

X

X

Al-

Qata

wn

ez e

t al

. (2

01

1)

X

X

X

X

Bab

oli

et

al. (2

01

1)

X

X

X

X

X

De

Vri

es (

20

11

) X

X

X

Aro

no

vic

h e

t al

. (2

01

0)

X

X

X

X

X

X

X

Lan

ckzw

eirt

et

al. (2

01

0)

X

X

X

X

Au

gu

sto e

t al

. (2

00

9)

X

X

X

Di

Mar

tin

elly

(2

00

9)

X

X

X

X

X

Nac

htm

ann e

t al

. (2

00

9)

X

X

X

X

X

Par

k e

t al

. (2

00

9)

X

X

Lit

tle

et a

l. (

20

08

) X

X

X

Lap

ierr

e et

al.

(2

00

7)

X

X

X

Dan

as e

t al

. (2

00

6)

X

X

X

X

Bab

oli

et

al. (2

00

5)

X

X

Ep

stei

n e

t al

. (2

00

0)

X

CHAPTER 3

42

Typical problems in the transportation system involve inflexibility, communication

and time management issues and distribution inefficiencies, such as long walking

distances, excess transportation movements, elevator problems, etc. The performance

of the internal hospital distribution activities can be evaluated on several factors, such

as on-time delivery of supplies, response time to urgent requests, errors, waste,

satisfaction of patient and personnel, streamlined organisation, etc. (Miller 2009). The

Supply Chain Operation Reference (SCOR) model addresses, improves and

communicates SCM activities within and between all involved stakeholders (Lenin

2014). It can be used to define the current logistics processes, get benchmarks or best-

practices and define performance measures. Di Martinelly et al. (2009) apply the

Porter-SCOR modelling approach to the hospital supply chain and define reliability,

responsiveness, flexibility, costs and assets as five types of performance indicators.

Rossetti and Selandari (2001) use the AHP model to formulate the hospital delivery

system and select three groups of performance indicators – technical, economic and

qualitative – related to the distribution process to decide on the replacement of a

human-based delivery system by mobile robots to distribute pharmaceuticals. The

hospital pharmacy activities are highly intertwined with the primary patient care

activity and it is important to have a perfect coordination between these two flows (Di

Martinelly 2008). Baboli et al. (2005) evaluate the performance of the internal flow of

pharmaceutical products to care units by measuring the delivery performance, order

fulfilment, lead time, supply chain response time, inventory days of supply, storage

costs, distance covered, etc. Sarno (2014) considers the logistics costs related to

pharmacy management policies in terms of personnel and physical resources

(transportation resources, inventory costs). The number and type of logistics resources

depend on the frequency and quantity of transports from the pharmacy to point-of-use

locations, characteristics of material handling equipment, etc. Personnel shift

scheduling also influences internal transport frequency. Augusto and Xie (2009)

develop a supply and transportation planning in order to balance workloads for both

pharmacy assistants and transporters, while respecting the availability constraint of

having a mobile medicine closet at each medical unit. The supply planning determines

the refilling of medicine closets at the central pharmacy based on the number of

assistants available and the average time needed for inventory checking and refill,

whereas the transportation planning is similar to a classical vehicle routing problem in

which the number of pick-up routes for transporters are minimized. Table 3-2 shows

an overview of indicators identified in literature to measure the performance of

distribution activities.


43

T

able

3-2

. O

ver

vie

w o

f p

erfo

rman

ce i

nd

icat

ors

for

dis

trib

uti

on a

ctiv

itie

s

Q

uali

ty

Tim

e

Fin

an

cia

l P

ro

du

cti

vit

y

D

eliv

ery

accu

racy

Cen

tral

i-

zati

on

degre

e

Dis

rupti

on

of

dis

trib

uti

o

n a

ctiv

itie

s

Pre

par

atio

n t

ime

Res

pon

siv

e-ness

(on

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X

CHAPTER 3

44

3.2.1.3 Performance management frameworks

The existence of conflicting interests and power relationships among different

stakeholders in the internal healthcare supply chain result in a multi-dimensional

character of the inventory and distribution system. It is a difficult task to determine the

contribution of supply chain activities to the performance of the healthcare delivery

system (Nachtmann and Pohl 2009). The different expectations and perceptions of the

stakeholders complicate the performance definition in the healthcare sector. A few

studies in OR/OM literature are concerned about performance measurement

frameworks in healthcare logistics. MCDM can be applied as a quantitative approach

to assess a number of alternatives on multiple criteria in order to find the best choice.

AHP/ANP are popular methods for solving MCDM problems in various industries,

such as maintenance. The research by Van Horenbeek and Pintelon (2014) serves as

an interesting opportunity to transfer the knowledge to develop a performance

measurement framework in a maintenance setting to a hospital setting. In their ANP-

based framework, relevant KPIs for specific business processes are selected in relation

with the company’s objectives. Recently, other researchers also recognize the potential

of AHP/ANP in the healthcare context. El Mokrini et al. (2018) propose a multi-criteria

framework using AHP to select the appropriate distribution network by considering

supply chain responsiveness and costs. Hoeur and Kritchanchai (2015) and

Kritchanchai et al. (2018) develop a logistics performance framework based on ANP

to develop a set of KPIs to assess the operational performance. Their results show that

inventory management and information and technology management are the most

important strategies for improving logistics performance in hospitals, with inventory

visibility and availability as the key strategic KPIs. In this dissertation, we will further

elaborate on operational KPIs covering all aspects of inventory management and

distribution to improve overall supply chain performance. Supeekit et al. (2016)

combine DEMATEL (i.e. Decision Making Trial and Evaluation Laboratory) and ANP

to evaluate the internal hospital supply chain performance. Their framework mainly

focuses on the interaction between clinical care processes, supporting processes and

patient safety while considering the main performance aspects of cost, reliability, time

and productivity. However, a more complete and detailed framework is required to

further link the performance aspects to business processes of the hospital supply chain

and to obtain KPIs for each process in order to improve overall hospital supply chain

performance. Finally, Feibert et al. (2017) and Feibert and Jacobsen (2015) assume a

process-oriented perspective and present an ANP-based tool for designing and


45

benchmarking efficient healthcare logistics processes. They identify a set of

performance indicators for assessing track-and-trace technologies in healthcare,

including quality, resources, productivity, satisfaction and service delivery.

3.3 Conclusion

3.3.1 Research gaps

Hospitals are forced to become operationally efficient by implementing effective SCM

and improving integration between patient care and logistics flows. The body of

literature on hospital logistics optimization is constantly growing, resulting in complex

OR/OM models mainly in the area of procurement and scheduling. However, there are

ample directions for promising future research as shown in Table 3-3.

What is missing in literature is a comprehensive work analysing how logistics

contribute to healthcare. Therefore, an interesting research opportunity is

understanding how the healthcare supply chain is currently performing. Different

stakeholders do not share the same perspectives, which complicates performance

management of SCM in hospitals. Performance measurement is important to address

inefficiencies in the logistics activities and it serves as a good managerial input for

decision makers in the healthcare supply chain. The literature review in Section 3.2.1

addresses this research gap and describes the performance indicators that impact the

internal logistics flow in hospitals. MCDM is proposed as a useful OR/OM technique

for determining relationships and prioritizing between performance indicators, while

considering the diverse interests from stakeholders.

In addition, there is still a missing link in literature of how the successful applied

industrial engineering techniques can be adopted in the healthcare sector, taking into

account the specificities of healthcare logistics. Although many complex mathematical

models have been presented in literature, there is little implementation in real-life cases

and the models are only valid for a specific department (e.g. hospital pharmacy), and

thus ignore integration among the hospital departments. The field of hospital logistics

lacks a structured approach for selecting the appropriate logistics concepts in order to

improve the overall performance of the internal hospital supply chain. Logistics

concepts have to be tailored to the specific needs of the healthcare organisation.

CHAPTER 3

46

Tab

le 3

-3.

Over

vie

w o

f re

sear

ch g

aps

and

op

port

unit

ies

Au

tho

r(s

) O

verv

iew

G

ap

s in

lit

era

ture

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ture r

ese

arch

You

ng (

20

04

) E

xp

lori

ng i

nd

ust

rial

con

cep

ts i

n

hea

lth

care

con

text:

lea

n,

six

sig

ma a

nd

th

eory

of

con

stra

ints

Lack

of

imp

lem

enta

tion

in

rea

l-

life

hosp

ital

case

s

Sim

ula

tion

stu

dy a

nd

sce

nari

o a

naly

sis

to t

est

eff

ect

iven

ess

of

ind

ust

rial

con

cep

ts i

n h

ealt

hca

re.

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Vri

es a

nd

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ijsm

an

(20

11)

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ilari

ties

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wee

n

man

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ctu

rin

g a

nd

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lth

care

sup

ply

ch

ain

Ad

dre

ssin

g c

om

ple

xit

ies

of

ad

op

tin

g S

CM

in

hea

lth

care

Alt

hou

gh

recogn

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g t

he

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ort

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ce,

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e are

lit

tle

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s to

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es

of

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SC

M

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hn

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s en

ab

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to S

CM

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tion

Imp

act

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s on

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M

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rics

in

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M

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ly c

hain

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(2

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itie

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f O

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tech

niq

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lth

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ited

in

form

ati

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ap

pli

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are

as

an

d u

sed

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hod

olo

gie

s

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to a

dap

t O

R/O

M t

ech

niq

ues

to s

pecif

icit

ies

in

hea

lth

care

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etti

et

al.

(20

12)

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vie

w o

f lo

gis

tics

pra

ctic

es i

n

inte

rnal

hosp

ital

sup

ply

ch

ain

Tech

no

log

ical

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lers

for

SC

M

Ap

pli

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on

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ly

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ori

ng i

mp

act

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rial

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ag

em

ent

on

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ent

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ty

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lem

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ng b

est

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om

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d t

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etail

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craft

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stry

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ent

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g d

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d i

nven

tory

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ing o

f m

edic

al

sup

pli

es

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imp

lem

enti

ng b

est

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ctic

es

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llan

d e

t al.

(20

17)

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mp

reh

ensi

ve

sum

mary

of

lite

ratu

re r

ela

ted

to h

osp

ital

mate

rials

man

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emen

t w

ith

a

focu

s on

qu

an

tita

tive

met

hod

s

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clu

ded

top

ics:

in

form

ati

on

flo

w,

per

ish

ab

le m

edic

al

pro

du

cts

(e.g

. b

lood

), p

lan

nin

g

an

d s

ched

uli

ng a

ctiv

itie

s

Iden

tify

ing p

erfo

rman

ce

mea

sure

s in

hosp

ital

log

isti

cs

Tra

nsf

erri

ng c

on

cep

ts f

rom

in

du

stri

al

to h

ealt

hca

re

sett

ing

Inves

tigati

ng t

ech

no

log

ical

enab

lers

, su

ch a

s R

FID

syst

ems

Ass

essi

ng w

hy h

ealt

hca

re i

s st

ill

lag

gin

g b

ehin

d i

n S

CM


47

Thus every organisation requires a unique and customized concept, fitting its strategy

and objectives, which will yield significant cost savings and improve intangible

aspects such as staff and patient satisfaction. Therefore, this dissertation adds to the

literature by developing a rigorous performance management framework in internal

healthcare logistics.

According to Maestrini et al. (2017), supply chain performance measurement systems

are defined as:

“A set of metrics used to quantify the efficiency and effectiveness of supply

chain processes and relationships, spanning multiple organisational

functions and multiple firms and enabling supply chain orchestration”.

Designing effective frameworks ensures a clear definition of performance and requires

selecting meaningful performance indicators, which can be financial, non-financial,

quantitative or qualitative, and are related to the organisation’s strategy (Carlucci

2010; Piratelli, Claudio et al. 2010).

3.3.2 Research contributions

This dissertation presents a framework to bridge the gap between theory and practice.

In addition to the theoretical evidence provided in literature indicating the significant

share of logistics in care delivery processes, empirical evidence will be provided

through real-life case studies to support existing knowledge, gain new operational

insights for process improvement, and enhance evidence-based decision making in the

healthcare logistics context. Inspiration for the framework originates from existing

logistics concepts described in literature and successfully applied in industrial sectors,

though they are not ready-for-use in real-life cases and need adaptation to the unique

characteristics of a healthcare setting. Moreover, simple strategies with reasonable

implementation time and high degree of success are preferred rather than investing in

sophisticated mathematical models (Melo 2012).

The framework development starts at the heart of logistics with the OT logistics

manager, aiming for care delivery with great efficiency and quality, at reasonable cost,

matching the resources for care to where and when they are needed most (Hall 2012).

From this logistics perspective, other non-logistics oriented stakeholders gain insights

into the impact of logistics in supporting clinical care processes, which stimulates more

informed decision making and is an essential prerequisite for successful

CHAPTER 3

48

implementation of process improvement initiatives. The cyclical feature of the

framework brings together all stakeholders’ perspectives, resulting in a feedback loop

which reduces uncertainty in decision making and encourages continuous

improvement to achieve the desired output related to the organisation’s strategy.

Moreover, it allows to verify whether the logistics indicators, as monitored in other

industrial sectors, can be adapted to healthcare processes.

Unlike other research, this framework integrates multiple aspects of the internal supply

chain and enables to quantify trade-offs between cost and service levels. The major

challenge will be to overcome obstacles to efficient SCM, such as conflicting

stakeholder goals, physician preference items, lack of standardization, no visibility,

etc. Finally, the proposed approach is transparent, systematic, flexible and easy-to-use,

and thus leaving enough room for applications in other healthcare logistics settings.

The obtained outcomes are valuable for healthcare providers who are streamlining the

internal supply chain by implementing effective inventory and distribution systems.

Altogether, this dissertation contributes to literature from both an academic and

practical point of view:

The P²DC²A-cycle promotes a structured methodology to develop a

theoretically sound and objective decision-support framework. The

healthcare logistics performance management framework integrates multiple

logistics indicators as considered by ‘traditional’ industrial engineering

concepts, tailors logistics concepts to healthcare specificities and orchestrates

supply chain integration by considering multiple stakeholders’ perspectives.

In addition, the cyclical feature stimulates continuous improvement.

The developed framework contributes to literature domains covering

healthcare systems engineering and management science by identifying

performance measures impacting the internal logistics flow in hospitals and

addressing OR/OM techniques to analyse how logistics contributes to value-

based healthcare.

MCDM, and in particular ANP, is used as a powerful OR/OM technique to

select and prioritize KPIs according to organisation-specific objectives. The

optimal KPI ranking constitutes the core elements, which can be quantitative

or qualitative, when defining operational excellence to the benefit of the

overall health system.


49

By combining ANP and Discrete-Event Simulation, we provide a sound basis

for performance management in healthcare. The hybrid ANP-DES tool

allows for empirical testing of the framework by evaluating several logistics

policies as well as determining parameter values to improve inventory and

distribution systems for the studied application. We introduce the Internal

Logistics Efficiency Performance (ILEP) index as the synthesized value

which allows to quantify trade-offs among possibly conflicting objectives,

find appropriate improvement initiatives and encourage benchmarking

opportunities by pursuing the best-in-class KPIs according to the process

type.

In contrast to early-participation, the modules are verified and validated by

incorporating stakeholder feedback in the final development phase of the

framework. The multi-level, multi-stakeholder framework stimulates

transparent and more informed decision making by increasing awareness of

how logistics can service health systems in order to reduce supply chain

fragmentation and offer a common language for stakeholders on all

organisational levels. Hence, the framework promotes perfect alignment

between processes and stakeholders to orchestrate supply chain integration

for value-based healthcare.

Overall, the framework uses a closed-loop approach to support decision

making and bridge the gap between theory and practice-based SCM

applications in healthcare. From a practical point of view, the healthcare

logistics performance management framework has potential for building

implementation paths and thus building trust in the research findings. The

merits of the framework lie in its generic nature of the modules and the ability

to customize the content of the modules to the studied application. Hence, the

structured approach provides a reference platform for addressing various

logistics needs by adapting industrial engineering techniques according to the

context and stakeholders’ perspectives.

51

CHAPTER 4

4 Prioritization of Performance Indicators using

an ANP-based Prototype for the Internal

Hospital Supply Chain3

This chapter starts with introducing MCDM as a suitable decision-support

technique in healthcare. In this research, ANP is applied as a popular

MCDM method serving as the foundation for developing the logistics

performance management framework. The ANP-based prototype in Section

4.2 provides an answer to the second research question, and is illustrated

by applying it to one hospital department, namely the Operating Theatre

(OT). Finally, some challenges encountered in a typical MCDM study are

discussed using another application of the AHP in medical decision making

for value-based healthcare. This shows the versatility of AHP/ANP in a wide

range of healthcare applications focusing both on logistics and patient care

aspects.

3 This chapter partially corresponds to the following papers/poster/supervised Master’s Theses:

Moons, K., Pintelon, L., Jorissen, P., De Ridder, D., Everaerts, W. (2020). Identification of multi-stakeholder

value in medical decision-making. International Journal of the Analytic Hierarchy Process, vol. 12 (1), 82-103.

Moons, K. Waeyenbergh, G., Pintelon, L., Timmermans, P., De Ridder, D. (2019). Performance indicator

selection for operating room supply chains: an application of ANP. Operations Research for Health Care, vol. 23,

December 2019.

Jorissen, P., Moons, K., Pintelon, L., De Ridder, D., Everaerts, W. (2019). Identification of multi-stakeholder

value in prostate cancer treatment by application of multi-criteria decision-making. European Urology

Supplements, 18 (1), 2184-2185.

De Bie, R. (2019) Application of Multi-Criteria Decision Making in healthcare: Optimization of questionnaires

used in prostate cancer treatment. KU Leuven.

Jorissen, P. (2018). Identification of multi-stakeholder value in prostate cancer treatment by application of multi-

criteria decision making. KU Leuven.

CHAPTER 4

52

4.1 Multi-Criteria Decision Making in healthcare

Measuring the performance of internal hospital supply chain processes is a major

challenge faced by hospital management. The performance definition reflects a multi-

dimensional character, representing the possibly conflicting medical or logistics

objectives depending on the various stakeholders’ preferences. Input from all

stakeholders is required to identify relevant performance indicators and to evaluate

medical supplies for safety, clinical value to patients and cost-effectiveness (Hoeksema

2011). The complex nature of healthcare SCM decisions, involving uncertainty, trade-

offs and imperfect information, necessitates the use of a rigorous performance

measurement approach (Adunlin et al. 2015). This approach will guide hospital

managers in making transparent and systemic decisions, subject to conflicting criteria

and multiple objectives. A technique often used in literature to deal with multi-criteria

problems in all kinds of sectors is called Multi-Criteria Decision Making (MCDM).

MCDM stems from the field of OR/OM and is a quantitative technique to assess a

number of alternatives based on multiple criteria by assigning relative weights to these

criteria (Beck and Hofmann 2014). Typical application areas in SCM include design

(e.g. distribution network), purchasing (e.g. supplier selection), manufacturing (e.g.

production planning), distribution (e.g. distribution planning), logistics (e.g. supply

chain effectiveness) and performance management in general. In recent years, there

has been increased awareness for using MCDM techniques in Health Technology

Assessment (HTA), hospital resource optimization, benefit-risk analysis of drugs,

priority setting and clinical practice guidelines (Marsh et al., 2017). Marsh et al. (2017)

recommends MCDM as a supporting approach for better healthcare decision making,

because it allows to select relevant performance indicators, elicit stakeholder

preferences and assess alternatives depending on both performance and stakeholder

preferences. Moreover, MCDM is suitable for healthcare supply chain performance

management because it utilizes quantitative and qualitative information, accounts for

multiple objectives and makes the decision process more explicit, rational and

objective (Adunlin et al. 2015; Beck and Hofmann 2014).

In this dissertation, MCDM is used to develop a generic performance management

framework for healthcare SCM. Our work aims to help healthcare stakeholders in

translating strategic or tactical goals into relevant KPIs, which reflect the multi-

dimensional character of the supply chain and thus ensure a holistic viewpoint. In

PRIORITIZATION OF PERFORMANCE INDICATORS USING ANP

53

addition, we propose to combine MCDM and simulation modelling to quantify the

logistics impact in healthcare and highlight the areas for efficiency improvement.

The next section describes different types of MCDM methods, whereas an overview

of the most commonly used MCDM methods has been introduced in our healthcare

logistics toolbox (see Section 2.2.2).

4.1.1 MCDM classification

Literature describes many types of MCDM methods appropriate to different problem

settings. Marsh et al. (2014) define MCDM as:

“Any method that establishes criteria, weights them in terms of importance,

and scores each alternative on each criterion to create an overall assessment

of value.”

MCDM is suitable to make consistent, rational and transparent decisions in complex

problems when multiple stakeholders are involved with possibly conflicting

viewpoints (Thokala et al., 2016). However, there is no guidance in selecting

appropriate MCDM methods, though the chosen method should be suitable for the

problem at stake requiring a rational thought exercise. The choice of the method

strongly influences the level of transparency and accountability.

Typically, MCDM methods are divided into three categories: value measurement,

outranking and goal programming approaches (Figure 4-1). Value measurement

methods aim to construct a ranking of alternatives based on a real number consistent

with decision-maker value judgments, such that alternative A is preferred to alternative

B whenever the value for A is higher. Value or utility functions are used depending on

decision-maker preferences to convert performance into value on each criterion. In a

simple additive model, the overall evaluation of each alternative is found by summing

the product of each criterion weight and the performance score. The value

measurement approach is widely adopted with common examples being Analytic

Hierarchy Process (AHP), Analytic Network Process (ANP), and Multi-Attribute

Utility (Value) Theory (MAU(V)T) (Marsh et al., 2014a). A second category uses

outranking (Thokala et al. 2016). In this approach, no numerical value is required to

select the best alternative as compared to the former approach. Outranking relies on

the principle of dominance between the alternatives. An alternative is stated to be

dominant when it scores better on a sufficient number of criteria. A drawback of this

CHAPTER 4

54

working principle is that outranking methods are not compensatory, or in other words,

alternatives are downgraded when they perform poorly on one criterion, in contrast to

value measurement methods. PROMETHEE and ELECTRE are examples of

outranking methods. The final approach is categorized as goal programming with

TOPSIS being the most commonly used technique. This technique measures how good

alternatives achieve the pre-defined desirable levels of achievement for each criterion,

by computing the distance from the ideal point and most negative point.

Not every MCDM approach is recommended for solving any decision problem. In this

dissertation, the desired outcome of applying MCDM is a priority setting of the

performance indicators and alternatives. Thus, the nature of this problem does not

allow the application of goal programming as we are interested in identifying the most

ideal result, rather than using it as input. In further research, however, the obtained

priorities can be used as input to apply the goal programming approach. What remains

is the choice between value measurement and outranking. Since the goal is to find and

interpret priority setting, precise values for the criteria weights are required. Therefore,

the value measurement approach is preferred to the outranking approach for solving

the prioritization problem. In addition, value measurement approaches are the most

applied methods in healthcare (Marsh et al., 2014a).

Figure 4-1. MCDM taxonomy.


55

Typically, there are six main elements when conducting a value measurement approach

(Marsh et al., 2016):

Clear definition of problem statement

Identifying exhaustive, coherent and non-redundant criteria (French and Roy

1997)

Measuring performance

Scoring alternatives

Weighting criteria

Calculating aggregate scores

MAU(V)T and AHP/ANP are value measurement methods which enable to synthesize

preferences across indicators into a quantitative score. The former method, however,

is data-intensive to create utility/value functions, which is difficult to obtain in a

healthcare setting. The latter methods use pairwise comparisons to support decision

making by defining and prioritizing objectives and indicators which are important to

measure, monitor, control and improve the overall performance of the internal hospital

supply chain. Since the decision needs to account for interdependency relations

between indicators, AHP would oversimplify the problem. ANP is less transparent

compared to AHP for generalizing the results to similar problem settings, though ANP

can deal with complexity inherent in the hospital environment by taking into account

interdependencies. In the next section, we will elaborate more on the ANP approach

and why its application is believed to be the most suitable to the formulated research

problem. For more detailed information on the principles of the AHP/ANP

methodology, we refer to the papers by Saaty (1996, 2006; Saaty and Vargas, 2006).

4.1.2 Analytic Hierarchy/Network Process

AHP and its extended form ANP are used to assign relative weights to indicators

representing their importance as a quantitative measure. Therefore, this approach

enables to define and prioritize healthcare logistics objectives and indicators

(Jharkharia & Shankar, 2007; Saaty & Vargas, 2006; Van Horenbeek & Pintelon,

2014). AHP simplifies complex decision problems by using a hierarchical structure,

consisting of the goal, objectives and (sub-)indicators (Saaty, 1990a). The goal or

control criterion is associated with the decision problem, and refers in this work to

finding the best set of indicators that influence the performance of the internal hospital

supply chain the most. Several indicators are assigned to specific healthcare logistics

CHAPTER 4

56

objectives. A pairwise comparison process is used to express preferences among

indicators on a 1-9 ratio scale based on decision makers’ judgments (Van Horenbeek

and Pintelon 2014). The principal eigenvector method is used to extract local and

global priority vectors (Saaty 1996). A consistency ratio of 10% is allowed to check

for inconsistent stakeholder judgments.

ANP is similar to AHP with the exception that the former transforms the decision

problem into a network rather than a hierarchy. The network structure consists of

different clusters and elements. ANP takes a holistic view by taking into account

connections among these elements and clusters and thus allows for capturing

relationships between or within clusters (i.e. outer or inner dependence). This feature

of interdependence is ignored in AHP, resulting in no feedback between different

levels and independent elements within each level. The four main steps of the ANP

process are listed below (Carlucci 2010) and illustrated in Section 4.2 to a test design:

Step 1 – Model construction and problem formulation

Step 2 – Building pairwise comparison matrices

Step 3 – Supermatrix formation

Step 4 – Prioritization and selection of KPIs

ANP is believed to be the most appropriate method for the KPI selection and

prioritization problem in this hospital logistics context:

First of all, ANP models are successfully used in many applications as a

strategic decision-support tool (Jharkharia and Shankar 2007; Verdecho et

al. 2012). In the healthcare context, AHP/ANP are the most commonly used

MCDM methods, especially when addressing topics related to HTA,

diagnosis or performance management (Marsh et al., 2014a). An inherent

characteristic of AHP/ANP is that they are powerful tools for decision

making because of their flexibility and ability to capture both quantifiable

and non-quantifiable aspects of decisions (Hummel et al., 2012). This is

essential for criteria, such as patient safety, which are difficult to be

expressed quantitatively.

Second, ANP offers a participatory approach where decision makers are

directly involved in the pairwise comparison process to derive priorities for

all indicators. A better understanding of the problem, in turn, leads to more

informed decision making. As a result, the right logistics strategy can be


57

formulated based on the decision maker’s personality (Van Horenbeek and

Pintelon 2014). In addition, pairwise comparisons are perceived to be easier

than comparing all criteria at once, and the difficulty to express preferences

between criteria is countered by allowing inconsistent judgments until a

specified threshold (i.e. consistency ratio) to achieve a good solution.

Third, trade-offs exist as not all performance indicators are equally important

to the value creation in health systems. ANP is a useful tool for balancing

different stakeholders’ perspectives, such as the conflicting objectives of the

medical and logistics professionals. Optimal performance of the internal

supply chain in hospitals can be achieved by trading-off logistics objectives,

such as cost minimization, maximization of supply availability or

maximization of staff productivity.

A final reason to adopt the ANP approach is its structuring characteristic to

transform the decision problem into a network form. The network allows for

specifying objectives on all organisational levels (i.e. strategic, tactical and

operational), whereby higher-level objectives are translated to relevant

indicators at operational levels (Van Horenbeek and Pintelon 2014). The

network also accounts for interdependent criteria, which is not valid in AHP

(Lee 2010). For example, “a change in supporting process efficiency can

cause a change in clinical care process efficiency” (Supeekit et al. 2016).

Ignoring dependencies may result in low quality of the outcomes for the

indicator prioritization process.

Despite the many advantages, the decision maker should pay attention to the

limitations and challenges incurred when performing an ANP study. MCDM methods

have been criticized by many researchers. The main reason for this criticism is that

MCDM may create a false sense of accuracy due to the possibility of manipulation.

Another weakness relates to uncertainty in both input data as well as the decision-

making process (Zardari et al. 2015). However, other researchers advocate the MCDM

approach due to its systematic and transparent methodology which increases

objectivity (Janssen 2001; Macharis, Verbeke, and De Brucker 2004). According to

some authors, the theoretical basis provided for AHP is limited (Dyer 2008; Pirdashti

et al. 2011), while this claim is countered by others (Ayag & Gürcan Özdemir, 2012;

Podvezko, 2009; L. Saaty & Vargas, 2006). ANP is a tool for comparative evaluation,

rather than providing an absolute measure of goodness of alternatives (Barfod and

CHAPTER 4

58

Leleur 2014). Another issue mentioned by Pérez et al. (2007) is that the method suffers

from rank reversals when introducing new alternatives. Hariharan et al. (2004) claim

that deriving criteria weights remains subjective. Finally, ANP solves the problem of

interdependence, though it is more demanding in terms of the number of pairwise

comparisons (i.e. n(n-1)/2 where n is number of criteria) and more advanced

calculations. Because of the resource-intensive and time-consuming nature of pairwise

comparisons, a maximum of 15 criteria is recommended. Despite all criticism about

AHP/ANP, the popularity of this method increases in different applications, which is

mainly motivated by the intuitive structure, the simplicity of application, its flexibility

and the user-friendly supporting software available (Ishizaka and Labib 2011).

4.2 Performance indicator selection and prioritization: an

ANP-based prototype for the operating theatre supply

chain

4.2.1 Introduction

Achieving operational excellence poses a major challenge to hospital managers as they

lack systems for assessing performance of the current processes in order to set future

goals according to the hospital’s strategy and objectives (Anupindi et al. 2012).

Performance management is a major research opportunity in healthcare SCM and

OR/OM literature (Volland et al. 2017). Hospitals using a performance management

framework gain a competitive advantage as they have control of their supply chain

strategy, can implement continuous improvement programs, and have improved

decision-making capabilities by focusing on relevant indicators (Maestrini et al.

2017).The performance metrics described in Chapter 3 provide a foundation for

establishing a performance management framework for internal hospital supply chain

processes. A stakeholder analysis is presented to customize the performance metrics

to the studied application.

MCDM techniques provide effective decision-support tools to select and prioritize

performance indicators, reflecting what is most important or contributes most to

process performance. ANP is a well-known MCDM method applied for performance

management by taking into account the complexity of the problem, multiple

conflicting criteria, information availability, stakeholders’ preferences and expertise in

modelling and analysis (Poh and Liang 2017). Therefore, we propose a prototype,


59

based on ANP, to measure the performance of internal supply chain processes by

aligning the hospital objectives and KPIs in order to provide a holistic view of the

health system performance. An illustration of the ANP methodology is presented by

implementing the prototype to one hospital department, namely the operating theatre

(OT). The OT is a critical resource and cost driver in the health system and hence, it is

an excellent opportunity for identifying efficiency gains. Starting from the logistics

perspective, the OT logistics manager is closely involved in the design and testing

phase of the prototype. In Chapter 6, more details will be provided on extending the

framework to a wider healthcare logistics context by incorporating all stakeholders in

the OT supply chain. The purpose is to define the best set of KPIs for evaluating

internal logistics processes (e.g. inventory and distribution systems) in accordance

with the objectives of the hospital department under study. The final outcome will be

a prioritization of KPIs, which serves as a useful input for hospital stakeholders to gain

managerial insights into tackling the trade-offs which are inherent to OT logistics

processes. The ANP method aligns with the value-based care model by striving

towards a shared goal that unites the interests of all stakeholders based on a clear

definition for operational excellence as obtained by the KPI prioritization. Hence, we

provide a common language for stakeholders to enhance alignment between patient

care and logistics processes in order to maximize value creation by identifying

efficiency gain opportunities.

4.2.2 Stakeholder analysis: selecting logistics performance

metrics for the OT

Selecting meaningful performance metrics is a challenging task for decision makers,

as different organisations use different metrics based on company goals, strategies or

policies (Ordoobadi 2012). In order to design a generic performance management

framework applicable to various healthcare logistics processes, the purpose is to select

a list of traditional performance metrics as considered in logistics engineering, which

can be customized according to the specific context or situation needs (Van Horenbeek

and Pintelon 2014; El Mokrini et al. 2018). Based on the extensive literature review in

Chapter 3, the OT logistics manager selects a set of performance indicators and

categorizes the indicators according to four objectives – quality, time, financial and

productivity/organisation – to represent the interests of the OT department. A single

stakeholder is consulted in this second module of the framework development

methodology based on his logistics expertise, tactical responsibility and accessibility

CHAPTER 4

60

to logistics data in order to interpret the relevance of KPIs for the problem situation

and to avoid potential conflicts as powerful stakeholders largely impact the weighting

procedure which might lead to biased results that maximize their own objectives. In

contrast to early-participation, the indicators are verified by including stakeholder at

the strategic, tactical and operational level in the final testing phase of the framework

to ensure more informed decision making and reduce supply chain fragmentation.

Table 4-1 and Table 4-2 give an overview of objectives and indicators for measuring

the performance of inventory and distribution systems respectively in the internal OT

supply chain. The first column contains KPIs categorized per objective, followed by a

definition in column two. The KPI definitions ensure uniform performance

measurement and thus allow for comparing different departments and learning from

best practices to achieve operational excellence. Depending on the process type, the

indicators need to be customized to monitor relevant KPIs for the problem situation.

In the third column, we propose potential sub-indicators that are related to the KPIs.

An interpretation of dependencies between the indicators is given in the final column

based on the guide to KPIs for public health managers by Aronovich et al. (2010). The

interrelationships among KPIs are generic as the indicators are based on traditional

logistics engineering processes regardless of the context. For instance, increasing the

level of safety stock will change the inventory turnover ratio, reduce the stock-out rate

and increase the capital tied up to stock. However, the strength of the dependency

depends on context-specific characteristics.


61

Tab

le 4

-1.

Over

vie

w o

f in

ven

tory

per

form

an

ce m

etri

cs.

Qu

ali

ty

Qu

ali

ty s

pecif

ies

ho

w w

ell

a s

pec

ific

act

ivit

y h

as

bee

n p

erfo

rmed

, en

suri

ng t

hat

pati

ents

rec

eiv

e ca

re s

ervic

e in

a s

afe

man

ner

an

d t

hat

pro

ble

ms

such

as

med

ical

erro

rs a

re m

inim

ized

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Inven

tory

ser

vic

e

level

(IS

L)

Th

e ab

ilit

y t

o f

ulf

il i

nco

min

g o

rder

s

fro

m s

tock

on

-han

d

Inven

tory

avail

ab

ilit

y

Sto

ck-o

ut

rate

ISL

is

main

ly d

eter

min

ed b

y t

he

sto

ck-o

ut

rate

, w

hic

h

shou

ld b

e as

low

as

poss

ible

to e

nsu

re i

tem

avail

ab

ilit

y.

Inven

tory

vis

ibil

ity (

IV)

Th

e ab

ilit

y t

o m

on

itor

the

am

ou

nt

of

stock

on

-han

d i

n t

he

cen

tral

stora

ge

an

d p

oin

t-o

f-u

se l

oca

tion

s

Inven

tory

level

Safe

ty s

tock

Su

pp

ly l

oca

tion

(ce

ntr

al

or

dec

entr

al)

Un

der

stan

din

g w

hat

you

have

in s

tock

an

d w

her

e it

is

loca

ted

is

imp

ort

an

t to

in

crea

se I

V t

hro

ugh

ou

t th

e

sup

ply

ch

ain

an

d r

edu

ce i

nven

tory

(sa

fety

) le

vels

. In

ad

dit

ion

, it

en

sure

s a b

ette

r accu

racy

rate

, lo

wer

sto

ck-

ou

t ra

te,

red

uce

d c

ost

s an

d s

trea

mli

ned

pro

ces

ses.

Inven

tory

acc

ura

cy (

IA)

Th

e ab

ilit

y t

o c

om

pare

sto

ck r

ecord

s

an

d a

ctu

al

stock

on

-han

d

Inven

tory

dis

crep

an

cy

Sto

cked

accord

ing t

o p

lan

Item

s in

over

sto

ck

Item

s in

un

der

sto

ck

Ass

ess

the

qu

ali

ty o

f in

ven

tory

con

tro

l b

y m

easu

rin

g

devia

tion

s o

f d

ata

on

in

ven

tory

levels

in

ord

er t

o

acc

ura

tely

tra

ck i

tem

s. L

ow

er i

s b

ette

r b

y r

ati

on

ali

zin

g

item

s to

im

pro

ve

ISL

an

d I

V t

hro

ugh

ou

t th

e su

pp

ly

chain

.

Inven

tory

crit

icali

ty (

ICr)

Th

e im

pact

of

erro

rs o

r d

ela

ys

of

sup

pli

es o

n t

he

surg

ical

pro

ced

ure

Avail

ab

ilit

y o

f su

bst

itu

te

sup

pli

es

% o

f er

rors

(w

ron

g i

tem

or

qu

an

tity

of

item

s)

Dela

ys

of

pro

ced

ure

s

ICr

ind

irec

tly m

easu

res

pati

ent

safe

ty b

y m

easu

rin

g t

he

eff

ect

of

inacc

ura

te o

r in

com

ple

te s

up

ply

pro

vis

ion

on

the

con

tin

uati

on

of

the

pro

ced

ure

. T

his

mig

ht

be

cau

sed

by b

ack

ord

ers,

pic

kin

g e

rrors

, et

c. L

ow

er i

s b

ette

r,

wh

ich

als

o i

mp

roves

IS

L.

CHAPTER 4

62

Fin

an

cia

l F

inan

cial

ind

icato

rs i

den

tify

su

pp

ly c

hain

cost

dri

ver

s, s

uch

as

exp

ense

s in

curr

ed b

y d

epart

men

ts f

or

pro

vid

ing s

ervic

es,

inclu

din

g d

irec

t an

d o

ver

hea

d c

ost

s fo

r in

ven

tory

an

d i

nte

rnal

dis

trib

uti

on

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Inven

tory

cost

(IC

o)

Th

e an

nu

al

cost

of

ho

ldin

g i

nven

tory

at

a s

pec

ific

sto

rag

e ro

om

Ord

erin

g c

ost

Ho

ldin

g c

ost

Sh

ort

ag

e cost

Over

sto

ckin

g c

ost

Sto

rag

e sp

ace

cost

Th

e an

nu

al

ICo c

an

be

bro

ken

do

wn

in

to d

iffe

ren

t co

st

com

pon

ents

. M

on

itori

ng t

he

cost

s en

sure

s b

ette

r IS

L,

IV a

nd

IA

.

Eff

ect

ive i

nven

tory

con

trol

ensu

res

low

er c

ost

s an

d t

hu

s

less

cap

ital

tied

up

to i

nven

tory

.

Valu

e o

f

inven

tory

(V

oI)

Th

e d

eter

min

ati

on

of

the

cost

of

aver

ag

e in

ven

tory

at

the

end

of

a

per

iod

Valu

e o

f in

ven

tory

per

stora

ge

roo

m

Valu

e o

f exp

ired

/dam

ag

ed

stock

Week

ly a

nd

dail

y v

alu

e fo

r re

spect

ively

th

e cen

tral

an

d

dec

entr

al

sto

ck.

Inaccu

rate

mate

rial

han

dli

ng c

au

se a

hig

her

am

ou

nt

of

dam

ag

ed o

r exp

ired

sto

ck,

wh

ich

has

a n

egati

ve

imp

act

on

th

e fi

nan

cial,

qu

ali

ty a

nd

pro

du

ctiv

ity o

bje

ctiv

es.


63

Pro

du

cti

vit

y/

Org

an

isa

tio

n

Pro

du

ctiv

ity

/Org

an

isati

on

in

volv

es

op

erati

on

al

con

tro

l m

etri

cs f

or

log

isti

cs d

epart

men

ts u

sed

for

stre

am

lin

ing p

roce

sses

,

red

uci

ng c

ost

s, f

acil

itati

ng i

nfo

rmati

on

flo

w a

nd

en

han

cin

g p

rovid

ed c

are

ser

vic

es

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Inven

tory

turn

over

(IT

u)

A m

easu

re o

f h

ow

fast

in

ven

tory

is

use

d i

n t

he

stora

ge

room

Inven

tory

days

on

-han

d

Sto

ck r

ota

tion

: sl

ow

-,

norm

al-

, fa

st-m

ovin

g

item

s

Th

e su

b-i

nd

icato

rs m

easu

re t

he

nu

mb

er o

f d

ays

inven

tory

is

store

d.

Low

er i

s b

ette

r si

nce

les

s ca

pit

al

is

tied

up

to i

nven

tory

an

d t

he

inven

tory

level

is l

ow

er.

More

over,

it

ind

icate

s h

ow

mu

ch i

nven

tory

is

con

sum

ed i

n a

cer

tain

tim

e p

erio

d a

nd

en

cou

rages

stan

dard

izati

on

.

Inven

tory

usa

ge

(IU

)

Th

e am

ou

nt

of

inven

tory

un

its

con

sum

ed d

uri

ng a

cer

tain

tim

e p

erio

d

Con

sum

pti

on

rate

Ph

ysi

cia

n p

refe

ren

ce

card

usa

ge

rate

% o

f re

turn

ed i

tem

s

Sto

rag

e sp

ace

uti

liza

tion

Pro

du

ctiv

ity i

s m

easu

red

by t

rack

ing I

U r

eco

rds,

wh

ich

inclu

des

con

sum

pti

on

as

well

as

retu

rn o

f it

em

s. T

his

pro

vid

es e

ssen

tial

info

rmati

on

for

sett

ing i

nven

tory

levels

an

d i

mp

rove

dem

an

d f

ore

cast

ing.

In a

dd

itio

n,

stora

ge

space

uti

liza

tion

can

be

imp

roved

by m

on

itori

ng

IU.

Pro

du

ct

stan

dard

izati

on

(PS

)

Th

e ab

ilit

y t

o r

edu

ce

vari

ati

on

of

med

ical

sup

pli

es i

n o

rder

to i

ncr

ease

tim

e sp

ent

at

core

act

ivit

ies

Pro

du

ct d

up

lica

tion

Un

iform

work

flow

s

Pro

du

ct/l

oca

tion

iden

tifi

cati

on

sta

nd

ard

s

Pro

du

ct d

up

lica

tion

in

mu

ltip

le l

oca

tion

s an

d d

iffe

ren

t

refe

ren

ce

nu

mb

ers

neg

ati

vely

im

pact

pro

du

ctiv

ity.

Vari

ati

on

in

pro

du

ct s

tan

dard

s m

ust

be

min

imiz

ed t

o

ensu

re q

uali

tati

ve a

nd

un

iform

in

ven

tory

con

trol,

wh

ile

con

tain

ing c

ost

s.

CHAPTER 4

64

Tab

le 4

-2.

Over

vie

w o

f d

istr

ibu

tion

per

form

ance

met

rics

.

Qu

ali

ty

Qu

ali

ty s

pec

ifie

s h

ow

well

a s

pecif

ic a

ctiv

ity

has

bee

n p

erfo

rmed

, en

suri

ng t

hat

pati

ents

receiv

e ca

re s

ervic

e in

a s

afe

man

ner

an

d t

hat

pro

ble

ms

such

as

med

ical

erro

rs a

re m

inim

ized

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Dis

trib

uti

on

acc

ura

cy (

DA

)

Th

e ab

ilit

y

to

pic

k

an

d

del

iver

the

corr

ect

item

s an

d

qu

an

titi

es

from

stora

ge

to p

oin

t-o

f-u

se l

oca

tion

Deli

ver

y/p

ick

ing a

ccu

racy

rate

Per

fect

ord

er f

ulf

ilm

ent

DA

is

mea

sure

d b

y t

he

nu

mb

er o

f er

rors

in

deli

ver

ing

or

pic

kin

g i

tem

s to

su

pp

ort

care

pro

ces

ses.

Th

e lo

wer

the

nu

mb

er o

f er

rors

, th

e b

ette

r th

e qu

ali

ty a

nd

org

an

isati

on

of

the

dis

trib

uti

on

sy

stem

.

Cen

trali

zati

on

(CI)

Th

e ab

ilit

y t

o p

ick

ite

ms

at

a c

entr

al

stora

ge

roo

m,

aw

ay f

rom

th

e op

erati

ng

roo

ms

Mate

rial

mo

vem

ents

to

dec

entr

al

loca

tion

s

Nu

mb

er o

f tr

ips

to s

tora

ge

roo

ms

Lo

gis

tics

act

ivit

ies

clo

se

to O

R

Th

e im

pact

of

CI

is m

easu

red

by t

he

para

llel

flo

w o

f

item

s to

man

y d

ece

ntr

al

sto

ckin

g l

oca

tion

s. T

he

less

mate

rial

mo

vem

ents

an

d t

rip

s to

dif

fere

nt

stora

ge

roo

ms,

th

e m

ore

ite

ms

can

be

pic

ked

at

cen

tral

pla

ces,

the

low

er t

he

dis

trib

uti

on

cost

an

d t

he

less

hin

der

for

care

pro

cess

es.

Dis

trib

uti

on

serv

ice

level

(DS

L)

Th

e avail

ab

ilit

y o

f lo

gis

tics

ser

vic

es t

o

sup

port

cli

nic

al

care

pro

ces

ses

Nu

mb

er o

f ad

dit

ion

al

item

s

Urg

ent

mate

rial

requ

ests

Nu

mb

er o

f re

turn

ed i

tem

s

Th

e su

b-i

nd

icato

rs m

on

itor

the

exte

nt

to w

hic

h

dis

trib

uti

on

act

ivit

ies

ensu

re t

hat

the

righ

t it

em

s are

at

the

righ

t p

lace

at

the

righ

t ti

me.

Th

e le

ss a

dd

itio

nal

mate

rial

requ

ests

, th

e le

ss n

urs

es n

eed

to l

eave

the

op

erati

ng r

oom

, th

us

the

bet

ter

the

log

isti

cs s

up

port

an

d

DA

. H

ow

ever,

pro

vid

ing t

oo m

an

y i

tem

s re

sult

s in

a

rever

se l

og

isti

cs f

low

, w

hic

h n

egati

vely

aff

ect

s D

SL

.


65

Tim

e

Tim

e in

vo

lves

the

per

iod

to c

om

ple

te t

he

log

isti

cs o

per

ati

on

s to

en

sure

th

at

the

righ

t it

em

s are

at

the

righ

t p

lace

an

d t

ime

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Rep

len

ish

men

t

lead

tim

e (R

LT

)

Th

e to

tal

am

ou

nt

of

tim

e th

at

ela

pse

s

fro

m

the

mo

men

t an

it

em

is

ord

ered

un

til

the

item

is

back

on

th

e sh

elf

Ord

er +

pic

kin

g +

tran

sport

atio

n +

un

load

ing

tim

e

RL

T i

nclu

des

ord

erin

g,

pic

kin

g,

tran

sport

ing a

nd

un

load

ing t

ime.

Sh

ort

er l

ead

tim

es w

ill

imp

rove

the

eff

icie

ncy o

f b

oth

th

e d

istr

ibu

tion

an

d i

nven

tory

syst

em

(e.g

. le

ss s

afe

ty s

tock

).

Res

pon

se t

ime

(RT

)

Th

e ab

ilit

y t

o d

eli

ver

ite

ms

on

tim

e,

pre

ven

tin

g d

ela

ys

in s

urg

ical

pro

ced

ure

s

Avera

ge

deli

ver

y t

ime

On

-tim

e d

eli

ver

y

Rati

o o

f d

eli

ver

y t

ime

to

dis

tan

ce t

ravel

led

Th

e su

b-i

nd

icato

rs m

easu

re t

he

ord

ers

deli

ver

ed b

y t

he

requ

este

d t

ime

an

d p

oss

ible

del

ays

of

surg

ical

pro

ced

ure

s. O

n-t

ime

deli

ver

ies

posi

tively

aff

ect

th

e

qu

ali

ty a

nd

pro

du

ctiv

ity o

bje

ctiv

es,

wh

ile

avo

idin

g

cost

s o

f ex

ped

itin

g o

rder

s.

Cli

nic

al

staff

invo

lvem

ent

(CS

I)

Th

e am

ou

nt o

f ti

me

cli

nic

al st

aff

is

bu

sy

wit

h

logis

tics

ta

sks,

ra

ther

th

an

thei

r

core

act

ivit

ies

Tim

e sp

ent

at

log

isti

cs

act

ivit

ies

Tim

e sp

ent

to r

equ

est/

retr

ieve

sto

ck-o

ut

item

s

Fre

qu

ency

an

d t

ime

of

leavin

g O

R

Cli

nic

al

staff

mu

st b

e re

lieved

as

mu

ch a

s p

oss

ible

fro

m

an

y l

og

isti

cs a

ctiv

itie

s in

ord

er t

o f

ocu

s on

th

eir

core

act

ivit

y.

CHAPTER 4

66

Fin

an

cia

l F

inan

cial

ind

icato

rs id

enti

fy su

pp

ly ch

ain

cost

d

river

s, su

ch as

exp

ense

s in

curr

ed b

y d

epart

men

ts fo

r p

rovid

ing se

rvic

es,

inclu

din

g d

irec

t an

d o

ver

hea

d c

ost

s fo

r in

ven

tory

an

d i

nte

rnal

dis

trib

uti

on

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Dis

trib

uti

on

cost

(DC

o)

Tota

l cost

of

han

dli

ng a

nd

tra

nsp

ort

ing

to m

ove

sup

pli

es f

rom

sto

rag

e ro

om

s

to p

oin

t-of-

care

loca

tion

s

Han

dli

ng c

ost

Tra

nsp

ort

ati

on

cost

Rati

o o

f tr

an

sport

ati

on

cost

to v

alu

e of

item

s

Mon

itori

ng c

ost

co

mp

on

ents

to e

nsu

re c

ost

con

tain

men

t

an

d e

ffic

ien

t d

istr

ibu

tion

syst

ems.

Per

son

nel

cost

(PC

o)

Th

e co

st r

ela

ted

to t

he

tim

e p

erso

nn

el

is i

nvo

lved

wit

h l

og

isti

cs a

ctiv

itie

s

Per

son

nel

tim

e

Th

e la

bou

r cost

ass

ocia

ted

wit

h h

an

dli

ng a

nd

mo

vin

g

sup

pli

es m

ust

be

min

imiz

ed.


67

P

ro

du

cti

vit

y/

Org

an

isa

tio

n

Pro

du

ctiv

ity

/Org

an

isati

on

in

vo

lves

op

erati

on

al

con

tro

l m

etri

cs

for

log

isti

cs d

epart

men

ts u

sed

fo

r st

ream

lin

ing

p

roce

sses

,

red

uci

ng c

ost

s, f

acil

itati

ng i

nfo

rmati

on

flo

w a

nd

en

han

cin

g p

rovid

ed c

are

ser

vic

es

KP

Is

Defi

nit

ion

S

ub

-in

dic

ato

rs

Inte

rp

reta

tio

n

Case

cart

eff

icie

ncy (

CC

E)

Th

e avail

ab

ilit

y a

nd

uti

liza

tion

of

case

cart

s to

pro

vid

e su

rgeon

s w

ith

th

e

requ

ired

su

pp

lies

Case

cart

avail

ab

ilit

y

Case

cart

uti

liza

tion

Th

e p

rod

uct

ivit

y o

f th

e lo

gis

tics

tea

m i

mp

roves

if c

ase

cart

s are

eff

icie

ntl

y u

sed

in

ord

er

to e

lim

inate

wast

e or

rever

se l

og

isti

cs f

low

s. C

ase

cart

uti

liza

tion

dep

end

s on

the

acc

ura

cy o

f p

hysi

cian

pre

fere

nce

card

s an

d t

hus

has

an

im

pact

on

both

th

e d

istr

ibu

tion

an

d i

nven

tory

syst

em.

Deli

ver

y

frequ

ency (

DF

)

Th

e n

um

ber

of

vis

its

to d

ecen

tral

stora

ge

loca

tion

s to

del

iver

or

rep

len

ish

item

s in

th

ese

loca

tion

s

% o

f it

em

s re

ple

nis

hed

Sca

nn

ing f

requ

ency

Revie

w p

erio

d

Th

e n

um

ber

of

vis

its

to d

ecen

tral

stora

ge

roo

ms

mu

st b

e

red

uce

d b

y i

ncr

easi

ng t

he

nu

mb

er o

f it

em

s re

ple

nis

hed

or

incr

easi

ng t

he

revie

w p

erio

d,

wh

ich

in

tu

rn w

ill

dec

rease

dis

trib

uti

on

cost

s.

Pro

ces

s

stan

dard

izati

on

(S)

Th

e ab

ilit

y t

o s

imp

lify

work

flow

s

bet

wee

n o

per

ati

ng r

oom

s an

d i

mp

rove

work

ing c

on

dit

ion

s

Un

iform

/sta

nd

ard

ized

work

flo

ws

an

d p

ick

ing

list

s

% o

f sc

an

nab

le i

tem

s

Th

e su

b-i

nd

icato

rs m

on

itor

init

iati

ves

to s

imp

lify

work

flo

ws

by i

ncr

easi

ng t

he

nu

mb

er o

f sc

an

nab

le i

tem

s

an

d s

tan

dard

izin

g t

he

lay

ou

t o

f p

ick

ing l

ists

th

rou

gh

ou

t

the

OT

, w

hic

h i

n t

urn

wil

l re

du

ce

DC

o a

nd

im

pro

ve

the

qu

ali

ty o

f th

e d

istr

ibu

tion

syst

em.

Per

son

nel

man

agem

ent

(PM

)

A m

easu

re o

f h

ow

to o

bta

in,

use

an

d

main

tain

a s

ati

sfie

d w

ork

forc

e

Per

son

nel's

sati

sfact

ion

Work

load

dis

trib

uti

on

Per

son

nel

flexib

ilit

y

Per

son

nel

uti

liza

tion

Erg

on

om

ics

frie

nd

lin

ess

Em

plo

yee

invo

lvem

ent

is a

cru

cia

l su

cces

s p

ara

met

er

for

eff

icie

ntl

y o

rgan

isin

g t

he

log

isti

cs a

ctiv

itie

s. I

n

ad

dit

ion

, in

crea

sin

g t

he

ab

ilit

y t

o d

o a

ll t

ask

s

(fle

xib

ilit

y)

hel

ps

to s

trea

mli

ne

the

op

erati

on

s.

CHAPTER 4

68

4.2.3 Performance indicator selection and prioritization using

ANP

KPI selection and prioritization problems can be solved as a MCDM problem (Carlucci

2010). The KPIs do not equally contribute to the overall performance of logistics

processes. ANP is recommended as a prioritization methodology which takes into

account stakeholder judgments. Moreover, the interrelationship property of ANP is

particularly important when addressing topics related to performance management due

to the existence of feedback relationships between indicators. For example, Supeekit

et al. (2016) show that a change in supporting process efficiency results in a change in

clinical care process efficiency. This section presents an ANP-based prototype to select

and prioritize indicators that could be of any importance for measuring the efficiency

of the healthcare supply chain processes, illustrated for a case study at the OT of the

University Hospital in Leuven (UZ Leuven). For more detailed information on the

ANP method, the interested reader is referred to the work of Saaty (2008).

Step 1 – Model construction

In the first step of the ANP methodology, the model is constructed based on the

problem formulation. ANP structures decision problems into a network of clusters,

nodes and dependency relations among the network elements. Figure 4-2 displays the

generic network structure for performance management of inventory and distribution

systems in hospitals. The clusters and nodes refer to the objectives and KPIs

respectively, which are based on Table 4-1 and Table 4-2.

At the top level, the goal is defined as ranking KPIs to measure efficiency of OT

logistics processes. A distinction is made between inventory and distribution activities,

where the former represents the process of storing disposable medical-surgical

supplies and the latter refers to replenishment policies for getting materials at the right

place and time for surgical procedures. The ANP ranking will guide hospital managers

to identify improvement opportunities for both processes that will enhance overall OT

logistics performance.

At the second and third level, the clusters contain respectively the objectives and KPIs

for inventory and distribution processes. The definitions can be found in Table 4-1 and

Table 4-2. Remark that the KPIs in the tables slightly deviate from the ones presented

in Table 3-1 and Table 3-2 as extracted from the literature review. Customized to the

OT setting, the time objective is important for determining replenishment or


69

distribution policies, but it is not considered as an objective for inventory management.

Clinical Staff Involvement (CSI) is identified as a time-related KPI and thus needs to

be re-categorized to the distribution indicators. Furthermore, we decompose

preparation time into Replenishment Lead Time (RLT) and Response Time (RT) to

distinguish between the time needed for replenishing and the ability to deliver on time.

Moreover, finding a workload balance is considered as a time-related KPI, though in

this setting it is shifted to productivity/organisation to measure staff satisfaction within

the Personnel Management (PM) KPI. In addition, patient safety is a key parameter to

determine the quality of the inventory system. In this work, patient safety is indirectly

included within Inventory Criticality (ICr), which measures the impact of errors or

delays of supplies on the surgical procedure (see Table 4-1). Patient safety is difficult

to measure quantitatively, whereas the Vital-Essential-Desirable (VED) analysis

provides a quantitative measure for ICr.

Finally, ANP considers inner dependencies (i.e. elements of a cluster depend on each

other) or outer dependencies (i.e. feedback between clusters from different levels)

among the network elements (Van Horenbeek and Pintelon 2014). The direction of the

arrow determines the relationship between the elements or clusters. For example,

Inventory Visibility (IV) is inner dependent on Inventory Service Level (ISL) since

higher stock levels will decrease the stock-out rate and thus improve ISL. In addition,

an increase in stock levels implies a larger Inventory Cost (ICo) which specifies the

outer dependence of ICo on IV and ISL. The relationships are described by inner link

A and outer link B respectively in Figure 4-2.

CHAPTER 4

70

Step 2 – Building pairwise comparison matrices

After customizing the network structure to the context-specific problem, pairwise

comparison matrices are constructed for each relationship between clusters and/or

nodes in the network. The pairwise comparison approach is based on Saaty’s nine-

point scale to derive decision makers’ preferences (Karpak, 2017; Saaty, 2008, 2010).

Next, the priorities or weights of each criterion can be derived from the pairwise

comparison matrix Wi by solving the principal eigenvector problem in Equation 4.1.

In this equation, λmax is the largest or principal eigenvalue of matrix Wi and w is the

local priority vector containing the weights (Saaty, 1990a). As an illustrative example,

Table 4-3 presents a pairwise comparison matrix for the inventory problem, indicating

the preferences of the OT logistics manager, namely how much an indicator in a row

is preferred to indicators in the columns. Table 4-3 illustrates that, with respect to the

‘Quality’ objective, ‘Inventory service level’ is five times more important or preferred

than ‘Inventory visibility’. The column ‘Priorities’ contains the local priority vector,

Figure 4-2. A generic hospital logistics performance measurement framework.


71

representing the weight of each element in the pairwise comparison matrix while

always considering the goal criterion in the network.

𝑊𝑖 𝑤 = 𝜆𝑚𝑎𝑥 𝑤 (4.1)

Table 4-3. Pairwise comparisons of indicators with respect to 'Quality' objective.

Performing pairwise comparisons in a consistent way is almost never the case.

Therefore, Saaty (2008) recommends calculating a Consistency Ratio (CR) with a

threshold of 10% for all judgments of the decision makers. If this ratio exceeds the

threshold, judgments must be revised or the weighted consistency method by Jarek

(2016) can be used to reduce inconsistency without changing decision makers’

judgments. CR is included in the final column of Table 4-3. CR is calculated as the

ratio of the Consistency Index (CI) and the average random consistency index (RI),

which is dependent on the rank n of the square matrix Wi as can be seen in Table 4-4

(Saaty, 2013). CI is calculated in Equation 4.2. Finally, it is important to note that other

challenges can be encountered in this step, such as uncertainty or vagueness of

preferences, gaps in knowledge or lack of problem understanding, or conflicts between

different decision makers, which may lead to wrong decisions based on the framework.

𝐶𝐼 =𝜆𝑚𝑎𝑥 − 𝑛

𝑛 − 1 (4.2)

Table 4-4. Random index (RI) (Saaty, 2013).

Step 3 – Supermatrix formation and overall priority calculation

The third step of the ANP methodology consists of composing a supermatrix including

all interactions between clusters and/or nodes in the network to describe the impact of

a row element on the column elements. The local priority vectors obtained in step 2

form the segments of the supermatrix. Three types of supermatrices can be

distinguished, namely the unweighted, weighted and limit supermatrix. The

Quality ISL IV IA ICr Priorities CR

Inventory service level 1 5 5 1/3 0.305 0.058

Inventory visibility 1/5 1 1 1/5 0.078

Inventory accuracy 1/5 1 1 1/5 0.078

Inventory criticality 3 5 5 1 0.538

n 1 2 3 4 5 6 7 8 9 10 …

RI 0 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 …

CHAPTER 4

72

unweighted supermatrix (see Table 4-5) contains the local priority vectors as obtained

by the pairwise comparisons of the OT logistics manager in this test design. Table 4-3

provides the priority vectors for the quality-related KPIs in the first column. The

priority vectors for the other elements are derived in a similar way.

Table 4-5. Unweighted supermatrix for inventory management network.

Quality Cost Productivity O ISL IV IA ICr ICo VoI ITu IU PS

Objectives (O) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Inventory

service level

(ISL)

0.305 0.000 0.750 0.800 0.614 0.625 0.540 0.429 0.439 0.594

Inventory

visibility (IV)

0.078 0.088 0.000 0.200 0.117 0.136 0.163 0.429 0.265 0.249

Inventory

accuracy (IA)

0.078 0.195 0.250 0.000 0.268 0.238 0.297 0.143 0.110 0.157

Inventory

criticality (ICr)

0.538 0.717 0.000 0.000 0.000 0.000 0.000 0.000 0.187 0.000

Inventory cost

(ICo)

0.750 0.000 0.200 0.500 0.000 0.000 1.000 0.800 0.000 0.750

Value of

inventory (VoI)

0.250 0.000 0.800 0.500 0.000 1.000 0.000 0.200 0.000 0.250

Inventory

turnover (ITu)

0.167 1.000 0.655 0.750 0.000 0.648 0.540 0.000 0.167 0.125

Inventory

usage (IU)

0.667 0.000 0.250 0.000 0.750 0.230 0.297 0.500 0.000 0.875

Product

standardization

(PS)

0.167 0.000 0.095 0.250 0.250 0.122 0.163 0.500 0.833 0.000

The clusters are not equally important as the objectives differ in how they contribute

to the overall performance of OT logistics processes. Therefore, clusters are also

pairwise compared to establish their relative importance by deriving cluster weights.

Link C in Figure 4-2 represents the inner dependence loop within the objective cluster

and the cluster weights can be found in Appendix A. The cluster weights are used to

normalize the unweighted supermatrix and subsequently form the weighted

supermatrix. Finally, the weighted supermatrix is raised to large powers in order to

reach convergence or stability. This matrix with equal scores in all rows is called the

limit supermatrix, and the scores denote the overall or global priorities of each element

in the network. The weighted supermatrix and limit supermatrix are shown in

Appendix A.


73

Step 4 – Prioritization and selection of KPIs

The limit supermatrix contains the overall priorities of the indicators, representing the

impact of each element on every other element in the network with which it interacts.

Table 4-6 presents the global priorities of the indicators (column 4), together with a

ranking of performance indicators (column 5) for measuring the efficiency of the OT

supply chain operations. The objective weights (column 2) are determined based on

the weights of the individual indicators. For further analysis, the indicator weights are

used to assess different logistics policies. The highest-ranked indicators represent the

most important KPIs with the greatest contribution to improving inventory

management and distribution systems.

4.2.4 Results

The example illustrates a methodological approach for ranking logistics objectives and

selecting KPIs that contribute most to improving OT supply chain processes, such as

inventory and distribution. The generic network in Figure 4-2 is established based on

literature and interviews with hospital logistics experts. In this initial prototype of the

logistics performance management framework, one stakeholder, namely the OT

logistics manager at UZ Leuven, has judged the pairwise comparisons. According to

this stakeholder’s preferences, the following preliminary results are found.

For improving inventory management systems, quality is the first-ranked objective

with a weight of 68% (see Table 4-6) and thus the greatest contributor to efficiency

improvements. Also note the low weight for the financial objective (i.e. 6%) which is

surprising as the logistics manager’s goal is to reduce costs while maintaining high

service levels. The objective weights are based on the individual indicator weights,

though they are independent of the number of indicators per objective. A sensitivity

analysis has been performed to verify the objective weights when removing one KPI

(e.g. inventory visibility) in the quality objective. Similar results can be found, even a

small increase in the quality weight to 69% has been observed because the cluster

weights in Appendix A show that quality has the most impact on both financial and

productivity objectives. The final column in Table 4-6 computes the cumulative

weights by sorting the indicators in a descending order according to their weight-based

ranking. When exploring the results into detail, the cumulative percentage shows that

the four highest ranked indicators account for almost 70% of the total weight assigned

to all indicators. These four indicators are inventory service level, inventory criticality,

CHAPTER 4

74

Tab

le 4

-6. R

ank

ing

of

inv

ento

ry a

nd

dis

trib

uti

on K

PIs

bas

ed o

n A

NP

wei

ghts

.

Inven

tory

ob

ject

ives

W

eigh

ts

Ind

icato

rs

Wei

gh

ts

Ran

kin

g

Cu

mu

lati

ve

wei

gh

ts

Qu

ali

ty

0.6

8

Inven

tory

ser

vic

e le

vel

0.2

89

1

0.2

89

Inven

tory

vis

ibil

ity

0.1

06

5

0.8

02

Inven

tory

acc

ura

cy

0.1

23

3

0.5

76

Inven

tory

cri

tica

lity

0.1

64

2

0.4

53

Fin

an

cial

0.0

6

Inven

tory

cost

0.0

32

8

0.9

72

Val

ue

of

inven

tory

0.0

27

9

1.0

00

Pro

du

ctiv

ity/

org

an

isati

on

0.2

6

Inven

tory

turn

over

0.1

20

4

0.6

96

Inven

tory

usa

ge

0.0

75

6

0.8

77

Pro

duct

sta

ndar

diz

atio

n

0.0

63

7

0.9

40

Dis

trib

uti

on

ob

ject

ives

W

eigh

ts

Ind

icato

rs

Wei

gh

ts

Ran

kin

g

Cu

mu

lati

ve

wei

gh

ts

Qu

ali

ty

0.3

2

Del

iver

y ac

cura

cy

0.0

92

5

0.6

30

Cen

tral

izat

ion

0.0

91

6

0.7

21

Dis

trib

uti

on s

ervic

e le

vel

0.1

36

2

0.2

96

Tim

e 0.1

5

Rep

lenis

hm

ent

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75

inventory accuracy and inventory turnover. Inventory service level is the most

important indicator in the OT setting, representing the ability to fulfil orders from stock

on-hand. At the operational level, inventory service level can be measured using sub-

indicators inventory availability or stock-out rate. The second most important indicator

is inventory criticality, measuring the impact of errors, delays of supplies or adverse

effects on the surgical procedure. Inventory criticality is indirectly related to patient

safety, which is the ultimate goal in any healthcare process. Third, having accurate

inventory is important to eliminate overstocking or hoarding behaviour of clinical staff

who hold many items in stock as a response to the high variability encountered in the

OT. Furthermore, product standardization and the cost indicators appear to be less

important compared to the quality-related indicators. However, improving product

standardization and introducing appropriate product/location identification standards

is crucial to reduce variability due to physician preferences, and hence improve

inventory control and reduce inventory costs.

The framework can also be customized to guide hospital management in selecting and

prioritizing KPIs related to internal distribution or replenishment policies to point-of-

care, ensuring that the right materials are at the right place and time. Table 4-6 shows

that productivity/organisation (48%) and quality (32%) are the main objectives when

evaluating distribution processes. It is remarkable that productivity is ranked higher

compared to quality. A plausible explanation is that the logistics manager emphasizes

process standardization, personnel management and delivery frequency as the relevant

KPIs to simplify the workflow and enhance staff satisfaction. Together with

distribution service level, these four indicators make up more than 50% of all

indicators. Notice that the time indicators have lower weights, although the logistics

manager aims to provide all materials for surgical procedures in time.

The findings show that different objectives and KPIs are selected dependent on the

process type in order to create a clear performance definition. The ANP-based

prototype serves as a reference model providing hospital stakeholders with a better

understanding of KPIs that are most important for a specific problem, or in other

words, it helps identifying the best set of KPIs for assessing the performance of

inventory and distribution systems. This study contributes to literature by proposing a

methodological and transparent approach, integrating multiple indicators considered

by traditional OR/OM techniques to enhance decision making based on context-

specific objectives. The prototype is based on ANP to calculate global priority weights,

which allows to select and prioritize relevant KPIs to improve inventory and

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76

distribution systems. Moreover, the prototype enables quantification of trade-offs

between different objectives, and thus improves evidence-based decision making when

the OT logistics manager defines service levels in order to reduce costs or reduce

number of stock-outs.

4.2.5 Discussion

The increased pressure to reduce costs forces hospital managers to develop new

managerial tools to ensure efficient operations of their care delivery systems (Serrou

and Abouabdellah 2016). The proposed approach selects a set of KPIs to support

decision making and guides hospital stakeholders in identifying performance gaps or

deficient areas. Step 1 discusses the inclusion or exclusion of criteria compared to the

literature review in Section 3.2.1. Some indicators are combined or eliminated due to

overlap or too broad interpretation. The prototype can be used with any set of criteria,

and thus is generic enough to evaluate other healthcare supply chain processes by

customizing it to specific contexts. In this case study, the prototype is adapted to the

OT setting to evaluate internal logistics processes such as inventory and distribution

activities. Logistics-oriented KPIs in the OT setting are similar to any industrial setting,

but the weights attached to the KPIs can differ significantly between sectors. Indicators

such as inventory service level, inventory turnover or inventory cost are used to assess

traditional supply chain concepts, though the consequence of having a stock-out is far

more severe in a healthcare setting than in a manufacturing process and thus requires

a higher weight. In addition, hospitals have poor inventory management, lack of data

and non-standardized procedures due to physician preference cards, which complicates

KPI monitoring and results in costly and ineffective systems (Camp et al. 2014). This

prioritization approach contributes to supporting evidence-based decision making in

healthcare environments. Quantitative methods, such as ANP, can be applied to

prioritize a set of indicators. ANP is preferred over AHP because logistics-related

objectives are often interdependent, like for example quality and cost (Link B and C

in Figure 4-2). Moreover, ANP allows for balancing opposing influences, such as

different performance perspectives between logistics and medical stakeholders.

Two internal logistics activities are considered, namely inventory and distribution.

Both activities interact since distribution ensures that material stocks are replenished

in time such that stock-outs can be minimized. However, the prototype considers one

activity at a time, as the goal is to understand which decision criteria are important for

different process types. The results show that different objectives are important to


77

evaluate inventory or distribution policies, with respectively quality and

productivity/organisation being the most important objectives. By obtaining efficient

inventory and distribution processes, the overall performance of the OT supply chain

will improve which in turn will enhance care processes. Similar results can be found

in literature. Hoeur and Kritchanchai (2015) and Kritchanchai et al. (2018) show the

significant impact of inventory management to the overall performance of healthcare

logistics. We elaborate on this work by suggesting a structured approach to translate

inventory objectives into relevant indicators and to prioritize between KPIs in order to

identify opportunities for efficiency gains.

The results show that the four highest-ranked KPIs – inventory service level, accuracy,

criticality and turnover – contribute most to the overall performance of the inventory

system. In Chapter 5, several inventory control policies will be evaluated by tracking

KPIs and determining optimal parameter values. Moreover, our findings also confirm

the dominance of patient safety, which is represented by the indicator Inventory

Criticality, as being the ultimate goal in healthcare. Furthermore, product

standardization proves to be a significant enabler to reduce costs and variability and

improve healthcare supply chain performance (Camp et al. 2014; Longaray et al.

2018), though its importance has not yet been emphasized by the logistics manager.

Inventory costs are ranked in the last position, however the main goal of the logistics

flow is to hold inventory to guarantee availability while reducing costs (Carrus,

Marras, and Pinna 2015).

Besides proper inventory management, the logistics flow should also ensure timely

provision of medical supplies at the right place in the most efficient way. Therefore,

the prototype also covers KPIs measuring the performance of internal distribution

systems and it provides valuable insights, e.g. in the case study productivity has a

higher weight than quality. This is mainly due to the high importance attached to

simplifying the logistics flow for hospital staff, which is considered as the fourth goal

in the Quadruple Aim strategy for hospitals. Carrus et al. (2015), Pinna et al. (2015)

and Robinson and Kirsch (2015) also show the importance of balanced workload

distribution and process standardization in their work. Furthermore, the impact of

centralization on costs, service level and the workflow is also considered important,

which is confirmed by Pinna et al. (2015), Iannone et al. (2014) and Landry and

Beaulieu (2013).

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78

Furthermore, the case study draws the attention of the OT logistics manager to the

potential of the ANP-based prototype to be highly relevant in improving the logistics

flow. The outcome is a ranking of indicators used as a guideline for optimizing

inventory systems and distribution activities in order to provide high-quality patient

care in the most efficient way. The KPI ranking stimulates evidence-based decision

making, reflecting the contribution of logistics supporting activities to value-based

healthcare. For example, the logistics manager can justify decisions to hold items in

stock close to the operating room and to define a standardized workflow to increase

staff satisfaction. Other advantages are its transparency and systematic approach. The

prototype enables to evaluate the impact of new logistics practices, which improves

managerial communication significantly and in turn enhances stakeholder

commitment. Engaging stakeholders in the design and testing phase of the prototype

is crucial to develop a generic framework addressing various healthcare logistics

needs.

4.2.6 Sensitivity analysis

A final sensitivity or ‘what-if’ analysis is performed to validate the robustness of the

KPI prioritization approach. It helps in understanding which indicators drive changes

in the results and to what extent the ranking is valid when changing the importance of

the indicators (Piratelli, Claudio et al. 2010; Poh and Liang 2017). Typical ‘what-if’

questions can be: what if all stakeholders have equal importance in their judgments,

what if all criteria are equally important, what if one criterion is given more

importance, etc. Since the approach is heavily dependent on possibly uncertain

judgments of the decision makers to derive the weights of the indicators, the accuracy

of these judgments is crucial. A sensitivity check is performed whereby the ANP

network is extended with alternatives. These alternatives represent potential strategies

for controlling inventory or distribution activities in the OT setting. For inventory

management, three alternative inventory control policies are included based on the

objectives: cost minimization, service level maximization or inventory

standardization. Similarly for distribution, four objective-related alternative strategies

are added: centralization, time/distance ratio, distribution cost minimization and

balanced workload.


79

In a single-factor sensitivity analysis, the weight of an indicator is varied, one at a time,

while keeping the remaining weights relatively unchanged, to examine the effect on

the ranking of alternatives (Modak, Ghosh, and Pathak 2018). The Super Decisions4

software is utilized to conduct the sensitivity analysis by varying the sensitivity

parameter, α, which provides a measure for comparative judgments of adjustments in

the priority vectors with respect to a specific node (Dobrea, Molănescu, and Busu

2015). The original ANP values of the alternatives are displayed when α equals 0.5.

Adjusting α with respect to a specific node can lead to changes in the ranking of

alternatives. For example, when the parameter value of inventory service level is

adjusted from 0.0001 to 0.9999 (see left plot in Figure 4-3), cost minimization (green

line) is found to be the best strategy for inventory control with a weight of 0.423,

followed by service level maximization (red line) and standardization (blue line) with

a weight of 0.306 and 0.271 respectively. However, beyond a parameter value

α=0.274, service level maximization raises to 0.751, whereas for cost minimization the

weight decreases to 0.103 and standardization to 0.146 at α=0.9999. Similarly, the

right plot of Figure 4-3, shows how the ranking of alternatives changes by adjusting

α for the inventory cost indicator. At α=0.580, which is similar to an increase of 15%

in the inventory cost weight, cost minimization policies are preferred above service

level. At smaller parameter values, service level maximization is the recommended

strategy. This analysis guides hospital stakeholders in selecting appropriate inventory

control policies by quantifying the trade-off between costs and service level. The plots

provide insights into the stakeholders’ preferences to maximize service levels, which

comes at the expense of increasing inventory costs. Strategies for inventory

standardization in order to improve productivity are dominated by the main trade-off

between balancing inventory levels and costs. However, improving product

standardization will reduce costs and simplify inventory control, and thus is important

when tackling the trade-off between cost and service level.

4 Super decisions is a computer software for decision-making purposes within several areas ranging from

manufacturing and aviation to service organisations. AHP and ANP are implemented in the software to combine

judgment and data to obtain rankings and predict outcomes (Creative Decisions Foundation 2019).

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80

Finally, when conducting the single-factor sensitivity analysis for identifying

distribution strategies, no clear trade-offs appear in the plot. The strategy to centralize

distribution activities, by moving away activities from the operating rooms, is found

to be the dominant policy to implement followed by time/distance ratio, balanced

workload and cost minimization.

4.3 Challenges of AHP/ANP

Although the ANP-based prototype provides useful insights into performance

measurement systems, some limitations need to be mentioned and a number of

challenges need to be tackled to gain its full potential. ANP is limited by its inability

to deal with human subjective judgments during the pairwise comparison process,

resulting in inconsistent results, uncertainty issues or knowledge gaps (Marsh et al.,

2017). Inconsistencies can occur due to limited experience of the decision maker or

the complex nature of the decision problem. Obtaining perfect consistency is almost

infeasible, therefore Saaty (1996) introduces a consistency ratio of 10% to achieve a

good solution. Whenever this ratio is exceeded, a method is needed to reduce

inconsistency while guaranteeing high accuracy. In this work, the weighted

consistency method proposed by Jarek (2016) is applied to reduce inconsistency.

Moreover, people usually are uncertain about their answers or have difficulties to

express preferences by the ratio scale. This can be resolved by incorporating a fuzzy

extension to ANP or by conducting a sensitivity analysis (Diaby, Campbell, and

Goeree 2013; Jharkharia and Shankar 2007; Poh and Liang 2017). Fuzzy ANP makes

Figure 4-3. Sensitivity analysis of Inventory service level (left plot) and Inventory cost (right

plot) ranging from 0.0001 to 0.9999.


81

use of crisp numbers to assess the pairwise comparisons. Crisp numbers are often

defined as triangular numbers representing three parameters, namely the smallest

value, the most promising value and the largest value. However, Chatterjee and

Mukherjee (2013) argue that Fuzzy AHP does not outperform AHP. In case of

knowledge gaps, simple heuristic rules or learning algorithms can be used to complete

the pairwise comparison matrix (Hua, Gong, and Xu 2008). This work prefers to use

simple heuristic rules because learning algorithms require time-consuming teaching

examples.

Another limitation is related to the sample size of the ANP application. A biased

attitude of the single decision maker may result in subjectivity when selecting the

alternatives, indicators and their weights. However, multiple stakeholders with

possibly conflicting goals for efficiency management are involved in the OT supply

chain. ANP accommodates a group decision-making approach by aggregating

individual opinions to prevent a single decision-maker’s bias, and hence improve the

robustness of the framework (Marsh et al., 2017; Van Horenbeek & Pintelon, 2014).

The expert group preferably represents a responsible person of each department at

different organisational levels. However, when more than one expert is involved, the

challenge pops up to deal with potentially conflicting views, which may lead to

discrepancies in the final ranking. In literature, aggregation techniques such as

consensus, vote, compromise or geometric mean of individual judgments are proposed

(Dyer and Forman 1992; Jharkharia and Shankar 2007). Consensus is often believed

to be the best approach to balance conflicting views by discussion. Aggregating

individual results can lead to an average result, which can cause dissatisfaction of all

stakeholders. Alternatively, Song and Hu (2009) suggest to cluster stakeholders

according to similarities between priority vectors and calculate the weighted arithmetic

mean for all clusters. In case of biased decisions, sensitivity analysis is useful to

investigate the impact of changing weights.

Finally, we assume that all the proposed KPIs can be measured. However, the lack of

information systems and missing product identification numbers or data standards

typically lead to poor performance management at the hospital supply chain

(Kritchanchai et al. 2018). Different types of technologies, such as RFID or cloud-

based platforms are potential enablers for a successful implementation and enhanced

system efficiency.

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82

Table 4-7. Solutions to deal with challenges in a MCDM study.

Table 4-7 presents an overview of solutions to the challenges that occur in AHP/ANP

studies. In the next section, the methods indicated in italic are applied to identify multi-

stakeholder value for medical decision making by using AHP. According to Porter

(2010), the overarching goal for any stakeholder must be to maximize value for

patients. Value is defined as health outcomes (i.e. quality of care) achieved per dollar

spent (Mukherjee 2008), and value improvement reflects the shared goal that unites

the interests and activities of patients, payers, care providers and suppliers. However,

measuring value in multiple dimensions is a major challenge to OR/OM modellers who

need to ensure sound modelling of contradicting preferences by engaging multiple

stakeholders. MCDM is aligned with value-based healthcare as it provides synthesis

and enables aggregating value in multiple dimensions (Goetghebeur et al. 2012;

Oliveira, Mataloto, and Kanavos 2019).

4.3.1 Medical decision making for value-based healthcare:

challenges in AHP

This case study is conducted at UZ Leuven during the first half of 2018 (Jorissen 2018).

We focus on the urology department, and more particular prostate cancer since it is

among the most commonly diagnosed type of cancers observed among men after skin

cancer (Belgian Cancer Registry 2017; Richman et al. 2005; Roth, Weinberger, and

Nelson 2008). Different treatment options are available, such as surgery, radiation or

active surveillance, though we focus only on surgery or (robot-assisted) radical

prostatectomy in this work. Recently it has become more important to guide patients

in the treatment selection process and follow-up of the disease. Patient preferences and

Challenges Solution methods References

Inconsistency Weighted consistency method

Non-linear programming approach based on

transitive cycles principle

Complete transitivity convergence algorithm

Jarek, 2016

Pereira and Costa, 2015

Tseng et al., 2007

Knowledge

gaps

Simple heuristic rules

Learning algorithms

Dempster-Shafer (DS) AHP

Hua et al., 2008

Uncertainty Fuzzy AHP/ANP

Sensitivity analysis

Diaby et al., 2013

Jharkharia and Shankar,

2007

Conflicting

judgments

Weighted geometric mean

Cluster similarity approach

Moreno-Jiménez et al.,

2016

Song and Hu, 2009


83

value for prostate cancer treatments are studied in the literature (Loeb, 2016), but

discrepancies remain between what patients value most and what medical

professionals (e.g. urologists, surgeons, radiotherapists, etc.) think that patients value

most. The increasingly central role of patients suggests an emerging role for shared

medical decision making in treatment selection (Litwin & Tan, 2017). In shared

decision making, patients need to be informed about the available treatment options

and possible consequences of the selected therapy. This section presents a pilot study

to detect misalignment between patient preferences and medical experts in order to

identify the most important aspects that determine value in radical prostatectomy. By

focusing on the modelling perspective, we show the feasibility and challenges of

applying AHP as a guideline for medical decision making and identify opportunities

for future research.

4.3.1.1 Methodology

The decision problem is to identify a definition for value (i.e. quality/cost) in this

treatment, considering multiple, possibly conflicting stakeholders’ perspectives. Value

measurement should encompass all provided services that aim for meeting the desired

patient outcomes. In case of radical prostatectomy, this involves the three stages of the

treatment including the pre-operative, per-operative and post-operative stage. Value

measurement over these stages is needed to realign reimbursement practices with

value, so that it provides payments covering the full care cycle as a reward for value

creation. However, different perceptions of quality of care between various

stakeholders and institutions influence the outcomes. Therefore, we present a model

for measuring the quality of prostate cancer surgery that encompasses the three

treatment stages. Multiple patient outcomes, however, can be competing (e.g. near-

term safety versus long-term functionality), and thus must be traded-off against each

other. In this work, we map the trade-offs, set priorities and construct a ranking of

quality criteria according to their relative importance depending on each stakeholder’s

perspective. This is a problem including multiple criteria, which necessitates the use

of MCDM techniques.

AHP is selected to be most suitable for solving this prioritization problem. It is a

transparent tool to support decision makers by structuring the problem into a hierarchy

and assigning weights to the quality criteria. The overall goal is shown at the top of the

hierarchy in Figure 4-4, namely identifying multi-stakeholder value definition in

radical prostatectomy treatment. At the second level of the AHP hierarchy, quality and

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84

costs are determined to be the objectives to measure value. For the quality objective,

criteria are identified from the standard set of outcomes and risk factors for localized

prostate cancer proposed by the International Consortium for Health Outcomes

Measurement (ICHOM) (Kelley 2015) and a discussion with surgeons of the urology

department. The surgeons verified the extensive list of criteria and selected 13 criteria

in order to satisfy the time constraint for judging pairwise comparisons in the next step

of the AHP approach. The ICHOM criteria can be divided into three groups depending

on the treatment stage. The pre-operative stage includes criteria that are relevant for

measuring services before the surgery, such as PSA-level, Gleason score and clinical

stage. In the second stage, the per-operative criteria measure the quality of the surgical

procedure, and whether or not it is assisted by a robot. These criteria involve total time

spent in the operating room, surgical margins, surgeon experience and the necessity of

blood transfusions. Finally, the post-operative stage refers to longer-term Patient

Reported Outcome Measures (PROMs) that are a result of the treatment, such as

urinary incontinence, erectile dysfunction and time to return to normal functioning.

This categorization of the criteria allows them to be compared within the same

treatment stage, but ignores interdependencies between different treatment stages.

According to experts, it is difficult to compare criteria at different stages because of

the different nature of the criteria (e.g. operative factors versus operation outcomes).

The AHP is a suitable method because it ignores these interdependencies, whereas

ANP would overcomplicate the problem.

Figure 4-4. AHP hierarchy structure (Jorissen, 2018).


85

After constructing the hierarchy, AHP uses pairwise comparisons to extract

stakeholders’ preferences in order to prioritize the selected criteria. In total, 33 patients

(i.e. 11 pre-operative and 22 post-operative patients), 5 urologists, 3 members of the

nursing staff, 2 representatives of hospital management, 2 general practitioners (GPs)

and 1 representative of health insurances participate in this case study. From the

pairwise comparison matrix, the weights for the respective criteria, representing a

measure for the relative importance of the quality criteria in each treatment stage.

In the next step, the overall quality score can be calculated. Quality of care is defined

by multiple outcomes collectively. The Institute of Medicine (IOM) (2001) defines six

dimensions of quality of care, namely safe, effective, patient-centred, timely, efficient

and equitable care. In consultation with the urology department, the ICHOM criteria

in the treatment stages are reassigned to these pillars of quality in order to derive the

final weights for measuring the quality of prostate cancer surgery. The quality

objectives as defined by the IOM are discussed in Table 4-8.

Table 4-8. Overview of six dimensions of quality of care according to IOM (2001).

Quality of care Description Criteria

Safe Avoiding injuries to patients from the care that is

intended to help them

Complications (C)

Blood transfusions (BT)

Effective Providing services based on scientific knowledge

to all who could benefit and refraining from

providing services to those not likely to benefit

(avoid overuse and underuse, respectively)

Surgical margins (SM)

Surgeon experience (SE)

Additional therapy (AT)

Patient-centred Providing care that is respectful of and responsive

to individual patient preferences, needs and

values, and ensuring that patient values guide all

clinical decisions

Urinary incontinence

(UI)

Erectile dysfunction

(ED)

Time to return to normal

functioning (TTRF)

Timely Reducing waiting time and sometimes harmful

delays for both those who receive and those who

provide care

Waiting time is not

included in this study

Efficient Avoiding waste, including waste of equipment,

supplies, ideas and energy

Total time in operating

room (TOR)

Length of stay (LOS)

Equitable Providing care that does not vary in quality

because of personal characteristics such as gender,

ethnicity, geographic location, and socioeconomic

status

PSA level (PSA)

Gleason score (GS)

Clinical stage (CS)

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86

4.3.1.2 Results

In this section, we propose solutions to address typical challenges in AHP/ANP

studies, such as inconsistencies in pairwise comparisons, knowledge gaps and conflicts

between stakeholders. AHP is used to determine weights representing the relative

importance of several quality criteria while considering possibly conflicting interests

of different stakeholder groups. As an example, we discuss the AHP ranking of quality

criteria according to 11 pre-operative patients. The same procedure is applied to other

stakeholder groups.

A priority vector, containing the weights for each criterion, is obtained based on the

pairwise comparison matrix for each patient. Equation 4.3 presents the original

pairwise comparison matrix WO and priority vector wO for criteria involved in the pre-

operative treatment stage. More information on deriving weights according to the

principal eigenvector method can be found in Section 4.2 or in Saaty (1990).

𝑊𝑂 = (𝑃𝑆𝐴𝐶𝑆𝐺𝑆

) (1 9 1 7⁄

1 9⁄7

15

1 5⁄1

) 𝑤𝑂 = (

0.23440.06060.7050

) (4.3)

𝐶𝑅𝑂 = 0.7276

The consistency of this matrix is checked by calculating the consistency ratio. This

ratio should be lower than 10% to ensure qualitative judgments. In this example, the

ratio exceeds the threshold value and thus further computations are required. The

weighted consistency method by Jarek (2016) is applied to reduce inconsistency. In

this technique, the pairwise comparison matrix WO is adapted by multiplying the

priority vector wO with the reciprocal of its transpose. Then, the new pairwise

comparison matrix W1 takes into account the original stakeholder judgments by taking

the geometric mean of the original and adapted matrix (see Equation 4.4). This process

is repeated until the consistency constraint is satisfied. Equation 4.5 contains the final

pairwise comparison matrix WN and the corresponding priority vector wN.

𝑊1 = √[(𝑤0 ∗ (1

𝑤0

)𝑇) ∗ 𝑊0] (4.4)

𝑊𝑁 = (𝑃𝑆𝐴𝐶𝑆𝐺𝑆

) (1 5 1 4⁄

1 5⁄4

18

1 8⁄1

) 𝑤𝑁 = (

0.23700.06430.6986

) (4.5)


87

𝐶𝑅𝑁 = 0.0904

The adjusted AHP-based priority vector wN, containing weights for every criterion in

the pre-operative treatment stage, is calculated for each stakeholder in each treatment

stage. Whenever the consistency ratio exceeds its limit, the weighted consistency

method, as described above, is applied.

The next step is to determine one ranking for the pre-operative patients group, by

integrating the priority vectors of the 11 pre-operative patients. However, different

experiences during the treatment or personal characteristics may cause conflicts within

the same stakeholder group. Taking normal average would not satisfy any stakeholder.

Song and Hu (2009) propose to use the cluster similarity approach to combine

stakeholders’ conflicting views within one stakeholder group. The degree of similarity

is calculated for all priority vector combinations by determining the distance between

two vectors dij. This distance value ranges between 0 and 1, indicating the degree of

similarity between the vectors. For example, vector 3 and 5, representing the criteria

weights for pre-operative patient 3 and 5 have a similarity degree of:

𝑑35 =𝑤3𝑤5

‖𝑤3‖‖𝑤5‖=

∑ 𝑤3𝑤5

√∑ 𝑤32 √∑ 𝑤5

2= 0.7638 (4.6)

The distance values for all vector combinations are combined into a similarity score

matrix, which is used for deciding on cluster groups. A threshold value of dij equal to

or greater than 0.80 is used to decide if the two vectors belong to the same cluster. This

step is iterated for every row in the matrix, resulting in a final cluster grouping. Next,

cluster weights are determined based on the cluster size. A bigger cluster gets a higher

weight as it represents more stakeholders, and thus will have higher impact on the final

result. The cluster weights are calculated as follows:

𝑤(𝐶1) =𝑆1

𝑆12 + 𝑆2

2 =8

82 + 32= 0.1096 (4.7)

𝑤(𝐶2) = 0.0411

Assume the pre-operative patients are divided into two clusters C1 and C2, with

respective cluster sizes S1=8 and S2=3. Cluster C1 contains the most stakeholders and

thus has the greatest weight. Finally, one ranking of quality criteria is defined,

reflecting the preferences of one stakeholder group. The Weighted Arithmetic Mean

(WAM) is used for multiplying cluster weights w(Ci) and priority vectors wi in the

CHAPTER 4

88

respective cluster Ci, and summing for all clusters. The final KPI prioritization for pre-

operative patients is shown in Table 4-9.

Table 4-9. Prioritization of quality criteria according to 11 pre-operative patients (Jorissen,

2018).

PSA CS GS TOR SM SE BT

0.094 0.061 0.125 0.041 0.127 0.249 0.080

LOS COMP UI ED TTRF AT

0.019 0.065 0.061 0.028 0.027 0.024

This priority vector is calculated based on the hierarchy shown in Figure 4-4, which

divides the criteria in the pre-, per- and post-operative treatment stage. However, the

main goal is to determine a definition for quality of care. Therefore, the criteria weights

are reassigned to the corresponding pillars of quality according to IOM (2001). Table

4-10 presents the resulting quality weights according to the pre-operative patient

group.

Table 4-10. Weights of quality of care indicators according to 11 pre-operative patients vs.

all stakeholder groups (Jorissen, 2018).

Quality of care Weight (pre-operative patients) Weight (all stakeholders)

Equitable 0.280 0.214

Effective 0.340 0.397

Efficient 0.060 0.050

Safe 0.145 0.134

Patient-centred 0.116 0.214

For each stakeholder group, the quality criteria are prioritized in the same way as

described above. The final goal is to combine all stakeholder viewpoints into one

priority vector, containing the overall quality criteria weights. However, the obtained

quality weights show conflicts between stakeholder groups. In addition, not every

stakeholder group is equally important to decision making. For example, pre-operative

patients have no experience with the per- and post-operative stages and thus might

underestimate the impact of the operation-outcome criteria. Therefore, we need a

weighting method to determine the weights of each stakeholder group in order to

define the overall ranking of quality criteria which represents all stakeholder groups.


89

A similar approach is used as before when tackling conflicts within stakeholder groups,

namely the WAM based on cluster similarity, to compute the overall priority vector

(Song and Hu 2009). The seven stakeholder groups are assigned to clusters based on

their similarity degree, as explained in Equation 3.6. Three clusters remain, cluster

weights are computed according to Equation 4.7, and the WAM is applied to derive

the final weights. A reallocation of the weights is required to measure quality according

to the IOM definition of quality of care (see final column in Table 4-10). From the

table, we find that effectiveness, patient-centredness and equity greatly contribute to

high quality of radical prostatectomy. Effective care is represented by the high weights

attributed to surgical margins and surgeon experience, whereas equity and patient-

centredness are determined by the high weights for respectively Gleason score and

post-operative consequences, such as urinary incontinency or additional therapy.

4.3.1.3 Discussion

This study aims to propose a multi-stakeholder definition of value for improving

medical decision making in radical prostatectomy. AHP proves to be most suitable for

solving the prioritization problem among the quality criteria to address the multiple

stakeholders’ preferences. However, a few challenges must be overcome to unfold the

full potential of the AHP methodology.

Saaty (2008) allows for 10% of inconsistent judgments to achieve qualitative output.

However, when this threshold is exceeded, a method is needed to reduce the

inconsistency. In this work, the weighted consistency method by Jarek (2016) was

applied to reduce the inconsistency without changing the stakeholder’s judgments. On

the other hand, knowledge limitation can lead to incomplete pairwise comparisons.

These gaps can be filled by taking the geometric mean of other stakeholder judgments

for the respective criterion as a simple heuristic (Hua et al. 2008). Furthermore,

stakeholders often have conflicting views, which may lead to discrepancies in the

ranking of quality criteria. Reaching a consensus is often believed to be the best

approach to balance conflicting views (Dyer and Forman 1992). In this work, we

applied a cluster similarity approach, as proposed in Song and Hu (2009), to deal with

conflicts between and within stakeholder groups.

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90

The results in Figure 4-5 show differences between pre-operative patients and post-

operative patients. The former group values equitable care (e.g. PSA, GS, SE) more

than the latter group, who focuses mainly on the quality of outcomes (e.g. TTRF, UI,

AT) and therefore assigns higher weights to the post-operative, patient-centred criteria.

This difference can be explained by the situation of the patient. One has already been

through all of the treatment stages, and the other is just starting the treatment. For

example, the PSA-level received a high priority from the pre-operative patients,

whereas the other stakeholders gave it lower weight. The interpretation of the PSA-

level, however, depends on the treatment stage. At the pre-operative stage, the level of

PSA determines the risk of metastases and therefore impacts the survival rate of the

patient which will dominate other patient-reported outcomes, whereas in a follow-up

after treatment, PSA evaluates the probability for biochemical recurrence and is

considered to be less important than quality of life indicators. Overall, the equitable

criteria are very important to the payers, nursing staff and pre-operative patients as

they assigned higher weights to the pre-operative criteria. Based on these results, we

gain insights to better inform the stakeholders about the medical implications of the

pre-operative criteria (PSA, CS, and GS). Although these criteria define the

aggressiveness of the tumour and therefore the treatment selection for each patient, the

criteria are not the main determinants for equity. In Belgium, however, the public

0,0000

0,2000

0,4000

0,6000

0,8000Equitable

Effective

EfficientSafe

Patientcentered

URO

PRE-OP

NUR

HM

GP

PAY

POST-OP

Figure 4-5. Radar plot showing different stakeholder group preferences for quality of care

indicators.


91

health system guarantees accessibility to care for everyone, whereas in private health

systems other criteria, such as level of income, race/ethnicity or the specialization

degree of the hospital, could affect equity.

Another discrepancy was observed in the post-operative phase, where urologists,

hospital management and post-operative patients attach more importance to patient-

centredness, such as complications (COMP) and additional therapy (AT) since these

indicate how they performed during the per-operative stage. The patient-centred

indicators mainly consist of Patient Reported Outcome Measures (PROMs) rather than

Patient Reported Experience Measures (PREMs). The latter focuses on the wellbeing

of patients and their care process by measuring satisfaction (e.g. quality of information,

communication with care providers, etc.), whereas the former aims to measure the

impact of the disease on the health-related quality of life and therefore allows to

compare different institutions in terms of value improvements. This pilot study helps

healthcare policy makers to identify the relevant PROMs for measuring value based

on the relative importance between the indicators. However, assigning weights to the

PROMs also depends on the patient perspective, since priorities might change when

patients go through the treatment stages.

From the results, we also observe some similarities among the stakeholders, such as

low importance for the operation time (TOR) and length of stay (LOS), as these criteria

reflect cost factors or efficiency, which are relatively unimportant compared to criteria

related to quality of care. Furthermore, safety is considered to be less important

because the indicators are objective measures, such as blood transfusions. Effective

care, on the other hand, has high weights for all stakeholders involved. Literature

shows that surgical margins (SM) are related to the quality of patient outcomes and

can predict recurrence (Fontenot and Mansour 2013), and surgeon experience (SE)

impacts the patient outcome as well as the probability of complications (Fossati et al.

2017; Di Pierro et al. 2014).

In addition, preferences can deviate within stakeholders groups due to personal

characteristics or previous experiences with medical treatments. A further

classification of the pre- and post-operative patient groups according to their age and

level of education would give more insights into the different preferences. The

preliminary results showed that age does not necessarily influence preferences, since

older patients assign similar weights to erectile dysfunction and complications when

compared to younger patients. Furthermore, level of education had an influence on the

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92

pre-operative criteria, as patients with a lower level of education attached more

importance to the PSA level than highly educated patients. As described above, a better

understanding of this medical term is required. Moreover, level of education could

impact equity since it determines how easy patients can have access to care.

4.3.1.4 Limitations and further work

The research also has some limitations. The quality criteria are limited to 13, which

are verified by the urologists at the hospital under study. The more indicators, the more

time-intensive the pairwise comparison process becomes. Moreover, the AHP process

starts with an inherent bias since only one stakeholder group was included who

assigned the criteria to the six pillars of the quality definition according to their

expertise. Hence, a pragmatic approach is used in this pilot study to prioritize criteria

for measuring value improvement. The overall goal was to show the feasibility of using

AHP for this medical decision making problem and provide insights to what extent the

criteria are relevant for measuring quality in radical prostatectomy in order to improve

future decision making. Due to the small sample size in this pilot study, the results

cannot be validated for all of the stakeholder groups. A larger sample size would lead

to more reliable results and allow for stratification to identify discrepancies within

stakeholder groups. Furthermore, the AHP approach does not allow for

interdependency between criteria to simplify the problem. However, the relative

importance of the treatment stages is not necessarily independent, since poor pre- or

per-operative conditions may cause undesired patient outcomes. Moreover,

uncertainty perceived by stakeholders during pairwise comparisons is not taken into

account in this AHP approach. This can be resolved by incorporating fuzzy logic into

AHP or conducting a sensitivity analysis as in Section 4.2.6.

The equity and time dimension in the quality of care definition are inaccurately

represented in this pilot study because the selected criteria do not measure any

difference between race/ethnicity (Orom et al. 2018) or waiting time respectively. In

further work, the value model will be adjusted to account for these indicators. Another

future research avenue concerns the weighting method used for calculating an

aggregate priority score for all the stakeholder groups. Instead of cluster similarity,

weights for stakeholder groups can be calculated based on the size of the groups or

based on the knowledge of the stakeholder for the respective criterion. In the latter

method, post-operative patients could have higher weights for the patient-centred

criteria when compared to pre-operative patients who have not been through the


93

treatment stages yet. However, still a large deviation could be found within the post-

operative patient group depending on the patient perspective. Patients attach different

weights to for example having a high risk on Urinary Incontinence (UI) when they

know they would otherwise not survive compared to patients who have a high survival

rate. Hence, the priorities depend on the patient perspective and this pilot study aims

to bring together all perspectives in order to measure overall quality of care. Moreover,

the resulting weights can be varied in a sensitivity analysis to investigate the impact

on the overall ranking. Finally, alternatives can be added to the hierarchy. Alternatives

represent the different treatment options (i.e. surgery, radiation therapy or active

surveillance). The established criteria weights are used to assign an overall score for

each alternative. As a result, AHP guides decision makers in choosing the treatment

that provides most value based on a ranking of the alternatives. Extending the model

with alternatives allows to validate the model in terms of functionality and consistency.

4.4 Conclusion

Healthcare logistics decisions are often based on intuition or experience, and thus lack

transparency and rationality, though the decisions are affected by a range of indicators

involving trade-offs between, sometimes conflicting, objectives. The complex

structure, multi-faceted decisions and the high impact of decisions increase the need

for MCDM. ANP is a popular MCDM technique known as a valuable, analytical tool

for supporting managerial decision making related to performance measurement. It

generates insights in understanding the complex relationships in the decision problem

and thus, improves the reliability of the corresponding decisions. MCDM is designed

to reflect, assess and better understand the problem in order to ensure rational and

consistent decision-making processes (Marsh et al., 2017).

This chapter presents an ANP-based prototype to guide hospital stakeholders in

translating logistics objectives to relevant KPIs measuring the overall performance of

the hospital supply chain. This prototype provides an answer to the second research

question which relates to building module 2 in the logistics performance management

framework development process. The findings are relevant for stakeholders involved

within the hospital logistics flow to gain insights into priority-setting and quantifying

trade-offs among indicators to focus on what matters most to the performance of the

internal hospital supply chain. As surgical supply costs take up more than 40% of the

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hospital operating budget, a well-defined supply chain strategy is needed to align

logistics and care processes and reduce costs. Although this case study deals with the

OT logistics flow, the decision-support framework can also be customized according

to specific objectives and process-related KPIs in other hospital logistics processes. In

Chapter 6, we will extend the prototype to a generic framework as a guide for hospital

practitioners and academics to improve transparency in decision-making processes by

considering multiple stakeholders’ perspectives.

Finally, several challenges inherent to applying AHP/ANP in a healthcare context are

addressed in a pilot study concerning medical decision making. AHP is used as an

innovative and structured approach to reflect healthcare decision-makers’ preferences

to the benefit of value-based healthcare by providing synthesized value in multiple

dimensions. The major challenge is to map all stakeholder preferences into one value

definition in order to identify opportunities for quality improvement and cost

containment. Based on these findings, we prove the versatility of the AHP/ANP

methodology for performance management problems in a wide range of healthcare

applications focusing both on logistics and medical aspects.

95

CHAPTER 5

5 Empirical Research at the Operating Theatre:

Policy Decision Making, Parameter Setting

and Performance Monitoring5

This chapter presents the third module in the framework development

procedure. The objective is to answer the third RQ by searching an efficient

logistics strategy which improves the overall performance of the healthcare

supply chain. We start by providing a state-of-the-art on inventory and

distribution models relevant for solving healthcare logistics problems. Then,

the ANP-based prototype, as developed in Chapter 4, is illustrated for one

test study design, namely the OT department, using Discrete-Event

Simulation. Four case studies are conducted to adapt the prototype to fit

different process types, to identify appropriate policies for inventory and

distribution processes and to determine optimal parameter values. Since no

single optimal policy exists, the items are first classified according to their

cost, demand and criticality characteristics in Section 5.2.2. Figure 5-1

conceptualizes the chapter.

5 This chapter is based on following papers/supervised Master’s Theses:

Moons, K., Waeyenbergh, G., Timmermans, P., De Ridder, D., Pintelon, L. (2020). Evaluating replenishment

systems for disposable supplies at the operating theatre: a simulation case study. Healthcare Systems Engineering.

HCSE 2019. Springer Proceedings in Mathematics & Statistics, vol. 316, 147-162. Moons, K., De Gang, W., Oor, A., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). Measuring the

performance of different distribution strategies in the operating theatre – A simulation case study. In Proceedings

of the 21th International Working Seminar on Production Economics, vol. 3 (221-244), Innsbruck, Austria, 2020.

Moons, K., Vandermeulen, E., Waeyenbergh, G., De Ridder, D., Timmermans, P., Pintelon, L. (2018). Operating

room supply chain management: a simulation case study. In Proceedings of the 20th International Working Seminar

on Production Economics, Innsbruck, Austria, 2018.

Karkera, K. (2020). Inventory control optimization for operating theatres. KU Leuven.

De Bie, R. (2019). Application of Multi-Criteria Decision Making in healthcare: Optimization of questionnaires

used in prostate cancer treatment. KU Leuven.

Lauwers, B. (2019). Optimization of a two-echelon inventory system at the operating theatre of a large hospital.

KU Leuven.

Robben, L. (2019). Analyse van de efficiëntie van ingrepenfiches in het operatiekwartier. KU Leuven.

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5.1 Inventory management and distribution systems in

healthcare: State-of-the-art

The internal hospital supply chain is identified to be the sore spot in process integration

and optimization (Rivard‐Royer et al. 2002). The literature review paper by Volland

et al. (2017) provides an up-to-date summary of quantitative methods applied in four

streams of hospital material logistics: supply and procurement, inventory management,

distribution and scheduling and finally holistic SCM. This section gives a brief

summary of relevant literature for this dissertation on inventory classification,

inventory modelling and distribution methods in hospitals (Karkera 2020; Lauwers

2019).

5.1.1 Overview of inventory classification techniques

Although planning and scheduling has received considerable attention in literature,

there has been limited research on inventory management in the healthcare setting.

Inventory management refers to balancing holding and ordering costs against levels of

patient service (Gebicki et al. 2014). PAR (i.e. Periodic Automatic Replenishment)

levels are widely used in hospitals; however, these PAR levels tend to reflect

experience-based inventory levels rather than data-driven actual inventory levels. As

a result, some products are overstocked while others are short in supply (De Vries

Figure 5-1. Overview of Chapter 5.

EMPIRICAL RESEARCH AT THE OPERATING THEATRE

97

2011). Managing medical-surgical supplies is more complex compared to production-

oriented environments, due to lack of standardization, lack of traceability of items, lack

of accurate consumption data, and high risk of obsolescence, expiration or damage.

Moreover, industrial sectors closely control inventory cost, which is typically not the

case in healthcare where several stakeholders are involved (Ahmadi et al. 2018).

Hospital stakeholders are working towards different objectives, impacting inventory

management and causing fragmented logistics responsibilities. In addition, medical

supplies differ in characteristics, such as cost, criticality or usage rate, which require

different levels of control to manage stock in hospitals.

Several methods for inventory classification exist to determine the level of

management control. Classification is required since no single inventory model can be

used for every article in stock. Therefore, depending on supply characteristics, other

inventory policies can be applied. Overall, we distinguish between three types of

medical supplies used at the operating theatre: disposables (e.g. gloves, syringes),

reusables (e.g. surgical instruments) and perishables (e.g. blood, medicines). This

dissertation focuses on disposable items, as they take up a large portion of the storage

space and they are an important source of waste and costs in hospitals.

The most familiar classification method used in both industrial and healthcare settings

is the ABC analysis, which classifies inventory items based on the dollar/euro usage

value (i.e. product of annual demand and unit cost). It is based on Pareto’s principle of

“vital few and trivial many”, which states that approximately 10% of the items are

classified in class A, constituting about 70% of the total dollar/euro usage value, and

thus requiring strict management attention (Gupta et al. 2007). Though, it is commonly

used and easy to apply, ABC classification considers only a single criterion, often

costs, and hence ignores the holistic view of the inventory system (Smith et al. 2017).

For example, non-availability of low-cost critical items can endanger a patient’s life.

Since cost is the only parameter in the ABC analysis, it might occur that low-cost items

are getting too little attention, and as a consequence, can have stock-outs (Devnani,

Gupta, and Nigah 2010). Therefore, the VED classification is proposed, which

classifies items into Vital (V), Essential (E), and Desirable (D) categories based on

criticality (Gupta et al. 2007; Molenaers et al. 2012). Criticality of items is determined

by the impact of stock-outs on the quality of the treatment to patients. As some items

are more critical than others, or in other words, a hospital cannot function without these

items, availability of items plays a pivotal role in inventory classification. Huiskonen

(2001) suggests two approaches to criticality in the context of spare parts management:

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98

Process criticality describes the impact of a failure on a process causing

damage to the environment. In a healthcare setting, this type of criticality is

reflected by the impact of medical supplies on surgical procedures.

Control criticality is associated with supply shortage problems due to lead

time variability or erroneous deliveries. In a healthcare setting, factors such

as scarcity, lead time or number of suppliers determine the control criticality.

In addition, the turnover ratio is an important inventory parameter, determining how

many days items are held in stock (i.e. Fast-, Normal- or Slow-moving items). This

characteristic is taken into account in the FNS-analysis (Devarajan and Jayamohan

2016).

Finally, a combined ABC-VED-FNS analysis considers all supply characteristics for

inventory classification, including costs, item criticality and inventory rotation. Al-

Qatawneh and Hafeez (2011) and Devnani et al. (2010) propose a multi-criteria

classification approach with cost and criticality as attributes, namely the ABC-VED

system. Simulation results show that assigning different service levels to different item

categories lead to cost savings and guaranteed availability of vital items (Al-Qatawneh

and Hafeez 2011). Similarly, Bošnjaković (2010) proposes an inventory classification

by combining ABC, VED and the usage rate. Multi-Attribute Spare Tree Analysis

(MASTA) is used for classifying spare parts inventory in a maintenance setting

(Braglia et al. 2004). Danas et al. (2006) modify it to be applicable in healthcare, and

call it Med-MASTA. The Med-MASTA tree accounts for four criticality categories,

namely patient treatment, supply characteristics, inventory problems and usage rate.

Moreover, as multiple criteria are involved within each category (see Table 5-1),

Molenaers et al. (2012) suggest to assign weights to the criteria using AHP. Finally, a

single criticality level is determined for each spare part, considering all criteria and

categories. Similarly, Flores et al. (1992) suggests a multi-criteria ABC analysis using

AHP to assign criteria weights.

Table 5-1. Categories and criteria considered in Med-MASTA (Danas et al., 2006).

Patient treatment Supply

characteristics

Inventory problems Usage rate

Danger of loss of life Quality of treatment Replace with other treatment

Lead time Substitutes available Number of potential suppliers

Price Space required Special inventory conditions Expiry date

Over-stocking Frequency of use


99

5.1.2 Overview of inventory models in healthcare

Depending on the classification, different inventory models are suitable for different

item categories. Inventory models typically provide answers to the fundamental

questions of when to order and how much to order (Rossetti et al. 2012). Furthermore,

inventory systems are characterized by the number of storage units through which

inventory is moved. A single-echelon inventory system represents the management of

one storage unit, whereas multiple storage units are controlled by a multi-echelon

inventory system. The latter system is often applicable in the operating theatre, where

multiple point-of-care units are supplied by a central storage (Ahmadi et al. 2018).

Table 5-2 provides an overview of the most commonly used inventory policies in

healthcare, distinguishing between continuous and periodic review policies (Ahmadi

et al. 2018; Rosales, Magazine, and Rao 2015). In the former, the inventory levels are

continuously monitored and a re-order quantity is placed whenever the inventory level

drops below the reorder point, s. Typical examples are the (s, Q) and (s, S) policies

with a fixed re-order quantity, Q, or a variable order-up-to level, S, respectively. On

the other hand, periodic review policies, such as (R, S), also known as PAR level, or

(R, Q), monitor inventory levels at fixed moments in time (i.e. review period, R). In

addition, the two-bin system or (R, s, Q) policy, is a simple periodic policy for

maintaining supplies at point-of-use locations (Beaulieu and Landry 2010; Rosales et

al. 2015). The two-bin system is an example of a Kanban system, introduced for

Toyota Production Systems, which introduces lean principles to eliminate waste,

decrease costs and improve service quality (Papalexi, Bamford, and Dehe 2016).

Rosales et al. (2015) suggest a RFID-enabled two-bin system to compare periodic and

continuous inventory control policies. According to them, average inventory cost is

minimized in the continuous policy. However, this policy is only preferred for Fast-

moving items supported with technological infrastructures, such as RFID tags or

barcodes, whereas periodic review policies are appropriate for slow-movers (Rossetti

et al. 2012). Most hospitals use periodic review policies because they are easy-to-

manage, compared to continuous monitoring of inventory which requires high set-up

costs and more complicated workforce schedules (Rosales et al. 2015). We refer to

Rossetti et al. (2012) for a comprehensive overview of inventory modelling, such as

the state of inventory over time, periodic or continuous review policies, Just-In-Time

or stockless inventory, etc.

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100

Table 5-2. Overview of inventory policies (Lauwers, 2019).

Periodic review Continuous review

(R, S) Every review period (R), an order

is placed up to level S.

(s, S) When inventory reaches the reorder

point (s), an order is placed up to

level S.

(R, Q) Every review period (R), a fixed

order quantity (Q) is placed.

(s, Q) When inventory reaches the reorder

point (s), a fixed order quantity (Q)

is placed.

(R, s, S) Every review period (R), when

inventory is below reorder point

(s), an order is placed up to level

S.

RFID-

enabled

two-bin

An empty bin automatically triggers

a replenishment.

(R, s, Q) Every review period (R), when

inventory is below reorder point

(s), a fixed order quantity (Q) is

placed.

Two-bin Every review period (R), an

empty bin is replenished.

Mostly, literature focuses on the cost minimization objective when controlling

inventory. The Economic Order Quantity (EOQ) model aims to minimize inventory

holding and ordering costs while computing the optimal order quantity (Rossetti et al.

2012). The EOQ model holds true under assumptions of deterministic demand, zero

lead times, constant unit costs and no back-orders. However, real-life demand is often

stochastic, which violates the assumption, and thus more advanced techniques are

required to determine the reorder point. A great variety of inventory models exist in

literature. However, not all models are representative for problems in a healthcare

setting, and therefore a number of inventory models are selected based on model

characteristics, shown in Table 5-3. First of all, we distinguish between optimization

and simulation models. In the former, unrealistic assumptions need to be made as

analytical models are unable to model the complexity of the health system. Although

an optimization model will more likely find an optimal solution compared to

simulation, the assumptions may lead to biased solutions (Jun, Jacobson, and Swisher

1999). Therefore, this work focuses on simulation models to identify initial values for

decision parameters subject to predefined constraints and to perform scenario analysis.

A second model characteristic relates to input requirements. A well-known problem in

healthcare settings is the scarcity of accurate logistics data. Hence, the model needs to

provide a proof-of-concept using limited data requirements. Furthermore, the great

variety of items requires different policies to be assigned to different item categories,

and thus model flexibility is required. Finally, the degree of complexity of the model


101

determines transparency and ability to replicate the model for different hospital

contexts. Simple inventory policies with reasonable implementation costs and efforts

are preferred due to limited OR/OM experience, rather than investing in sophisticated

mathematical models with considerable computing times (Melo 2012).

Table 5-3. Model characteristics (Lauwers, 2019).

Authors Simulation Limited data

requirements

Flexibility Transparency

Smith et al. (2017) X X X X

Gebicki et al. (2014) – Policy 1 and 2 X X X X

Gebicki et al. (2014) – Policy 3 and 4 X X

Guerrero et al. (2013) X

Little and Coughlan (2008) X

Bijvank and Vis (2012) X

Kelle et al. (2012) X X

Four optimization models are included in Table 5-3, from which we derive interesting

constraints or decision variables. Guerrero et al. (2013) model a one-warehouse, n-

retailer distribution system to decide on the reorder point and order-up-to level under

a service level constraint. Little and Coughlan, (2008) develop a single-echelon

optimization model with the objective function to maximize the minimum and average

service level subject to a space constraint in a single storage room for multiple

products. In their model, delivery frequency is added as a decision variable. Similarly,

Bijvank and Vis (2012) develop two models to control inventory at point-of-use

locations in hospitals, with review period and order quantity as the main decision

variables:

Capacity model maximizes service level under storage space constraint

Service model uses service level as a constraint to minimize inventory holding

costs.

Finally, Kelle et al. (2012) use constraint-based optimization to define values for

reorder point and order-up-to level of items held in Automated Dispensing Machines

(ADMs). The model aims to minimize replenishing and holding costs subject to service

level and space constraints.

To summarize, Table 5-4 provides an overview of different inventory optimization

models. In this work, the aim is to minimize the overall inventory cost and stock-outs

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102

subject to service level. Potential decision variables are reorder point, order up-to level,

review period and item locations.

Table 5-4. Overview of healthcare inventory optimization models (Karkera, 2020).

Authors Model Objective Decision

variables

Constraint Multi-

echelon

Guerrero et al. (2013)

Markov optimization

Minimize stock on-hand value

Reorder point and order up-to level

Service level and storage space

Two-echelon

Little and Coughlan

(2008)

Constraint-based

optimization

Maximize minimum and

average service level

Service level, delivery

frequency and order up-to level

Storage space

Single-echelon

Bijvank and Vis (2012)

Markov optimization

Minimize capacity and maximize service level

Order quantity and review period

Service level and capacity

Single-echelon

Kelle et al. (2012)

Constraint-based optimization

Minimize total refill (ordering) cost plus the inventory holding cost

Reorder point and order up-to level

Service level and storage space

Single-echelon

A multi-echelon inventory system, on the other hand, requires more advanced

modelling techniques to determine inventory parameters in each echelon (Rossetti et

al. 2012). Lapierre and Ruiz (2007) present a multi-echelon inventory model to

schedule logistics activities while aiming for minimal inventory costs and balanced

workload schedules for better inventory control. Ahmadi et al. (2018) suggest a robust

stochastic mixed-integer programming model for surgical supplies to reduce costs by

determining the optimum item location and quantity. However, sophisticated

analytical models often make unrealistic assumptions, and thus fail to represent the

complexity of the multi-echelon inventory system. Most closely related to this work

are the simulation models proposed by Smith et al. (2017) and Gebicki et al. (2014) in

Table 5-3.

Smith et al. (2017) use Monte Carlo simulation to develop a centralized inventory

model and a stockless model. In the latter model, the central storage is eliminated as

suppliers directly replenish the care units. The authors find a lower cost for slow- to

normal-movers in the stockless system. The centralized model, on the other hand, uses

a central pharmacy to deliver supplies to different care units in a single-item, two-

echelon system. The model can easily be extended to a multi-item model as PAR levels


103

or cost factors can be varied depending on the classification group. The objective is to

minimize inventory costs and stock-outs by controlling the order up-to level in the

central pharmacy and care units.

Secondly, Gebicki et al. (2014) compare four inventory policies with increasing

complexity to determine the appropriate policy based on varying model characteristics.

The simulation model consists of multiple pharmaceutical items, one central pharmacy

and several dispensing machines at point-of-use locations. The objective is to

minimize total costs and number of stock-outs subject to a service level constraint.

Policy 1 and 2 compute the order-up-to level (i.e. the sum of the reorder point and the

EOQ) using demand during lead time. Policy 2 differs from policy 1 by using

differentiated service levels for each classification group, considering the criticality of

the drug. Policy 3 and 4 determine the location of a drug, either in the central pharmacy

or the dispensing machine. The decision parameters are the expiration window,

probability that an item is in stock and criticality. The results show that a critical item

with a low probability of being in stock requires more safety stock compared to non-

critical items or items with high probability of supply availability. However data on

the probability of items being in stock is hard to obtain in hospital settings.

Another non-traditional approach for controlling inventory is implementing Just-In-

Time (JIT) or stockless applications. Both systems reduce the need for a central

storage, allowing direct delivery to point-of-use locations and thus enhanced

responsiveness to fluctuating demand. Moreover, stock levels and associated costs

decrease, which facilitates inventory management. In a stockless system, the supplier

delivers to point-of-use locations and thus virtually eliminates need for storage,

whereas JIT allows to deliver items to different locations from a hospital’s warehouse,

which is managed by the distributor (Kim and Schniederjans 1993). Hence, a JIT

system need not be stockless or vice-versa (Kim and Schniederjans 1993). Although

the JIT system reduces holding costs, it causes more frequent deliveries and thus

increases ordering costs. In addition, the role of demand management is crucial in

managing JIT operations. Kim and Schniederjans (1993) provide a comparative

analysis of conventional inventory systems with JIT and stockless systems.

Finally, information technology systems play an important role to continuously track

inventory, and thus increase visibility throughout the supply chain (Mathew, John, and

Kumar 2013). Other benefits of automated inventory systems are up-to-date

information, minimal clinical personnel involvement and less errors for hospitals

(Beaulieu et al. 2013). RFID and ADMs are examples of technological enablers to

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capture data and thus stimulating demand-related inventory parameter optimization.

Rossetti et al. (2012) use ADMs to generate a realistic bill of materials. Rosales et al.

(2015) use RFID technology to implement a continuous two-bin inventory system,

together with a periodic review policy to improve inventory visibility. Beaulieu et al.

(2013) present the CHUM case (Centre Hospitalier de l’Université de Montréal) in

which inventory management systems are combined with RFID technology to improve

operating room supply chain performance. However, automation should start at the

manufacturer stage in the external supply chain, providing labels with standardized

barcodes, such as GS1 standards with additional logistics parameters (e.g. lot and serial

number, GTIN) to enable medical supply tracking and improve inventory control. Data

standards are required to ensure effective communication and information exchange

among various supply chain partners (Varghese et al. 2012). Implementing data

standards will positively contribute to performance metrics such as staff productivity,

inventory accuracy or item/location identification standards.

This review discusses several inventory models used in a healthcare setting. Based on

the decision variables and constraints used in the above mentioned models, a two-

echelon inventory system will be established for disposable supplies at the operating

theatre. Providing inventory management solutions is vital for achieving efficiency

targets in hospital supply chain operations. However, the unique characteristics and

possibly conflicting interests among different stakeholders need to be considered to

overcome the main obstacles to inventory management in hospitals. A detailed

situation description, the inventory classification methods and the simulation model

development are discussed in Sections 5.2.1, 5.2.2 and 5.2.3.

5.1.3 Overview of healthcare distribution and scheduling systems

Supply availability is crucial in any healthcare setting, and thus coordination and

distribution of time-critical supplies and medical equipment are of utmost importance.

In addition to optimizing inventory parameters, optimal replenishment policies and

distribution strategies should be developed to efficiently move medical supplies to

point-of-use locations. Well-coordinated replenishment policies and effective delivery

strategies positively contribute to the performance of the internal hospital supply chain,

which results in streamlined, cost-effective processes and fewer cancelled or delayed

interventions (Lanckzweirt 2010; Rohleder, Cooke, et al. 2013). The models discussed

in literature, as discussed below, use several decision variables to determine the impact

of distribution and scheduling policies at point-of-use locations.


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5.1.3.1 Scheduling decisions for optimal replenishment

The supply department has to coordinate the replenishment activities. Therefore,

scheduling decisions, such as how often and when to visit each care unit, should be

made related to inventory. Lapierre and Ruiz (2007) develop such a scheduling

approach to coordinate distribution activities while respecting inventory capacities.

Moreover, they incorporate workload balancing to find an optimal replenishment

schedule by evaluating holding costs, time consumption and the uniformness of the

workload distribution. Bijvank and Vis (2012) investigate the impact of different

inventory models on medical supplies by establishing simple replenishment rules to

determine reorder levels and reorder quantity. The optimal replenishment policy

depends on whether inventory levels are monitored continuously, by introducing RFID

technology, or periodically. Although a continuous review policy utilizes the available

capacity more efficiently, periodic review policies perform almost equally well with

high service levels. Other studies regarding the use of continuous review policies can

be found in Rosales et al. (2015, 2014), who analyse the impact on costs, inventory

levels and replenishment frequency at point-of-use locations. The main disadvantage

of continuously reviewing stock is the high investment cost of introducing a RFID

system. Moreover, it incurs a higher fixed cost due to a more complex workload

schedule for logistics staff and more frequent interruptions to perform inventory tasks.

Periodic review, on the other hand, is more straightforward scheduling-wise, though it

requires higher stock levels. Choosing the appropriate replenishment system is also

dependent on the number and size of stocking locations, which is typically

architecturally fixed for most hospitals. Therefore, the trade-off should consider

operating several small stocking locations using a continuous review policy or a large

central storage with a periodic review policy (Rosales et al. 2015).

5.1.3.2 Distribution and routing of case carts

In a traditional distribution network, suppliers ship their products to distributors, from

where the products continue to the hospital’s warehouse. Typically, a large amount of

stock is held in hospitals with a low delivery frequency, low ordering costs and high

inventory holding costs. Supply chain integration can be achieved by setting up a

centralized warehouse system for direct shipments from supplies, as proposed by the

Mercy Health System (Rossetti et al. 2012), which results in improved fill rates and

cost savings. Landry and Beaulieu (2013) consider two aspects when determining

distribution methods to point-of-use locations:

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Reorder quantities are set by the centralized materials management

department or the decentralized nursing units

A continuous or periodic inventory review policy is used.

Essoussi and Ladet (2009), Baboli et al. (2011), Iannone et al. (2014) and Pinna et al.

(2015) show the benefits of using a centralized model, rather than decentralizing

logistics processes, such as lower costs, lower inventory levels due to more frequent

replenishments, reduced workload and higher service levels (Carrus et al. 2015). On

the other hand, the impact of decentral inventory locations on the performance of

replenishment systems in nursing units is investigated in the work of Bélanger et al.

(2018), by focusing on logistics staff productivity.

This work focuses on the internal distribution network, where supplies move from the

hospital warehouse to multiple point-of-use locations. Case cart systems are an

efficient and organised method of supply distribution for the operating theatre as they

ensure that the right materials are available at the right time and place for the right

procedure (Diconsiglio 2005). Benefits of such a case cart system include time savings,

reduced travel times, improved material flow, more standardized practices, better

tracking of materials, reduced costs, improved care for patients, etc. (Bett et al. 2010).

The hospital layout is fixed and determines the routing of the carts. The content of the

case carts is based on physician preference cards (i.e. materials each surgeon prefers

for each procedure type). An accurate and up-to-date preference card is crucial to

guarantee material availability for each procedure (Lanckzweirt 2010). RFID

technology is useful to update the preference cards based on actual consumption

patterns (Landry and Beaulieu 2013).

Finally, one of the most important drivers of logistics costs in a warehouse, regardless

the industry or sector, are the order picking costs accounting for over 50% of

warehouse operating expenses (Tompkins et al. 2011). Moreover, 50% of the order

picking time is attributable to traveling, which is a non-value adding activity (Bartholdi

and Hackman 2008). Since travel time increases with travel distance, both are

important objectives when optimizing the order picking process. For this reason,

distribution strategies are also aiming for picking the orders as efficiently as possible.

Any underperformance when picking orders can result in increased operational costs

and decreased service. Since order picking is an integral part within the distribution

network, improving efficiency is not only important for the performance of the

warehouse, but for the overall importance of the supply chain.


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In hospital warehouses, typical distribution problems involve inefficient picking path,

long walking distances, excess movements or elevator problems. According to Chan

and Chan (2011) and Le-Duc and De Koster (2005), assigning Stock Keeping Units

(SKUs) to a storage location in the picking area, also known as the storage location

assignment problem, significantly improves the performance of the order picking

system. Storage space allocation policies, such as class-based storage, are widely used

to optimize space and distance, retrieval time as well as handling costs (Chan and Chan

2011; Reyes, Solano-Charris, and Montoya-Torres 2019). In addition, routing has a

direct impact on the travel time and thus the overall performance of the system.

Routing algorithms determine the sequence of visits to pick the requested products in

order to find the optimal picking path that minimizes the travelled distance, picking

time and congestion in the aisles (Khodabandeh 2016). Key and Dasgupta (2015) solve

two common routing problems, namely the Travelling Salesman Problem (TSP) and

the Shortest Path Problem (SPP) to find the optimal picking path. The Nearest-

Neighbour algorithm and Dijkstra, or the enhanced A*, algorithm are the most

frequently used algorithms for solving TSP and SPP respectively. However, Horvat

(2012) indicates that these heuristic approaches for optimizing picking routes are

difficult to implement due to complexity and inflexibility to adapt to different

warehouse layouts. As an alternative, routing strategies determine the picking path in

a simple and logical, but not necessarily optimal way. The three most common routing

strategies are the S-Shape, Return and Largest Gap, as shown in Figure 5-2.

The following studies show real-world applications of routing or distribution strategies

in hospitals. Little and Coughlan (2008) incorporate delivery resources and routing

information in their inventory model. Banerjea-Brodeur et al. (1998) model the linen

distribution as a periodic vehicle routing problem to determine the routing and delivery

frequency to care units. Augusto and Xie (2009) develop a hybrid supply and

transportation planning for a French hospital pharmacy, where the transportation

Figure 5-2. Three routing strategies (Horvat, 2012).

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planning is similar to a classical vehicle routing problem in order to minimize pick-up

routes for transporters. The number of movements can be further reduced by

reorganising the supplies in the shelves. In addition, the supply planning problem

respects the availability constraint of having a mobile medicine closet at each care unit.

Rossetti and Selandari (2001) show the technical, economic and qualitative benefits of

replacing a human-based delivery system by mobile robots to distribute

pharmaceuticals. Although the number of studies on partially automated order picking

systems, robotic couriers or Autonomous Guided Vehicles (AGVs) is increasing,

manual operations remain a necessity in hospital environments since the automated

systems do not offer enough capacity and flexibility to support the complex material

transfers. De Koster et al. (2007) state that up to 80% of all Western European

warehouses using ‘picker-to-part’ order picking systems are managed manually. On

the other hand, automated ‘parts-to-picker’ systems have become popular in recent

years with the introduction of robotized material handling solutions such as Automated

Storage and Retrieval systems in e-commerce warehouses (Tappia et al. 2019).

However, a large investment cost comes along when integrating automation

technology (Rais et al. 2018). Finally, Chen et al. (2013) indicate the incorporation of

picker congestion when solving routing problems as an interesting research

opportunity.

Altogether, the organisation of the internal distribution within hospitals is complex, as

different types of medical supplies are stored in various central and decentral storage

locations. Implementing an effective internal distribution system requires determining

optimal replenishment policies, case cart distribution systems and efficient picking

paths to improve the performance of the hospital supply chain. Increasing the

efficiency of these activities allows for significant cost savings, while improving staff

satisfaction and service levels. Sections 5.2.4 and 5.2.5 evaluate potential distribution

strategies for the operating theatre setting.

5.2 Empirical research at the operating theatre

The ANP-based prototype as developed in Chapter 4 serves as a first step towards

defining operational excellence in healthcare logistics. We assess the prototype by

providing empirical evidence through the use of case studies. Case studies are chosen

as research design to experiment with SCM concepts in real-life problems to generate

a proof-of-concept of how SCM can contribute to value-based healthcare. In particular,

the case studies are illustrated for the operating theatre (OT) at UZ Leuven, which


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serves as a test setting for implementing the prototype by measuring performance and

identifying process improvement initiatives. This section describes the potential of

combining simulation modelling and ANP to identify opportunities for efficiency

gains by introducing the Internal Logistics Efficiency Performance (ILEP) index.

5.2.1 Discrete-Event Simulation in healthcare logistics

In Chapter 2, the healthcare logistics toolbox is presented with Discrete-Event

Simulation (DES) being one of the most commonly used types of simulation for a wide

variety of healthcare problems. DES allows to understand the healthcare operations

and has an impact on the complex system by conducting “what-if” analyses

(Kammoun, Loukil, and Hachicha 2014; Thorwarth and Arisha 2009). Although DES

has been mainly used for quantitatively supporting health and care systems operations

(e.g. patient/staff scheduling, workflows of emergency departments, etc.), it has not

been widely applied to healthcare SCM problems (Günal and Pidd 2010; Zhang 2018).

Jun et al. (1999) describe healthcare simulation as follows:

“… DES is an operational research technique that allows the end user

(namely hospital administrators or clinic managers) to assess the efficiency

of existing healthcare delivery systems, to ask ‘what if?’ questions and to

design new systems. “

Due to the complex and highly stochastic nature of hospitals, sophisticated methods

are required to support decision making. The complex problem structure makes

traditional tools, such as queueing theory or other analytical models, impractical as

they require many assumptions to simplify modelling, making models

unrepresentative of reality (Hu et al. 2018). Physically changing the healthcare supply

chain would disrupt daily operations, cost money and could endanger patient safety.

In the OT, the complexity mainly comes from the multitude of materials, stocking

locations and replenishing systems, which makes simulation a prime tool for analysing

and improving logistics services (Abukhousa et al. 2014; Rego and Sousa 2009; Rytile

and Spens 2006). The popularity of DES increases due to its flexibility, ability to

construct versatile models of complex systems and consideration of uncertainty and

variability by integrating stochasticy into the model (Kelton et al., 2015; Mielczarek

& Uziałko-Mydlikowska, 2012; Thorwarth & Arisha, 2009). Moreover, it enables to

model several scenarios and predict the system’s performance and the impact of

alternative improvement strategies (Pidd 2004). Multiple, often competing,

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performance objectives are simultaneously evaluated, which makes simulation

particularly well-suited to tackle healthcare problems where stakeholders with

conflicting perspectives are involved (Lowery 1996). Another benefit is the visual

representation and animations to clearly communicate the savings of a new system to

stakeholders (Kelton et al. 2015). Hence, visualization improves understanding and

awareness, which in turn leads to commitment and impact. Even though simulation is

a powerful tool, it also requires a large amount of effort in time, cost and data (Jun et

al. 1999). A trade-off should be made between modelling accuracy and time

consumption prior to building the simulation model. Therefore, the modeller should

first develop a conceptual model describing the problem situation, data requirements

and desired level of detail (Robinson, 2008a). Analysing the system step by step gives

the modeller a deeper insight into the studied problem and uncovers issues not

previously noticed.

The main advantage of using DES are the building blocks, representing separate

activities of inventory and distribution systems. This modular logic allows for better

understanding, verification and modifications in the future. In addition, it facilitates

the transfer of knowledge and models between different hospitals or logistics processes

and thus encourages generalization. Furthermore, DES is used as only the specified

events from a time schedule are of interest, such as demand generation or inventory

counts. The model also reports global variables, which pertain to the overall system

rather than individual entities, such as monitoring the inventory level. Other

advantages of simulation are its ability to evaluate multiple KPIs simultaneously,

incorporate the stochastic nature of the problem and it is easy to gain an in-depth

knowledge of how the system works and where the bottlenecks occur (Roberts 2011).

Although these benefits make simulation a very powerful tool to model logistics

operations in any setting, it needs to be noted that there are also some shortcomings

when using simulation. The simulation output can be only as good as the data it

receives. Therefore, accurate data collection is of great importance to build a model

that imitates reality. Moreover, simulation models require a thorough understanding of

the system and can be time-consuming (Thorwarth and Arisha 2009). Typically, a

trade-off should be made between model accuracy and time consumption. Uncertainty

is another drawback in a simulation study (Kelton et al. 2015). Assumptions and

simplifications are made to account for the components that are not included in the

model, but are present in the real world, in order to improve transparency and model

development speed. Finally, simulation is not an optimization technique, but it can be


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combined with quantitative models, such as integer programming or Markov models

(Mielczarek & Uziałko-Mydlikowska, 2012).

5.2.1.1 Conceptual modelling

Conceptual modelling is defined as the process of abstracting a model from a complex

real-world problem by depicting the processes using a computer-specific logic

(Robinson, 2008a). However, designing the right model (i.e. what to model and what

not to model) is generally agreed to be the most difficult and least understood task to

be carried out in the beginning of a simulation study. Including too many details will

make the model too complex to be built within the time frame and knowledge

restrictions, as opposed to making the model too simple to accurately represent reality.

Furthermore, using correct and relevant data is crucial for a functioning valid model.

An often repeated phrase is “garbage in, garbage out”. In general, hospitals do not have

extensive logistics databases, so relevant data must be collected manually from time

studies, interviews or observation studies (Gebicki et al. 2014). The design of the

model will determine the data requirements, the speed with which the model can be

developed, the validity of the model, the speed of experimentation and the confidence

that is put in the model outcome. The four steps in a simulation study as described by

Robinson (2018) are:

System description: providing a thorough understanding of the problem

situation and those elements of the real world that relate to the problem.

Conceptual model: a blueprint for the computer model, though it is not

specific to any software. It describes the modelling objectives, system’s

boundaries, inputs, outputs, assumptions and simplifications of the model.

The conceptual model facilitates the development of the computer model and

ensures that the relevant KPIs are measured to compare different

improvement scenarios.

Model design: the design of the constructs for the computer model (e.g. data,

components, model execution). The object flow diagram symbols, suggested

by Greenwood et al. (2013), are often used to depict key system elements and

their relationships.

Computer model: software-specific representation of the conceptual model.

Typically, general-purpose software is used for simulation models rather than

industry-specific tools (e.g. MedModel for healthcare), because most

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academics are familiar with this software and appreciate its flexibility (i.e. not

limited to healthcare issues). In this chapter, two simulation software

packages, namely Arena and Simulink, are used for modelling inventory

policies, replenishment systems and distribution flows. In addition, a systems

engineering software, LabVIEW, is applied to visualize order picking paths

in the hospital warehouse.

Finally, models must be properly validated and verified for their intended purpose

(Persson 2002; Sargent 2013). However, objective validation and verification

techniques, such as confidence intervals or hypothesis testing, are difficult to adopt in

a healthcare setting due to lack of logistics data. Formal statistical tests might lead to

the conclusion that the model is not accurate, though the model might still be useful

for its intended purpose to compare alternative scenarios, rather than predicting

absolute answers (Lowery 1996). For more information on conceptual modelling

frameworks for simulation, we refer to papers by Robinson (2018, 2013, 2011, 2008a,

2008b).

5.2.1.2 Application of simulation in healthcare SCM

Most healthcare simulation literature focuses on patient flows and care pathways,

making the modelling of supply chain problems in hospitals rather novel. Brailsford et

al. (2009) examine the literature on simulation in the healthcare sector by analysing

the frequency of using modelling methods as well as the application domains. This

section presents a short overview of problems in healthcare SCM that have been

attempted to solve by simulation. For more applications of healthcare simulation, we

refer to review papers by Günal and Pidd (2010), Katsaliaki and Mustafee (2011),

Mielczarek (2016) and Zhang (2018).

Holm et al. (2013) investigate the bed allocation problem among hospital wards by

using DES to reduce hospital crowding. Furthermore, DES is applied to assess

efficiency improvement in the emergency department by evaluating multiple

performance objectives (Komashie and Mousavi 2005). Several studies show the

benefits of simulation to healthcare SCM, such as improved process flows, location

and capacity of facilities or inventory management (Abukhousa et al. 2014). Recently,

Duan and Liao (2013) propose a simulation-optimization framework for managing

highly perishable products in inventory by developing a new age-based replenishment


113

policy. Zhou and Olsen (2018) analyse long-life perishable products. They use a

simulation-optimization model for rotating medical supplies to reduce expiration.

Nevertheless, a wide application of simulation studies in healthcare SCM is limited, as

it requires a large effort in time and data analysis (Katsaliaki and Mustafee 2011). Lack

of accurate logistics data complicate the validation and verification steps to determine

the accuracy of the model. Furthermore, Brailsford et al. (2009) and Katsaliaki and

Mustafee (2011) show that most of the developed models in academic settings are not

accepted by healthcare organisations due to high implementation costs. In addition,

models are often built specifically for the hospitals they were developed for, indicating

a need for generalized models with a high degree of flexibility and scalability. The

adoption of promising technologies, such as RFID, will mitigate the aforementioned

challenges as they enable supply chain managers to collect accurate data and hence,

support simulation modelling in healthcare SCM (Abukhousa et al. 2014).

5.2.1.3 Methodology for simulation study design

This chapter mainly focuses on identifying appropriate logistics policies for inventory

and distribution systems and determining the best set of parameter values. DES is the

recommended tool to understand the system’s behaviour and integrated with the ANP-

based prototype, it allows to visualize the impact of efficiency improvement

opportunities on the performance of the healthcare supply chain. Values for the

relevant KPIs are monitored based on simulation output, and scenario analysis is

conducted to support policy decision making. Based on a thorough system description

and by additionally consulting the OT logistics manager, alternative logistics policies

are formulated by mapping the current As-Is situation and identifying bottlenecks,

while ensuring that the policies fit to the overall hospital’s goal. This stakeholder is

responsible for a team of executives on the OT-floor and thus represents both the

tactical and operational level. Figure 5-3 presents a high-level overview of the

simulation methodology.

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The operating theatre in UZ Leuven is located in one of Belgium’s largest hospitals.

This hospital distinguishes between day care surgery (OT2) and more elaborate

surgeries (OT1) in two operating theatres. OT1 consists of 33 operating rooms,

clustered in seven sectors according to their respective medical discipline (e.g.

orthopaedic, urology, reconstructive or trauma surgery). Figure 5-4 shows a floor map

of OT1. The smaller OT2 includes only two clusters, however, the logistics flow in

OT2 is out-of-scope.

Figure 5-5 provides a schematic representation of a cluster in OT1. One cluster or

sector is typically equipped with four to six operating rooms, separated by a central

core or midcore, where infrequently used medical-surgical supplies are stored. Nurses

have to leave the operating room to retrieve items from the central core. The commonly

used supplies, on the other hand, are held in three relay cabins – infusion (A), induction

(B) and surgical supplies (C) – located within each operating room. Together, the

central core and relay cabins form the decentral stock, which is daily replenished from

central storage rooms. Two central storage rooms are located close to OT1, namely the

OT1 storage at the same level (as marked in pink in Figure 5-4) and the CSA (Central

Figure 5-3. Overview of simulation methodology.


115

Sterilization Department) storage one level below OT1. The former storage contains

surgery-specific items only used at the OT, whereas the latter storage room stores

hospital-wide supplies since it distributes items to all hospital departments. Both

central storage rooms are used for replenishing decentral stock, as well as for

assembling surgical case carts which are prepared for each surgical procedure.

Figure 5-4. Floor map of OT1 and the central sterilization department (CSA).

Figure 5-5. Schematic representation of a cluster with four operating rooms and the

corresponding decentral storage locations, namely the central core and relay cabins (A,B,C).

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The scope of this simulation project is limited to disposable medical-surgical supplies

(e.g. surgical drapes, gloves, syringes, custom procedure trays). OT1 has to store

around 2600 SKUs in more than 5000 decentral storage locations, requiring daily

replenishment. These items may have high unit costs or demand volumes, which

explains this internal OT supply chain’s large portion of logistics costs (Bijvank and

Vis 2012; Landry and Beaulieu 2013). Figure 5-6 gives a snapshot of the As-Is

situation at the start of this research in November 2017. We observe a higher inventory

cost for OT1-items (i.e. surgery-specific supplies) compared to CSA-items (i.e.

hospital-wide supplies). Moreover, the decentral stock represents data of one cluster,

and thus extrapolating this data to seven clusters results in a decentral supply cost of

€78750. Furthermore, Figure 5-7 provides more details on the unit cost range.

Although the majority of the SKUs have a low unit cost (i.e. less than 5€), aggregating

all items results in a significant amount. We also observe that less frequently used

items, stored in the central core, are more expensive. On the other hand, reusable

supplies are also provided during surgery, but these supplies are stored in another

storage room, which is not included in this study. Another remark to be made here, is

that blood products are not considered, as these are managed in a distinct flow.

Figure 5-6. As-Is situation of disposable item cost at OKa1 in November 2017.


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5.2.2 Classification of inventory items in healthcare – A case

study

Inventory classification is pivotal to managing storage units in hospitals. It helps in

identifying item-specific characteristics, such as costs, demand patterns or criticality.

Due to the large amount and wide variety of medical supplies and their divergent

attributes, no single inventory policy applies to all supplies and complicates inventory

management. Thus, inventory classification is a crucial prerequisite when developing

appropriate inventory models depending on the item characteristics. Based on the

classification, different service levels can be specified for different types of supplies,

serving as a starting point for choosing appropriate scenarios of the inventory model.

In this section, two classification methods are compared, both featuring multiple

criteria. The case study includes data on 1541 SKUs stored in OT1 storage room at UZ

Leuven, collected in December 2018.

Figure 5-7. Unit cost (€) range of disposable supplies in decentral stock.

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5.2.2.1 ABC-FNS-VED classification cube

First, we discuss a combination of single-criteria classification methods, namely ABC,

VED and FNS accounting for euro usage value (i.e. product of unit cost and annual

demand), criticality and annual demand respectively. This method goes beyond the

disadvantage of using a single criterion to classify items due to the increasing

importance of non-cost criteria, such as availability, obsolescence or criticality. The

latter criterion comprises sub-factors including the impact of a stock-out, lead time for

replenishment and availability of substitutes (Flores and Whybark 2008). The ABC-

FNS-VED analysis allows to categorize items along three perpendicular axes, forming

a cubic representation of 27 classes of items. The classification cube is displayed in

Figure 5-8.

The Y-axis represents the ABC classification to classify items from a perspective of

inventory value. For each item, the euro usage value is calculated and all items are

arranged in decreasing order. The thresholds for classifying items into A, B or C are

determined by the changes in the slope of the cumulative euro usage Pareto chart (see

Figure 5-9). According to the Pareto chart, 10% of the SKUs accounting for 70% of

euro usage value are assigned to category A, and require strict management control.

Category B accounts for 20% of euro usage and 15% of SKUs, whereas category C

represents 10% of euro usage value and 75% of SKUs. The majority of items is thus

Figure 5-8. ABC-FNS-VED classification cube with euro usage, demand and criticality along

the axes.


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assigned to the latter category, which is typically characterized by cheap, non-critical

and slow-moving items.

Figure 5-10 depicts the annual euro usage for each item category. From this bar chart,

we observe that category A accounts for the highest inventory value in terms of euro

usage (8.35 million euros) corresponding to only 10% SKUs. Similarly, category B

includes 18% of SKUs accounting for 2.37 million euros and the majority of the items

(72%) is assigned to class C, representing 1.18 million euros. Since the demand in all

categories is at a similar level, we state that items in category A are mainly expensive

items, whereas category C consists of cheaper items.

A B C

Annual demand 184.428 162.912 131.426

Number of SKUs 159 270 1.112

Annual euro usage 8.354.515 2.378.934 1.188.553

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

0

50.000

100.000

150.000

200.000

Annual

euro

usa

ge

(€)

Annu

al D

eman

d (

SK

Us)

Figure 5-9. Pareto chart of cumulative annual euro usage.

Figure 5-10. ABC analysis.

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Since ABC classification only considers euro usage value, an in-depth analysis is

required to determine the nature of demand in a particular category. Therefore, the

Fast-, Normal- and Slow-moving items are classified according to the FNS analysis,

providing an inventory picture based on demand. The procedure for classifying items

is similar to the ABC analysis. The thresholds are based on the cumulative annual

demand slope based on the Pareto chart. Moreover, the FNS analysis provides insights

on calculating the inventory turnover ratio, which is the ratio of annual consumption

over average inventory. Due to lack of data on average inventory levels, we assume

that items with high demand are fast movers. Fast-movers have a demand ranging

between 60 and 1800 items per month, whereas normal- and slow-movers consume

less than 60 and 15 items respectively. Figure 5-11 shows the results based on the FNS

classification. A minority of the items (9%) causes the majority of annual demand (i.e.

70%) and are called fast-moving items. However, these items represent the lowest euro

usage value. Hence, F-items are mostly cheap items which are frequently used in

surgery. In contrast, items in category S have the lowest demand, but account for the

highest volume in euros as well as the majority of SKUs. These items are slow-movers,

infrequently used during surgery, and are expensive. S-items cannot be ignored, as

they take up a lot of storage space and account for a large portion of inventory holding

costs.

In addition, by combining the ABC and FNS inventory pictures, more information can

be extracted on the monetary value of an item (see Figure 5-12). The number of fast-

movers are quite evenly distributed among the ABC classification. However, the

corresponding euro usage value is highest (80%) for fast-movers in category A, or in

other words, fast-movers in category A are more expensive than FB or FC items. The

D E V

Sum of Annual Demand 27941 333969 116856

Count of ITEMS 175 820 546

Sum of Total Cost 285886 3489841 8146275

0

2000000

4000000

6000000

8000000

10000000

0

100000

200000

300000

400000A

nual

euro

usa

ge

(€)

Annual

dem

and (

SK

Us)

Figure 5-11. FNS analysis.


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majority of S-items (82%) are classified in class C, being cheap. In contrast, slow-

movers classified as A or B items are expensive, though they are rarely used in surgery.

A similar pattern can be observed for normal-moving items. Furthermore, when

looking at the ABC classification, we observe an even distribution of fast, normal and

slow movers in category A. The fast-movers (28%), however, account for the majority

of demand (88%) and the lowest euro usage value (28%) in this category. Hence, we

can conclude that the FA items are less expensive, but they are used in daily surgery.

In contrast, the normal- and slow-moving items in category A are very expensive and

less frequently used. In addition, half of the B-items are expensive slow-movers. 80%

of euro usage is represented by N- and S-items which account for 21% of annual

demand. Fast-moving B-items are cheap. Finally, items in category C are mainly slow-

moving items (83%). Due to the high euro usage and low demand, these medical-

surgical supplies are considered to be expensive.

Although costs and demand are important criteria from a logistics perspective, quality

of patient care and patient safety rank higher than the monetary benefits. Therefore, a

third type of classification is added to the X-axis in Figure 5-8, namely VED analysis,

to consider criticality of items. The items are classified by consulting the logistics

manager and nursing staff, who takes into account the impact of stock-outs on surgical

procedures, the ease of substitutability and the lead time for replenishment. The

criticality classification is based on the Med-MASTA approach (Braglia et al. 2004;

Danas et al. 2006), taking into account three attributes: surgery criticality, availability

A B C A B C A B C

F N S

Number of SKUs 44 52 41 52 75 147 63 143 924

Annual demand 162290 128183 45054 17430 25749 52175 4708 8980 34197

Annual euro usage 2334462 493204 99022 3040217 637935 208039 29798361247795 881492

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Figure 5-12. Combination of ABC and FNS analysis.

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of substitutes and lead time for replenishment. Surgery criticality is a parameter of

utmost importance as it measures the impact of an item on the surgical procedure and

thus can directly affect patient safety. Therefore, the tree structure in Figure 5-13 starts

with this attribute. Second, substitutability considers items with no substitutes to have

a more adverse impact compared to items which can easily be substituted. Finally, lead

time measures the period of procurement of an item and thus determines the ease to

have an item in stock within the desired time frame. The Med-MASTA classification

tree for this work is displayed in Figure 5-13. For each node, three levels of importance

(1 – Critical, 2 – Important, 3 – Not important) are indicated, except for availability of

substitutes, which is considered to have a yes/no condition. First, surgery criticality is

determined and depending on this outcome one of the three different tree structures

according to the importance level is followed until one reaches the final classification

into V, E or D. In general, as more parameters are evaluated with high importance, the

resulting category will be V. Table 5-5 contains the definitions for assigning items to

node levels for each attribute.

Table 5-5. Definitions of criticality attributes.

Attribute Importance level

Surgical

criticality

Critical – Non availability of items cause postponement of urgent

surgery (e.g. reanimation or trauma)

Important – Non availability of items cause postponement of scheduled surgery

Not important – Non availability of items do not affect urgent or scheduled surgery

Availability of

substitutes

Yes – Item has one or more substitutes.

No – Item has no substitutes.

Lead time for

replenishment

Critical – Lead time is more than two weeks.

Important – Lead time is between one and two weeks.

Not important – Lead time is less than one week.


123

Figure 5-14 shows the results of the VED analysis. Remarkable is that V-items account

for an intermediate demand, but a peak in euro usage value. This is plausible as V-

items are often medical equipment, such as oxygenators, which have a unit cost of

approximately €1140 and an annual usage of €187000. Approximately 5% of vital

items have an average unit cost range between €1000 and €4000. Although these items

are less frequently used, they require stricter control in order to prevent critical

shortages which have adverse impact on surgery. The majority of items (53%) are

classified to be essential and they represent the highest annual demand. Most E-items

are priced below €200. Finally, the desirable items account for the least euro usage and

annual demand with an average unit cost less than €50. Examples of D-items are

surgical face masks, gloves, etc. These items have several substitutes, in varying sizes

or colours, and have short replenishment lead times. Therefore these items are

classified as desirable.

Figure 5-13. Med-MASTA tree structure for determining criticality of items (based on Danas et

al., 2006).

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124

Linking the VED analysis to the combined ABC-FNS classification provides further

insights into the item characteristics. A remarkable aspect is the high euro usage value

for slow-moving items in Figure 5-11 and Figure 5-12. The combined FNS-VED-ABC

analysis in Figure 5-15 and the inventory classification matrix in Figure 5-16 show that

the majority of this euro usage value (32.4%) is taken up by vital items in category A.

Since the SAV items do not have very high demand and account only for 3% of SKUs,

we can conclude that these items are very expensive and require strict management

control. A similar pattern is shown for NAV items, accounting for a high euro usage

value (23.8%) due to expensive and critical items. Furthermore, essential items

account for the highest number of SKUs as they are used on a daily basis and thus are

essential for surgeries. Two peaks in annual demand are observed for FBE and FAE

items. Both are fast-moving items which are frequently used in surgery. Since these

classes have low euro usage value, they are considered to be cheaper items. Finally,

the class of desirable items have extremely low annual demand (5.98%), annual euro

usage (2.30%) and the least number of SKUs. Most of these items are cheap, have high

substitutability and short lead times which lowers the surgical impact (e.g. gloves,

clipper blades, etc.).

D E V

Annual demand 27941 333969 116856

Number of SKUs 175 820 546

Annual euro usage 285886 3489841 8146275

01000000

2000000300000040000005000000

60000007000000

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Figure 5-14. VED analysis.


125

Altogether, the ABC-FNS-VED analysis is a useful classification technique to

distinguish between items that vary in costs, demand or criticality. Moreover, the

classification helps in identifying inventory hot spots, and separate them from lower

priority items, especially those that are numerous but not so important for guaranteeing

patient safety. No single inventory policy applies for all items. It is important to assign

different service levels depending on the item class in order to prevent under- or over-

stocking. For example, a simple policy such as the EOQ model, can be implemented

for non-critical items with low demand. For critical items, however, more advanced

inventory models are required to determine optimal inventory parameters (e.g. reorder

point, reorder quantity, review period) and safety stock in order to achieve higher

service levels. Safety stock is maintained to mitigate the risk of stock-outs when

demand is unpredictable.

0

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A C A B C A B C A B C A B C A B C A B C A B C A B C

D E V D E V D E V

F N S

Annu

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Figure 5-15. FNS-VED-ABC classification.

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126

Fig

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127

5.2.2.2 AHP-based classification

A second classification method uses a well-known MCDM technique to calculate a

classification score for each inventory item, while considering multiple attributes. The

AHP is developed by Saaty (2008) as a tool supporting decision-making processes

when several influencing criteria are involved to find the solution that best suits their

goal and understanding of the problem. In a first step, AHP decomposes the complex

decision problem into a hierarchical structure consisting of branching nodes as in

Figure 5-17. Applied to the OT1 setting, the influencing criteria for ranking inventory

items are average unit cost, euro usage, demand pattern, criticality and surgeon’s item

preferences. Surgeon preferences can significantly impact inventory management as

the item assortment continuously changes due to the launch of new, innovative

surgical-medical items or due to surgeon’s partnerships with medical companies.

Increased transparency is required by engaging surgeons and improving awareness

regarding the costs of surgical instruments in order to limit item variety and ensure

proper management of physician preference cards. In this study, however, surgeon

preferences are disregarded due to limited data.

The second step of AHP uses pairwise comparisons to express preferences for each of

the influencing nodes on a 1-9 scale based on expert knowledge and judgment of

decision makers. Table 5-6 shows the values for the pairwise comparisons as judged

by the OT logistics manager.

Inventory classification

score

Average unit cost

Annual euro usage

Annual demand Criticality

Figure 5-17. Hierarchy of criteria for inventory classification (Karkera, 2020).

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128

Table 5-6. Pairwise comparison table in AHP procedure.

Average

unit cost

Annual

euro

usage

Criticality Annual

demand

Priorities

Average unit cost 1 1/9 1/8 1/6 0.04

Annual euro

usage

9 1 5 2 0.45

Criticality 8 1/2 1 2 0.31

Annual demand 6 1/2 1/2 1 0.20

In the next step, AHP converts these judgments to numerical weights or priorities for

each of the influencing criteria. A consistency ratio of 10% is achieved to check for

inconsistent judgments. The resulting priorities can be found in the final column of

Table 5-6. The highest weight is attributed to annual euro usage, followed by

criticality, annual demand and average unit cost. Based on these priorities, critical

items or items with high euro usage value will be ranked higher, whereas items with a

high unit cost do not necessarily have a high-ranked classification score due to its low

weight. Since euro usage value is the product of annual demand and unit cost, a high

unit cost does not necessarily imply a high euro usage value when demand is very low.

This difference between the priorities emphasizes the importance of patient safety,

rather than costs, when classifying items for inventory optimization.

Finally, the classification of items based on the AHP priority vector is determined. The

goal is to calculate a classification score for each inventory item by taking into account

the priorities for each attribute in Table 5-6, representing the relative importance of the

item characteristics. In addition, for each item a value on each criterion is required.

The classification score is computed as the sum product of the criteria weights and the

corresponding criteria values (see Equation 5.2). The criteria values are determined by

data on average unit cost, annual euro usage value and annual demand. Since no

numerical value is available for criticality, Flores and Whybark (2008) assign values

of 1, 0.5 and 0.01 for vital, essential and desirable items respectively. However, the

criteria values differ significantly in units of measure, with annual euro usage

amounting up to hundreds of euros whereas criticality takes up values in the range of

0 and 1. Therefore, the criteria values are normalized using:

𝐹𝑛𝑒𝑤 =𝐹𝑎𝑐𝑡𝑢𝑎𝑙 − 𝐹𝑚𝑖𝑛𝑖𝑚𝑢𝑚

𝐹𝑚𝑎𝑥𝑖𝑚𝑢𝑚 − 𝐹𝑚𝑖𝑛𝑖𝑚𝑢𝑚

(5.1)


129

In Equation 5.1, Fnew represents the normalized criterion value, Factual is the initial value

for each criterion, and Fminimum/maximum are the minimum and maximum criterion values

for all items included in the dataset. After normalization, the classification score for

each inventory item is calculated:

𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑠𝑐𝑜𝑟𝑒 = 0.04 ∗ 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 + 0.45 ∗ 𝑎𝑛𝑛𝑢𝑎𝑙 𝑒𝑢𝑟𝑜 𝑢𝑠𝑎𝑔𝑒+0.31 ∗ 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙𝑖𝑡𝑦 + 0.20 ∗ 𝑎𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 (5.2)

The classification scores are now arranged in decreasing order and thresholds are

defined to classify items into different groups. Flores and Whybark (2008) suggest to

use cut-offs of 10%, 20% and 70% of the classification score to classify items into

three groups. The items accounting for the top 10% of classification scores are the

most important items (i.e. category 1), requiring strict management control, whereas

category 2 and 3 receive moderate or low priority respectively. Category 1 contains

mostly expensive and vital items, characterized by the high euro usage and lower

annual demand. In category 2, slow-moving vital items are observed. However, these

items also need strict management control as a stock-out can adversely impact a

surgery. The majority of SKUs is classified as category 3, reflecting high annual

demand and euro usage. The classification cut-offs, however, should be decided based

on the understanding of the problem and the goals of the organisation. Unlike the ABC

and FNS analysis, the slope variations in the Pareto chart cannot be used as reference

points for cut-offs due to the linear relationship in Equation 5.2. Further analysis of the

cut-offs suggested by Flores and Whybark (2008) shows that 189 vital items are

classified under category 2 and 292 vital items under category 3. Figure 5-18 provides

an overview of the number of SKUs by linking the ABC-FNS-VED classification to

the AHP categories.

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130

If these classification results would be used to assign service levels for the

development of the inventory model, it could be catastrophic as several vital or

essential items have insufficient control due to their classification in category 2 and 3.

Therefore, the threshold values as suggested by Flores and Whybark (2008) are

adapted to the current situation in consultation with the OT logistics team, and one

category is added as proposed by the Med-MASTA classification by Danas et al.

(2006) to determine the importance of inventory items. The goal when setting the

thresholds is to assign as many vital items as possible to category 1 and 2, the majority

of essential items to category 3 and the remainder, mainly desirable items, in category

4 based on their classification score. The thresholds are changed to 25%, 35%, 39%

and 1% of the cumulative classification score. As a result, the vital items fall into

category 1 and 2. 95% of essential items are classified in category 3 and all desirable

items are grouped in category 4. Figure 5-19 shows the results for the AHP

classification based on the adapted cut-offs, tailored to the specific inventory problem

at OT1. Based on this classification, different service levels can be assigned to four

classes of items and appropriate inventory parameters can be found depending on the

item characteristics.

Figure 5-18. Combination of AHP-based (category 1, 2, 3) and ABC-FNS-VED classification.

A C A B C A B C A B C A B C A B C A B C A B C A B C

F N S F N S F N S

D E V

AHP category 3 1 10 1 5 22 1 5 130 19 48 28 15 51 111 8 36 499 13 2 277

AHP category 2 3 1 6 4 3 14 19 1 24 100 18

AHP category 1 1 14 22 29

Number of SKUs


131

The AHP method is a simple and transparent approach to classify inventory items,

while incorporating judgments from stakeholders. A classification score is computed

as a single weighted measure integrating several parameters that influence the

inventory picture. The AHP method, like the ABC-FNS-VED classification, uses

attributes such as annual demand, annual euro usage and criticality as important item

characteristics. However, the latter method uses these attributes to classify items

independently into 27 categories, whereas the former method computes a classification

score which takes into account all attributes to classify items into four categories. Cut-

offs, tailored to the specific problem setting, are selected to obtain four classification

groups, which will further act as input when identifying appropriate inventory policies

in Section 5.2.3.

5.2.2.3 Comparison of classification methods

The AHP-based classification technique is suggested because it integrates decision

makers’ preferences by computing weights and performance for the item

characteristics that affect inventory control, and hence it is a combination of

quantitative and qualitative input. Moreover, a single weighted AHP classification

score for each item is used to categorize the items in four groups, which is easier to

manage as compared to the 27 inventory classes from the hybrid ABC-FNS-VED

classification. Although the AHP-based classification is a useful method to categorize

items considering multiple, possibly conflicting, criteria, the method by itself is not as

intuitive as when compared to the ABC-FNS-VED classification. Without insights

1 2 3 4

Annual demand 141880 98911 210071 27904

Number of SKUs 208 368 777 188

Annual euro usage 7948907 1387264 2390915 194915

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AHP categories (1 - 4)

Figure 5-19. AHP-based classification with category 1 requiring close management attention

and category 4 the least management attention.

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132

from the latter analysis, the AHP method fails to identify why items are categorized in

a class since no distinction can be made between fast-moving or slow-moving,

expensive or cheap, and critical or desirable items in one category. By linking the

AHP-based classification to the ABC-FNS-VED analysis, we gain insights to

determine threshold values for assigning items to the four categories such that items

requiring stricter management control are clustered according to the organisation’s

goals. Determining these characteristics for all of the categories is essential to assign

appropriate inventory policies. For example, items that are highly critical, have long

replenishment lead times and/or high demand require strict management control by

implementing shorter review periods and optimizing reorder point, reorder quantity,

safety levels under space and service level constraints. Table 5-7 compares both

classification methods.

Table 5-7. Comparison of two classification methods.

AHP-based classification ABC-FNS-VED classification

Classification

criteria

Annual euro usage Annual demand Criticality Average unit cost

Annual euro usage Annual demand Criticality

Number of

classes

A single classification score for each item allows for classifying into four categories

Classification cube with 27 categories.

Decision

maker’s

preference

Pairwise comparisons to calculate weights for classification criteria.

No input: classification criteria are equally weighted.

Inventory

characteristics

Difficult to identify critical, fast-moving or expensive items from the classification score.

Transparent grouping of items based on characteristics: criticality, demand, cost.

Thresholds Choice of thresholds based on

organisation’s goal.

Choice of thresholds based on

Pareto principle.

Ease-of-use Simple method Simple method

Rank reversal Rank reversal could occur due to adding criteria or changing thresholds.

Rank reversal could occur due to a change in thresholds.

Before developing different inventory models, a minimum service level needs to be

defined for each item. This service level acts as a constraint to decide if a policy is

acceptable or not. Based on the threshold values for management control, the service

levels can be appropriately defined for the corresponding classes and serve as input to

the inventory model to balance costs, inventory turnover and stock-outs (see Table


133

5-8). For category 1 and 2, a service level of 99.7% and 98% respectively should be

achieved since a stock-out of these items has adverse impact on the surgical procedure

and patient safety. Category 3 and 4 have service levels of 95% and 90% respectively.

A lower service level can be maintained as these items have several substitutes and

short lead times. In addition, increasing the items in stock for items in category 4 has

limited impact on holding costs due to the low unit costs, and hence a longer review

period can be implemented for this type of items. As a conclusion, no single inventory

model can be used for every item in stock. Depending on the item characteristics, other

policies will be applied.

Table 5-8. Service level assigned per classification group

AHP-classification 1 2 3 4

Service level [%] 99.70 98 95 90

5.2.3 Optimization of a two-echelon inventory system at the

operating theatre – A case study

Inventory control of medical-surgical supplies is a challenging field in OR/OM due to

dynamic and critical supply needs compared to other hospital units. The nature of

surgical procedures varies widely depending on the medical discipline (e.g. cardiology,

neurology, and traumatology), physicians often have preferences for different supplies

and the patient’s medical history plays a central role. Today, the OT is dealing with

several inventory issues, such as stock-outs, backorders, lack of visibility or paper-

based exchange of information between different units. These problems can adversely

affect patient safety, decrease service levels and lead to higher inventory costs. In this

case study, a two-echelon inventory system is established where the central storage

room and the operating rooms constitute the two echelons (Lauwers 2019). The main

objective is to identify inefficiencies in the current situation and propose possible

improvements based on the ANP-based prototype, as suggested in Chapter 4. The

scope is limited to disposable supplies used at the OT. The inventory classification

based on AHP serves as an input for identifying multiple scenarios by assigning

appropriate service levels for each item group in order to minimize costs, improve

inventory turnover and reduce stock-outs. A DES model is developed to determine

values for the operational inventory parameters, and potential trade-offs among the

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134

KPIs are quantified by introducing the Internal Logistics Efficiency Performance

(ILEP) index that combines the ANP and DES output.

5.2.3.1 Methodology

Figure 5-20 shows the materials flow within OT1. A situation description and

schematic overview of OT1 were given in Section 4.2.1.4, Figure 4-4 and Figure 4-5.

The flow starts downstream in the supply chain where demand is generated by using

supplies during surgery. For each procedure, a surgical case cart is prepared in the

central storage room with the minimum required amount of items. Any additional

supplies required during surgery can be pulled from decentral stock. Logistics

employees check the consumption in the decentral storage (i.e. central core and relay

cabins) by daily scanning the amount of used items. The requested items are collected

in the central storage to replenish the central core and relay cabins. Similarly, the

central storage room is scanned on a weekly basis and is replenished by external

suppliers. This study focuses on the two-echelon inventory system, where the decentral

stock is replenished by the central storage. Currently, a periodic (R,S) policy is applied

where, every review period R, the supplies are counted to generate a picking list with

the orders that bring inventories up to the maximum stock level S, or the PAR level.

However, these PAR levels are based on intuitive reasoning to satisfy average demand

for a period of four days, or the longest possible weekend. Moreover, limited visibility

in inventory costs and stock-outs, high inventory turnover, stocking excessive amounts

of items, etc. are typical problems encountered due to lack of appropriate inventory

policy.

Figure 5-20. Schematic overview of supply chain in operating theatre.


135

5.2.3.2 Conceptual model

Modelling objective

The overall objective of this work is to identify opportunities for efficiency gains by

selecting the most appropriate inventory policy for each item category and improving

inventory parameters accordingly. A two-echelon inventory model is developed as a

first step towards the optimal inventory allocation decision, which aims to determine

where to locate supplies between different locations (Ahmadi et al. 2018). The supplies

are categorized according to common characteristics, such as surgical criticality, price

or usage rate. Each category requires different management control by assigning

different service levels (see Table 5-8). The simulation model runs different scenarios

for each item category by adjusting the decision parameters, namely maximum stock

level and reorder point. The best performing scenario for each category is identified

by comparing the ILEP index, which integrates three KPIs, namely inventory cost,

service level and productivity, based on the ANP-based prototype as designed in

Chapter 4.

Model structure

A conceptual model is constructed providing an overview of the inventory system logic

and facilitating the development of the computer model. The inventory items are

divided into four categories based on their AHP-based classification score to determine

the importance of the inventory items. Ten items for each group are selected to test the

applicability of different inventory policies using service level as a constraint, being

99.7%, 98%, 95% and 90% for the four categories respectively. Every day at 6:00 PM,

demand is generated in the decentral storages, which needs replenishment from central

storage, and thus also creates demand in the latter. The model structure consists of two

main parts, namely fulfilling the daily, decentral demand based on the consumption

rate of an item during surgery, and replenishing the stock. Figure 5-21 represents the

conceptual model for part 1, namely fulfilling demand during surgery. If demand is

fulfilled from decentral stock, the inventory level is updated according to Equation 5.3.

𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝑡 = max(𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝑡−1 − 𝑑𝑒𝑚𝑎𝑛𝑑𝑡 ; 0) (5.3)

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136

Whenever the demand cannot be completely fulfilled from decentral stock, the central

storage or other clusters are consulted in this predetermined order. Similarly, the

inventory level of the visited storages are updated. In addition, stock-outs are recorded

when demand cannot be fulfilled. Figure 5-21 also presents different types of stock-

outs. A local and main stock-out can be solved from central stock or other clusters

respectively, whereas a global stock-out means the item is not available. A remark

should be made that stock from other clusters cannot be completely depleted. At all

times, one item should remain in the respective stock in case of a main stock-out as it

is not the intention to cause shortage problems.

The second process involves replenishing the stock. Decentral stock is daily

replenished and central stock on a weekly basis. For both storages, the current stock

level is compared to a specified reorder point. If the stock level drops below the reorder

point (s), replenishment is requested up to the maximum stock level of that item (S).

Equation 5.4 determines the reorder quantity, with S representing the order up-to level

in the (s, S) inventory policy:

𝑅𝑒𝑜𝑟𝑑𝑒𝑟 𝑞𝑢𝑎𝑛𝑡𝑖𝑡𝑦 = 𝑆 − 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝑡 (5.4)

Since the decentral stock is scanned daily at 7:00 PM and replenished between 6:00

AM and 10:00 AM the next morning, no decentral demand is generated within this

timeframe. The central inventory, however, is only monitored once a week for

replenishment, namely on Thursday 8:00 AM and is replenished on Monday 8:00 AM,

resulting in a lead time of 2 days. As demand is daily generated at decentral stocking

locations, central demand during the replenishment lead time should be considered.

Equation 5.5 calculates the inventory level for the central stock:

𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝑡 = 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝑡−2 − 𝑑𝑒𝑚𝑎𝑛𝑑𝐿𝑇 + 𝑟𝑒𝑜𝑟𝑑𝑒𝑟 𝑞𝑢𝑎𝑛𝑡𝑖𝑡𝑦𝑡−2 (5.5)

Figure 5-21. Conceptual model for daily demand generation at decentral stock (Lauwers, 2019).


137

Data

Data availability is a well-known problem in any healthcare setting. At the hospital

under study, scanning data is available covering consumption in almost all operating

rooms in a period of one year (i.e. April 2018 – March 2019). Therefore, we include

demand data based on the consumption rate of items which are stocked decentral.

Demand is generated in the model using an empirical distribution, which means that

occurrence of demand is proportional to the data sample. These data also partially

represent the central storage demand. The remaining demand in the central storage

room originates from preparing surgical case carts. However, due to lack of data,

surgical case cart demand is omitted from this study and thus it can be expected that

the final inventory parameters for the central storage room will be an underestimation.

Although the simulation output will deviate from reality, our approach provides

interesting insights as proof-of-concept for optimizing inventory management.

Another problem relates to the information on stock-outs. When a stock-out occurs in

the operating room, a local stock-out is recorded and the item is retrieved from the

central storage. However, this information is not included in the consumption data of

the decentral and central stock. As a consequence, the demand data of the central stock

contain information on decentral consumption, surgical case cart consumption and

local stock-outs. In case of a global stock-out, the item is not scanned and thus not

consumed in the data. Instead, demand is generated for a substitute item.

Key performance indicators

Three KPIs are identified to measure the cost, quality and productivity of the two-

echelon inventory system at the OT. These objectives are specified in the prototype to

improve the efficiency of inventory management in a healthcare setting. Table 5-9

includes the weights attributed to these KPIs, which are defined using the Analytic

Network Process (ANP). More information about the ANP methodology and the

weight factors can be found in Chapter 4 and Saaty (2010, 1990).

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138

Table 5-9. Ranking of inventory KPIs based on ANP weights (Lauwers, 2019).

Inventory

objectives

Weights Indicators Weights Ranking Normalized

weights

Quality 0.68 Inventory service level

0.289 1 0.655

Inventory visibility 0.106 5 Inventory accuracy 0.123 3 Inventory criticality 0.164 2

Financial 0.06 Inventory cost 0.032 8 0.073

Value of inventory 0.027 9

Productivity/

organisation

0.26 Inventory turnover 0.120 4 0.272 Inventory usage 0.075 6 Product standardization

0.063 7

In this case study, inventory service level, cost and turnover are measured as model

outputs, representing quality, cost and productivity objectives respectively. These

KPIs have the highest weight in each objective, and thus are perceived to be the main

parameters to improve inventory management. Service level is defined as the

percentage of demand that can be fulfilled in time. Inventory cost includes item,

holding and ordering cost. Finally, inventory turnover refers to the average amount of

days that an item is in stock. The expressions for calculating the KPIs are provided in

Appendix A. Due to lack of data, the other indicators could not be recorded in the

model. However, further research could include space utilization and improve

standardization of the inventory system through rationalizing product selection by

physicians and increasing hospital staff involvement. The KPI weights are converted

to obtain a sum of one in the final column of Table 5-9. Notice the big difference

between quality and cost, which represents the main trade-off in most inventory

models.

5.2.3.3 Simulation model

A simulation model is presented to model different inventory policies instead of an

analytical model, which is mostly used in literature. Analytical models often need

unrealistic assumptions as it is more difficult to implement complex materials flows

compared to simulation. DES is the appropriate simulation method to incorporate

stochasticy when modelling demand generation and inventory replenishment at

periodic time intervals. Moreover, DES allows to ask “what-if” questions by changing

the operational inventory parameters and analysing the impact of different scenarios.


139

For this case study, the Simulink simulation platform is used which enables data

processing within the software package Matlab.

Figure 5-22 gives a high-level overview of the different building blocks in the

simulation model. The modular build-up allows for easy understanding, flexibility and

modifications by the end user. The model is characterized by a single-item, two-

echelon inventory system. Since there is no interaction among items, extending the

model to multiple items would be too time-consuming. The inventory modules (e.g.

InventoryA_Item1) contain the current inventory level of one item in each storage

room, central and decentral. At the start of the simulation, two attributes, namely the

maximum stock level and reorder point, are assigned to each data module, which can

easily be changed to test different scenarios. The ‘Read inventories’ module saves the

current inventory levels to a data file with a fixed time interval of one hour in order to

calculate the average inventory. The decentral and central storage modules contain the

daily demand generation and inventory replenishment. An overview of the demand

generation in decentral stock and the model logic of the inventory check are shown in

Appendix B.

Validation and verification

Verification and validation are crucial steps in any simulation study. A verified model

ensures that the outcome of the model is as expected according to the conceptual

model. A simple Material Requirement Planning (MRP) logic in which the inventory

level and stock-outs can easily be predicted is compared to the simulation outcome.

Similar results are obtained. The robustness of the model is also verified by creating a

Figure 5-22. High-level overview of simulation model.

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140

similar model in a different DES software, namely Arena. In the long run, both models

converge to the same outcome, though there are negligible deviations in demand due

to random number generation.

Validation deals with building the right model. Since data availability is a well-known

issue in healthcare, limited validation is performed by calculating the expected

consumption rate and number of decentral orders. Table 5-10 gives an overview of the

number of possible scanning days of one item in decentral stock and the frequency.

Extrapolating this data to one year (i.e. 260 days) gives similar results as the simulation

output, and thus validation ensures that the model gives a good representation of

reality.

Table 5-10. Validation of annual demand and order size.

Decentral

stock

Scanning

days

Scanned

amount

Frequency Expected

frequency

(260 days)

Expected

consumption

A 189 1 7 9.6 9.6

CD 249 1/2/3 57/29/6 96.1 139

F 247 1/2/3/4 43/17/10/1 74.7 116.6

G 250 1/2/3/4/5 8/2/1/3/2 16.6 38.4

Expected total

Simulated total

197 303.6

197.7 306

Scenario analysis

For each item of the four classification groups, three scenarios with varying stock

levels (S) and reorder points (s), are simulated and compared by monitoring the impact

of these decision parameters on the service level. The service level is the restricting

factor as a minimum level must be achieved for each classification group. The As-Is

scenario represents the current situation of the item, with experience-based inventory

parameters (s, S). Due to the large differences between items, no general description

of this scenario can be provided since the service level is too low for 12% of the items,

while the remaining items have excessive safety stocks. In the second scenario, the

goal is to adapt the parameters to the demand characteristics of the items while

respecting the requested service level in each classification group. A box plot of the

order sizes (see Figure 5-23) is used to determine the maximum stock level and reorder

point. For the most critical categories, the maximum stock level and reorder point are

equal to the maximum peak demand (Dp) to maintain sufficient safety stock and thus


141

ensure high service levels. The lower the item criticality, the less safety stock will be

allowed and thus the decision parameters decrease to the maximum (Dm), first quartile

(D1q) or median (Dmed) demand. Finally, in the third scenario more risk is taken by

reducing the safety stock and thus lowering inventory levels, holding costs and

inventory turnover. As a consequence, the service level will be lower, but still

acceptable for non-critical items. Table 5-11 provides an overview of the decision

parameters for each scenario, each storage location and each classification group. Due

to varying demand characteristics within one classification group, different

combinations of parameters are tested in a trial-and-error approach in order to

determine the combination generating the lowest costs and stock-outs. If for all items

within the same group acceptable service levels are obtained, the inventory policy can

be generalized for the whole group and the KPIs are measured to find the most efficient

inventory policy for each group in terms of cost, quality and productivity.

5.2.3.4 Results

The simulation model is used to assess three scenarios summarized in Table 5-11 and

find the most appropriate inventory policy for each classification group. No single

inventory policy applies to all supplies due to divergent item characteristics, such as

criticality, price or usage rate. After a warm-up period of one year to achieve steady

state, the simulation model has run for ten years. For each scenario, ten items of each

classification group are simulated. Yearly statistics are extracted to obtain average

values for the three KPIs, namely costs, stock-outs and inventory turnover. By

aggregating the normalized values into the ILEP index, using the ANP weights, the

best-performing scenario is selected for each item group. Finally, an inventory policy

matrix is proposed.

Figure 5-23. Boxplot of order size as a guide to determine decision parameters.

CHAPTER 5

142

Tab

le 5

-11

. O

ver

vie

w o

f d

iffe

ren

t sc

enar

ios.

Scen

ario

G

ro

up

A

Gro

up

B

Gro

up

C

Gro

up

D

Cen

tra

l D

ecen

tra

l

Cen

tra

l D

ecen

tra

l C

en

tra

l D

ecen

tra

l C

en

tra

l D

ecen

tra

l

1

(As-

Is)

S,s

G

ut

feeli

ng

2

S

1.2

∗𝐷

𝑝

1.2

∗𝐷

𝑝

𝐷𝑚

𝑎𝑥

𝐷𝑝

1

.2∗

𝐷1

𝑞

𝐷𝑝

2

+𝐷

1𝑞

𝐷

𝑝

s

𝐷𝑝

𝐷

𝑝

0.9

∗𝐷

𝑚𝑎

𝑥

𝐷𝑝

−𝐷

𝑚𝑖𝑛

𝐷

𝑚𝑒

𝑑

𝐷𝑝

−𝐷

𝑚𝑖𝑛

1

1

3

S

1.1

∗𝐷

𝑝

1.1

∗𝐷

𝑝

𝐷𝑚

𝑎𝑥

𝐷𝑝

𝐷

𝑚𝑎

𝑥

𝐷𝑚

𝑎𝑥

/ /

s

0.9

∗𝐷

𝑝

0.9

∗𝐷

𝑝

𝐷1

𝑞

𝐷𝑝

−𝐷

𝑚𝑖𝑛

−1

𝐷𝑚

𝑒𝑑

𝐷

𝑚𝑒

𝑑

/ /


143

Single-item inventory model

First, the outcome of a single-item inventory model is explained. One item from group

C is arbitrarily chosen and its inventory diagram is plotted in Figure 5-24, showing the

first 50 days of year 1. In the As-Is scenario, the maximum stock level equals the

reorder point (i.e. max = min), or in other words, every time an item is used an order

is placed. The diagram also clearly shows the weekly and daily review period for the

central and decentral (cluster F) inventory respectively. In the former stock, however,

incomplete refills (i.e. stock is not filled to the maximum level) occur due to demand

during the 2 days lead time. Further analysis shows sufficient safety stock or no stock-

outs in both storages, and thus a service level of 100% is achieved, which is higher

than the requested 95%. As the inventory parameters are mainly based on gut feeling,

the inventory tend to be overstocked as S typically exceeds the maximum peak

demand. Therefore, scenario 2 and 3 adapt the inventory parameters to the demand

characteristics, which can be read from the boxplot in Figure 5-23. The inventory

diagram for scenario 2 and 3 show decreased S and s values, though there are more

stock-outs in these scenarios. Table 5-12 provides further details on the differences

between the three scenarios.

It is clear that the two improvement scenarios outperform the As-Is situation in terms

of costs and productivity, whereas quality of care seems to decrease at first sight. First,

the yearly total cost decreases and is lowest in the risk-taking scenario 3. The lower

holding cost is due to decreasing the S and s values, which results in lower average

inventory levels. The low reorder point also causes the ordering cost to drop, as the

item will be ordered less frequently. Furthermore, the item cost decreases, however,

this saving should be carefully interpreted. Since there are more global stock-outs in

scenario 3 and backorders are not allowed, less items are ordered and thus the item

cost goes down. In reality, the item is substituted or an emergency order is placed. Due

to lack of data, it is not considered in this study. We assume to have constant item costs

for all scenarios with equal demand, and the item cost can thus be omitted in further

analysis. Second, inventory turnover is used as a productivity metric to streamline

inventory processes and improve logistics staff utilization. Since it represents the

period that an item is in stock, it is related to the average inventory level as can be seen

in the formula in Appendix C.

CHAPTER 5

144

Fig

ure

5-2

4.

Cen

tral

an

d d

ecen

tral

in

ven

tory

dia

gra

m f

or

single

ite

m.


145

Hence a lower average inventory level causes a drop in the number of days the item is

in stock, or a faster inventory rotation. This, in turn, determines, the frequency and

time spent by logistics staff to replenish the items. Finally, the stock-outs increase due

to lower inventory levels and thus lower safety stock. Consequently, the service level

drops from 100% to 97.20%, but stays within acceptable limits for group C items. A

remark should be made that service level is calculated based on global stock-outs (see

Appendix C). However, local and main stock-outs should be penalized as well since

logistic employees spend more time in visiting other stocks, which increases the

handling cost. Scenario 3 has more local stock-outs due to lower reorder points in

decentral stock, but the higher maximum central stock level implies less main stock-

outs as central inventory can fulfil demand and other decentral inventories should not

be visited as often as in scenario 2.

Table 5-12. Yearly single-item inventory parameters.

Scenario As-Is Scenario 2 Scenario 3

(s, S) Turnover [yrs] (s, S) Turnover [yrs] (s, S) Turnover [yrs]

Central (65, 65) 0.0738 (13,

20)

0.0094 (13, 27) 0.0157

Cluster A (7, 7) 0.1107 (3, 5) 0.0628 (1, 1) 0.0138

Cluster E (2, 2) 0.0242 (1, 2) 0.0174 (1, 2) 0.0179

Cluster F (6, 6) 0.0140 (4, 6) 0.0110 (2, 6) 0.0096

Cluster G (20, 20) 0.0982 (6, 8) 0.0346 (4, 6) 0.0257

Costs [€] Holdi

ng

Orderi

ng

Item Holdi

ng

Orderi

ng

Item Holdi

ng

Orderi

ng

Item

387 1413 1613

2

103 1222 1596

1

98 1055 15660

Total cost 17932 17286 16813

Stock-

out

Local Main Global Local Main Global Local Main Global

0 0 0 16.6 38.7 7.7 55.2 19.4 20.2

Service

level [%]

100 98.93 97.20

CHAPTER 5

146

Multi-item inventory policy per classification group

The simulation model has run for ten items in each classification group and the relevant

KPIs are computed. For each KPI, the simulation output is normalized using the As-Is

scenario as a baseline. The lower the KPI value, the better. The ILEP index is obtained

by calculating the weighted sum of all normalized KPI values using the ANP weights.

The best scenario is identified as the one with the highest ILEP index.

Items in category A are mostly critical items due to lack of substitutes or high surgical

impact, whereas the average unit cost and yearly demand vary between cheap and

expensive items and fast and slow movers respectively. Table 5-13 presents the annual

average values of ten items for three KPIs. The service level is specified to be 99.7%

for this category, which is based solely on global stock-outs. However, only

considering global stock-outs results in higher service levels, and thus rewards

overstocking. Therefore, the service level-1 in Equation 5.6 is determined based on

local and main stock-outs, replacing the formula in Appendix C. Still, service level-1

is defined as the percentage of demand that can be fulfilled in time, though Equation

4.6 takes the inverse to ensure that lower KPI values in the framework represent higher

service levels. For example, due to the high amount of safety stock in the risk averse

scenario 2, no stock-outs are observed resulting in a value of 1 or 100% and a

normalized value of 98.85, whereas scenario 3 has a service level value of 1.01 (or

98.81%) based on local and main stock-outs, reflecting the higher normalized value

100.12 in Table 5-13 and Equation 5.6:

𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝑙𝑒𝑣𝑒𝑙−1 = ( 𝑑𝑒𝑚𝑎𝑛𝑑 − (𝑙𝑜𝑐𝑎𝑙 + 𝑚𝑎𝑖𝑛 𝑠𝑡𝑜𝑐𝑘𝑜𝑢𝑡)

𝑑𝑒𝑚𝑎𝑛𝑑)

−1

= 1.01 (5.6)

From a cost perspective, savings of approximately 28% and 37% are generated in

scenario 2 and 3 respectively. Further analysis shows that the order cost is the main

contributor as reorder point is below the maximum stock level and thus orders are

placed less frequently compared to the As-Is situation. As most items are cheap, the

impact of lowering the inventory parameters is limited for the holding cost. Finally,

inventory turnover is halved, or in other words, items rotate 50% faster compared to

the As-Is, where items are overstocked and thus stay longer in stock. A weighted

combination of the normalized KPI values gives the ILEP index for this classification

group. Although the service level in scenario 3 is lowest, it performs better than the

other scenarios. However, the logistics manager should always consider the trade-off


147

between multiple inventory objectives when choosing an appropriate policy. Making

decisions based on a single score may be misleading.

Table 5-13. KPI scores for classification group A.

KPI As-Is Scenario

2

Scenario

3

Normalized values

(weights) As-

Is

Scenario

2

Scenario

3

Service

level

1.01 1.00 1.01 (0.66) 100 98.85 100.12

Cost [€] 10441.84 7513.07 6544.50 (0.07) 100 71.95 62.68

Turnover

[yrs]

0.25 0.12 0.11 (0.27) 100 48.00 44.00

ILEP index 0 16.88 17.88

A similar analysis is performed for categories B, C and D. Category B contains mainly

expensive items and normal to fast movers. Remarkably, the As-Is scenario has a

service level far below the target of 98% for four out of ten items due to a large amount

of global stock-outs. However, this is not visible in the KPI value, since this calculation

only takes into account local and main stock-outs (see Equation 5.6). Although Table

5-14 provides a distorted overview, scenario 2 and 3 perform still better than the As-

Is. Scenario 2 saves 4% in costs, while these savings go up to 18% in scenario 3. The

savings will be even larger, since the holding cost is underestimated in the As-Is

scenario due to understocking. As category B contains mainly expensive items, the

holding cost has a significant impact on the total cost savings. Finally, items stay on

average seven days longer in stock in the As-Is scenario. Again, this is an

underestimation due to lack of safety stock.

Table 5-14. KPI scores for classification group B.

KPI As-Is Scenario

2

Scenario

3

Normalized values

(weights) As-

Is

Scenario

2

Scenario

3

Service

level

1.04 1.01 1.02 (0.66) 100 97.11 98.30

Cost [€] 17273.26 16611.63 14205.47 (0.07) 100 96.17 82.24

Turnover

[yrs]

0.08 0.05 0.05 (0.27) 100 62.50 62.50

ILEP index 0 12.37 12.61

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148

Less critical items are assigned to category C. In general, these items are cheaper and

have high demand. The target service level is 95%, however, Table 5-15 shows that

the As-Is scenario reaches a service level of almost 100%. Scenario 2 and 3 tackle the

overstocking problem by lowering maximum stock levels and as a results have more

local and main stock-outs. This also leads to a turnover drop from 44 days to 8 days

and cost savings of up to 40% in scenario 3. Based on the total KPI score, scenario 2

performs best, so the cost savings in scenario 3 are not compensated by the difference

in service level, as is the case for category A and B.

Table 5-15. KPI scores for classification group C.

KPI As-Is Scenario

2

Scenario

3

Normalized values

(weights) As-

Is

Scenario

2

Scenario

3

Service

level

1.00 1.04 1.10 (0.66) 100 104.34 110.38

Cost [€] 9799.18 7076.37 5908.07 (0.07) 100 72.21 60.29

Turnover

[yrs]

0.17 0.04 0.03 (0.27) 100 23.53 17.65

ILEP index 0 19.99 18.50

Finally, classification group D contains slow-moving items and the service level to

achieve is 90%. However, because demand is low, one improvement scenario is

simulated. The general policy for slow-movers is to reorder an item if it is used. Local

and main stock-outs increase compared to the nearly 100% service level in the As-Is

scenario. Cost savings are around 38% due to lower stock levels and less frequent

replenishment orders, and inventory turnover drops from 48 days to 16 days in stock.

Summarizing the results in Table 5-16 shows that scenario 2 outperforms the As-Is,

despite the higher number of stock-outs.

Table 5-16. KPI scores for classification group D.

KPI As-Is Scenario 2 Normalized values

(weights) As-Is Scenario 2

Service level 1.00 1.09 (0.66) 100 109.35

Cost [€] 4832.16 2981.70 (0.07) 100 61.71

Turnover

[yrs]

0.18 0.06 (0.27) 100 33.33

ILEP index 0 14.80


149

5.2.3.5 Discussion

The hybrid ANP-DES tool acts as a decision-support tool to select the most appropriate

inventory policy at the operating theatre by evaluating the ILEP index, which gives a

more holistic overview of internal inventory control. In general, the two improvement

scenarios outperform the As-Is situation in terms of costs and productivity. Although

quality is slightly lower for class A, C and D, the service levels remain in the requested

range. The current situation exists of a two-bin system, in which inventory parameters

are set based on intuition to fulfil demand for the longest possible weekend. Moreover,

all items are treated in the same way, regardless of their criticality, which results in a

100% service level for most items. However, no consistency can be found in the

current policy, or in other words, some items are in understock while others are

overstocked.

In scenario 2 and 3, the inventory parameters are adapted based on the item

classification attributes. Scenario 2 is more risk averse compared to scenario 3, which

contains less safety stock. Overall, scenario 3 has a positive effect on the financial and

productivity factor, but a negative effect on service level. Due to ANP weighting

factors, the cost saving cannot always compensate for lower service levels, which

emphasizes the main trade-off between cost and quality in healthcare. In classification

group A and B, an inventory policy as proposed in scenario 3 performs best. Stock-

outs are limited due to the high service level target. As a result, the additional cost

saving and productivity gain in scenario 3 due to reduced safety stock have more

impact on the ILEP index, and thus compensate for the stock-outs. On the other hand,

the lower requested service levels in category C and D allow for more stock-outs

compared to the As-Is, so the negative effect on service level grows in scenarios 2 and

3. Scenario 2 performs best, where more items are held in decentral storage room and

less items in central storage compared to scenario 3. For these classification groups,

the cost saving by decreasing safety stock in scenario 3 does not compensate for the

difference in service level. In Table 5-17, we propose an inventory policy matrix

depending on item classification groups. If for ten items with different characteristics

acceptable service levels can be obtained, we assume that a policy can be generalized

for the whole classification group. However, making decisions based on a single ILEP

index may be misleading. Therefore, the logistics manager should always consider the

trade-off between multiple criteria when choosing an appropriate inventory policy.

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150

Table 5-17. Inventory policy matrix.

A B C D

As-Is policy

Risk averse policy 2 X X

Risk taking policy 3 X X

Some remarks can be made regarding the simulated inventory policies and the

performance framework using the KPIs. Service level is the main contributor when

determining the score for each scenario, as it has the highest weight in Table 5-9.

Although the main intention is to find policies that meet the service levels based on the

global stock-outs, only the local and main stock-outs are included when calculating the

service levels. Using a service level based on global stock-outs, as shown in Table

5-12, results in a service level of 100% for the As-Is scenario due to holding excessive

safety stocks. As a consequence, scenarios with lower service levels are penalized,

though they are within the acceptable range as required for their classification group.

Therefore, global stock-outs are left out of the service level calculation in Equation 5.6

in order to not reward overstocking. Moreover, in case of local or main stock-outs,

valuable time is wasted to obtain any stock-out items, potentially disrupting patient

care (Rosales et al. 2015), whereas the wage cost of a global stock-out is limited as no

extra actions are needed. In the future, a study can be executed to calculate a

penalization cost for a local and main stock-out and consider service level as a

constraint. In addition, the need for this high weighting factor for service level is

questionable for less critical items. Reconsidering the weights for different

classification groups may have an impact on the ILEP index to select the appropriate

inventory policy, as other criteria will get more influence. Next, another policy not

considered in this work relates to group C and D. As the majority of the items are

cheap, holding costs are negligible and order costs can be reduced even more by

increasing the gap between the maximum stock level and reorder point. As a result,

inventory rotation will increase (i.e. items are longer in stock) and service levels are

higher. However, one should remark that other restrictions, such as expiry date or

limited storage space, also influence the inventory level. Furthermore, review period

is also a potential decision variable, but changing this parameter is equivalent to

lowering the reorder point, since no costs are charged for manually counting the

inventory and not ordering any items.


151

Furthermore, the inventory model also allows to analyse the impact of decentralization

as a key dimension when designing a distribution network (Abdul-Jalbar et al. 2003;

Bélanger et al. 2018). In this stockless case, the central inventory is eliminated and the

external suppliers deliver directly to decentral storages that are closer to point-of-use.

This behaviour can be imitated by increasing the inventory level to a very high amount

and a reorder point of one, so that the central inventory acts as an external supplier.

The simulation results of decentralization for an expensive item show a large decrease

in holding costs compared to the above mentioned scenarios, due to elimination of the

central stock. However, the savings in holding cost will be less noticeable for cheap

items. The order costs, on the other hand, increase since the external order cost plus a

cost for the scanning process and internal logistics problems is charged every time an

order is placed. Moreover, the replenishment lead time could be problematic,

especially for critical items. From the simulation, we can conclude that optimizing

inventory parameters for the As-Is scenario results in a better policy. This outcome

reinforces the hospital’s past decision to not implement decentralization.

Finally, due to assumptions and simplifications made in the simulation model, there

are some limitations. A trade-off should be made between computational effort, model

complexity and time spent modelling. From the organisational perspective, we assume

only one inventory per cluster. However, one cluster exists of four operating rooms

with their own stock. The obtained inventory parameters therefore apply to four

stocking locations in the cluster, and still need to be separated to individual operating

rooms. Moreover, the available data only contains daily consumption. We assume no

demand during replenishment lead time in decentral stock, though in reality demand

occurs at several times during a day. Also, no data is available on surgical case carts

which are prepared in the central storage. The same policies can be applied, however,

the maximum stock levels and reorder points need to be reconsidered (i.e. upscale).

Finally, information sharing between echelons is very inefficient. If a shortage occurs

at the central storage, no signal is made to the decentral stock. When checking the

decentral stock, a replenishment order is placed, even if the item is out-of-stock in the

central storage room. As a consequence, an order cost is charged multiple times for

placing the same order without replenishing the items. Since no information is

available on the fulfilment of replenishments, the same order cost is applied. However,

the time spent replenishing will be lower in case of a central stock-out. Further research

could conduct a time study to consider a broader management cost perspective.

Furthermore, hospitals could change to continuous review policies by investing in

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152

technology such as RFID to reduce inventory cost and increase inventory visibility

(Rosales et al. 2015). Kochan et al. (2018) propose cloud-based hospital supply chains

to enhance collaborative information-sharing in a multi-echelon inventory system with

improved inventory visibility and increased supply chain responsiveness.

5.2.4 Evaluating replenishment systems for disposable supplies at

the operating theatre – A case study

From a distribution perspective, coordination of the resources, effective delivery

strategies and efficiently managing healthcare delivery services are a necessity to

provide high-quality patient care (Rohleder, Cooke, et al. 2013). Implementing

effective internal distribution systems require determining optimal replenishment

policies to minimize stock-outs at the OT. Increasing the efficiency of replenishment

activities allows for significant cost savings, while improving staff satisfaction and

satisfying service level requirements. In this case study, the hybrid ANP-DES tool is

customized to assess internal OT replenishment policies at decentral stocking

locations. The goal is to measure performance of three replenishment policies in the

OT to identify efficiency targets.

5.2.4.1 Methodology

Currently, a periodic review replenishment policy is used with a fixed stocking limit,

based on gut feeling. However, stock-outs, imperfect order fulfilment, stock

duplication on replenishment carts, non-standardized processes and ergonomic burden

for logistics staff are typical problems. With this study, we aim to improve the

efficiency of the internal replenishment process at OT, focusing at the flow of

disposable supplies (e.g. surgical drapes, gloves, syringes). Figure 5-25 shows the

study design. The decentral storage locations represent the final link in the internal

supply chain before consumption during surgery. Different replenishment policies are

investigated to move materials from the central storage room to the decentral stock.


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Scenario analysis

Three scenarios, representing alternative replenishment systems for disposable

supplies, are introduced. In the As-Is scenario, the decentral stock is replenished using

copy carts from the central stock (see Figure 5-25). These carts contain a duplicate of

all disposables stored in decentral stock, meaning that the carts are a permanent double

stock. The great amount of items on these carts cause heavily loaded carts, which

increases ergonomic burden for logistics staff. Furthermore, all information exchange

is paper-based and no record is made of what has been consumed. This complicates

effective inventory control, requiring consumption data of each surgery. The As-Is

scenario serves as the baseline scenario throughout the rest of this study. The

alternative scenarios represent two potential logistics improvements, both featuring

barcode scanners. The Standard scenario eliminates the copy carts to reduce the

amount of stock in circulation. Barcode scanners are introduced to scan decentral stock

(see Figure 5-25). Based on the scanning data, the requested items are picked in central

stock to replenish decentral stock. Moreover, scanning enables collecting daily

consumption data in operating rooms, which is essential for inventory optimization as

suggested in Section 5.2.3. A disadvantage of this scenario, however, is that decentral

locations are not immediately restocked, resulting in more visits and higher incomplete

refills (i.e. location cannot be refilled to the stocking limit). This drawback is countered

by the Copy carts scenario, which features both barcode scanners and copy carts. Three

copy carts, for each relay cabin (see Figure 5-5), are used to immediately replenish

decentral stock. The copy carts hold a selection of commonly used items to avoid

heavily loaded double carts. In addition, scanning enables daily consumption data on

the copy cart level. Table 5-18 gives an overview of the replenishment scenarios.

Figure 5-25. Internal replenishment process at operating theatre.

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Table 5-18. Overview of replenishment scenarios.

As-Is Standard Copy carts

No double stock X ✓ X

Barcode scanner X ✓ ✓

Immediate replenishment ✓ X ✓

Consumption data X ✓ (OT level) ✓ (copy cart level)

Simulation model

We use simulation to model replenishment policies in the OT, because of the

advantages of simulation in solving complex problems (Hu et al. 2018). DES provides

the flexibility of evaluating multiple KPIs, and can be used as a decision-support tool

for evaluating the efficiency of the logistics processes. The models are developed using

Arena Simulation Software©. The scenarios are translated into three distinct models,

using a similar modular logic and sharing most variables. The models contain entities

representing copy carts, resources or logistics staff, attributes (e.g. type of disposable

supply), variables (e.g. current inventory level) and queues. Figure 5-26 displays the

Standard scenario in its conceptual form. Four sections are highlighted representing

the core activities: (1) scanning decentral stock (i.e. calculate reorder quantity by

comparing the maximum stock limit and the actual stock), (2) uploading scanning data

(i.e. waiting for the printer to print the list with scanned items and transfer the list to

the central storage room), (3) assembling case carts (i.e. picking items in central

storage room) and finally (4) replenishing decentral stock.


155

Fig

ure

5-2

6.

Conce

ptu

al m

odel

of

Sta

nd

ard s

cenar

io.

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Data collection

The simulation model requires two types of data. During a ten-day observation period,

a time study is performed on replenishment jobs (e.g. scanning, picking and

replenishing times). Due to the stochastic nature of these process times related to

human-performed tasks, the time data fit statistical distributions (e.g. Beta

distribution). The second type of data deals with daily item usage of decentral stock.

The introduction of barcode scanners allows collecting data over one year (August

2017 – July 2018). In total, 264 disposable SKUs stored at 454 locations in one OT

cluster are included in the simulation study. A Poisson distribution is fit to the data,

expressing the probability of items consumed in one day.

Verification and validation

Attention must be paid to verification and validation, ensuring that the model performs

as intended to the modelling assumptions and behaves the same as the real system,

respectively (Sargent 2013). Due to lack of accurate logistics data, we focus on best

judgment of the expert to explore the model as thoroughly as possible. First, adding

animation, by showing the (de)central storage rooms, logistics staff, copy carts and a

clock, facilitates communication with various stakeholders, increases confidence in the

simulation results and thus enhances commitment and impact. Second, graphical

visualizations aid in monitoring the values of various performance indicators, such as

stock-outs, incomplete refills and items in stock per item per storage room (see Figure

5-27). Finally, task completion times and workload distribution are validated by

comparing them to the real system. The OT logistics manager is knowledgeable about

the direction of the output behaviour and knows the acceptable range of the value

magnitudes. Graphs are commonly used to check for operational validity when

statistical assumptions cannot be satisfied or when there are insufficient data. Based

on these graphs, the model developer and system expert decide the model accuracy is

within its acceptable range for its intended purpose, namely evaluating three

replenishment scenarios to identify the best-performing one.


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5.2.4.2 Results

The computer models are used to assess three replenishment policies in order to realize

efficiency opportunities. The simulation output provides a score for the performance

indicators, which is normalized across all scenarios using the As-Is scenario as a

baseline. All of the indicators are measured such that a lower score represents a better

outcome. By aggregating the normalized values into a single score for each objective

using the ANP-based prototype, the best-performing scenario is selected on the level

of one objective for which it holds that the lowest total objective score is better. More

information on how the KPIs are computed can be found in Appendix D. Table 5-19

shows the values for these KPIs in each scenario as well as the ILEP index to select

the appropriate policy, which integrates the four efficiency dimensions of quality, time,

financial and productivity. In contrast to the performance indicators, the best scenario

is selected as the one with the highest ILEP index.

Figure 5-27. Simulation output visualizing items in stock, incomplete refills and stock-

outs for item FA366823 in one operating room of cluster D.

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Table 5-19. KPI scores and ANP weights.

The Standard scenario provides the best quality, due to the high weight attributed to

the distribution service level. Distribution service level allows to monitor the urgent

delivery rate and the additional items needed, and thus is a critical KPI to reduce the

number of stock-outs. Furthermore, delivery accuracy is performing badly in the As-

Is scenario due to the time gap between making picking lists and replenishing stock,

which causes incomplete refills. The Copy carts scenario eliminates this time gap by

immediately restocking decentral locations and thus improving the delivery accuracy

by guaranteeing a maximal order fulfilment. Finally, the impact of centralization is

investigated to free up space for primary patient care services in the OT by reducing

parallel material flows to decentral stocking locations. The Standard scenario

eliminates copy carts which need additional replenishment as a permanent double

stock and also benefits from reducing decentral stocking limits.

Metric/Scenario

Standard Copy

carts

As-Is Normalized values

Standard Copy

carts

As-Is

Quality (0.32) Distribution service level

(0.136)

12.86 16.04 13.32 96.99 115.30 100

Delivery accuracy

(0.092)

78.67 35.33 139.69 56.32 25.29 100

Centralization (0.091) 5963.31 8836.84 7480.47 39.88 109.07 100

Total Quality Score 68.97 87.57 100

Time (0.15) Replenishment lead time

(0.057)

219.53 202.21 373.46 58.87 54.24 100

Response time (0.053) 11h41 11h29 14h13 80.74 79.89 100

Clinical staff

involvement (0.036)

171.42 197.14 105.11 163.09 187.57 100

Total Time Score 92.51 96.43 100

Financial (0.06) Inventory cost 2798.05 3907.12 3416.97 81.89 114.34 100

Total Financial Score 81.89 114.34 100

Productivity/

Organisation

(0.48)

Delivery frequency

(0.121)

307.89 279.03 293.67 83.73 49.80 100

Standardization (0.16) 291 291 291 100 100 100

Personnel management

(0.121)

0.46 0.42 1.78 25.79 23.76 100

Total productivity

Score

72.77 61.94 100

ILEP index 24.21 20.92 0


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In addition, the model keeps track of the time required to perform replenishment

activities by logistics staff. The time-related KPIs in Table 5-19 indicate the best (i.e.

lowest) score for the Standard scenario. However, the results should be carefully

interpreted. All items are delivered on time (i.e. before the end of the day), with the

As-Is scenario having the latest response time at 14h13. Both the response time and

the replenishment lead time are considerably higher in the As-Is scenario due to

discontinuities in the replenishment process. These interruptions are mainly caused by

the involvement of CSA employees, who are not included in the study. Although the

time CSA employees are working adds to the replenishment lead time in the As-Is

scenario, it does not apply for clinical staff involvement since it only considers OT

employees. This also explains the low value for clinical staff involvement, whereas the

Standard and Copy carts scenarios only use OT employees to perform replenishing

activities.

From a financial perspective, an important indicator for assessing replenishment

policies is the inventory holding cost. Table 5-19 shows a 20% reduction in holding

cost in the Standard scenario, whereas the Copy carts scenario incurs a 15% increase.

The lower holding cost in the former scenario is a result of eliminating copy carts,

which carry permanent double stock. The Copy carts scenario, on the other hand,

requires a careful selection of items to be held on the copy cart in order to reduce

holding costs. In future work, inventory on copy carts must be optimized in

collaboration with the nursing staff. Although costs are an important indicator to

evaluate replenishment policies, the financial factor has the least impact (0.06) on the

overall performance.

Furthermore, productivity and organisational KPIs aid in streamlining processes,

facilitating information flows and improving staff satisfaction. Delivery frequency

determines productivity by the number of item locations that need replenishment and

the visits to decentral locations. In all scenarios, an average of 40% of decentral item

locations are daily replenished, though these items are classified as fast-movers when

they are justified to be held in stock close to the operating room. On the other hand,

the Copy carts scenario improves productivity most because employees visit the

decentral stocking locations only once for immediate replenishment. Furthermore, a

recent trend in healthcare management stimulates process standardization to simplify

workflows and create uniform replenishment practices throughout the OT.

Standardization is measured by the number of items that could be scanned for

replenishment, and is thus equal for all scenarios. The scanning data provides accurate

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consumption details which can be used during product selection negotiations between

different stakeholder to foster standardization and thus decreasing product variety,

reducing costs and facilitating inventory control.

Finally, the ILEP index is developed by converting the scores of the four KPIs into a

single score using ANP priorities, demonstrating the Internal Logistics Efficiency

Performance for each scenario. This index provides an objective evaluation basis for

logistics managers to identify the best strategy for managing internal healthcare

logistics processes. The higher this index, the better the performance of the process.

The ILEP index is added to Table 5-19. Overall, we observe that the Standard scenario

provides the best service quality at the lowest cost, although it is slightly more

demanding for logistics staff compared to the Copy carts scenario. Hence, the logistics

manager should always consider the trade-off between multiple criteria when choosing

an appropriate replenishment policy.

5.2.4.3 Discussion

The hybrid tool, combining the simulation and ANP model, supports decision making

when selecting the most appropriate scenario for organising the internal OT

replenishment process. The identified KPIs serve as a guideline for monitoring the

performance of internal healthcare logistics processes. Both the Standard and Copy

carts scenarios demonstrate better performance in terms of quality, time and

productivity/organisation compared to the As-Is scenario. From a financial

perspective, the Standard scenario is preferred due to lower inventory holding costs.

When analysing the findings of this case study, some limitations must be considered.

One limitation is related to the sample size of the ANP application. This work presents

preliminary results of a healthcare logistics performance management framework as

only one stakeholder is included, namely the OT logistics manager expressed his

preferences for KPIs. Second, the accuracy of the data used in the simulation model

must be critically assessed. The input data contain 264 of 404 (65%) unique SKUs

stored at 454 of 743 (61%) supply locations in decentral stock, representing 98% of

total items ordered. The remaining items are excluded due to limited scanning data for

various reasons, such as scanning problems, new items, backorders or IT-related

problems. For inventory optimization purposes, accurate data is essential to determine

appropriate stock limits and reorder points in order to minimize costs and maximize

service levels. Despite these limitations, this case study provides insights on the impact


161

of three replenishment scenarios on the workflow, quality and costs of the operating

theatre.

In further research, the simulation model can be extended by including other clusters,

thereby improving the scalability of the findings. The ILEP index for one cluster shows

a 24% improvement for the Standard scenario compared to the As-Is situation.

Extension to all clusters will reveal even better results due to standardization

opportunities for obtaining a uniform replenishment process throughout the entire OT.

In addition, the methodology can be customized to various healthcare logistics

problems using the ILEP index as an objective evaluation basis for assessing

improvement scenarios. Hospitals using a performance management framework have

a competitive advantage. The corresponding ILEP index enables hospitals to control

their supply chain strategy, implement continuous improvement programs and

improve decision making by focusing on relevant KPIs (Maestrini et al. 2017).

5.2.5 Evaluating case cart distribution systems in the operating

theatre – A case study

With the OT being one of the most patient- and supply-critical departments in a

hospital, the significance of adopting efficient logistics supporting services must be

emphasized. Delays in surgical procedures due to missing or erroneous medical

supplies can endanger patients’ lives. By simulating the logistics flow and analysing

multiple scenarios, we show that our ANP-based prototype is a powerful decision-

support framework for a hospital environment to determine efficiency gain

opportunities. In this case study, two main aspects of the surgical case cart distribution

system are assessed, namely the logistics flow of case carts and picking paths within a

storage room. Current practice relies on experience and intuition of logistics staff to

pick the right items and visit the different storage locations, which often leads to extra

travelling times and distances. In the logistics flow, standardization efforts are

analysed by focusing on employee workload in order to increase staff flexibility and

implement uniform distribution practices. The impact of standardization is further

investigated by modelling the workflow within a storage room to identify optimal

picking path strategies. In addition, two central storages are combined to examine the

impact of centralization on the distribution process. The overall objective is to

determine potential efficiency gains in the organisation of the logistics process for

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162

distributing surgical case carts, by combining ANP and DES modelling in terms of

quality, time, cost and staff productivity.

5.2.5.1 Methodology

The study is conducted at the operating theatre of UZ Leuven. For every surgical

procedure in OT1 (i.e. elaborate surgery OT), a surgical case cart is prepared, one day

ahead, based on a physician preference supply list. Case carts are assumed to be

available at all times. Two central storage rooms, namely CSA storage and OT1-

storage, are used to prepare the carts. The cart assembly process starts in the CSA

storage, where two OT employees prepare all carts for all clusters. Because CSA

storage is located one level below OT1 (see Figure 5-4), four elevators are used to

transport the carts to the assigned clusters. The surgical case carts are finished in the

OT1-storage, where cluster-specific employees pick the requested items on the

preference list and bring the cart to the operating room. Figure 5-28 gives a schematic

representation of the logistics flow in OT1. On the other hand, day care surgeries in

OT2 use baskets instead of case carts, which are prepared in the OT2-storage room,

due to the smaller size of these interventions. The clusters in OT2 are excluded, as this

study only examines the impact of combining the CSA and OT2 storage rooms, rather

than the logistics flow in OT2. Finally, case carts for emergency surgeries are

excluded, as they are not planned in advance. Overall, the objective is to improve the

organisation of the surgical case cart distribution, namely the logistics flow between

different storage locations as well as the order picking process within a storage room.

Figure 5-28. Schematic representation of logistics flow in OT1.


163

Conceptual model

A conceptual model is established to better understand the problem and facilitate the

development of the computer model, without being specific to one software language.

The first challenge relates to improving the case cart flow between different storage

and point-of-care locations. Seven possible improvement scenarios are proposed by

changing three parameters. These input parameters reflect three improvement

strategies assessing the impact of standardization, centralization and elevator usage as

shown in Table 5-20. First, the impact of standardization is measured by creating more

uniform distribution practices in OT1-storage. Currently, two types of OT employees

are involved in the case cart distribution process. The general OT employees prepare

carts for all clusters in the CSA storage, whereas cluster-specific OT employees only

prepare carts for their cluster in the OT1-storage causing overcrowding in this small

stocking location. By increasing the number of general OT employees in the OT1-

storage, the distribution processes will become more standardized. Second, the impact

of centralization is analysed by combining the CSA and OT2-storage room to reduce

product duplication and free up space in the small OT2-storage. With this

centralization effort, the impact on the workload in the central CSA-storage must be

measured and a third general OT employee is added in this strategy. Finally, the

elevator is detected to be a bottleneck in the logistics flow. Time savings could be

achieved by changing the elevator usage. In the current situation, the elevator only

operates when all case carts are assembled in CSA storage. A new strategy suggests to

operate the elevator whenever the maximum number of case carts for the elevator is

reached. Table 5-20 provides all combinations of the three strategies for conducting

scenario analysis. Scenario 1 reflects the As-Is scenario without any efforts on

standardization, centralization or elevator usage.

Table 5-20. Overview of input parameters and scenarios.

Input/Scenario 1 2 3 4 5 6 7 8

Standardization X X X X

Centralization X X X X

Elevator usage X X X X

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164

The conceptual model ensures that the relevant KPIs are measured to evaluate the

impact of different organisations of the distribution system. However, measuring the

efficiency of the logistics flow is not straightforward in a healthcare context. In Chapter

4, a prototype is presented based on ANP, where multiple KPIs are identified for

monitoring distribution processes and the ILEP index is developed from the point of

view of the OT logistics manager. More details on the relevant KPIs can be found in

Table 5-21.

Table 5-21. Overview of KPIs.

Objective KPI Description Formula

Time [s] Average

waiting time

(for elevator)

Average time

that case cart

spends waiting,

without any

handling of

logistics staff

=𝑤𝑎𝑖𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒

𝑡𝑜𝑡𝑎𝑙 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐𝑎𝑟𝑡𝑠

Average time

to finish cart

Average time

needed to finish

all case carts

=𝑇𝑖𝑚𝑒 𝑛𝑒𝑒𝑑𝑒𝑑 𝑡𝑜 𝑓𝑖𝑛𝑖𝑠ℎ 𝑎𝑙𝑙 𝑐𝑎𝑟𝑡𝑠

𝑡𝑜𝑡𝑎𝑙 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑐𝑎𝑟𝑡𝑠

Time to finish

in CSA/OT1

storage

Time that

employee needs

to finish daily

case cart task

= 𝑇𝑖𝑚𝑒 𝑓𝑖𝑛𝑎𝑙 𝑐𝑎𝑟𝑡 − 𝑆𝑡𝑎𝑟𝑡 𝑡𝑖𝑚𝑒

Financial [€] Personnel cost The cost to hire

the needed

employees,

regardless the

required time to

perform the task

= 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠∗ 𝑑𝑎𝑖𝑙𝑦 𝑤𝑎𝑔𝑒

Quality Centralization A qualitative

measure on how

centralized a

scenario is

{1 𝑖𝑓 𝐶𝑆𝐴 𝑎𝑛𝑑 𝑂𝑇2 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Risk of errors Indicator of the

risk that logistics

employees make

mistakes

=% 𝑡𝑖𝑚𝑒 𝑏𝑢𝑠𝑦 ∗ 𝑡𝑖𝑚𝑒 𝑤𝑜𝑟𝑘𝑖𝑛𝑔

𝑡𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑤𝑜𝑟𝑘𝑠ℎ𝑖𝑓𝑡

Productivity/

Organisation

Avg percentage

busy in OT1

storage

Percentage of

time that

logistics staff is

working

=𝑇𝑖𝑚𝑒 𝑤𝑜𝑟𝑘𝑖𝑛𝑔

𝑇𝑖𝑚𝑒 𝑡𝑜 𝑓𝑖𝑛𝑖𝑠ℎ 𝑖𝑛 𝑠𝑡𝑜𝑟𝑎𝑔𝑒

Standardization A qualitative

measure on how

standardized a

scenario is

{1 𝑖𝑓 𝑛𝑜 𝑐𝑙𝑢𝑠𝑡𝑒𝑟 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑒

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒


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Furthermore, the hospital is interested in tackling inefficient picking paths travelled by

logistics employees within storage rooms. Current picking practice is similar to the

Return strategy, as shown in Figure 5-2 (Horvat 2012), whereby the aisle with the

picking location is entered and left from the same baseline-aisle. In the hospital,

however, the travelled path depends largely on experience and intuition of logistics

staff, resulting in extra travelling distance and picking times. Other factors, such as

small aisles and illogical organisation of supplies in the shelves, also increase waste in

the picking process. In this case study, three models for picking path optimization in

the CSA storage are established. Although the high-level conceptual model is similar,

different routing strategies or algorithms are applied to sort the picking locations and

visualize the path. The models reflect two commonly used routing strategies, namely

the Return and S-shape strategy, and one routing algorithm that solves the well-known

Shortest Path Problem (SPP) and Traveling Salesman Problem (TSP) consecutively.

For each model, the picking location in the aisle, the walking speed and the picking

time are implemented as inputs in order to visualize a picking path. First the picking

locations (i.e. aisle and row) are extracted from the picking list and sorted in the right

order according to the respective strategy. The second step involves visualizing the

travelled path of the cart to the requested location. The overall objective is to minimize

travelling distances or time, resulting in a standardized or uniform picking path for

preparing case carts in the CSA storage. In addition, the impact of reassigning supplies

in the storage area is investigated. Currently, supplies are ordered in aisles according

to product families, such as custom procedure trays, anaesthesia materials, etc. as

represented by the different colours in Figure 5-30.

Finally, both problems are integrated by implementing the optimal picking path into

the logistics flow. The logistics flow model, however, makes abstraction of the picked

items. Instead, the model considers picking times based on the order quantity,

regardless of the type of medical supplies. By integrating the optimal picking path in

the logistics flow model, the picking time is more accurate, since it considers the exact

location of the requested supplies in the storage room. Potential opportunities for

efficiency gains are identified and recommendations are provided to the OT logistics

manager.

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Data requirements

Data from the UZ Leuven hospital is collected in the year 2018-2019. Two types of

data are required: number of items to be picked and time data. First, the average

number of items to be picked per case cart are identified from frequently used

physician preference supply lists in OT1. This data will be used as input in the logistics

flow model. From the supply list, the exact locations of the items can be derived as

input for the routing model. Second, an observation study is performed to estimate the

duration of certain tasks to complete the surgical case cart, such as picking time,

travelling time, waiting for elevator, etc. The average time per item is assumed to be

independently distributed among different employees or clusters. The logistics flow

model incorporates stochastic into the model by assuming a normal distribution of the

picking time in each cluster.

Computer model

In this section, the software-specific representation of the conceptual model is

described for both aspects of the surgical case cart distribution process. The first

challenge is modelled using a DES software package from Matlab (i.e. Simulink),

whereas the picking path is visualized using a systems engineering software, namely

LabVIEW.

In Simulink, different models representing different organisations of the logistics flow

are built, but they all share the same modular logic. The modular build-up increases

flexibility to adapt models by changing the input parameters and customize to different

hospital layouts. An overview of the As-Is model is given in Figure 5-29. The models

consist of entity generators, representing surgical case carts that will move through the

model. Every day, a random number between n=80 and n=100 carts are created.

Servers divide the carts over the clusters with equal probability (1/n). Every line in the

model represents the tasks performed in one cluster, which is similar to all clusters.

The orange rectangle represents queues in the model, which can be a waiting time

before picking starts at the CSA or OT1-storage or waiting time in front of the elevator.

The black box shows the picking process in the CSA storage. The function block,

circled in yellow, regulates the number of carts that queue in front of the elevator

(yellow rectangle). This parameter can be changed to analyse the impact of elevator

usage. The blue part represents the picking process at the OT1-storage and the

travelling times to the specific cluster. Note that the employees who work in the blue


167

rectangle are cluster-specific employees, whereas the black rectangle seizes general

employees who work for all clusters in the CSA storage. The impact of standardization

is evaluated by changing the number of employees. Finally, the bottom line in Figure

5-29 represents the picking process for OT2. Only the OT2-storage is used to prepare

baskets for each surgical procedure, which explains the limited number of building

blocks. In scenario 2, 4, 6 and 8, the impact of centralization is evaluated by combining

OT2- and CSA storage to reduce product duplication. Although OT2 is located

elsewhere in the hospital, travelling times are not modelled.

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9.

Over

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169

Second, a LabVIEW model is constructed for modelling the picking process within the

CSA storage room. The main advantage of this software is its graphical approach of

programming which facilitates picking path visualization. In the model, the items are

organised in aisles, scaled from the real-life storage area, and the travelled distances

are converted to actual distances. A walking speed of 3 km/h and a picking time of 10s

is estimated, regardless of the employees.

First, the Return strategy (see Figure 5-30) is modelled whereby the employee always

returns to the baseline aisle. The cart is parked in the baseline aisle due to space

restrictions in-between aisles. The picking locations are sorted such that the location

farthest from the baseline aisle is first reached. The distance array stores the distances

from the starting position to all possible locations. For each model, the total travelling

distance per picking list is calculated in meters.

In the second strategy, a picking path in the shape of an S is developed in order to solve

the problem that only one cart at a time can traverse an aisle in the storage room. As

can be observed in Figure 5-30, the carts follow one line while being transported along

the aisles. To formulate the S-Shape, the picking locations are sorted alternately from

lowest to highest or highest to lowest location. The total travelling distance is similar

for each cart, since the entire S-Shape need to be traversed.

Third, a routing algorithm is tested which implements both Dijkstra and Nearest

Neighbour algorithm, solving respectively the SPP and TSP problem. The Dijkstra

algorithm searches for the shortest distance between a current location and all other

locations that need to be visited, whereas the Nearest Neighbour algorithm selects the

nearest location starting from the current location and it requires that the cart starts and

ends the picking path at the same position. A two-dimensional distance array is

constructed to calculate every possible travelling distance between two locations. In

this scenario, the cart can be carried along the picking path or be placed in the baseline

aisle.

Figure 5-30. Difference between two routing strategies (Return and S-shape) and one routing

algorithm (right).

CHAPTER 5

170

Verification and validation

Another simulation software, FlexSim, is used as a means of verifying the simulation

model in Simulink. FlexSim is used for modelling industrial applications and contains

similar building blocks as Simulink which facilitates the verification process. If both

models behave similarly, this is a strong indicator that the initial model works as

intended. We observe a 10% deviation in the average time to finish all case carts

between both models. This difference is plausible because the models incorporate

stochastics by introducing random distributions and the limited amount of replications

due to time restriction. Figure 5-31 shows the arrival time of the final case cart in

cluster A for ten iterations in Simulink. As can be observed from this graph, the arrival

times vary between 3000 and 6000 seconds, and thus the average time of 3575 seconds,

as obtained by the FlexSim model, falls within this range. In addition, a structured

walk-through allows the developer and OT logistics manager to visually check similar

behaviour in both models, such that we can conclude that the model behaves as

intended. Furthermore, the LabVIEW model is verified by adding a size control, which

tracks the size of the input arrays where the picking items are stored during the run.

Since the program can only stop when all picking items have been visited and the size

control indicates the right amount of picking items, we conclude that all items have

been picked for each simulated case cart. Moreover, the cart is successfully restored at

the start/end position in each run.

Second, the validity of the model must be evaluated to ensure accurate representation

of reality. However, the output of both the Simulink and LabVIEW models cannot be

compared due to lack of empirical data. Therefore, a subjective validation is performed

Figure 5-31. Arrival time of the final cart in cluster A for each replication.


171

where the OT logistics manager acts as the expert and confirms that the results of the

model are in line with his daily experience in the hospital.

5.2.5.2 Results

This section presents the results as obtained by the models in Simulink and LabVIEW

for the logistics flow and picking path problem respectively.

Logistics flow of surgical case carts

Three improvement strategies are formulated aimed at increasing efficiency in the OT

distribution process in terms of quality, time, cost and productivity. The strategies are

as follows: implementing standardized distribution practices, centralizing storage

rooms and improving elevator usage. The impact of the alternative improvement

strategies is measured by the simulation output, which provides a value for each KPI.

This value is normalized across all scenarios using the As-Is as a baseline, and a lower

score is better. The ANP weights as proposed in Chapter 4 are applied to define the

overall impact of the indicators on the logistics flow. They show that quality (0.32)

and productivity (0.48) are the most important objectives with the greatest contribution

to improving the internal distribution process in the OT. More details on the ANP

weights can be found in Appendix E. Finally, the most efficient scenario is chosen by

calculating the ILEP index, which is computed by multiplying the ANP weights with

the performance scores on the four efficiency dimensions. The higher the ILEP index,

the better the scenario. The results are obtained by running the simulation model ten

times, where one run represents an eight-hour work shift. Table 5-22 shows the

normalized data as obtained from the Simulink models. Note that the normalized

values for centralization and standardization are reversed, as a lower score is better. In

other words, a value of 1 represents no centralization or standardization effort.

Furthermore, a ninth scenario is added to this table. This scenario has the same inputs

as the baseline scenario, but the picking times are adapted to mimic the behaviour of

the enhanced picking path based on a routing algorithm as suggested in the LabVIEW

model. This scenario will be discussed more extensively in Section 5.2.5.3.

CHAPTER 5

172

Tab

le 5

-22. N

orm

aliz

ed K

PI

val

ues

ob

tain

ed f

rom

Sim

uli

nk o

utp

ut.

Met

ric/

Sce

na

rio

1

2

3

4

5

6

7

8

9

Qu

ali

ty (

0.3

2)

Ris

k o

f er

rors

(0.1

7)

1

1.1

88

0.8

13

1.1

25

1

1.1

88

0.9

38

1.0

63

1

Cen

tral

izat

ion (

0.1

7)

1

0

1

0

1

0

1

0

1

Tota

l Q

uali

ty K

PI

0.3

4

0.2

02

0.3

08

0.1

91

0.3

40

0.2

02

0.3

29

0.1

81

0.3

40

Tim

e

(0.1

5)

Res

pon

se t

ime

(0.1

0)

1

0.6

74

0.7

03

0.6

71

0.7

21

0.9

20

1.0

44

0.9

10

0.8

43

Avg

wait

ing tim

e 1

0.0

42

0.1

14

0.0

42

0.1

43

0.7

64

1.0

70

0.7

64

0.8

60

Avg

fin

ish t

ime

1

0.8

03

0.7

94

0.8

03

0.8

31

0.9

54

1.0

10

0.9

54

0.8

30

Tim

e C

SA

1

0.9

57

1.0

20

0.9

76

1.0

30

0.9

6

1.0

40

0.9

39

0.8

50

Tim

e O

T1

1

0.8

88

0.9

00

0.8

52

0.8

83

0.9

96

1.1

00

0.9

71

0.8

60

Tota

l T

ime

KP

I 0.1

0

0.0

67

0.0

70

0.0

67

0.0

72

0.0

92

0.1

04

0.0

91

0.0

84

Fin

an

cial

(0.0

6)

Per

sonnel

cost

(0.0

5)

1

1.2

50

1.1

30

1.1

30

1

1.2

50

1.1

30

1.1

30

1

Tota

l F

ina

nci

al K

PI

0.0

5

0.0

63

0.0

56

0.0

56

0.0

50

0.0

63

0.0

56

0.0

56

0.0

50

Pro

du

ctiv

ity

(0.4

8)

Sta

ndar

diz

atio

n (

0.2

9)

1

0

0

1

1

0

0

1

1

Per

sonnel

uti

liza

tion (

0.2

2)

1

1.3

70

0.9

30

1.3

00

1.1

70

1.2

00

0.8

30

1.1

00

1.0

30

Tota

l P

rod

uct

ivit

y K

PI

0.5

1

0.3

01

0.2

05

0.5

76

0.5

47

0.2

64

0.1

83

0.5

32

0.5

17

ILE

P I

nd

ex

0

0.1

49

0.1

61

0.0

20

-0.0

13

0.1

63

0.1

59

0.0

41

-0.0

01


173

The impact of the different improvement strategies will now be discussed. First,

standardization efforts are measured in scenarios 2, 3, 6 and 7. In these scenarios,

general employees prepare carts in the storage rooms, while cluster-specific employees

are responsible for transporting the carts to their respective clusters. Scenario 7

represents the standalone effect of standardization, which significantly increases

overall performance. This increase is mainly due to a good score on productivity KPIs

which encourage uniform practices and balanced staff utilization rates. However, the

negative impact on the time perspective cannot be ignored, as general employees need

to sequentially prepare all case carts compared to cluster-specific employees who

prepare carts at the same time. Moreover, an additional employee is hired in this

strategy, which increases costs without any time savings. In the other scenarios,

standardization interacts with other improvement strategies. In scenario 3 and 6, we

observe that standardization increases performance more when respectively the

improved elevator strategy and centralization are adopted. Centralization positively

affects quality by decreasing number of trips to storage rooms and reducing logistics

activities close to the operating rooms. Changing the elevator usage, on the other hand,

allows for more evenly spread arrival of carts, reduces workload variability and has a

positive impact on time needed in OT1-storage. Furthermore, risk of errors depends

on personnel productivity. The utilization rate is better in scenario 3 and 7, ensuring a

balanced workload. An argument could be that the working time of general employees

is lower than the working time of all cluster-specific employees since standardization

allows for pooling tasks. Centralization, on the other hand, increases workload. The

longer an employee is busy, the higher the probability of making a mistake. However,

it is difficult to draw an unambiguous conclusion for delivery accuracy due to limited

number of replications.

Scenarios 2, 4, 6 and 8 combine the CSA-storage and OT2-storage to analyse the

impact of centralization. The results show a decrease in the time needed in CSA

storage, though the workload in CSA storage increases. As discussed in Section

5.2.5.1, a third employee is seized for preparing surgical case carts to compensate the

higher workload. However, the additional baskets from OT2 require less items, and

thus the extra employee overcompensates the extra work which results in a lower

number of items to be picked per employee and thus less time needed to finish tasks

in the CSA storage. Moreover, the sooner the employees finish carts in the CSA

storage, the sooner employees start picking in the OT1-storage, and thus the total time

to finish case carts decreases. The total time saving is reinforced when centralization

CHAPTER 5

174

interacts with elevator strategies and standardization. Finally, the risk of errors appears

to be highest in centralization scenarios due to the extra workload for picking baskets

for OT2 in the CSA storage.

Finally, the impact of changing the elevator strategy is clearly visible in the average

waiting time. In scenario 2, 3, 4 and 5, the elevator operates every time the maximum

number of case carts according to the elevator size is reached. As a consequence, case

carts are sent in smaller batches to the respective clusters compared to the current

strategy where the elevator starts to operate as soon as all carts at the CSA storage are

finished. This results in an average waiting time that is seven times lower compared to

As-Is. This time saving is also noticeable in the average time to finish all carts, though

not proportionally due to shifting waiting times to OT1-storage. Nevertheless, this

waiting time is shorter than waiting in front of the elevator. From a time perspective,

implementing the elevator strategy is beneficial, whereas overall performance

decreases by adopting it as a standalone strategy. Combining elevator usage and

standardization in scenario 3 provides the best results due to the positive impact on

productivity. In the other scenarios, personnel utilization rates increase because the

elevator strategy decreases waiting times and thus results in less idle time for

employees, which also has a positive effect on time needed in OT1-storage.

Picking path problem

The LabVIEW model has been used to visualize three possible routing solutions for

27 physician preference supply lists, which are most frequently used in OT1. These

picking lists differ in number of items depending on the respective clusters or types of

surgeries. For example, trauma surgeries are non-elective and less predictable,

resulting in a low number of items on the case cart. On average, 18 items need to be

picked for each surgical case cart.

Three routing solutions are evaluated by comparing the travelled distance, as an output

from the LabVIEW model, which is expressed in meters (m). Moreover, this distance

provides an indication for the travelling time of employees to pick all the items on the

list as well as the average time required per item. These times, expressed in seconds

(s), are useful to calculate the impact of an optimized picking path on the total logistics

flow of the surgical case cart, which is presented in Section 5.2.5.3. As illustrated in

Table 5-23, the total travelling distance is calculated for three possible picking paths.

For example, the first picking list for cluster D contains 13 picking items. The results


175

show that the routing algorithm performs best with a gain of 42m and 11m compared

to the S-Shape and Return strategy respectively. Converted to travelling times, using

Equation 5.7, results in a time saving of 50s and 14s respectively.

𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 =𝑇𝑟𝑎𝑣𝑒𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒

0.833𝑚𝑠

+ (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑡𝑒𝑚𝑠 ∗ 10𝑠) (5.7)

In total, for 52% of the picking lists, the routing algorithm has the shortest picking

path, whereas the Return strategy performs best in 30% and the S-Shape strategy is

most efficient for 18% of the case carts. On average, the savings in distance or time

are negligible between the Return strategy and the optimal routing algorithm. The

average distance is 78m and the average time per item is 16s. The small difference can

be explained as the Return strategy operates with an ordered sequence of picking items

in LabVIEW. In reality, however, logistics employees use their intuition to pick items

according to the order of the items on the picking lists, resulting in longer travelling

times. In addition, a time delay for parking the case carts in the baseline aisle is not

included in the model, but it takes a considerable amount of time to place the items on

the right shelve or basket on this cart. Furthermore, the largest time saving is achieved

when changing from the S-Shape strategy to the routing algorithm, amounting up to

10s on average. On a daily basis with 90 surgeries, this represents a saving of 900s or

15 minutes.

Finally, the supplies are reorganised in the CSA storage area according to their usage

pattern in each aisle rather than product type. In other words, the most frequently used

items are placed in the upper half of the aisle, such that employees do not need to

traverse the entire aisle, unless for rarely picked items, which reduces the total

travelling distances. In LabVIEW, this reorganisation is implemented only for the

routing algorithm. The fourth column in Table 5-23 presents the impact of this storage

reorganisation. On average, the routing algorithm with reorganisation saves 0.21m in

distance compared to the current organisation. However, the impact of reorganisation

depends on the respective cluster, as for cluster D, the reorganisation is not favourable,

whereas a gain of 12.61m can be achieved in cluster F.

CHAPTER 5

176

Tab

le 5

-23

. R

esult

s fr

om

Lab

VIE

W m

odel

s.

Clu

ster/

Item

s

To

tal

tra

vell

ing

dis

tan

ce (

m)

To

tal

tra

vell

ing

tim

es

(s)

Avera

ge t

ime p

er i

tem

(s/

item

)

Retu

rn

S

R

ou

tin

g

Ro

uti

ng

+ l

ayo

ut

Retu

rn

S

R

ou

tin

g

Ro

uti

ng

+ l

ayo

ut

Retu

rn

S

R

ou

tin

g

Ro

uti

ng

+ l

ayo

ut

A

29

10

3.9

1

87

.15

10

7.0

4

97

.14

41

4.7

4

39

4.6

2

41

8.5

0

40

6.6

1

14

.30

13

.61

14

.43

14

.02

A

17

89

.69

87

.15

81

.57

10

6.6

2

27

7.6

7

27

4.6

2

26

7.9

2

29

8.0

0

16

.33

16

.15

15

.76

17

.53

A

28

99

.17

87

.15

10

4.2

5

92

.4

39

9.0

5

38

4.6

2

40

5.1

5

39

0.9

2

14

.25

13

.74

14

.47

13

.96

A

16

85

.97

87

.15

74

.29

69

.55

26

3.2

1

26

4.6

2

24

9.1

8

24

3.4

9

16

.45

16

.54

15

.57

15

.22

B

8

51

.95

87

.15

64

.82

77

.93

14

2.3

6

18

4.6

2

15

7.8

2

17

3.5

5

17

.80

23

.08

19

.73

21

.69

B

16

78

.52

87

.15

82

.25

72

.6

25

4.2

6

26

4.6

2

25

8.7

4

24

7.1

5

15

.89

16

.54

16

.17

15

.45

B

16

83

.43

87

.15

10

0.0

2

96

.97

26

0.1

6

26

4.6

2

28

0.0

7

27

6.4

1

16

.26

16

.54

17

.50

17

.28

B

18

72

.6

87

.15

84

.11

89

.69

26

7.1

5

28

4.6

2

28

0.9

7

28

7.6

7

14

.84

15

.81

15

.61

15

.98

D

13

56

.69

87

.15

45

.02

45

.02

19

8.0

6

23

4.6

2

18

4.0

5

18

4.0

5

15

.24

18

.05

14

.16

14

.16

D

5

55

.68

87

.15

48

.06

48

.06

11

6.8

4

15

4.6

2

10

7.7

0

10

7.7

0

23

.37

30

.92

21

.54

21

.54

D

9

98

.32

87

.15

58

.38

58

.38

20

8.0

3

19

4.6

2

16

0.0

8

16

0.0

8

23

.11

21

.62

17

.79

17

.79

D

10

33

87

.15

51

.78

49

.25

13

9.6

2

20

4.6

2

16

2.1

6

15

9.1

2

13

.96

20

.46

16

.22

15

.91

D

20

85

.12

87

.15

82

.42

82

.42

30

2.1

8

30

4.6

2

29

8.9

4

29

8.9

4

15

.11

15

.23

14

.95

14

.95

D

8

51

.78

87

.15

48

.4

48

.4

14

2.1

6

18

4.6

2

13

8.1

0

13

8.1

0

17

.77

23

.08

17

.26

17

.26

F

22

83

.43

87

.15

10

9.8

3

96

.8

32

0.1

6

32

4.6

2

35

1.8

5

33

6.2

1

14

.55

14

.76

15

.99

15

.28

F

12

66

.34

87

.15

61

.09

76

.24

19

9.6

4

22

4.6

2

19

3.3

4

21

1.5

2

16

.64

18

.72

16

.11

17

.63

F

29

84

.28

87

.15

93

.75

99

.59

39

1.1

8

39

4.6

2

40

2.5

5

40

9.5

6

13

.49

13

.61

13

.88

14

.12

F

30

83

.6

87

.15

11

0.6

8

10

4.2

5

40

0.3

6

40

4.6

2

43

2.8

7

42

5.1

5

13

.35

13

.49

14

.43

14

.17

F

34

10

2.3

8

87

.15

98

.07

90

.62

46

2.9

1

44

4.6

2

45

7.7

3

44

8.7

9

13

.61

13

.08

13

.46

13

.20

F

32

93

.25

87

.15

10

3.7

4

97

.05

43

1.9

4

42

4.6

2

44

4.5

4

43

6.5

1

13

.50

13

.27

13

.89

13

.64

G

29

10

2.2

2

87

.15

80

.72

86

.48

41

2.7

1

39

4.6

2

38

6.9

0

39

3.8

2

14

.23

13

.61

13

.34

13

.58

G

16

77

.85

87

.15

87

.32

79

.88

25

3.4

6

26

4.6

2

26

4.8

3

25

5.8

9

15

.84

16

.54

16

.55

15

.99

G

32

10

8.8

2

87

.15

10

1.5

4

11

3.1

3

45

0.6

4

42

4.6

2

44

1.9

0

45

5.8

1

14

.08

13

.27

13

.81

14

.24

G

22

86

.82

87

.15

80

.72

89

.02

32

4.2

3

32

4.6

2

31

6.9

0

32

6.8

7

14

.74

14

.76

14

.40

14

.86

G

8

68

.88

87

.15

51

.62

53

.14

16

2.6

9

18

4.6

2

14

1.9

7

14

3.7

9

20

.34

23

.08

17

.75

17

.97

G

6

65

.66

87

.15

58

.05

46

.71

13

8.8

2

16

4.6

2

12

9.6

9

11

6.0

7

23

.14

27

.44

21

.61

19

.35

G

6

55

87

.15

53

.48

50

.09

12

6.0

3

16

4.6

2

12

4.2

0

12

0.1

3

21

.00

27

.44

20

.70

20

.02

Avg

18

7

8.6

8

87

.15

78

.63

78

.42

27

6.3

1

28

6.4

7

27

6.2

5

27

6.0

0

16

.41

17

.94

16

.19

16

.18


177

5.2.5.3 Discussion

The ANP-based prototype and KPIs as suggested in Chapter 4 are useful to monitor

the performance of internal distribution processes in a transparent and rational way.

The simulation output provides several insights and recommendations for improving

the logistics flow of surgical case carts. Most scenarios, except for scenario 5,

demonstrate better performance based on the ILEP index compared to the As-Is

situation. Overall, scenario 6, which incorporates standardization and centralization

actions, performs best. From the ANP weights, as shown in Appendix E,

standardization is the most important KPI influencing the choice of distribution

policies as it improves productivity of logistics personnel by balancing workload and

promoting uniformity. Moreover, centralization greatly contributes to high quality of

the distribution process by decreasing materials flows to several storage locations and

as a result reduces product duplication. Scenario 3 and 7 occupy the second and third

place respectively. However, remark that scenario 7 only performs well on

productivity KPIs, which have the highest weights according to the OT logistics

manager. A trade-off should be made between multiple criteria to evaluate the benefits

of centralization, standardization and balanced elevator usage in terms of quality,

productivity, cost or time improvements.

Second, the LabVIEW models visualize different picking paths to minimize travel

distances. Based on the average output, the optimal routing algorithm performs best as

it searches for the shortest picking path, in contrast to a routing strategy. However,

heuristic approaches for optimizing picking paths are difficult to implement due to

complexity and inflexibility to adapt to different storage rooms (Horvat 2012).

Moreover, optimal algorithms provide non-intuitive routing solutions which often

result in employees deviating from this path, and thus causing a sub-optimal routing.

One picking path is displayed in Figure 5-30 for the three routing methods, where the

optimal routing solution does not follow a logical route among the aisles. As shown in

Table 5-23, the Return strategy behaves equally well and has a more logical picking

path. A possible improvement of the current, Return-like picking practice is to order

the picking locations per aisle, instead of following the order of the items on the

picking lists. This improvement is ready-to-use, accepted by employees with less

resistance and the results show that this Return strategy performs similarly as the

routing algorithm. In general, we can conclude that the routing algorithm is preferred

for picking lists with a low number of items, whereas a high number of items favours

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the Return or S-Shape strategy. In addition, the location of the items in the aisles plays

a key role in selecting the appropriate strategy. If items are spread evenly across all

aisles, the S-shape strategy is more efficient compared to the Return strategy. For

example, for clusters B and F, the Return strategy performs better because many

picking items are situated in the same aisle. Finally, it should be noted that the travelled

distance is equal for each path in the S-Shape strategy due to the assumption that the

entire storage room needs to be traversed. In reality, however, logistics employees will

stop the picking path at the final item location and go back to the start position.

Therefore, the S-Shape strategy only pays off when many items need to be picked,

equally spread over all aisles in the storage room.

Although a storage reassignment could have a positive influence on the travelled

picking path of the employees, the models in LabVIEW cannot support mixed aisle

configurations. As a result, only full aisles can be replaced in order to develop more

efficient picking paths. Table 5-23 presents the small gain by a reorganisation

implemented in the routing algorithm. Other possibilities for reorganising the storage

room involve changing the locations from different aisles. This can be done by locating

the most commonly picked items from the entire storage room in the two aisles closest

to the start position in order to decrease travelling distances. The remaining aisles can

be ordered according to product families to ensure a logical organisation of the storage

room. Another approach stores the most frequently used items across the entire storage

room in the upper half of each aisle, whereas the less commonly picked items are

located farther away from the baseline aisle. However, this rearrangement may be

confusing for logistics employees.

The enhanced picking path is implemented in the current logistics flow (i.e. scenario

9 in Table 5-22) to analyse the impact on the distribution process. The routing

algorithm with the new storage organisation is selected as it has the minimal travel

distance and average time per item (16.18s). The logistics flow, however, represents

the current picking path travelled by logistics employees, which is a free interpretation

of the Return strategy. Hence, a time difference is observed between picking times per

item in the Simulink model (19.79s) and those obtained from the Return strategy in the

LabVIEW model (16.41s). The final column in Table 5-22 presents the ninth scenario,

integrating the optimal picking path in the As-Is situation. The improved picking times

result in a saving of 53 minutes in the CSA storage. However, the enhanced picking

path does not provide any benefits to the performance compared to the As-Is situation.

The time KPI has a relative low weight in the framework, and thus the picking time


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savings should be big to generate a positive impact on the logistics flow performance.

Although the enhanced picking path presents no clear advantages in the logistics flow,

the reduction in travelled distances cannot be underestimated when evaluating

productivity. In addition, an optimal picking path results in more uniform and

streamlined picking processes, and thus less obstacles along the storage aisles.

Finally, delivery accuracy is difficult to measure due to lack of data. In this case study,

we assume a relationship between risk of errors and working time. Moreover, the

impact of picking errors depends on the time of detection (Rammelmeier, Galka, and

Gunthner 2011). Especially in the OT, a picking error on the surgical case cart can

have tremendous effects on the procedure, leading to delays or endangered patients’

lives. Several picking error prevention technologies can be implemented to improve

picking accuracy and thus improve the quality of the OT distribution flow. These

technologies differ with respect to implementation costs and accuracy rates. Cameras

are often used as a means for detecting errors. However, the picking errors are not

prevented by camera identification. Therefore, we focus only on error prevention. A

first technology is 2D or QR-code scanning. The QR codes can easily be generated,

maintained and are readable to any kind of mobile device with camera (Nilsson and

Elmar Merkle 2018). Moreover, QR-codes provide more storage capacity, flexible

design and assured visibility compared to 1D barcode scanning. In addition, a low

implementation and maintenance cost make this technology financially attractive,

though a relabelling of codes is necessary to implement this strategy in the hospital.

Second, a pick-by-light system can be introduced to prevent picking errors. The picker

receives all picking information on small display boards attached at the storage

shelves. A pick is indicated with the lightning of a lamp and a display shows the

quantity to pick. Although this technology guarantees high picking accuracy, it is

something to bear in mind for the future due to long implementation times and high

costs.

When analysing the findings of this case study, some limitations should be considered.

First, the ANP methodology as proposed in Chapter 4 calculates weights for the KPIs

based on one stakeholder’s preferences, namely the OT logistics manager. Second, the

Simulink model does not model any employees to operate the elevator or to transport

baskets between CSA storage and OT2-storage, which will have a negative impact on

the time needed in CSA storage. Third, the number of replications as well as data

availability is limited. As a result, no statistical validation can be performed on the

simulation outcome. Next, the picking path optimization problem does not include the

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exact spot of the item on the shelf. Moreover, in the S-Shape strategy, the final aisle

(F) must have at least one picking location in order to return the cart automatically to

its start position, resulting in equal travel distances as the full S-Shape needs to be

traversed. Finally, reorganising the storage room layout is not possible due to lack of

integration within LabVIEW.

In future work, extensions to both models are possible. In LabVIEW, the current model

serves as a baseline for further research to obtain a routing algorithm adjusted to the

organisation of items in each storage room and to determine optimized storage layout.

In contrast, the model can optimize the routing solution for each cluster by

implementing cluster-specific picking lists. However, using a different strategy or

algorithm is confusing for logistics employees and stimulates deviation from the

optimal path. Second, the logistics flow model in Simulink is only applicable for the

current situation in the hospital under study. However, DES studies have proven its

use in multiple industrial applications (Babulak and Wang 2012). This study provides

a proof-of-concept that building a simulation model is universally applicable and also

works in a healthcare environment, especially in distribution processes without any

patient interactions (Kuljis et al. 2007). Furthermore, the centralization scenario should

be further elaborated to investigate the impact on inventory. Pooling inventory into

one location often translates into cost savings for the hospital by reducing product

duplication. Finally, it is difficult to isolate the impact of the surgical case cart process

on the total logistics flow. The next section considers integrating inventory and

distribution processes in the OT.


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5.3 Integrating inventory and distribution systems using

surgical procedure preference lists

An important aspect of the internal logistics processes at the OT relates to preparing

surgical case carts for elective procedures. Section 5.2.5 discusses the organisation of

the distribution process to ensure timely delivery of surgical case carts and optimal

routing paths for picking items in storage rooms. However, the content of the case carts

has been ignored, though outdated preference lists for these carts might cause delays

of surgical procedures due to erroneous materials provision. In addition, Section 5.2.3

propose to improve the inventory levels in central and decentral stock, where we

disregard standardization efforts or physician preferences to update surgical case carts.

Surgeons have preferences for their own brands/suppliers of products, which results in

lack of product and process standardization and hence it presents a major challenge to

implement effective materials management systems (Nguyen et al. 2014; Schneller,

Burns, Lawton, and Smeltzer 2006). Surgeons represent the final link in the OT supply

chain, where operating time, quality of patient outcomes and supply availability are

crucial success parameters (Gitelis et al. 2015). Healthcare logistics aims to integrate

inventory and distribution systems in order to ensure supply availability by preparing

case carts based on procedure-specific preference lists. These lists are composed of

materials required to perform the surgery. The goal of this study is to investigate the

impact of up-to-date procedure preference lists on both medical and logistics processes

within the OT (Robben 2019). Efficient surgical preference lists are an effective

enabler for standardization initiatives, which results in cost containment and improved

transparency, safety and value for the overall health system (Huntley, Howard, and

Simpson 2018).

5.3.1 Methodology

Data on 14 surgical preference lists are collected between January and February 2019

from one OT cluster at UZ Leuven. An example of a preference list is added in

Appendix F. The data contain empty packages of materials used during each surgery

as well as the associated procedure-specific preference lists. The cluster is specialized

in abdominal surgery. More specifically hepatobiliary surgery is selected for this case

study since a great variety of materials are used in the preference lists of this sub-

discipline. Although only one surgeon is working in this field, this section presents an

exploratory study to enhance cost awareness among surgeons in general. Second,

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variability in supply consumption by surgeon is investigated by analysing 72 procedure

lists. A multi-disciplinary OT cluster at UZ Leuven is considered, including neuro,

reconstructive and plastic, oral and maxillofacial, and breast surgery. The individual

procedure-specific preference lists allow to explore standardization opportunities

because two to six different surgeons perform similar procedures depending on the

sub-discipline. For both analyses, the focus is on disposable medical-surgical supplies,

such as covering materials, surgical materials or anaesthesiological materials. Due to

the great amount and great variety, disposables provide significant opportunities for

standardization, whereas sterile instrument sets are more or less constant. Therefore,

sterile sets are excluded from this research. It should be noted, however, that much of

the literature focuses on the content of the sterile sets, which needs to be optimized to

reduce waste (Copenhaver et al. 2017).

5.3.2 Results

First, we analyse the efficiency of the preference lists used in hepatobiliary surgery by

comparing the materials on the lists and actual material consumption during surgery.

In total, 38 elective surgeries have been performed in the study interval. Figure 5-32

displays how the consumed items deviate from the preference lists for the included

surgeries. Five categories are identified:

Not – Items included in the list, but not consumed.

Less – Items included in the list, but consumed in a lower quantity.

Correct – Items included in the list, and consumed in the predefined quantity.

More – Items included in the list, but consumed in a larger quantity.

Extra – Items not included in the list, but consumed.

In addition, a distinction can be made between different types of items (‘Count’) and

the total number of items (‘Sum’) consumed. On average, each surgery uses 60

different types of items and 82 items in total, whereas the preference lists contain 43

different items. Hence, the preference lists obtain an accuracy level of 72%, or in other

words, the lists contain 72% of the different types of items which are actually

consumed during surgery though the quantity consumed may differ. This can be found

in Figure 5-32 by adding the ‘Count’ percentages for categories Not, Less, Correct and

More. Since one item can only be attributed to one category, all items on the preference

list are divided over these four categories. Taking a closer look at total item

consumption (‘Sum’), we observe that 39% of the items are consumed as prescribed


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in the list and 35% of the items are additionally retrieved from decentral stock. A minor

percentage of items is consumed in a different quantity. A large percentage of items,

however, is not consumed, though the logistics employees indicated the items as being

prepared. Plausible explanations could be that the items have no package, the package

is lost, the items are from the central sterilization department or the items have been

restocked. Further research is required to justify the inclusion or exclusion of these

items on the list.

Figure 5-33 gives an overview of the total number of items consumed (‘Sum’) for

different surgery types. We observe that surgeries B, G and A represent the best

correctness ratio, namely 50%, 48% and 46% respectively. Notice that surgeries J and

K have used the least number of correct items, whereas they represent the highest ratio

for retrieving extra items. The ratios provide an objective justification for evaluating

the surgical preference lists, or in other words, the higher the ratio of extra items, the

less efficient the list, and thus the more room for improvement. Figure 5-32 shows that

35% of total consumed items are additionally provided during surgery. This

corresponds, on average, to 28 items extra. The majority of the extra materials (49%)

are suture materials. Clinical staff is involved with preparing suture, since it is stored

in the operating room and therefore not included on the preference list. Moreover,

gloves account for 18% of the extra material types. The size and type of gloves depend

on the surgical team, and therefore are not included on the list. Other extra materials

20,58%

0,57%

2,02%

28,48%

48,35%

19,38%

0,55%

6,71%

34,60%

38,76%

Not

Less

More

Extra

Correct

0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00%

Item

cat

egori

es

Percentage of items

Sum items Count items

Figure 5-32. Deviation in item types and item consumption between preference lists and actual

practice.

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are harder to catalogue, such as laparoscopic materials, clips, reloads, needles,

catheters, etc. In general, improvement initiatives are mainly useful for suture materials

in order to minimize clinical staff involvement, increase cost transparency and improve

inventory control, whereas other extra materials are subject to large variability

dependent on the surgical team and thus request more standardization efforts.

Furthermore, procedure F uses, on average, more items than provided in the list. For

one item, a systematic pattern can be found which justifies an increase in the specified

quantity. In contrast, a limited number of items are used less often than indicated.

Unused items are often referred to as waste, which should be avoided as much as

possible. Moreover, restocking unopened items is a costly and time-consuming task.

Although Figure 5-33 shows very small ratios for ‘Less’, the items categorized as ‘Not’

might also include unused items. Finally, data on ‘Extra’, ‘More’, and ‘Less’ categories

are especially useful for inventory control as it helps identifying where to locate

supplies, such that frequently added materials are stored closer to the point-of-care

locations and clinical staff spend less time in retrieving these items.

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

A B C D E F G H I J K L M N

Percentage of

items

Surgery types

Not Less Correct More Extra

Figure 5-33. Overview of different surgery types in terms of item consumption deviation from

the preference lists.


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Furthermore, information on product use is valuable for hospital management to

accurately compute costs of each surgical procedure. Figure 5-34 compares average

item consumption (column graph) and costs (line graph). The trend shows that costs

increase in function of the number of items consumed, though the increase in cost is

not proportional. The actual average consumption is twice as much as prescribed in

the preference lists, whereas the actual average cost ‘only’ increases by 40%. The extra

items account for an average unit cost of €8.64, which is 44% lower than the average

unit cost of items on the preference list. Typically, the preference list contains surgery-

specific materials, which are more expensive, compared to the frequently-used extra

retrieved items. However, lack of registration of extra materials is an obstacle to

accurately record consumption and in turn, balance stock levels and compute the cost

per surgery. In addition, accumulation of extra materials drives up costs with €236.89

on average per surgery, and thus this analysis aims to enhance cost awareness by

educating surgeons and clinical staff to update preference lists. Remark that the ‘Not’-

category is excluded in this cost perspective. ‘Not’-items are indicated as being

prepared for surgery, though they are assumed to be not consumed during surgery. Due

to the varying cost range of these items, the cost of the preference list would be biased

since the actual costs ignore this item category.

0

20

40

60

80

100

120

0

200

400

600

800

1000

1200

1400

1600

A B C D E F G H I J K L M N

Nu

mber

of

item

s co

nsu

med

Cost

(€

)

Surgery type

Items preference list Actual avg consumption

Cost preference list Actual avg cost

Figure 5-34. Overview of average costs and item consumption per surgery type.

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Finally, Figure 5-35 provides an overview of one surgery, which has been performed

12 times during the research period. On average, this procedure consumes 97 items

compared to the 48 items as suggested in the preference list. As a consequence, the

cost of the preference list is an underestimation, since the average cost of this

procedure is €295 more expensive. The standard deviation is also displayed. If we

assume that the costs are normally distributed, then 68.27% of the procedures are

within one standard deviation of the mean. We observe that one outlier reports a cost

increase of €588 and consumes up to 119 items.

This work presents a pilot study showing how to enhance cost awareness among

surgeons in general. Variability in supply consumption by surgeon often results in a

great variation in average costs while similar patient outcomes are obtained (Simon,

Frelich, and Gould 2018). Therefore, the second aim in this study is to identify

opportunities for standardization initiatives and increase surgeon engagement by

providing justification to rationalize their product portfolio and to ensure cost

containment. 72 procedure lists are evaluated for 21 surgery types in a multi-

disciplinary cluster, or in other words, on average three surgeons perform a similar

procedure using an individualized preference list. In contrast to analysing the

efficiency of the lists based on actual consumption, only materials on the lists are

evaluated. As a consequence, the cost saving represents an underestimation due to

ignoring any additional materials consumed during surgery. Figure 5-36 displays the

0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9 10 11 12

Cost

(€

)

Replications of surgery type F (#)

Surgery type FCost list

Actual cost

Avg actual cost

Standard deviation (+1σ)

Standard deviation (-1σ)

Figure 5-35. Comparison of costs between preference list and actual practice for surgery type F.


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average cost as well as the number of items for four surgery types performed by

respectively six, four, four and five different surgeons. The maximum cost difference

amounts €100, €32, €232 and €124 per case respectively, whereas the number of items

is less variable. The higher cost is often associated with a higher number of items,

however, the cost of the extra items greatly exceeds the average unit cost in the

procedure list. For example, in the first surgery type, surgeon F consumes four extra

items corresponding to a cost increase of €100. In terms of average unit cost, we thus

observe an increase from €5 to €25 for the extra items compared to the average number

of items on the list. Moreover, surgery types 2 and 3 are performed by similar surgeons.

As can be seen, surgeon B accounts for higher costs in both surgery types, whereas the

other surgeons perform similarly. Hence, the analysis provides valuable information

to negotiate which items to include on standardized preference lists. On average,

designing standardized preference lists in this multi-disciplinary OT cluster yields a

cost saving of 30%.

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Figure 5-36. Comparison of costs and number of items for different surgeons performing

similar surgery types.


189

5.3.3 Discussion

This work investigates how preference lists deviate from actual practice in terms of

consumption and costs. The findings show that 39% of the consumed items correspond

to the lists, whereas 35% additional items are retrieved from decentral stock. An up-

to-date preference list ensures minimal clinical staff involvement by decreasing the

frequency of leaving the operating room for retrieving additional materials. From a

logistics point of view, procedure lists provide useful information on materials

consumption for each surgery and hence, improve cost transparency. As a

consequence, inventory parameters can be adapted to material needs by balancing

stock levels and costs. The limited storage space within the operating room only

justifies to stock very frequently used or critical items. Moreover, accurate

consumption data and a balanced stock facilitate the replenishment process. Therefore,

surgical preference lists serve as a crucial link for integrating inventory and distribution

systems. The consumption data allow to identify where to locate materials, how many

to stock, and when to replenish in order to ensure supply availability. The lists also

contain details on product description and location identification to standardize the

distribution process. From a cost perspective, this work shows that the average cost is

40% higher than reported in the procedure lists. As we know that the hospital in this

case study performs 21000 surgical procedures per year (UZ Leuven 2018), the costs

of extra materials mount up to €5 million per year which is not recorded. This finding

stimulates to create cost awareness among hospital personnel.

Although the procedure lists are an enabler for achieving an efficient OT supply chain,

many lists are customized by surgeon preferences which results in lack of

standardization. Educating surgeons on material consumption and associated costs will

enhance surgeon engagement to identify standardization opportunities which allows

for rationalizing the products portfolio, minimizing costs and streamlining logistics

operations to the benefit of value-based healthcare. Simon et al. (2018) aim to reduce

variation by designing standardized procedure lists of disposable supplies. This

standardization effort yields a cost saving of 32% which is in line with the 30% as

found for the multi-disciplinary OT cluster in the hospital under study. Hence, we can

argue that in general, significant cost savings can be realized by educating surgeons on

disposable supply costs.

From an overall inventory perspective, the standardization cost savings need to be

integrated with the 30% cost saving obtained in the two-echelon inventory model

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without any standardization (Section 5.2.3). The latter model calculates the inventory

cost based on total item consumption throughout the OT, whereas the standardization

case study only accounts for materials on the preference lists and thus excludes any

additionally retrieved or returned items. From Figure 5-32, we learn that 39% of the

items on the preference lists are correct and therefore it is assumed that the inventory

cost saving in the two-echelon model distinguishes between 12% based on adjusting

inventory levels for items on the preference lists and 18% for all other items. Together

with the cost saving from rationalizing the product portfolio, the items on the

preference lists could reduce inventory costs up to 42% which corresponds to a saving

of 17% in the total inventory picture. Remark that this might be an overestimation,

because the savings from adjusting the inventory levels are partially double-counted.

In addition, the cost saving for all other items need to be considered, and thus overall,

the OT could save 35% of money tied up to inventory.

Finally, a major source of waste in the post-operative stage is associated with the cost

of unused materials, which are thrown away, are restocked or need extra sterilization,

and thus involve a resource-intensive logistics task, since individual items have no

location identification attached. A study by Harvey et al. (2017) shows a cost saving

of up to 289 dollar per surgery. Hence, educating and engaging surgeons when

composing preference lists bridges the gap between logistics and medical processes.

This will improve the efficiency of care processes in the pre-, per- and post-operative

stage, as well as integrate inventory and distribution systems by realizing cost savings,

reduce waste, balance stock levels, limit product variation and improve replenishment

processes.

This section presents an exploratory pilot study to investigate the impact of up-to-date

procedure lists on both medical and logistics processes within the operating theatre.

Due to the small sample size, some procedures are only performed once during the

research period. Increasing the number of procedures is likely to enhance trust in the

findings, which are valuable for discussing surgical preferences and increase cost

awareness. In further work, standardization efforts can be evaluated by creating action

groups of multiple stakeholders to rationalize product portfolios. Furthermore, items

in the category ‘Not’ need further analysis in order to decide to eliminate these items

from the procedure list, which will impact the distribution process to restock redundant

materials. In addition, optimizing sterile instrument sets provides opportunities to

eliminate waste, which has been ignored in this study. Finally, increasing awareness

in terms of costs and consumption requires implementing product identification


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standards (e.g. GS1 standards) in a uniform database for different hospital

departments, such as purchasing, logistics and medical departments.

5.4 Conclusion

Cost containment in healthcare is of increasing concern. Streamlining hospital supply

chains is critical to provide superior, low-cost patient care. This chapter answers to the

third research question by integrating different management aspects to improve the

performance of the internal hospital logistics flow using the prototype as suggested in

Chapter 4. The methodology is illustrated by applying the prototype to five case studies

conducted at the operating theatre. By combining Discrete-Event Simulation, scenario

analysis and the ANP prototype, we prove that the hybrid ANP-DES tool is supportive

in identifying appropriate logistics policies, determining parameter values and

quantifying trade-offs among KPIs to improve inventory and distribution processes,

while taking a holistic view of the hospital supply chain. Moreover, the ILEP index is

introduced as a transparent outcome of the hybrid tool and provides a sound basis for

evaluating logistics improvement initiatives.

Providing inventory management solutions is vital for achieving efficiency targets in

hospital supply chain operations. However, no single inventory policy applies to all

items due to divergent attributes, such as costs, demand patterns or criticality. The first

case study addresses inventory classification methods to develop inventory strategies

based on item characteristics. Two classification methods are compared, both featuring

multiple criteria. The AHP-based classification divides items into four classes based

on a weighted classification score compared to the 27 inventory classes for the ABC-

FNS-VED classification. Inventory classification helps in distinguishing between

inventory hot spots and lower priority items, and accordingly service levels are

specified for four classes of items based on the AHP method.

Second, a DES model is developed to represent a single-item, two-echelon inventory

system, where operating rooms are replenished by a central storage room. The overall

objective is to provide an efficient, yet simple model that varies the maximum stock

level and reorder point to identify the best inventory policy based on the ILEP index.

Service levels for the four item classes act as an important constraint when identifying

appropriate inventory policies. We observe that for critical items in category A and B,

the risk-taking policy 3 performs best due to limited amount of stock-outs, significant

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cost savings of 37% and faster inventory rotation. Items in category C and D are

assigned a lower service level target, though the positive effect on the financial and

productivity factor do not compensate for the 10% increase of stock-outs in policy 3.

Therefore, a risk-averse policy 2 is preferred for less critical or slow-moving items.

However, the high weighting factor in the ANP-based prototype for service level is

questionable for less critical items. Reconsidering the weights for different

classification groups may have an impact on the ILEP index to select the appropriate

inventory policy. Overall, the framework supports inventory policy decision making

by solving the trade-off between service level and costs, depending on item

classification. Further research will extend the model to identify where to locate

supplies in the internal supply chain. Applying the inventory-policy matrix to other

departments will enhance the scalability of the key findings.

Furthermore, the complex distribution channels within hospitals require determining

optimal replenishment policies, case cart distribution systems and efficient picking

paths to improve the performance of the hospital supply chain. The third case study

analyses the logistics workflow in one OT cluster by focusing on replenishment of

disposable supplies. Overall, we demonstrate that two replenishment scenarios both

result in efficiency improvements compared to the current As-Is situation. The

Standard scenario provides a better service than the As-Is at lower costs, whereas the

Copy carts scenario improves the logistics staff productivity. However, the latter

scenario contains a higher inventory cost due to non-optimal selection of items carried

on copy carts. In addition, both scenarios increase data collection through scanning,

resulting in valuable information for inventory control. Ultimately, the logistics

manager should consider the trade-off between service, time, costs and productivity

when defining efficiency targets, and discuss this trade-off with medical staff, as they

have critical insights into the challenges of achieving an operationally efficient

workflow, supporting care delivery.

The fourth case study demonstrates the impact of surgical case cart distribution and

order picking in storage rooms on the internal supply chain performance. In terms of

case cart distribution, the best strategy is to adopt standardization and centralization

resulting in increased uniformity and balanced workload in the logistics chain.

However, the multi-faceted character of achieving operational excellence often results

in different policy decisions. From a time perspective, centralization and enhancing

the elevator usage perform best, whereas the cheapest policy strives for improved

elevator usage only. Furthermore, modelling the optimal path for order pickers


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provides deeper insight into improving traveling time, which constitutes the largest

portion of the picking time. A comparison is made between optimal routing algorithms

and routing strategies. In each model, picker congestion is taken into account by

allowing pickers to cross aisles one at a time due to space restrictions. In addition,

optimizing the storage assignment decision further contributes to reductions in travel

time and distance. In our case study, it is optimal to implement centralization,

standardization and elevator policies, rather than improving the order picking process.

However, the reduced traveling time (i.e. saving of 53 minutes) and distance cannot be

underestimated from a time and productivity perspective. Summarized, improving

distribution activities can significantly impact system performance and should be

aligned with inventory control when streamlining internal supply chain operations in

hospitals. Although the recommended distribution policies are case study-specific,

using the prototype as a tool for policy decision making is generalizable to other

healthcare settings since the ILEP index allows for identifying efficient logistics

practices.

The chapter closes by linking inventory and distribution strategies to foster integrated

supply chain processes, which positively impacts performance. The case study

investigates surgical procedure lists and aims for increasing cost awareness among

hospital stakeholders as a crucial step towards standardization efforts. Standardization

will reduce product variety and thus facilitate inventory management and cost

containment. This work reveals a total inventory cost saving of 35% by implementing

standardization initiatives for items on preference lists as well as improving inventory

levels of all items consumed within the OT. Moreover, this finding provides insights

in identifying areas of low-hanging fruits to easily reduce costs as a prerequisite to

optimize the whole supply chain. Up-to-date surgical preference lists provide

information on supply consumption, which enables balancing of inventory levels and

costs as well as identifying optimal distribution processes to streamline the logistics

flow to the benefit of value-based healthcare.

Finally, we offer a critical reflection to the methodology related to the choice for

simulation packages. Guimarães et al. (2018) list a number of evaluation criteria for

selecting Discrete-Event Simulation software in a manufacturing setting. The most

important criteria assess technical requirements for model development and execution.

First, the Arena simulation package is known as a comprehensive tool for DES

modelling applied to a wide range of problems (e.g. manufacturing, SCM, patient

modelling, etc.) (Ait El Cadi, Gharbi, and Artiba 2016). The main advantage of this

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DES package is its library of icons, global variables and entity attributes as well as its

ability to show animations. The software is highly-flexibly and thus allows for high-

end modelling in an object-oriented environment and easy customization to different

application domains (Guseva et al. 2018). In addition, the graphical user interface

integrates specialized simulation languages (i.e. SIMAN) featuring easy-to-use drag

and drop modules, though it is found to be unintuitive and outdated. A major drawback

of the software is the time-intensive debugging process and the expensive acquisition

cost. Furthermore, a thorough introduction into any simulation software is

recommended as model developers need to learn and gain experience to overcome

technical complexities (Kelton et al. 2015). A second simulation model uses

SimEvents which adds DES to the Simulink environment and is fully integrated with

Matlab. The Simulink simulation platform allows to model discrete-event, dynamics

and agent-based simulation. In general, Simulink is more appropriate for continuous

time simulation, but the integration with Matlab facilitates data processing and offers

a multi-paradigm tool for optimization. Although both simulation software packages

provide similar results, Arena is preferred for entity-based DES, whereas Simulink

outperforms Arena for complex system simulation in terms of ease-of-use, speed,

integration of modelling paradigms and the optimization toolbox (Ait El Cadi et al.

2016).

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CHAPTER 6

6 Multi-Level Multi-Stakeholder Framework

Validation6

This chapter describes the final module in the development of the healthcare

logistics performance management framework. This module adds three

feedback loops in order to address the possibility of bias that comes with a

single decision-maker’s attitude and generalize the findings to a wider

healthcare logistics context. In addition, the iterations stimulate continuous

improvement by identifying benchmarking opportunities.

6.1 Introduction

Three modules in the development of the logistics performance management

framework have been described throughout Chapters 3 till 5, resulting in an initial

prototype which has been tested for an operating theatre (OT) setting. This prototype

considers only one stakeholder’s opinion as a first step towards increasing awareness

of how logistics can service healthcare systems. In this case, the logistics manager is

selected as the single decision-maker, as his main responsibility is managing the

material and information flows throughout the OT.

In this dissertation, a structured approach is proposed to measure the impact of

different logistics policies on the overall performance of the hospital supply chain, and

especially a prototype is evaluated for OT logistics. Savings in costs and time as well

as improvements in quality and productivity are identified from a logistics perspective.


Moons, K., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). A Framework for Operational Excellence in

Hospital Logistics: Implementation Roadmap. Health Care Management Science, Submitted.

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This understanding increases awareness how logistics support contributes to

qualitative care. Moreover, it promotes evidence-based decision making among

multiple stakeholders by defining the best set of indicators to achieve the overall goal

of operational excellence and value-based healthcare.

Recently, multi-stakeholder multi-criteria analysis is gaining attention to enhance

stakeholder commitment as well as to elicit divergent perspectives of each stakeholder

(Macharis, De Witte, and Ampe 2009). Group decision making has a positive impact

on the robustness of the approach, since any decision taken is more likely to be

accepted when considering a broader perspective. Therefore, the final step in the

framework development is to include different stakeholders’ objectives and

viewpoints. Alternative logistics concepts are evaluated from multiple stakeholders’

perspectives and the appropriate policy can be identified by quantifying the main trade-

off between costs and service levels. Dealing with this trade-off reduces logistics

fragmentation in healthcare organisations, and in turn improves overall performance

of the hospital. The goal of this chapter is two-fold, namely to find a generic ranking

of the indicators constituting the definition for operational excellence in healthcare

logistics and to demonstrate that the framework is robust for its intended purpose of

identifying efficiency improvement initiatives in the internal hospital supply chain.

The former objective faces a major challenge inherent to any MCDM study, namely

comparing different stakeholders’ perspectives, analysing conflicts between

stakeholders and generating consensus decisions when setting priorities for the KPIs.

By including medical-oriented stakeholders, the logistics indicators are verified on

relevance from medical perspective, which allows to gain insights on how logistics

principles can be transferred from industrial processes to a healthcare setting. For

example similarities to spare parts logistics in maintenance operations can be found,

though measuring logistics indicators in a healthcare setting is more complicated due

to outdated information technology systems and conflicting goals among stakeholders.

In addition, the importance of the indicators differs since endangering patient’s lives

is far more severe than having lost revenues due to a stock-out.

The second objective aims to propose a robust decision-support tool serving as a

guideline for practitioners and academics to quantify trade-offs between objectives and

reap the benefits from adopting SCM concepts in healthcare. For this purpose, two

prevalent MCDM techniques, ANP and PROMETHEE, are combined into a hybrid

tool to verify the performance management in healthcare logistics. Both techniques are

MULTI-LEVEL MULTI-STAKEHOLDER FRAMEWORK VALIDATION

197

capable of addressing complex decision problems, involving a group of stakeholders

and evaluating interdependencies among the indicators.

Finally, this chapter concludes with a benchmarking study to address the need for

effective performance measurement in healthcare logistics and to identify best

practices by pursuing the best set of KPIs among different hospital processes.

6.2 Literature

6.2.1 Stakeholder conflicts: how much do their preferences

matter to decision making?

A typical challenge in any MCDM study for healthcare problems relates to dealing

with disagreement among diverse stakeholders. This problem is roughly translated as

how to value and interpret the divergent opinions or perspectives in a transparent,

accountable, consistent and reliable manner (Marsh et al., 2014b). Conflicting

stakeholder goals often lead to discrepancies between rankings of alternatives, and thus

complicate the overall objective of finding a ranking that fits all stakeholders. A multi-

stakeholder MCDM analysis enhances stakeholder commitment and allows to gain

insights in the stakeholder perspectives (Macharis et al. 2009). Moreover, group

decision making increases the robustness of the approach, since a generalized result

often leads to a more successful implementation (Beierle 2002).

This section elaborates on different methods to solve this group decision-making

problem in a solid, mathematical manner and thus aggregating preferences that satisfy

all individuals. Belton and Pictet (1997) apply MCDM methods to group decision

making and distinguish between sharing (i.e. single decision-makers), aggregating (i.e.

pooling of individual judgments) and comparing. The AHP/ANP approach usually

adopts the aggregating approach for group decision making. From a qualitative

perspective, on the one hand, group decision-support techniques, such as voting,

consensus, compromise or the Delphi method are used (Ehrgott, Figueira, and Grego

2010; García-Melón, Gómez-Navarro, and Acuña-Dutra 2012; Jharkharia and Shankar

2007). In case not all stakeholders have equal stakes in the decision-making process,

the question arises how much their preferences matter to decision making and weights

can be assigned to different stakeholder groups. A larger weight can be given to

stakeholder groups, reflecting their experience or knowledge when judging a certain

criterion. The weights can be obtained by identifying the relative importance of

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decision makers by pairwise comparisons, or by a supra-decision-maker, who defines

the individual stakeholder weights (Rietkötter 2014).

From a quantitative perspective, on the other hand, the most straightforward

aggregation method is by calculating the arithmetic or geometric mean of each

individual stakeholder (Danner et al., 2011; García-Melón et al., 2012, 2016; Govindan

et al., 2014; Gumus, 2017; J. Hummel et al., 2014). The geometric mean aims to hold

central tendency by taking into account the product of the individual values, rather than

the sum of the values for calculating the arithmetic mean. Saaty (1989) recommends

to aggregate individual judgments using geometric mean. Less popular aggregation

methods are linear programming or the Bayesian approach (Altuzarra, Moreno-

Jiménez, and Salvador 2007; Mikhailov 2004; Moreno-Jiménez et al. 2016). A more

sophisticated method is the cluster similarity method as proposed by Song and Hu

(2009). This method is often used in complement with the AHP/ANP method as

already explained in Chapter 4, though it can be applied on every ranking as obtained

by a certain MCDM analysis. The main reasoning is that two stakeholders are assigned

to the same cluster whenever the degree of similarity exceeds a certain threshold. The

cluster weights are determined proportionally to the number of stakeholders in the

cluster. A final ranking considering multiple stakeholder viewpoints can be obtained

by calculating the Weighted Arithmetic Mean (WAM) or the Weighted Geometric

Mean (WGM) (Moreno-Jiménez et al. 2016). Finally, a sensitivity analysis can further

investigate the impact of changing stakeholder weights on the final result which can

lead to new insights in the decision-making process.

6.3 Methodology

In this chapter, three feedback loops are integrated to extend the prototype to a general

framework by considering multiple stakeholders’ perspectives and a wider healthcare

logistics context. The prototype starts from the heart of logistics, where the logistics

manager aims to efficiently store and move medical supplies to the right location at

the right time, while minimizing costs and improving satisfaction of logistics

personnel. The results based on the ILEP index as obtained from the pilot case studies

at the OT in Chapter 5 serve as input for more informed decision making in healthcare

logistics in general, which is useful when stakeholders have no clue of the potential

added value of logistics to the care delivery system. Two key questions for value

measurement in multi-level multi-stakeholder environments need to be answered: who

and how to involve multiple stakeholders. The former addresses the need for tools to


199

inform rationale for stakeholders identification and selection, whereas the latter

question aims to explore ways to capture knowledge and value judgments in health by

promoting transparency, feasibility and consensus building (Vieira, Oliveira, and Bana

e Costa 2019).

Stakeholders at three management levels are included to ensure that the operational

insights gained from the prototype can be extrapolated to improve tactical and strategic

decision making. The final goal is to create a multi-level, multi-stakeholder

performance management framework which is robust, supports systematic and

transparent decision making and is applicable to a wider healthcare context, addressing

various logistics needs. Figure 6-1 provides an overview of the methodology to

incorporate both logistics (in blue) and medical (in orange) stakeholders’ perspectives

in the OT supply chain. In addition, the logistics perspective is considered for two other

hospital departments, namely the hospital pharmacy and IRCC (Interventional

Radiological and Cardiovascular Centre). The framework thus includes different

processes or material types to prove generalizability of the findings to a wider

healthcare logistics context.

Figure 6-1. Overview of multi-level, multi-stakeholder approach.

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200

6.3.1 Stakeholder analysis

The stakeholder analysis helps to identify the people who are directly or indirectly

influenced by the consequences of any decisions taken, and thus whose perspectives

need to be considered in the validation process. Understanding the objectives of

different stakeholder groups is crucial when appropriately assessing the different

alternatives (Macharis et al., 2012). This chapter includes three levels of decision

making and three stakeholder groups – OT, IRCC and hospital pharmacy – to validate

the framework development procedure. Initially, the prototype has been developed for

disposable surgical supplies using input from the logistics manager at the OT. Building

on this tactical level, the medical point of view of surgeons can be added. Surgeons are

powerful stakeholders as they determine scheduling decisions and have individual

material preferences, which impacts daily execution of both clinical and logistics

processes. Furthermore, operational insights are gained by including nurses and

logistics employees at the department or hospital ward level. Nurses are responsible

for the proper execution of surgical procedures without any delays, whereas logistics

employees support the care process by timely storing and distributing the supplies to

the right operating rooms. Many OR/OM techniques are suitable for operational

models, however, they are restricted by the tactical and strategic decisions. The head

of logistics decides on the overall logistics strategy to improve performance of the

overall healthcare institution. Moreover, strategic decisions concerning policy-

making, information technology systems or organisation structure are made. Typically,

soft systems methodologies are used, such as cognitive mapping, influence

diagramming or system dynamics. By taking into account hospital upper management,

the framework is validated on a broader perspective and other hospital departments are

included as a way of generalizing the findings. In this work, the hospital pharmacy and

IRCC department are selected because different types of materials are handled by each

department requiring different objectives to be pursued. The pharmaceutical supplies

in the former department are characterized by obsolescence, product expiry or strict

regulatory requirements, whereas the IRCC department deals with expensive medical-

invasive supplies, such as catheters, compared to disposables used in the OT. For each

department, the materials manager iterates between the logistics indicators as included

in the prototype. Altogether, eight stakeholders from three stakeholder groups

participate in this study to quantify trade-offs between objectives in order to reduce

logistics fragmentation and streamline internal supply chain processes.


201

6.3.2 Data collection and analysis

We use an inductive approach to refine the prototype by iterating between the (1)

indicators and (2) logistics policies as well as (3) identifying best practices to ensure a

reference framework can be developed as an aid for more informed decision making

in healthcare logistics (see Figure 6-2). Data is collected from conducting interviews

with eight stakeholders at three control levels in the period between August and

September 2019. The average duration of an interview was around 90 minutes. The

interview consists of a semi-structured questionnaire (Appendix G).

First, a feedback loop is introduced in the final development stage of the framework

to avoid bias of a single decision-maker’s attitude when defining what is important for

efficiency management. All respondents iterate between the indicators by judging

pairwise comparisons and an individual prioritization of KPIs is determined using the

ANP approach. For each stakeholder group or department, the priorities are compared

and consensus is achieved using a similarity-based clustering approach as explained in

Chapter 4 (Song and Hu 2009). The overall priority vector is derived by computing the

WAM or WGM, which represents the multi-stakeholder KPI ranking for the context-

specific setting.

Figure 6-2. Converting the prototype to a robust multi-level multi-stakeholder framework:

three iterations.

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The formulas for WAM and WGM can be found in Equations 6.1 and 6.2:

𝑊𝐴𝑀 =∑ 𝑊𝑖𝑤𝑖

𝑛𝑖=1

∑ 𝑊𝑖𝑛𝑖=1

(6.1)

𝑊𝐺𝑀 = exp (∑ 𝑊𝑖 ln (𝑤𝑖)

𝑛𝑖=1

∑ 𝑊𝑖𝑛𝑖=1

) (6.2)

Where n determines the number of stakeholders, Wi is the cluster weight and wi

represents the priority vector using ANP.

A second iteration relates to policy decision making in healthcare logistics. The

purpose is to check the robustness of the framework when selecting appropriate

inventory and distribution policies in order to achieve operational excellence to the

benefit of value-based healthcare. A hybrid ANP-PROMETHEE ranking of policies is

determined to quantify trade-offs among objectives and ensure the robustness of the

framework by comparing it to the ANP ILEP-index. The PROMETHEE methodology

is briefly explained in Section 6.3.3. The input data can be quantitative or qualitative,

and is determined by respectively using the values as observed from the simulation

output or by using a five-point Likert scale where stakeholders express the importance

of indicators after being informed about the simulation results of alternative policies

in Chapter 5. In addition, a sensitivity analysis is conducted to address uncertainty

issues when measuring performance or weighting criteria according to stakeholders’

preferences.

Finally, the third feedback loop aims to gain insights in the current way of monitoring

performance indicators. Bottlenecks in performance management or deviations from

best practices are detected in order to identify benchmarking opportunities for potential

efficiency gains. In this case study, the logistics-oriented stakeholders are asked to

score the indicators on a five-point Likert scale, according to their current practice of

performance measurement, where five represents daily, up-to-date monitoring. Next

the actual performance score and the deviation from the ideal score are computed from

the proposed logistics performance management framework. The framework considers

all relevant stakeholders’ preferences using the generic ANP weights, and thus also

implicitly takes into account strategic and medical-oriented goals. The ideal levels of

performance monitoring are suggested by the head of the hospital logistics department,

the OT logistics manager and the materials manager of the IRCC, reflecting both

strategic and tactical decision-making levels for achieving operational excellence. In


203

general, the multi-level, multi-stakeholder benchmarking approach allows to identify

best practices by determining the best set of KPIs reflecting the objectives of the

overall health system and multiple stakeholders’ perspectives.

6.3.3 PROMETHEE methodology

In this study, the PROMETHEE method is applied as outranking approach to rank

alternatives from highly performing on all criteria to bad performance. PROMETHEE

stands for Preference Ranking Organisation Method for Enrichment Evaluations and

is developed by Brans et al. (1986). This method is chosen because of its simplicity in

conception and computation and its ability to outrank a finite set of alternatives

considering multiple, sometimes conflicting, criteria (Abdullah, Chan, and Afshari

2019). Another reason for its popularity is the existence of user friendly software

packages, such as ‘Visual PROMETHEE’ (Mateo 2012). PROMETHEE is widely

applied in many different fields, such as chemistry, energy management or logistics

and transportation (Behzadian et al. 2010). The main working principle of

PROMETHEE relies on creating preference functions based on pairwise comparisons

of alternatives across multiple criteria by several stakeholders. These judgments can

be qualitative or quantitative, depending on the criteria characteristics. Different types

of PROMETHEE exist, however, this work applies PROMETHEE II in order to obtain

a complete ranking of alternatives with respect to the desired objectives, compared to

a partial ranking in PROMETHEE I (Brans et al. 1986). The following steps explain

the PROMETHEE II procedure (Behzadian et al. 2010):

Step 1: Determine deviations based on pairwise comparisons between two evaluations:

𝑑𝑗(𝑎, 𝑏) = 𝑓𝑗(𝑎) − 𝑓𝑗(𝑏) (6.3)

Where 𝑑𝑗(𝑎, 𝑏) denotes the difference between the evaluations of alternative a and b

on each criterion. A non-negative value implies that the first alternative is favoured in

the pairwise comparison, the opposite is true for a negative value.

Step 2: The difference between two evaluations is translated into a preference matrix

using a preference function:

𝑃𝑗(𝑎, 𝑏) = 𝑃𝑗 [𝑑𝑗(𝑎, 𝑏)] (6.4)

Where 𝑃𝑗 (𝑎, 𝑏) represents the preference of alternative 𝑎 with regard to 𝑏 on each

criterion, as a function of 𝑑𝑗(𝑎, 𝑏), ranging from 0 to 1. For large deviations, a strong

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204

preference is allocated to the best alternative using values closer to 1, whereas small

deviations indicate small preference or indifference of the decision-maker.

PROMETHEE is underpinned by several preference functions, helping to translate the

decision maker’s preference between two alternatives on each criterion into a

judgmental score. In general, six basic types of preference functions, 𝑝(𝑥), are known

in literature, where 𝑥 is the difference between the judgments (Brans & Vincke, 1985;

Dağdeviren, 2008; Podvezko & Podviezko, 2010). Depending on the preference

function, different threshold parameters need to be defined. In general, no more than

two parameters, the indifference threshold for the lower boundary (q) and a parameter

indicating strict preference or the upper boundary of the argument (p), are requested.

For more information on preference functions and parameter values, the interested

reader is referred to Brans and Vincke (1985), Dağdeviren (2008) and Podvezko and

Podviezko (2010).

Step 3: Calculate the overall preference index for each pair of alternatives:

∀𝑎, 𝑏 ∈ 𝐴| 𝜋(𝑎, 𝑏) = ∑ 𝑃𝑗(𝑎, 𝑏)𝑤𝑗

𝑘

𝑗=1

(6.5)

Where 𝜋(𝑎, 𝑏) is defined as the weighted sum 𝑃𝑗(𝑎, 𝑏) for each criterion, ranging

between 0 and 1. 𝑤𝑗 represents the criteria weight. 𝜋(𝑎, 𝑏)~1 implies a strong global

preference of 𝑎 over 𝑏.

Step 4: Calculation of outranking flows:

∅+(𝑎) =1

𝑛 − 1∑ 𝜋(𝑎, 𝑏)

𝑛

𝑏=1

, (𝑎 ≠ 𝑏)

∅−(𝑎) =1

𝑛 − 1∑ 𝜋(𝑏, 𝑎)

𝑛

𝑏=1

, (𝑎 ≠ 𝑏) (6.6)

Where ∅+(𝑎) and ∅−(𝑎) denote the positive and negative outranking flow for each

alternative, respectively. The positive outranking flow is also called the leaving flow,

determining how 𝑎 dominates all the other alternatives and is thus a measure of power.

In contrast, the negative or entering outranking flow indicates how weak the alternative

is compared to all the others (i.e. dominated or outranked character). The best


205

alternative is characterized by high values of leaving flows and low values of entering

flows.

Step 5: Calculation of net outranking flow:

∅(𝑎) = ∅+(𝑎) − ∅−(𝑎) (6.7)

Where ∅(𝑎) refers to the net outranking flow for each alternative, and thus represents

the final ranking of alternatives. The best alternative is defined as the alternative with

the highest net flow value.

6.4 Results

6.4.1 Multi-stakeholder KPI ranking

This study aims to validate the findings by extending the prototype to a general

logistics performance management framework for healthcare settings. This section

describes the results from adding a first feedback loop to the single decision-maker

prototype by eliciting multiple stakeholders’ perspectives in order to obtain an overall

ranking which constitutes the definition for operational excellence in a healthcare

supply chain. The priority vectors resulting from the ANP method are determined for

stakeholders involved in the OT supply chain. In addition, the framework is applied to

other hospital departments including decision making at all organisational levels.

From the OT perspective, five stakeholders are included and Figure 6-3 presents the

normalized ANP inventory weights (i.e. sum of weights for each stakeholder equals

one). In general, the average weights of the OT stakeholder group are similar to the

preferences elicited by the logistics manager in the prototype developed in Chapter 4.

One exception is inventory criticality, which is ranked to be less important according

to the OT stakeholder group. However, the discrepancy between the weights can be

attributed to the stakeholder backgrounds. Medical-oriented stakeholders rank

criticality, which is indirectly related to patient safety, as one of the most important

elements to improve performance of inventory systems, whereas the logistics

stakeholders are less educated on the impact of items on the surgical procedure and

thus assign a lower weight. More conflicts can be observed for the productivity-related

indicators. Inventory usage and turnover are especially important to logistics personnel

for determining the cost of a surgery as well as to efficiently use expensive storage

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206

space close to the operating room. In contrast, medical personnel assign higher weights

to quality-related indicators.

Furthermore, conflicts occurring within the logistics stakeholder group can be

attributed to the level in the decision-making hierarchy. The logistics manager

prioritizes tactical decisions, such as ensuring accurate inventory levels to minimize

overstocking and linking medical and logistics processes by emphasizing inventory

criticality. At an operational level, however, more attention is focused on metrics to

streamline daily operations, such as monitoring inventory rotation and usage. Finally,

product standardization is considered to be moderately important as all stakeholders

recognize the potential benefits of engaging healthcare professionals and increasing

cost awareness for future improvements. However, the logistics manager has a lower

weight for this indicator since other components in the supply chain must first be

improved in order to express the importance of surgeon engagement when

standardizing products and thus facilitating inventory management.

To generalize the findings, two other settings are included in this study. Consequently,

two flows of materials are considered. Potential differences in the ANP ranking can be

attributed to the type of materials, namely disposables, pharmaceuticals and expensive

surgical-invasive items in respectively the OT, hospital pharmacy and IRCC

department. One would expect costs to be more important for the expensive materials,

0,000

0,050

0,100

0,150

0,200

0,250

0,300

0,350

AN

P w

eights

Inventory indicators

Logistics Manager OT

Average Logistics

Average Medical

Average OT ranking

Figure 6-3. ANP inventory weights of OT stakeholder group.


207

however, Figure 6-4 shows only a slight increase in the weights for financial indicators.

In any healthcare setting, costs are ranked at the lowest position as patient health will

always get priority.

The main conflict between the different settings is observed for inventory criticality,

turnover and usage. The former indicator is more important for pharmaceutical

supplies since the right drug must be provided based on the prescription. At the OT

and IRCC, on the other hand, many alternatives are available for surgical disposables

and the appropriate invasive item is selected from the full product assortment

depending on patient characteristics. Hence, these material types are stored close to the

operating room, which makes inventory turnover more important. Fast-moving items

requiring daily replenishment are justified to be in stock in the operating room in order

to avoid overstock and waste valuable space for care processes. Furthermore, the

weight assigned to inventory usage of pharmaceuticals is extremely low due to the

legal requirements, which enables easy monitoring of usage and therefore this factor

is considered to be not important.

Finally, we include the strategic decision-making level to present an overall logistics

perspective for all hospital departments. This viewpoint displays some conflicting

objectives compared to the studied settings, aiming at tactical and operational

performance metrics. Although service level is top-priority at all decision-making

levels to balance inventory levels and patient safety, the logistics director emphasizes

the importance of cost and productivity objectives to achieve operational excellence.

Quality indicators are lower ranked since these metrics typically impact the execution

of daily processes at the operational level, which is beyond the responsibility of

strategic management.

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208

A similar analysis is done for defining efficient distribution systems for the three

material flows. Figure 6-5 shows the weights representing the impact of different

indicators on moving disposable supplies at the OT. By comparing the single decision-

maker KPI ranking to the OT stakeholder group, we observe increased importance for

delivery accuracy and response time, while the productivity indicators are assigned a

lower rank according to the OT stakeholder group. All stakeholders agree that

delivering the correct items and quantities to point-of-use locations and maximizing

logistics support to clinical care processes are top priority. Conflicts within the

stakeholder group are mainly due to the roles of the involved stakeholders. The

logistics manager is mainly responsible for streamlining the distribution flow by

increasing uniformity, balancing staff workload and optimizing the replenishment

rounds, whereas other stakeholders focus more on qualitative and timely delivery of

supplies. Clinical staff is less concerned how logistics personnel is managed, as long

as the availability of supplies is guaranteed and items are delivered on time to prevent

delays in surgical procedures. Moreover, they assign a higher priority to case cart

efficiency, since this parameter ensures that the required supplies are provided based

on physician preferences. Remark, however, that case cart efficiency largely depends

on physician engagement to accurately schedule their appointments and update the

0,000

0,050

0,100

0,150

0,200

0,250

0,300

AN

P w

eights

Inventory indicators

OT

Pharmacy

IRCC

Head of Logistics Department

Overall ranking

Figure 6-4. ANP inventory weights of multi-level, multi-stakeholder groups.


209

material lists for each cart. Case carts are often inaccurately prepared due to missing

information on patient characteristics or the executing physician.

Overall, the distribution ranking is less ambiguous for different settings, as shown in

Table 6-1. For the OT and pharmacy, qualitative distribution systems gain in

importance, whereas the IRCC department prioritizes the productivity or organisation

of the workflow. Moreover, a higher weight is assigned to cost indicators for

pharmaceuticals, since the distribution of supplies goes beyond the OT and IRCC to

all hospital departments. Since the ranking depends on the material types, the

distribution ranking is considered to be context-specific. Therefore, three overall

rankings are established in Table 6-1. In addition, the strategic decision-making level

is added to the analysis to provide an overall perspective on distribution in hospitals.

From a holistic view, the time perspective is an important determinant for providing

high-quality patient care in the most efficient way, as logistics is typically defined as

delivering the right supplies to the right places at the right time. Moreover, process

standardization efforts require support from higher managerial levels, since this also

influences the cost and productivity of distribution systems.

0,000

0,050

0,100

0,150

0,200

0,250

AN

P w

eights

Distribution indicators

Logistics Manager OT

Average Logistics

Average Medical

Average OT ranking

Figure 6-5. ANP distribution weights of OT stakeholder group.

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210

In terms of inventory management, on the other hand, we observe more consensus

across stakeholders for the highest-ranked indicators, such as quality indicators, and

less consensus for the moderate-to-low ranked indicators, such as cost or productivity

indicators. This suggests that the criteria are of high importance and the inventory

ranking is generalizable, regardless of the material types which vary in terms of costs,

legal requirements or nature of the items. The overall KPI ranking can be found in

Table 6-1.

Table 6-1. Multi-level, multi-stakeholder ranking of inventory and distribution indicators.

Average ranking OT Pharm

acy

IRCC Average ranking OT/Pharmacy/

IRCC

Delivery accuracy 1 1 9 Inventory service level 1

Centralization 6 6 12 Inventory visibility 4

Distribution

service level

2 8 10 Inventory accuracy 2

Replenishment

lead time

7 4 4 Inventory criticality 5

Response time 5 5 2 Inventory cost 9

Clinical staff

involvement

12 10 6 Value of inventory 8

Distribution cost 9 3 8 Inventory turnover 3

Personnel cost 11 7 11 Inventory usage 7

Case cart

efficiency

8 9 5 Standardization 6

Delivery frequency 10 12 7

Process

standardization

3 2 1

Personnel

management

4 11 3

6.4.2 Robustness of the framework

The second feedback loop relates to policy decision making. The average OT ranking

in Table 6-1 serves as input for choosing the most appropriate policy based on the

ILEP index. In addition, a hybrid approach, combining ANP and PROMETHEE II, is

applied in order to check the robustness of the ILEP index and the developed

framework.

6.4.2.1 Hybrid ANP-PROMETHEE II ranking

The ANP-PROMETHEE II method is applied as a second-stage validation of the

logistics performance management framework, aiming to select the policy that


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improves efficiency in the internal hospital supply chain according to multiple

stakeholders’ preferences. The different steps as elaborated in Section 6.3.3 are

executed, ANP is applied as weighting procedure and the software ‘Visual

PROMETHEE’ is used to obtain the ranking of alternatives, which represent

improvement policies for inventory and distribution systems in the OT. The highest-

ranked alternative (i.e. rank 1) represents the best policy based on the normalized ANP

weights and the net outranking flow (PROMETHEE II). The policies are judged across

the indicators in the framework serving as input data, which can be qualitative or

quantitative. Quantitative judgments are derived from the simulation output as

provided in Chapter 5, whereas qualitative judgments are obtained by asking the OT

stakeholder group to score the importance of indicators with respect to the alternative

policies on a five-point Likert scale.

Quantitative analysis

Figure 6-6 displays the ANP-PROMETHEE II rankings for inventory and distribution

policies from a quantitative perspective. Individual stakeholder preferences as well as

the average OT ranking are shown in the graphs. For most stakeholders, a similar

ranking can be found for both inventory and distribution policies. The As-Is policy is

at the lowest spot (i.e. rank 3), which implies that all stakeholders recognize the impact

of improving logistics processes at the OT and thus increase awareness of logistics

value.

In terms of distribution, the Standard scenario (see Section 5.2.4) is identified to be the

most suitable policy to increase efficiency since it reduces product duplication and

enhances data collection for inventory optimization. The inventory picture, on the

other hand, shows different rankings dependent on the item classification. For critical

items, classified in group A, stakeholders agree to adapt inventory parameters to

demand characteristics (i.e. scenario 2 in Section 5.2.3) to become more efficient.

However, for C-items, conflicts arise among stakeholders. Stakeholders expressing a

stronger trade-off between service level and costs or turnover, prefer the As-Is scenario

over the risk-taking scenario 3. This trade-off is largest for nurses, who select the As-

Is to be the best policy. Due to the high weight assigned to inventory service level, a

decrease in this KPI in scenarios 2 and 3, though it still satisfies the requirements for

C-items, cannot be compensated by cost savings or improved turnover rates as their

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weights are low. On average, however, the trade-off is alleviated and therefore the risk-

averse scenario 2 is preferred.

Qualitative analysis

On the other hand, a similar analysis is done using qualitative judgments. The

stakeholders are provided with a detailed problem description from the logistics

viewpoint, focusing on the main bottlenecks in the As-Is situation and efficiency gain

opportunities. The potential improvements are presented by using the normalized KPI

values as obtained from the simulation output for the different inventory and

distribution policies. This understanding promotes more informed decision making

when stakeholders are asked to score the importance of relevant indicators with respect

to the different policies. Although all stakeholders were reported about the advantages

and disadvantages of each policy, the ANP-PROMETHEE II ranking may differ

depending on the stakeholders’ perception. Most stakeholders converge their

preferences for distribution systems as the Standard scenario is selected to be the best

policy. The quantitative analysis resulted in the same ranking, thus we assume that the

Figure 6-6. Quantitative PROMETHEE II ranking for OT stakeholder group.


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simulation results increase awareness of potential efficiency improvements in

distribution processes.

However, for inventory systems, the stakeholder rankings are divergent, as can be seen

in Figure 6-7. Surprisingly, logistics stakeholders show indifference or even prefer the

least efficient As-Is scenario, whereas clinical stakeholders gain deeper insights into

the importance of indicators and recognize the potential efficiency gain in the proposed

policies. The positive impact of information is thus especially noticeable for clinicians,

who learn about the impact of service level, inventory cost and turnover when

inventory levels are adapted. Further analysis are conducted using the quantitative

data, rather than the impacts as estimated by experts to avoid misinterpretation of the

indicators.

Trade-off quantification

Furthermore, the hybrid ranking can be graphically displayed in the GAIA

(Geometrical Analysis for Interactive Aid) plane, showing the relative alternatives’

point coordinates depending on their contributions to the indicators (Brans and

Mareschal 1994). Alternatives are represented by points and indicators by vectors.

Conflicting indicators are easily identified since their vectors point in opposite

directions, in contrast to indicators expressing similar preferences. The length of the

vectors represent the relative discriminating power of the indicator on the alternatives:

the longer the vector, the more discriminating power. In addition, the direction of the

decision vector 𝜋 reflects the overall preference by compromising between indicator

weights, and the best alternative according to the ANP-PROMETHEE II ranking is

oriented in the same direction (Wang & Yang, 2007). Figure 6-8 displays the GAIA

Figure 6-7. Qualitative PROMETHEE II inventory ranking for OT stakeholder group.

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plane for inventory policy decision making from the overall perspective of the OT

stakeholder group. From this plane, it can be observed that scenario 2 scores

particularly well on inventory service level, whereas scenario 3 improves turnover rate

and costs. Since inventory service level has the highest discriminating power, as

expressed by its vector length, the red decision axis points in the direction of this

indicator and hence scenario 2 is the best closest alternative for A-items.

Furthermore, this analysis enables trade-off quantification between indicators which

can be visualized using GAIA radar plots. Figure 6-9a displays the well-known trade-

off in any healthcare setting and how to balance service level and costs by

implementing the right inventory policy. Scenario 2 outperforms the As-Is scenario in

terms of service level, costs and turnover due to overstocking and wasteful processes

in the current practice. Choosing between the two improvement policies corresponds

to solving the trade-off between service level and costs. Scenario 2 has a higher service

level, whereas scenario 3 performs better on costs and turnover, though at a lower

service level.

Figure 6-8. GAIA plane for inventory (A-items) policy selection and trade-off quantification.


215

Similarly, the Figure 6-9b displays the trade-offs when selecting distribution policies.

As can be seen in the GAIA radar plots, distribution service level and delivery

frequency are conflicting criteria due to their opposite position. The Standard scenario

mainly focuses on distribution service level, centralization and costs, whereas the Copy

carts scenario emphasizes good performance on delivery frequency, accuracy and

replenishment lead time. Overall, the stakeholders value distribution service level

higher than delivery frequency, and therefore the Standard scenario is the preferred

policy. Remark that clinical staff involvement is the main factor favouring the As-Is

situation, however, this discrepancy is largely due to inaccurate staff representation as

explained in Chapter 5.

Figure 6-9a. GAIA radar plots for inventory policy selection and trade-off quantification.

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6.4.2.2 Sensitivity analysis

With the help of a sensitivity analysis, the robustness of the final ranking of alternatives

can be checked. Group decision making often implies larger variability or uncertainty

in the criteria weights, and thus complicates the process of obtaining a robust ranking.

A robust ranking is a ranking that does not change significantly when minor changes

are made to different steps in the MCDM procedure (Adunlin et al. 2015). Two ways

of performing sensitivity analysis are described, namely comparing two MCDM

approaches and evaluating the impact of changing indicator weights on the final

ranking.

A first method to check the sensitivity of the framework output is by comparing two

MCDM procedures. In the standalone ANP approach, the local priorities are

synthesized to obtain a global priority vector, indicating the extent to which

alternatives contribute to efficiency improvement in the OT supply chain. Different

logistics policies in terms of inventory and distribution systems are evaluated using

simulation (see Chapter 5). The ILEP index is introduced to find the most suitable

policy for each process type. Figure 6-3 and Table 6-1 display the average ANP

Figure 6-9b. GAIA radar plots for distribution policy selection and trade-off quantification.


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weights for the OT stakeholder group. According to this KPI ranking and the

corresponding ILEP index, scenario 3 is preferred for A-items, whereas C-items

request a more risk-averse scenario for inventory management. The Standard scenario

is the best option for replenishing supplies to operating rooms.

Alternatively, a hybrid ranking is proposed by combining ANP and PROMETHEE II.

Figure 6-10 shows the comparison between both methods. The last option is the As-Is

situation, which indicates that stakeholders are aware of the current bottlenecks in the

logistics system. For distribution systems, both methods rank the Standard scenario to

be the best policy for improving efficiency, demonstrating the robustness of the

framework for this process type. However, the best inventory alternative depends on

the MCDM procedure. ANP selects scenario 3 for A-items because this method allows

to compensate the slight decrease in service level with reduced costs and improved

turnover rate. The ANP-PROMETHEE II method, on the other hand, is non-

compensatory in nature and therefore reinforces this trade-off by ranking scenario 2

with the highest service level at the top. For less critical items, both methods agree on

the highest-ranked policy, whereas a change occurs in the adjacent lower ranks

depending on the trade-off between service level and costs or turnover. Although the

ranking differs for inventory management, both improvement alternatives present

significant cost savings and turnover improvements, such that the final choice depends

on the nature of the decision-maker, who is more risk-averse or risk-taking. Since the

PROMETHEE II method takes into account ANP weights, the ILEP index is assumed

to present a robust ranking.

Figure 6-10. ANP vs. PROMETHEE II ranking for inventory (left) and distribution (right)

policies.

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Secondly, a sensitivity analysis is conducted by varying the weights assigned to the

indicators (Diaby and Goeree 2014). ‘Visual PROMETHEE’ proposes an interactive

tool, namely ‘walking weights’. A value for an indicator weight is arbitrarily chosen,

and the other weights are adjusted accordingly. This analysis allows to assess within

which stability interval the weights can change without adapting the final ANP-

PROMETHEE II ranking. The smaller the interval of the indicator weight, the greater

its impact on the ranking. Table 6-2 displays the stability intervals for each indicator

for each process type.

For controlling inventory of A-items, there is a change in the first-ranked alternative

(i.e. scenario 2) if service level weight decreases below 6% or if cost or turnover

become very important (i.e. 83% or 95% respectively). Since A-items are classified to

be critical high-cost items, the service level will always have top priority, and thus

probability of changing the final ranking is small. In contrast, for C-items, the stability

intervals are smaller, or in other words, the indicator weights have more impact on the

ranking. Figure 6-11 presents the net outranking flows. Whenever the inventory

service level weight increases by 10%, the As-Is scenario is preferred over scenario 2.

However, service level weight is expected to decrease as the items are less critical. A

drastic decrease to 9% will change the ranking in favour of scenario 3, where cost

savings and improved turnover compensate for the reduced service level. From a cost

perspective, small changes in the weights results in an interchange of the second and

third rank (i.e. As-Is and scenario 3) unless costs become the main objective (i.e. 72%),

then scenario 3 outperforms scenario 2.

In terms of distribution systems, distribution service level, clinical staff involvement

and delivery frequency have the greatest impact on the ranking. However, the best

policy in the ranking is only changed when delivery frequency gets more important

(i.e. 27%). In this case, Copy carts scenario is preferred over Standard scenario. In

general, modifying the weights of all other indicators does not change the ranking, or

only changes in adjacent ranks 2 and 3. Therefore, we can conclude that the best policy

does not become the worst one, and vice versa, and thus, the framework is considered

to be robust.


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Table 6-2. Stability intervals for the weights of each criterion.

Criteria Stability

interval

Criteria A-items Stability

interval

Distribution service level

0 33 Inventory service level

6 94

Delivery accuracy 0 47 Inventory cost 0 83

Centralization 0 79 Turnover 0 95

Lead time 0 80

Response time 0 92

Clinical involvement 0 22 Criteria C-items Stability

interval

Cost 0 43 Inventory service level

48 70

Delivery frequency 0 27 Inventory cost 0 26

Standardization 0 100 Turnover 21 46

Personnel management 0 94

6.4.3 Benchmarking

In this section, benchmarking allows to compare logistics performance measurement

efforts between different hospital departments, depending on the material types used.

Benchmarking opportunities are identified by analysing how far the As-Is situation

deviates from the desired performance score, and thus highlights those areas that are

under-performing or misaligned. The best practice frontier contains the ideal

performance score, which defines the best set of KPIs to achieve operational

excellence in healthcare logistics. Figure 6-12 displays the As-Is performance score

Figure 6-11. Impact on ranking by changing inventory weights for C-items.

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for managing inventory systems at the OT, which is calculated as a weighted average

of ANP weights and a score of 1-5. Different performance shapes and sizes can be

observed for two reasons:

First, multiple stakeholders use different definitions for the indicators and

strive towards different objectives. As a consequence, the extent to which

KPIs are monitored is often biased by the decision-maker’s attitude, leading

to possibly conflicting performance scores. This work aims to solve the

disagreement among stakeholders on how to measure the indicators.

Second, the relative importance of indicators differs between different

stakeholders, even if they belong to a similar stakeholder group. Inventory

service level seems to outperform the other indicators, though the peak is

mainly caused by the high ANP weight factor (i.e. 0.229) assigned to this

indicator rather than a high performance score (i.e. 2.5 on 5). Due to this high

relative importance, service levels should be monitored closely in order to

achieve operational excellence. The same trend can be found in each

department, so we can conclude that service level plays a crucial role in

healthcare logistics in general. Moreover, a big difference can be observed

when measuring inventory criticality, which is mainly due to varying ANP

weights between the logistics stakeholders.

Figure 6-12. As-Is performance score of inventory indicators at OT.


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The radar plot in Figure 6-13 compares performance measurement in three

departments. The overall OT, IRCC and pharmacy weights are used as general ranking

(see Table 6-1). Therefore, differences in the radar plots are only due to differences in

performance monitoring scores rather than varying importance. The OT setting is

centred with an average total performance rate of 43%, whereas the performance

shapes for pharmacy and IRCC department are larger, 66% and 64% respectively. The

low performance score for the OT disposables (see blue line: Average As-Is score OT

in Figure 6-13) is mainly attributable to poor monitoring of productivity indicators,

though these indicators contribute greatly to efficient inventory management. As

mentioned in Section 5.2.2, 90% of the items stored at the OT are normal- to slow-

moving items with a low turnover rate, and the consumption rate is not recorded due

to inability of track-and-tracing. Also, lack of inventory visibility complicates efficient

inventory management. The pharmacy (see grey line: As-Is score pharmacy) scores

especially good on the inventory quality indicators, which are also highly valued as

shown in Section 6.4.2 due to strict legal regulations for drugs management.

Furthermore, the expensive invasive materials (see yellow line: As-Is score IRCC)

perform well in terms of recording consumption rate and managing stock rotation.

These indicators are more important because the items are directly charged to patients,

waste due to product expiry must be minimized and these items are allocated to space

close to the operating rooms resulting in high opportunity costs.

To achieve operational excellence, the three settings strive towards an average

performance rate of 85% by focusing mainly on inventory service level, accuracy and

productivity indicators. Criticality also contributes greatly to operational excellence by

balancing inventory levels and patient safety requirements. This ideal percentage is set

by taking into account the strategic and tactical mission of the organisation in this case

study. Figure 6-13 compares the As-Is and To-Be performance scores for managing

inventory systems. The ideal score for the OT and pharmacy (orange line) are

combined as we only consider pharmaceutical supply delivery to the OT in this case

study, whereas the IRCC (green line) operates independently. The best-practice

frontier allows the performance scores to increase by 42%, 17% and 21% for

disposables, pharmaceuticals and invasive supplies respectively, up to the optimal

score. The study shows that no records are made for incoming and outgoing flows of

surgical disposables, which results in typical issues such as overstocking, lack of

inventory visibility or high value of product expiry. Poor performance on these

indicators results in lower quality and therefore, hospitals must strive for qualitative

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inventory systems to improve efficiency while maintaining high service levels.

Pharmaceutical supplies, on the other hand, are strictly regulated resulting in more

information on the inventory status. However, actions are required to improve

productivity or organisation of the materials flow to the OT. In addition, supply

consumption during surgery is not properly recorded, except for invasive medical

supplies which are used at the IRCC department and are accountable to the patient’s

invoice. As a result, cost information per surgery is inaccurate, which might lead to

overall loss when costs exceeds the revenues. Altogether, hospitals face many

obstacles and resistance to adopt SCM techniques, standardize supplies and implement

information technology systems which would improve integration among departments.

By identifying these improvement initiatives, hospitals can improve supply tracking,

contain costs and facilitate inventory control.

Similarly, Figure 6-14 shows the benchmarking analysis for internal distribution

systems in hospitals. The distribution ranking is considered to be context-specific, and

therefore multiple rankings are used considering the perspectives of OT, pharmacy and

IRCC stakeholder groups. The goal, as determined by upper management, is to achieve

Figure 6-13. As-Is vs. To-Be performance score of inventory indicators for each department.


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an overall performance score of 86% for surgical disposables and pharmaceuticals

(blue line). However, waste in current distribution practices result in an average score

of 62% (yellow line) and 67% (green line) respectively and thus leaves room for

efficiency improvement. Delivery accuracy, distribution service level and process

standardization have the highest weighting factors, and thus contribute most to

achieving efficient distribution systems. However, the performance scores are

moderate which presents significant improvement initiatives. From a time perspective,

the indicators perform quite good. Clinical staff involvement is limited, enabling

medical personnel to focus on their core activities. Finally, working towards up-to-date

surgical case carts, uniform workflows and a balanced workload for personnel will

enhance the productivity or organisation in the OT supply chain. Especially,

standardizing processes by introducing information technology systems will increase

the efficiency in the distribution flow. Similar trends are observable for the IRCC

department (brown line), where the performance shape is oriented towards initiatives

improving standardization, balancing workload and timely delivering supplies to the

right locations.

Figure 6-14. As-Is vs. To-Be performance score of distribution indicators for each department.

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6.5 Discussion

This study represents the final link in the framework development by providing three

feedback loops to integrate multi-level, multi-stakeholder perspectives. The prototype

as developed in Chapter 4 provides insights on how logistics can service healthcare

systems, and hence stimulates more informed decision making among all stakeholders

from a logistics perspective. Based on this understanding, different stakeholders’

objectives and viewpoints are quantified and alternative logistics concepts are

evaluated using the logistics performance management framework. Involving

stakeholders in the final development phase of the framework is a crucial success

parameter for increasing awareness of logistics benefits in hospitals and implementing

efficiency improvement initiatives. This is in contrast to early-participation, where

stakeholders have no knowledge of how logistics contribute to a value-based

healthcare system and as a consequence strive for the objectives that maximize their

own benefits.

6.5.1 Feedback loop 1: individual versus shared stakeholder

ranking using ANP

The first feedback loop allows for eliciting multiple stakeholders’ preferences to

determine individual and shared rankings of KPIs, generalizing the elements that

constitute the definition of operational excellence in healthcare logistics. To ensure a

valid framework, group decision making enhances consensus on which performance

indicators are relevant for evaluating the internal logistics flow in hospitals. The ANP

methodology supports shared decision making by engaging stakeholders in

participatory processes to value and prioritize multiple outcomes or indicators

(Hummel et al. 2014).

Regardless of the process or material type, improving performance management will

enhance the generalizability of the findings. The formulas, as presented in Chapter 5,

ensure uniform measurements and thus allow for comparing different hospital

departments and pursuing operational excellence to the benefit of value-based

healthcare. In addition, the logistics indicators are verified by medical stakeholders to

enhance awareness of logistics benefits and to provide insights on tailoring SCM

techniques to a healthcare setting. The findings in Table 6-1 reinforce the well-known

trade-off, namely service levels need to be maximized without considering the costs,


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which are ranked as least important. However, recent trends force hospitals to cost

containment without compromising on quality of care. Thus the logistics performance

management framework serves as a powerful tool to support managerial decision

making, while considering multiple stakeholder perspectives, to identify improvement

initiatives and streamline the internal hospital supply chain.

The overall ranking integrates multi-level group decision making, and therefore

possibly conflicting preferences must be solved. A quantitative approach is used to

allocate weights to stakeholders. In this study, the stakeholders are clustered according

to their similarity as introduced in Chapter 4 (Song and Hu 2009). The number of

stakeholders allocated to a cluster determines the cluster weight. Next, the overall

ranking is computed using the WAM or WGM from Equations 6.1 and 6.2. The WGM

accounts for the central tendency and thus results in a more stretched ranking compared

to WAM. In other words, the most important indicators are assigned a higher score and

the least important indicators get a lower score compared to WAM, where all scores

are averaged. Figure 6-15 shows minimal differences between both approaches. In this

work, we use WAM for computing the overall ranking because WGM often ignores

smaller clusters, which is not desirable for the small sample size in this case study.

Further work could reconsider the group decision making approach by determining

stakeholder weights in terms of the nature of the respective indicators and the expertise

to judge the indicator. The weights can be obtained by identifying the relative

importance of decision makers by pairwise comparisons, or by a supra-decision-

maker, who defines the individual stakeholder weights (Rietkötter 2014). The limited

number of stakeholders is a shortcoming in this case study, where one stakeholder

sometimes represent an entire department. For further research, we suggest to validate

the overall ranking using a qualitative procedure. The Delphi method as suggested by

Freitas et al. (2018) and Falzarano and Pinto Zipp (2013) provides a useful web-

platform for monitoring stakeholder participatory processes in order to generate

consensus by going through several iteration rounds. This socio-technical multi-

criteria approach aims to create collective alignment while developing a sound and

coherent model of values synthesizing the multiple perspectives (Vieira et al. 2019).

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6.5.2 Feedback loop 2: robustness in policy decision making

6.5.2.1 Hybrid ANP – PROMETHEE II ranking

This work shows the relevance of integrating ANP and PROMETHEE II as a hybrid

decision-support tool for measuring performance and ranking improvement initiatives

in healthcare logistics processes, such as inventory and distribution. Weighting the

criteria is an important step in any MCDM procedure, showing the relative importance

of each criterion. In the PROMETHEE II method, the decision maker is assumed to be

able to appropriately determine criteria weights, though no explicit weighting

procedure is specified, whereas ANP weights can be determined by the eigenvector

method. Furthermore, ANP decomposes complex problems into networks of elements

to structure the problem and obtain a clear view of the importance of each criterion

(Macharis, Springael, et al. 2004). The problem with ANP, however, is that it implies

a large number of pairwise comparisons and thus is a time-consuming task. Moreover,

ANP allows for compensating trade-offs. This is avoided in PROMETHEE II, such

that bad scores on some criteria cannot be compensated by good scores on other criteria

(Wang & Yang, 2007). A combination of the advantages of ANP and PROMETHEE

II proposes a relevant tool for multi-criteria decision problems in healthcare. The

Figure 6-15. Comparison between different averaging methods.


227

relative importance of the weights are determined using the ANP method, serving as

an input to find the best alternative by going through the different steps of the

PROMETHEE II method. A crucial step in the PROMETHEE II methodology

involves defining preference functions for all indicators, which determines the

indicators’ thresholds and hence affects the final outcome of evaluation. The choice of

preference function is not supported by any guidelines, but is an arbitrary choice based

on the indicator characteristics and decision maker preferences. Depending on the

nature of the indicators, linear and level preference functions are suggested for

quantitative and qualitative indicators respectively (Abdullah et al. 2019). The

threshold parameter values are set based on the suggestion by ‘Visual PROMETHEE’,

which takes into account the individual judgment of experts.

The ranking obtained by analysing the net outranking flow values using the ANP-

PROMETHEE II methodology learns that most stakeholders tend to converge their

preferences, and thus increase awareness of the logistics impact to value-based

healthcare and strive for integrated inventory and distribution systems in the OT.

However, some discrepancies occur among stakeholders when selecting an appropriate

inventory policy for C-items (see Figure 6-6). Items classified in group C are

characterized by lower costs, low criticality and are less frequently used. However, the

ANP weight for service level remains top priority regardless of the inventory

classification. As a consequence, this weight dominates the ranking and hence policy

decision making, even though lower service levels suffice for C-items. Medical

personnel strives towards maximal availability by having items on-hand at all times.

No distinction is made between different inventory classes, though a lower service

level for C-items still guarantees high-quality of patient care. Reassigning weights

based on characteristics of the inventory items is an interesting opportunity for future

research, such that policy decision making is aligned with inventory classification.

Moreover, the framework increases evidence-based decision making, such that

medical personnel strives towards the overall goal of value-based healthcare that

balances care and logistics processes.

In addition, the framework is qualitatively assessed. Although stakeholders are

informed about the potential benefits of inventory and distribution policies based on

simulation models, a biased attitude remains when judging the importance of the

inventory indicators. Surprisingly, the logistics stakeholders prefer the least efficient

As-Is scenario, which is in conflict with their ANP weights as shown in Figure 6-3. A

plausible explanation could be misinterpretation of how the indicators relate to the

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logistics policies. For distribution, on the other hand, the information proves to be

valuable when defining efficiency gains. Further research should focus more on

practice-based SCM implementation programs and informing decision-makers of the

logistics impact on overall healthcare performance by promoting stakeholder

participation in logistics optimization processes.

Finally, the GAIA analysis is a powerful graphical tool to quantify trade-offs by

determining differentiation power and conflicting criteria. However, keep in mind that

Principal Components Analysis is applied to construct a two-dimensional plane in

Figure 6-8, and thus the aim is to cover the highest possible percentage of information

to improve reliability. The information covered by the GAIA plane guides hospital

practitioners and academics to understand the trade-offs and identify the best set of

KPIs to improve efficiency in the internal OT supply chain.

6.5.2.2 Sensitivity analysis

The criteria weights play a major role in any MCDM study, and therefore it is essential

to perform a sensitivity analysis. The framework is considered to be robust, because

the indicator weights can be modified considerably without changing the best

alternative in the ranking or by changes only in adjacent ranks. Moreover, ANP and

PROMETHEE II provide similar results in general. Some deviations can be observed

because ANP allows for compensation, which is ignored in PROMETHEE. A

distinction can be made between the PROMETHEE I partial ranking and

PROMETHEE II complete ranking.

The partial ranking is defined by the intersection of two, possibly non-identical

rankings, which are deduced from the positive and negative outranking flows (Brans

et al., 1986). For example, when selecting inventory policies for C-items from the

nurses’ perspective, the As-Is and scenario 2 are incomparable alternatives due to

conflicting indicators. The As-Is dominates the other alternatives in terms of service

level, but scenario 2 has a lower weakness (i.e. less negative outranking flow) because

the As-Is is dominated more in terms of costs and turnover. Therefore, two non-

identical partial rankings can be observed in Figure 6-16, where the decision-maker

should decide on the best policy.


229

Alternatively, the PROMETHEE II complete ranking finalizes the decision by

balancing the two outranking flows. Although the information is more disputable, the

complete ranking from the nurses’ perspective prioritizes the As-Is over scenario 2

(see Figure 6-6) and thus the difference in service level does not compensate for the

cost and turnover improvements. A similar trade-off is made in the inventory ranking

for the OT stakeholder group, though the cost and turnover improvements compensate

for the reduced service level due to different indicator weights. As a result, scenario 2

is ranked first. ANP also ranks scenario 2 first for C-items (see Figure 6-10), however,

there is a change in the adjacent ranks 2 and 3 between both MCDM methods. The

ANP-PROMETHEE II method prioritizes the As-Is scenario over scenario 3 due to

the high weight assigned to inventory service level. As a consequence, there is no

compensation for the cost savings or turnover improvements. Figure 6-11 indicates

that a decrease in service level weight to 8% adjusts the ranking in favour of scenario

3. The ANP-ILEP index, on the other hand, prefers scenario 3 at the initial weights

because the improvements in costs and turnover rate compensate for the reduced

service level. In further research, the sensitivity analysis can be extended by calculating

the final ranking multiple times using different preference functions, and thus

exploring the dependence of evaluation results on the choice of preference function

types and their parameters (Podvezko and Podviezko 2010).

Figure 6-16. The partial PROMETHEE I ranking for inventory policies of C-items.

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6.5.3 Benchmarking

Benchmarking is a powerful tool for performance evaluation (Ozcan 2014). Feibert et

al. (2017) identify the most important decision criteria and suggest performance

metrics to enable benchmarking in healthcare logistics processes by comparing bed

logistics and pharmaceutical distribution in Danish and US hospitals. Inspired on these

case studies, we provide insights on the current practice of performance measurement

in healthcare logistics and which KPIs are best-in-class indicators when designing

effective inventory and distribution policies in different departments. The analysis

considers benchmarking between three material flows – surgical disposables,

pharmaceuticals and invasive supplies – and supports decision making by identifying

KPIs which need close monitoring to reduce the gap to the ideal performance score.

This best set of KPIs (i.e. ideal performance score) is located on the outer frontier in

the radar plot, representing the highest performance scores while taking into account

multiple stakeholders’ perspectives. Depending on the material type, the extent to

which KPIs contribute to the efficiency definition differs and hence different logistics

policies constitute best practices among departments. Learning from best practices in

other departments and understanding the impact of logistics policies is pivotal to the

success of implementation and hence will improve overall hospital performance.

Volland et al. (2017) point out measurement of performance in hospital logistics as a

major research opportunity to promote continuous improvement. Healthcare

benchmarking is defined as “a continuous, systematic process of measuring products,

services and practices against organisations regarded to be superior with the aim of

rectifying any performance ‘gaps’” (Kouzmin et al. 1999). Further research should

evaluate performance management and best practice opportunities in other hospitals

as well as in maintenance settings which show similarities to healthcare logistics.

Altogether, the strategy for improving OT logistics performance should focus more on

quality and productivity objectives for inventory and distribution systems, since these

objectives constitute the most important elements of the efficiency definition as

obtained by the weights in the ANP procedure. An efficient inventory system also

enhances distribution performance by adopting efficient replenishment policies.

Depending on the process type or context, the priority of performance measures differs

and thus results in different best practices. The IRCC department is more productivity-

focused while maintaining high service levels, whereas the objectives of the OT and

pharmacy are quality-driven with increasing awareness for product and process


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standardization. Hence, the framework guides and supports decision-makers in

identifying context-specific improvement initiatives. The results can be extended to

healthcare logistics in general, as consistent results are found by Kritchanchai et al.

(2018), who emphasize the need for efficient inventory management and effective

information systems. Their strategy for improving healthcare logistics performance

focuses on four KPIs: inventory availability, inventory visibility, track-and-traceability

and standardized product identification. Feibert et al (2017) also focus on the quality

aspect, employee engagement and security of supplies as important decision criteria in

the literature on healthcare logistics benchmarking.

A remark should be made that the ideal performance score does not equal the

maximum performance score (i.e. 100%) in this case study. The logistics manager is

responsible for managing decentral wards, and therefore the ideal performance scores

reflect the strategic or tactical goals for efficiently managing internal supply chain

activities. Typically, a large part of hospital logistics flows (e.g. central warehouse) is

outsourced to a third-party logistics provider in order to focus on core activities. The

framework also applies to outsourcing decisions as monitoring KPIs is required for

effective performance management, regardless of who is responsible. Moreover, the

framework supports the logistics manager when negotiating the KPI measures and

standards with the external provider. For example, using a holding cost percentage

rather than a detailed cost breakdown structure suffices for monitoring the inventory

cost within the hospital, resulting in a To-Be performance score of 3 on 5.

Another drawback when measuring performance in hospitals relates to lack of data and

thus subjectivity in stakeholder judgments. As a consequence, different stakeholders

assess the current situation from their logistics or medical perspective. For example,

the logistics stakeholders rank inventory service level as the most important indicator

for handling surgical disposables. However, the scores for this indicator vary from bad

to moderate, resulting in a similar performance peak in the radar plot, though with a

different size. Therefore, more attention is needed to uniformly measure performance

indicators in a healthcare logistics setting in order to identify appropriate action plans.

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6.6 Conclusion

This study investigates the robustness of the framework by providing three feedback

loops to integrate multi-level, multi-stakeholder perspectives.

The first feedback loop involves eliciting stakeholder preferences by iterating between

the indicators and determining aggregated KPI rankings. The results show conflicting

rankings due to different professional backgrounds, decision-making levels, process

types and material characteristics. In terms of inventory management, consensus is

achieved on high-ranked indicators and thus a general ranking is proposed. The

distribution ranking, on the other hand, diverges among the stakeholder groups and is

considered to be context-specific. Regardless of the process type, improving

performance management will enhance the generalizability of the findings by

quantifying how logistics processes contribute to value-based healthcare. In addition,

the multi-stakeholder analysis improves transparency by dealing with the problem

from multiple viewpoints.

A second feedback loop to refine the framework relates to policy decision making. A

decision-support model is proposed integrating ANP and PROMETHEE II. The hybrid

method provides a powerful tool to quantitatively support practitioners and academics

in ranking improvement initiatives, making more informed and rational decisions, and

quantifying trade-offs when adopting SCM concepts in healthcare. The findings

reinforce the well-known trade-off, namely service levels need to be maximized

without considering the costs, which are ranked as least important. The impact of

changing criteria weights on the final ranking is examined by performing a sensitivity

analysis. Minor changes reflect a robust ranking. Although the hybrid MCDM

approach provides robust results, the awareness of the logistics impact on value-based

healthcare as well as physician engagement is at a low level of maturity, resulting in

decision making based on intuition.

Third, the proposed framework provides a good foundation for evaluating logistics

performance gaps between different hospitals or departments, and thus enables

benchmarking. The benchmarking study provides insights for decision makers to

identify which objectives and indicators are important for designing efficient logistics

processes and determining best practice opportunities in healthcare logistics.

Moreover, the performance gaps focus attention on which data to collect in order to

appropriately monitor the relevant KPIs. In addition, the benchmarking analysis can


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be extended by including more operational data, rather than measuring the extent to

which indicators should be monitored to become more efficient.

In further research, domain experts will assess the validity of the results. The

collaborative nature of this study improves the degree of confidence the experts have

in the findings and thus improves logistics awareness in healthcare. Consulting several

stakeholder perspectives enhances staff commitment and ensures robust results. Future

work should focus on supply chain integration by aligning inventory and distribution

policies to the benefit of value-based healthcare. The methodological approach can be

extended to a wider healthcare logistics context as an interactive decision-support tool,

engaging multiple stakeholders with conflicting perspectives.

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CHAPTER 7

7 Roadmap for implementation of the

framework7

Chapter 7 presents the healthcare logistics performance management

framework by bringing together the four modules discussed throughout this

dissertation. We explain how the framework works and we present a real-

life application to enhance trust building in the framework. Finally, an

implementation roadmap provides guidelines both for scholars and

healthcare practitioners to apply the framework on a wider scale in

healthcare logistics, without rebuilding from scratch.

7.1 Introduction

SCM plays an important role in the current transition from volume-based to value-

based healthcare. In contrast to the traditional financial model focusing on fee-for-

services and isolated care, the new fee-for-value model aims to balance qualitative

patient outcomes and healthcare expenses by encouraging coordination between

logistical and care processes across the healthcare continuum. From a logistics

perspective, however, healthcare organisations lack data and tools for containing costs

and thus delivering value-based care (Deloitte 2018b). A data-driven approach is

needed to evaluate logistics processes, monitor performance and gain actionable

insights to support decision making within the hospital supply chain.


Moons, K., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). A Framework for Operational Excellence in

Hospital Logistics: Implementation Roadmap. Health Care Management Science, Submitted.

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OR/OM is a rich field consisting of quantitative tools to support data-driven process

improvement. We present a toolbox to help healthcare stakeholders in understanding

SCM and increase awareness of logistics processes, which in turn will improve

stakeholder commitment and have an impact on the overall hospital performance. The

included tools originate from industrial engineering applications in the field of

OR/OM, such as decision-making methods, inventory control models, simulation

modelling, performance management, etc. However, transferring successfully applied

SCM tools from other sectors is not a simple copy-paste due to the unique challenges

inherent to the internal hospital supply chain. We aim to support decision making and

address the barriers to effective healthcare SCM by integrating the toolbox into a

healthcare logistics performance management framework that identifies efficiency

improvement opportunities and adapts best practices to the healthcare logistics needs.

With this framework we create a proof-of-concept, though the actions required for real-

world implementation cannot be neglected since it determines how the results

contribute to healthcare performance (Brailsford et al., 2019). The remainder of this

chapter discusses the framework, its building blocks, potential applications as well as

guidelines for implementing the framework in any healthcare logistics setting.

7.2 Healthcare logistics performance management

framework

We develop a healthcare logistics performance management framework to adopt SCM

strategies as a first step towards achieving operational excellence at the internal

hospital supply chain and accordingly delivering value-based care. “Operational

excellence is achieved through the use of best inventory management and distribution

systems, combined with continuous supply chain process improvements and better

integration with the patient care process” (Landry and Beaulieu 2013). We distinguish

between two components in the conceptual framework, namely healthcare logistics

and performance management:

Healthcare logistics is defined as “design, planning, implementation and

control of coordination mechanisms between supplies, equipment, services

and information from suppliers to point-of-care in order to enhance clinical

outcomes while controlling costs” (Di Martinelly, 2008). More particularly,

this work focuses on materials planning, including storage and distribution of

disposable medical supplies to achieve operational excellence.

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237

Performance management allows to understand the current system and

identify deficiencies, where performance indicators are used to monitor how

well the organisational goals are realized. In contrast to the fragmented

logistics responsibilities in hospitals, effective performance management

requires an integrated approach to control the supply chain strategy,

implement continuous improvement programs and improve decision-making

capabilities by focusing on relevant KPIs (Maestrini et al. 2017).

The healthcare logistics performance management framework is designed as a modular

structuring platform to provide decision support for applying industrial engineering

tools in order to determine the extent to which logistics contribute to healthcare. Four

modules or building blocks are designed with a special focus on describing and linking

the criteria to assess efficiency improvements. These modules are considered as critical

success factors for effective performance management by translating strategic or

tactical goals into operational indicators and establishing interdependency

relationships to account for a system-thinking perspective when adopting SCM

strategies that fit the specific context needs. Moreover, the cyclical feature in the

framework allows to iterate between the modules by bringing together all stakeholders’

perspectives in order to ensure SCM is tailored to the unique healthcare setting and

continuous improvement is supported to achieve the desired output according to the

organization’s goals. The framework addresses three barriers to adapt SCM to the

healthcare specificities:

Increasing transparency: through information-sharing across the healthcare

continuum. The framework guides healthcare managers to collect the right

data for KPI monitoring and thereby reducing uncertainty in decision making.

Reducing supply chain fragmentation: by transforming silo-based

practices into integrated supply chain processes provides a competitive

advantage, and therefore the framework also stimulates strategic thinking

(Maestrini et al., 2017).

Introducing common language for stakeholders: through uniform

performance measurement results in more informed decision making and

increased awareness of logistics impact to value-based healthcare. Hence, the

framework strives towards alignment between patient care and logistics by

implementing cost-effective, standardized processes.

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Overall, the healthcare logistics performance management framework uses a closed-

loop approach to adopt SCM in healthcare decision making, as shown in Figure 7-1.

The framework is generic as it can be used by any hospital or healthcare facility.

Regardless of the application on hand, the different modules can be applied to support

decision making, whereas the content of the module can be customized to the specific

context.

7.3 Working principle of the framework

From a logistics perspective, there is a need to gather data, evaluate processes and

monitor performance in the internal hospital supply chain. The main working principle

of the healthcare logistics performance management framework relies on

understanding the system, determining the best set of KPIs that define operational

excellence in healthcare logistics, evaluating SCM interventions by trading-off KPIs

and involving multiple stakeholders to have more informed decision making and an

integrated hospital supply chain. The framework consists of four main modules (see

Figure 7-1):

Selecting indicators

Prioritizing indicators

Evaluating logistics policies and monitoring performance

Incorporating stakeholder feedback

Figure 7-1. Modular, closed-loop approach for developing the healthcare logistics performance

management framework.


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7.3.1 Selecting indicators

The first module in the framework is concerned with defining a set of criteria to

measure the performance of the internal hospital supply chain. Based on a literature

review and in consultation with healthcare logistics experts, performance indicators

are assigned to four objectives – quality, time, cost and productivity/organisation –

representing the multi-dimensional character of SCM. More particularly, the indicators

are customized to assess the impact of two managerial functions or process types,

namely a set of 9 and 12 indicators for monitoring storage and distribution of

disposable medical supplies respectively. To obtain effective performance

management, the objectives need to be aligned with the performance indicators as

considered relevant to the managerial function. Choosing the right indicators is

essential to meet the objectives and build a widely applicable foundation for the

framework. The selected indicators must comply to the following specifications

(Marsh et al., 2016):

Exclusive: ability to distinguish among alternatives

Complete: justify clear definition covering the full range of factors relevant

to decision making

Non-redundant: avoid unimportant or unnecessary information

Measurable: provide operational and meaningful information to decision

makers

According to Marsh et al. (2014), the number of indicators ranges between 3 and 19 to

assess the value of healthcare interventions. The larger the number of indicators

included, the more complete the set of indicators, though it requires more time and

cognitive effort for both model developers and decision makers. Furthermore, the

framework involves indicators as used in traditional logistics engineering concepts to

ensure uniform performance measurement, enable benchmarking opportunities and

learn from best practices. Having streamlined standards drives hospital management

to clearly define operational excellence, reduce barriers to SCM implementation and

support decision making on all organisational levels.

7.3.2 Prioritizing indicators

Since not all criteria are equally important in various hospital departments or

healthcare organisations, a prioritization step is needed to customize the indicator list.

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The inter-relationships between objectives and indicators are determined in

consultation with hospital logistics experts, whereas priorities are established using

MCDM techniques. ANP is a popular, easy-to-use MCDM method that is frequently

used for performance management, providing a synthesized value that reflects the

possibly conflicting logistics and medical goals in healthcare SCM. The performance

management framework strives towards value improvement considered as a shared

goal that unites the interests of all stakeholders (Porter 2010), and therefore the ANP

method aligns with the value-based care model.

ANP decomposes the decision problem into a generic network structure, referred to as

the ANP-based prototype, to assign relative weights by eliciting stakeholder

preferences and assess alternatives by trading off KPIs. The prioritization problem is

initially translated into a prototype to model different aspects of decision making in

hospitals materials management and improve understanding of the logistics impact to

value-based healthcare. The prototype only takes into account the expertise of the

logistics stakeholder to define the role of logistics in healthcare by aligning objectives

and indicators as considered relevant for the studied process. The outcome shows the

best set of KPIs to assess supply chain improvement interventions while striving for

operational excellence.

7.3.3 Evaluating logistics policies and monitoring performance

The generic network structure in the ANP-based prototype offers a starting point for

customization to various logistics needs. Therefore, the third module uses case studies

to experiment and gain insights into real-life problems and potential efficiency gains.

Improvement opportunities are identified as the desired consequence of monitoring the

relevant KPIs and developing logistics policies triggered by these KPIs in different

managerial areas.

The ANP-based prototype is combined with Discrete-Event Simulation modelling as

a sound basis for the reference framework (see Figure 7-2). DES helps to define

operations, map processes and analyse available logistics data in a structured way to

understand the current situation, identify bottlenecks and test several improvement

scenarios (Roberts 2011). The simulation output determines objective values for the

selected KPIs, which are prioritized using the ANP-based prototype. The Internal

Logistics Efficiency Performance (ILEP) index integrates these KPI values into a

multi-dimensional evaluation score. Hence, the ILEP index is the main output from


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the hybrid ANP-DES tool to support data-driven process improvement by quantifying

trade-offs among the possible conflicting objectives (i.e. quality, time, cost and

productivity).

7.3.4 Incorporating stakeholder feedback

Finally, Figure 7-3 shows three external feedback loops to consider a broader

perspective and describe how the indicator selection and prioritization modules relate

to policy decision making and performance monitoring. The single decision-maker

perspective is extended to a multi-stakeholder, multi-level framework that ensures

SCM is tailored to the healthcare setting.

Figure 7-3. Incorporating three feedback loops to validate the multi-level, multi-stakeholder framework.

Figure 7-2. Hybrid ANP-DES tool to identify data-driven process improvement.

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In the first feedback loop, the indicators are verified by addressing potential conflicts

between multiple stakeholders using a clustering approach as proposed by Song and

Hu (2009). Stakeholders often have biased preferences due to fragmented logistics

responsibility in each department, their background (e.g. logistical, medical) or level

of control (e.g. operational, tactical, strategic). Via group decision making, ANP finds

a generic importance ranking of the indicators that constitute the definition for

operational excellence, dependent on the managerial function.

The second loop highlights the robustness of the framework as a transparent and

informed decision-making tool by promoting stakeholder commitment. A sensitivity

analysis is performed to increase the accuracy of the obtained ILEP index. The ILEP

index is compared to a hybrid ANP-PROMETHEE II method, which ranks the

logistics policies and identifies the characteristics of the best policy for the studied

application. Small deviations in the ranking of policies are attributed to the non-

compensatory nature of the PROMETHEE II method. However, compensation for the

high service level requirement is justified in any healthcare setting because the

productivity objective plays an intermediating role to neutralize the trade-off between

costs and service level by emphasizing the need for streamlined and integrated

workflows. Furthermore, a stability interval is determined for the indicator weights to

determine their impact on the choice of the best policy. The greater the interval, the

lower the impact and thus the framework and ILEP index are considered as a robust

assessment tool for SCM in healthcare. From a qualitative perspective, the framework

stresses the importance of stakeholder education to increase understanding of SCM,

which creates awareness of logistics and enhances stakeholder commitment. Hence,

the framework stimulates more informed decision making by providing a common

language for all stakeholders to maximize value contribution.

The two feedback loops discussed above provide insights on the extent to which

indicators impact policy decision making. A third feedback loop is added to identify

benchmarking opportunities and accordingly promote continuous improvement

programs. A comparison is made between the actual and ideal performance score to

identify the need for collecting the right data. Having accurate data availability is

essential input information for future work to validate the hybrid ANP-DES tool by

fine-tuning the modelling of the logistics policies and to ensure uniform ways of

monitoring the relevant KPIs. Uniform performance measurement allows for a

transparent comparison between operational efficiency and patient outcomes for value-

based healthcare.


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Altogether, the closed-loop framework as shown in Figure 7-3 provides actionable

insights for both academics and healthcare practitioners to tailor SCM to the healthcare

setting, encourage effective communication between stakeholders and increase data

transparency for modelling SCM interventions.

7.4 Practice-based application of the framework

The developed framework guides healthcare organisations in making logistics

adjustments to solve deviations from the desired performance outcome. The decision

makers can act in different ways: do nothing, change policies, change objectives,

change KPI prioritization, etc. Do nothing is a viable alternative in case the expected

resistance to change in the organisation is too big. Implementing SCM in healthcare

requires evaluating the extent to which logistics contributes to qualitative patient care

based on stakeholder feedback. An overlooked logistics culture and lack of stakeholder

commitment at different levels of control are significant obstacles to implementation

(Ageron, Benzidia, and Bourlakis 2018). Moreover, only 5% of literature reports the

practical use of healthcare simulation modelling results compared to the large amount

of suggested or conceptualized models without input from healthcare practices

(Brailsford et al., 2009; Brailsford et al., 2019; Katsaliaki & Mustafee, 2011). In this

work, we present a framework with potential for practice-based applications. Case

studies are chosen as empirical research design to illustrate the feasibility of the

framework for understanding various logistics needs in hospitals, gain actionable

insights and generate a proof-of-concept on how logistics can contribute to value-based

care.

7.4.1 Testing the framework for internal distribution at the

operating theatre

This section presents a practice-based application of a SCM intervention at the OT in

order to gain new insights by bridging the gap between theory and practice. The

outcome of the framework is compared to a real-world implementation in order to

enhance trust building in the framework. Referring to Section 5.2.4, the framework has

been tested to evaluate replenishment systems for disposable supplies at the OT, by

focusing on the materials flow between central and decentral stocking locations while

reducing logistics activities close to the operating room. According to the ILEP index,

the Standard scenario outperformed the other replenishment policies by eliminating

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double stock and collecting consumption data on the operating room level. In practice,

the most efficient replenishment scenario (i.e. the Standard scenario) has been

implemented and significant efficiency gains are reported by comparing the historical

situation in 2017 (i.e. As-Is) to the current situation in 2019, which uses the Standard

replenishment policy. The stock on-hand and the inventory value (i.e. product of

average inventory level and unit cost) of disposable medical supplies are given for both

the central and decentral stock:

First, two inventory pictures are taken for 616 SKUs in the central storage

rooms, namely the CSA and OT1 stock, reflecting the As-Is (i.e. 2017) and

2019 situation based on one-year data for the same SKUs. The data reflect the

weekly replenishment activity. Figure 7-4 shows a decrease in the number of

CSA items held in stock due to eliminating double stock on replenishment

case carts in the Standard scenario. Accordingly, we observe a drop of 22%

in the CSA value. A stronger effect is observed for the OT1 storage room with

a 55% reduction in OT1 value, mainly because these items are on average

more expensive than CSA items. Based on the included SKUs in the dataset,

a saving of more than €65.000 has been realized on a weekly basis.

Figure 7-4. Comparison of central stock in 2017 and after implementing the Standard scenario

in 2019.


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Second, Figure 7-5 compares the 2017 and 2019 situation for 253 SKUs that

need daily replenishment in the decentral stock of one OT cluster. It shows

an upward shift in the items stored from central to decentral locations, and

similarly the decentral stock value goes up with 30%. Although the majority

of these items have a unit cost less than €5, these items are considered fast-

moving items and justified to be stocked close to the operating room.

Assuming that half of the items are daily replenished, the annual decentral

stock value accumulates to €12 million (= 0.5*13599*7 clusters*5 days*52

weeks).

Combining the 2019 central and decentral inventory pictures results in a 4%

saving of overall OT inventory value, or in absolute numbers €700.000 per

year, while decentral consumption data slightly increase (i.e. 9% increase in

quantity shipped). Although the weekly saving in central stock is largely

offset by the increased amount of decentral items replenished on a daily basis,

focusing on low-hanging fruit allows to easily reduce costs. In addition, the

inventory holding cost needs to be computed to compare the implementation

results to the framework outcome in Table 5-19. The holding cost is defined

as the product of the average inventory level and 25% of the unit cost per

year. We observe a decrease of 23% in holding cost for the OT stock, which

is in line with the 20% financial saving as suggested by the framework for the

Figure 7-5. Comparison of decentral stock in 2017 and after implementing the Standard scenario

in 2019.

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Standard scenario. A discussion of the implementation results with all

stakeholders increases confidence in the framework, which in turn will

improve stakeholder commitment and align logistics and medical goals to

improve overall hospital performance.

It is important to note that the differences between the 2017 and 2019 situation are not

only attributable to implementing the Standard scenario in practice, as other parameter

values have also changed during the studied time period. Therefore, the improvement

cannot be interpreted as an isolated impact of adjusting the distribution system.

Effective materials planning requires integrated coordination of both inventory and

distribution systems. In addition, the studied application underestimates the real-world

savings due to a limited dataset (e.g. 616 out of 3241 SKUs included in the central

dataset). Lack of data availability is one of the largest obstacles to implement effective

performance management in the internal hospital supply chain. Although health

systems generate vast quantities of data, the quality of logistics data is often poor. The

output is unlikely to be informative if inaccurate or incomplete data is inserted in the

framework, or it can be stated as “garbage in, garbage out”. The framework guides

hospital management to collect the right data, which is a critical prerequisite for

creating a decision-support tool for adopting SCM in healthcare. Also, the scarcity of

right-skilled logistics staff hinders implementation of effective SCM. The framework

stimulates transparent decision making, however, lack of expertise and training

complicate the interpretation of the results. Finally, healthcare stakeholders often are

too busy dealing with today’s problems and have no spare money to invest in

improvement initiatives that will lead to future savings.

In general, research on healthcare logistics provides a fragmented picture of isolated

hospital processes, where the modelling is typically done by scholars without input

from healthcare practitioners. Our framework orchestrates supply chain integration,

encourages hospital stakeholders to identify data requirements for OR/OM modelling,

stimulates more informed decision making and enhances knowledge transfer to

increase SCM understanding. Identifying SCM implementation paths based on the

framework by combining performance measures and stakeholder information

enhances trust building in the findings. Overall, the framework guides hospital

management to identify areas of low-hanging fruit to easily increase efficiency by

addressing the unique SCM challenges, such as stakeholder commitment, OR/OM

training or standardization. In addition, disposable medical supplies are often


247

overlooked, though accumulating small bits results in significant savings and improved

hospital performance.

7.5 Level of replicability

From both a theoretical and practical point of view, the following question pops up:

“How can we use this framework on a wider scale in healthcare logistics”.

The level of replicability is a major academic concern addressed in literature, which

aims at maximizing the probability of reusing the framework and extending findings

to different settings without rebuilding from scratch (Monks et al. 2019; Uhrmacher et

al. 2016). The majority of healthcare frameworks focus on one managerial area or

hospital department (Hans et al. 2012), whereas we aim to develop a generic

framework that increases transparency by providing modelling details to both scholars

and healthcare practitioners for repeating the same process and achieving similar

results while orchestrating supply chain integration in hospitals. The ability to

customize the framework to various logistics needs significantly adds to the academic

value of this work by including information about the framework architecture in terms

of tools (e.g. ANP, DES) used and data requirements for modelling (Brailsford et al.,

2019). The modular building blocks as presented in Section 7.3 enhance the extent to

which the framework can be reassembled to fit to the specific context needs, regardless

of the data requirements. Moreover, stakeholder commitment is a crucial parameter to

ensure replicability of the framework, since incorporating stakeholder feedback by

adding iteration rounds to the framework development process improves confidence

in the findings to customize the framework to different needs. From a practical point

of view, we suggest to use the available software packages for visualizing features and

simple solutions rather than sophisticated black box operations in order to enhance

user-friendliness and encourage stakeholder participation. However, this work does

not provide any guidelines on selecting software as the modules in the framework are

software independent.

Our framework focuses on integrated coordination of inventory and distribution in

materials planning at the operating theatre. Aligning inventory and distribution

processes is essential to define parameter values for stock levels, identify where to

stock and when to replenish, reduce the reverse logistics flow, increase uniformity as

well as realize time and cost savings. The framework reflects the need for holistic SCM

by taking into account multiple aspects of decision making in hospital materials

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management and striving for supply chain integration. Hence, the framework is not

exclusively intended for addressing a single logistics need. The merit of the framework

lies in its generic nature and ability to customize to various departments, the entire

hospital or other managerial processes. The remainder of this section describes how

the different modules can be replicated for different departments (e.g. OT, emergency

department, hospital pharmacy, etc.) and processes (e.g. purchasing, demand planning,

resource management, etc.) as well as the extent to which the framework can impact

overall hospital performance. Finally, potential research avenues are suggested.

7.5.1 Implementation roadmap

We provide guidelines to successfully adopt SCM in different healthcare applications

using the framework as a starting point. The framework helps to incorporate the

OR/OM perspective in healthcare by transferring logistics concepts from other

industries, while accounting for the characteristics that are unique in care delivery

systems. The overall purpose is to structure the underlying problem situation using the

generic modules and support decision making by adapting the content to context-

specific needs. Figure 7-6 gives an overview of the steps that need to be undertaken to

effectively implement SCM interventions in healthcare. The implementation path

starts from a request for a logistics reengineering project in order to improve the

performance of the healthcare supply chain. The developed framework supports

decision making by understanding the current system, identifying bottlenecks and

performing a scenario analysis before implementing the most efficient solution.

Although the starting point varies depending on the problem description, following

this implementation roadmap will lead to transparent performance measurement and

finally a successfully integrated healthcare supply chain. We will describe ten steps of

the journey to understand how to apply these steps for practice-based implementations.

❶ Selecting indicators

The first step in the framework guides decision makers to derive a list of indicators for

measuring the performance of the processes included in the studied application. Based

on an extensive literature search and an additional discussion with healthcare logistics

experts, a maximum of 15 indicators are selected. The team of experts consists of

stakeholders on the tactical control level who are most experienced to interpret the

relevance of the indicators for the application in order to avoid potential conflicts or

biased attitude. For instance, logistical indicators are verified by the logistics/materials


249

manager who is responsible for tactical planning (e.g. what, where, how when and

who) for organising hospital operations. Moreover, the framework intends to create

uniform performance measurement by including traditional indicators as considered in

logistics engineering processes. Therefore, the same set of indicators is copied for a

similar process type in different departments.

❷ Constructing network with dependencies

Second, a network structure needs to be established using the ANP method by aligning

the objectives with relevant indicators and determining potential interdependencies.

The latter are defined by the healthcare logistics experts. For each process type, this

relationship is considered to be generic due to its traditional nature, and therefore it is

transferable to different hospital departments. For example, increasing the level of

safety stock will change the inventory turnover ratio, reduce the stock-out rate and

increase the capital tied up to stock.

❸ Prioritizing indicators

The next step in the ANP method aims to solve the prioritization problem according to

the logistics experts’ preferences. They determine how strong the objectives and

indicators relate to each other using Saaty’s 1-9 scale for pairwise comparisons (Saaty,

1990b). Context-specific characteristics, such as patient arrival rate, predictability or

complexity, are key drivers when setting priorities. The outcome of this step represents

the best set of KPIs that constitute the elements for defining operational excellence and

delivers a blueprint to customize the framework for reengineering the hospital logistics

processes. The ANP software ‘Super Decisions’ is used to capture and structure

stakeholder judgments into indicator rankings and examine the impact of changing

weights on the ranking.

❹ Mapping current situation

In the fourth step, Business Process Mapping tools provide a thorough understanding

of the problem situation, the people involved and the elements that have an impact on

the current system (Antonacci et al. 2018). Process flowcharts, swim lane diagrams or

value stream maps visualize the logistics flow and underlying bottlenecks are

identified in order to streamline processes and implement an efficient supply chain.

Moreover, adopting Lean management/Six sigma techniques (e.g. 5S) or Theory of

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Constraints principles enable to identify and eliminate non-value-adding activities

from a system-thinking perspective.

❺ Defining logistics scenarios

Based on the above system description, various scenarios are analysed to predict the

impact of possible future events on supply chain performance. The scenarios represent

alternative logistics policies or SCM tools, where the current (i.e. As-Is) situation acts

as a baseline scenario for comparing the potential logistics improvements. The highest-

ranked KPIs are considered to have the greatest contribution to realizing efficiency

gains. Healthcare SCM consists of a variety of traditional logistics tools for multiple

process types, such as EOQ or more sophisticated models for inventory control, Line

Balancing technique for distribution, vendor management for procurement, Systematic

Layout planning for hospital architecture, forecasting methods for demand planning,

etc.

❻ Modelling logistics processes

Next, the different scenario outcomes are modelled using simulation. DES is a popular

simulation method in the OR/OM toolbox because it enables simultaneous evaluation

of multiple KPIs to represent the multi-dimensional perspective of healthcare SCM.

Moreover, its visualization feature and user-friendly software programs (e.g. Arena,

Enterprise Dynamics, Simulink, etc.) encourage stakeholder participation. In addition,

simulation is a powerful method for scenario analysis and allows to compare different

SCM techniques (e.g. (R,Q) versus (s,S) inventory policy). The same simulation

model, however, does not apply to each healthcare setting, but requires context-

specific and structural modifications. The simulation logic can be reproduced to model

general inventory and distribution processes based on the included information about

the conceptual design, such as software used, system’s boundaries, data requirements,

assumptions, experimentation, etc. Modelling other process types is more challenging,

though the modellers can use the systematic approach for performing a simulation

study as a reference tool.

❼ Interpreting ILEP index

In step 7, the simulation outcome from the previous step and the KPI prioritization as

obtained in step 3 are integrated in an Excel worksheet. The hybrid ANP-DES tool

provides a reference platform for the decision makers to quantify trade-offs among


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objectives and indicators, compute a weighted single performance score (i.e. ILEP

index) and identify the most suitable policy for the hospital supply chain. A higher

ILEP index represents a better performing SCM tool for the studied application. Until

now, however, the framework only takes into account input from the logistics-oriented

stakeholder at the tactical level based on his background, expertise and accessibility to

logistics data in order to increase awareness of SCM in healthcare and avoid potential

conflicts due to biased stakeholder preferences.

❽ Sharing information between stakeholders

Step 8 considers a broader perspective to ensure a more robust framework by

incorporating stakeholder feedback. Stakeholder feedback plays a key role to pave the

way for potential efficiency gains in future logistics applications and generate insights

to overcome SCM implementation barriers in healthcare. The outcome presented by

the hybrid ANP-DES tool serves as input for more informed decision making in

healthcare logistics by promoting stakeholder commitment. The single decision maker

attitude is extended to create a multi-level, multi-stakeholder framework that

represents the goals of each stakeholder group (i.e. logistics or medical background)

on each organisational level (i.e. strategic, tactic, operational). Visual Management

techniques, such as dashboards, are effective communication tools to enhance the

information flow by providing feedback of the process performance to all stakeholders.

Moreover, visual management stimulates continuous improvement initiatives

(Torghabehi et al., 2016). In addition, the future trend towards digitalization suggests

technological enablers, such as cloud-based platforms, to facilitate information sharing

among all stakeholders in the supply chain.

❾ Choosing best policy tailored to healthcare setting

Next, all stakeholders verify the indicators to ensure that the suggested logistics tools

are tailored to the healthcare setting. Based on the information on dashboards, the

stakeholders repeat step 3 to express their preferences using pairwise comparisons.

Although most of the stakeholders are not familiar with SCM tools, the Visual

Management techniques improve understanding of SCM without any need for

interpreting raw data. By stimulating more informed decision making, stakeholders

will develop a positive attitude towards the framework and build trust in the findings.

However, as long as stakeholders strive towards achieving their own objectives,

conflicts will occur which can be solved using a clustering approach (Song and Hu

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2009). On the other hand, consensus could be achieved by the Delphi method, which

is a qualitative web-platform for stakeholder participation to generate collective

alignment by going through time-intensive iteration rounds (Falzarano and Pinto Zipp

2013; Freitas et al. 2018; Vieira et al. 2019). Based on the generic KPI prioritization,

the logistics policy that best suits the specific application is selected as described by

the ILEP index in step 7.

❿ Benchmarking

Finally, the framework can be used as a benchmarking tool to enable continuous

improvement in the healthcare supply chain. The visual cues allow to monitor current

performance and how it deviates from the target performance. Moreover, the uniform

performance measurements ensure a transparent comparison of operational efficiency

between hospitals and departments. By providing a common language to all

stakeholders, the overall goal is to pursue shared interests and thus converging KPI

priorities to ensure integrated processes on a hospital-wide level.

7.5.2 Implications for hospital-wide applications of the

framework

In addition, Figure 7-6 shows that the framework is an orchestrator for supply chain

integration in healthcare. By considering multiple stakeholder perspectives, the

framework provides fruitful insights to foster integration among functions and/or

departments in order to increase awareness of holistic SCM to value-based healthcare.

In this section, we describe how the framework can be used to further push

implementation of SCM on a hospital-wide level.

On the individual department level, the framework identifies improvement

opportunities based on the ILEP index to achieve operational efficiency for each

process type. Monitoring performance on the operational level provides feedback to

stakeholders on the extent to which tactical and strategic objectives could be realized.

Hence, the framework incorporates vertical integration by providing a common

language and negotiation power for stakeholders to enhance alignment between patient

care and logistics processes.


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The horizontal integration between departments and managerial functions reflects the

implications for hospital-wide applications of the framework. Hospitals contain a

variety of departments (e.g. operating theatre, emergency department, intensive care

unit, oncology, etc.) and logistics processes (e.g. purchasing, demand planning,

inventory control, etc.), working independently from each other and maximizing their

own interests. Breaking down this silo-based practice requires carefully coordinated

and integrated processes to positively impact the patient flow and operational

efficiency. The framework addresses this unique challenge in healthcare SCM by

reducing the fragmented logistics responsibilities and transferring knowledge between

departments and logistics processes. On the one hand, the framework supports

stakeholder education and more informed decision making to increase awareness of

logistics impact by adopting SCM in healthcare and promoting stakeholder

commitment. Closing the gap between awareness and willingness to commit will result

in taking actions to solve problems to the benefit of overall hospital performance. On

the other hand, we identify the need for uniform performance measurement which

enables benchmarking between departments or hospitals and learning from best

practices. It ensures a transparent comparison of operational efficiency between

hospitals and departments and thus the framework is aligned to the value-based

healthcare model.

Finally, the framework identifies the need for data transparency as a critical factor for

achieving integration in the hospital supply chain. Data capturing, data analytics and

information-sharing among stakeholders across the healthcare continuum is essential

to increase transparency and foster data-driven process improvements in healthcare

SCM. Future applications of the framework could focus on investigating consistent

information technology systems, uniform RFID technology and data standards across

hospital departments as potential enablers for holistic SCM.

7.5.3 Future directions

Further work can be undertaken to extend the framework to other departments,

managerial functions or hospitals. Stakeholder feedback plays a key role to tailor SCM

strategies to the unique healthcare context. The customized selection and prioritization

modules deliver a blueprint to apply the performance management framework in

different departments. However, validating the framework for other managerial

functions requires more effort, though we provide a systematic approach and reference

platform to reproduce each module. By increasing the sample size of this work, we


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could extract more stakeholder preference data for different departments and

managerial functions. The ANP prioritization procedure will become generic if

convergence of the KPI weights occurs, or in other words, if all stakeholders pursue a

shared goal based on a clear performance definition as obtained by the framework.

Another interesting future research initiative is to focus on improving the dynamic

character of the framework. More specifically, the ANP-DES tool is a dynamic model

because it takes into account interaction among network elements when deriving

priorities related to the context, though more work can be undertaken to automate the

network construction and weighting procedure (i.e. step 2 and 3) based on the collected

stakeholder preference data. Technological enablers for Big Data analytics, such as

Machine Learning or Internet-of-Things, could be integrated with ANP to reduce the

complexity of structuring the problem information (e.g. indicators and dependencies)

and dynamically change the KPI priorities according to the context. Furthermore, the

DES models allow to analyse the implications of changing inventory parameters (i.e.

reorder point and order up-to level) based on historical demand data. The parameter

values define different inventory policies dependent on the item classification. A

change in this classification will impact overall decision support as the inventory

parameters are highly variable. Future work could implement a robust data collection

procedure to periodically update inventory classification and use Machine Learning to

propose general inventory guidelines for each classification group.

Finally, promising research opportunities are related to analysing the impact of the data

analytics trend on healthcare SCM. The framework can be reproduced to align

information technology systems, logistics processes and stakeholders to the benefit of

supply chain integration and standardization. Having accurate data availability is

essential input information for further validating the hybrid ANP-DES tool by fine-

tuning the modelling of SCM interventions (e.g. inventory policies) from a holistic

viewpoint.

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7.6 Conclusion

The paradigm shift from volume-based to value-based care drives healthcare

organisations towards aligning logistics and medical processes. From a logistics

perspective, a data-driven approach is needed to evaluate processes, monitor

performance and gain actionable insights to manage the increasing healthcare

expenses. We propose a healthcare logistics performance management framework,

which guides hospitals in adopting SCM practices in order to improve overall hospital

performance, while covering multiple stakeholders’ perspectives as to comply with

possibly conflicting objectives. With this reference framework, we show how logistics

contributes to healthcare by adapting industrial engineering tools to unique

characteristics and by monitoring measurable KPIs to foster data-driven process

improvement.

Four modules make up the framework to ensure effective performance management

and strive towards operational excellence in healthcare. A structured ANP approach

provides guidelines for hospital management to translate strategic or tactical goals into

operational performance indicators. The generic network structure establishes inter-

relationships among indicators and takes into account multiple dimensions (i.e. quality,

cost, time, productivity) of decision making in holistic SCM. Furthermore, combining

ANP and DES allows to quantify the logistics impact by integrating relevant KPI

values into a multi-dimensional ILEP index to assess efficiency improvement

initiatives. Finally, each module is verified and validated based on stakeholder

feedback to stimulate more informed decision making, to reduce supply chain

fragmentation and to propose a common language for stakeholders on all

organisational levels.

Overall, the framework is a closed-loop approach to support decision making and to

bridge the gap between theory and practice-based SCM applications in healthcare. We

offer a proof-of-concept that industrial engineering tools are useful to identify

efficiency gains in healthcare settings by adopting SCM. Moreover, the

implementation roadmap provides guidelines for implementing SCM strategies using

the framework as a blueprint. Although each healthcare setting is different, following

the implementation path will lead to transparent performance management by

addressing the unique challenges to healthcare SCM, such as enhancing data

transparency, knowledge transfer and stakeholder commitment. The framework is not

exclusively intended for addressing materials planning problems, but the merits of the


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framework lie in the generic nature of the modules and the ability to customize the

content of the modules to the studied application.

Finally, the framework provides fruitful insights to healthcare policy makers,

managers and executives to implement the framework on a hospital wide-level by

orchestrating supply chain integration. Vertical integration can be achieved by

promoting stakeholder commitment and providing common language to support

decision making on all organisational levels, whereas data transparency is a critical

factor to ensure uniform performance measurement between departments and process

types in order to achieve horizontal integration.

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CHAPTER 8

8 Future Outlook: Digital Trends in Healthcare

Logistics8

An epilogue is added to discuss the digitalization trends in healthcare. We

identify four trends holding considerable promises for moving towards

digital, patient-centred and cost-effective healthcare. A future outlook is

presented describing the implications for supply chain optimization, where

we focus on turning the unique challenges as mentioned in Section 3.1.1 into

opportunities to achieve value improvement in healthcare.

8.1 Introduction

In this PhD dissertation, we focus on the importance of healthcare SCM and we call

for action to control the increasing healthcare expenses as one aspect of the Quadruple

Aim strategy to achieve value-based care. A healthcare logistics performance

management framework has been developed to address the unique challenges inherent

to the internal hospital supply chain. The adoption of effective SCM is typically

obstructed in healthcare organizations due to dispersed logistics responsibilities, lack

of data, conflicting stakeholder perspectives, poor understanding of OR/OM, etc.

However, the overall goal in value-based care is to perfectly align medical and SCM

processes, and therefore this chapter takes a broader perspective to reform the

healthcare system. Interoperability between processes is playing a vital role in this

reform due to the rapid evolution of digitalization in healthcare. With the increasing


Moons, K., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). Future outlook: Implications of digital trends in

healthcare logistics. Health and Technology, Submitted.

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number of patients pushed out of the hospital (i.e. outpatients) and a more important

role for patients in self-controlling their health (i.e. patient empowerment), the

digitalization trend has significant implications for coordinating primary and

supportive care delivery services across the extended healthcare continuum with

multiple service providers outside the hospital walls. Hence, moving towards a

“hospital” of the future brings additional optimization opportunities, where digital

technologies act as potential enablers to support this transformation by enhancing data-

gathering, real-time data analytics and automation.

8.2 Hospital of the future

SCM will play a crucial role in navigating hospitals on their innovation journey by

supporting future trends and technologies that enable this transformation (DHL 2017).

The hospital of the future facilitates collaboration between clinical and

logistics/facility services and provides a significant opportunity for cost reduction.

Figure 8-1 visualizes the care components which will constitute different layers in the

future hospital design, following the strategic care plan of Flemish hospitals (Belgium)

that aims for collaboration, qualitative care, efficient processes and staff or patient

satisfaction (Cardoen and De Ridder 2019). The clinical services contain patient flows

throughout a hospital platform consisting of five categories:

Primary care represents first-line actors supporting and coordinating care

services as long as possible in patients’ home environment (e.g. hospital at

home, telehealth).

Ambulatory care services distinguish between medical day care hospitals and

polyclinics. The latter are free-standing outside the hospital and provide

services without offering a patient room such as wound care, chemotherapy

or outpatient diagnostics. Due to low-technological requirements these

services can be located off-campus, in contrast to highly technology-

dependent day care hospitals for surgical procedures.

Chronic care facilities offer care in rehabilitation and residential care centres

with supervision and assistance in daily living, medical and nursing services

included.

Bridging homes or transition houses facilitate the return to normal functioning

after hospitalization including patient education, social services and active

FUTURE OUTLOOK: DIGITAL TRENDS IN HEALTHCARE LOGISTICS

261

engagement of family and surroundings in order to alleviate the bed-blocking

problem in bed houses.

Bed houses or focused clinics represent traditional hospitalization beds for

surgical, medical, paediatric or other pathologies. These tertiary centres are

often integrated with university and technology campuses, foster

specialization and aim to serve larger populations.

Creating hospital networks on a loco-regional level supports basic care provision in

the proximity of the patients. On the other hand, supra-regional collaborations provide

complex treatments in specialized tertiary centres embedded in the loco-regional

network. Moreover, the supra-regional network houses the logistics and facility

services. The technological platform involves technology-dependent services. A

distinction can be made between standard and advanced technologies, such as

operating rooms or imaging and radiotherapy or intensive care units respectively.

Finally, reengineering the logistics flows can yield significant savings. Healthcare

logistics engineering involves the activities of design, planning, implementation and

control of coordination mechanisms between supplies, equipment, services and

information from suppliers to point-of-care locations in order to enhance clinical

outcomes while controlling supply chain costs (Council of Supply Chain Management

Professionals, 2016; Di Martinelly, 2008).

Adopting effective hospital information systems is a prerequisite for reengineering the

supply chain and accurately balance patient care and logistics processes while

considering economic constraints (Di Martinelly et al., 2009; Iannone et al., 2013;

Kritchanchai et al., 2018). Digital technologies are an important enabler for

information-sharing between clinical and logistical aspects, which facilitates

integration among different supply chain actors by leveraging enhanced connectivity

and data analytics. Hospitals get a smaller role in the care continuum as they switch

from integrated providers to high-tech specialists or knowledge hubs in collaboration

with other care professionals and patients. As a consequence, the scope of future

healthcare logistics will be extended to a health system without walls, involving all

aspects of prevention, diagnosis and treatment ranging from primary to tertiary care.

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8.3 Global healthcare trends

Digital technology has become a buzzword in the tech industry providing opportunities

for an innovative future for healthcare. In addition to medical technologies, digital

innovation also focuses on patient empowerment and data analytics to ensure cost-

effective and transparent healthcare practices. Starting from exploratory research by

Flemish hospitals, the Green Paper by Cardoen and De Ridder (2019) provides a

roadmap of future trends to be incorporated in the hospital’s strategic plan, which is

also confirmed in the global healthcare outlook by Deloitte (2020). Based on these

papers, we distinguish between four trends impacting the global healthcare

community: organisation, Big Data analytics, technological enablers and hospital

architecture.

Organisation: Hospital networks or Integrated Delivery Networks (IDN) are

proposed between hospitals and multiple care providers with centralization of

logistics and facility services, and therefore providing opportunities for

efficiency and quality gains in all links of the care chain. The Green Paper

distinguishes between loco-regional and supra-regional networks, with the

former providing basic care within reasonable distance from patients with

optimal investment of equipment and the latter providing complex care in

high-tech, specialized reference hospitals (Cardoen and De Ridder 2019). In

addition, home care will be provided more frequently, which sets new

challenges to functionality, efficiency and competences of logistics processes

(KCE 2015). Finally, the human component is a unique feature in the field of

healthcare SCM. The shift towards patient empowerment significantly

influences the power relationship between healthcare professionals and

patients, where the latter take responsibility for managing their own health

decisions to ensure efficient, effective and transparent care services through

partnership rather than paternalistic (i.e. the doctor knows what is good for

me) behaviour.

Big Data analytics: The greater prevalence of outpatient care challenges the

health system to support data-gathering in order to improve patient, material

and information flows and accordingly enhance transparency and traceability

in the healthcare supply chain. The increasingly popular term “Big Data”

describes the technology of managing and analysing large amounts of data in

order to provide insights into individual patient data and public health data as

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well as process and logistics data, where traditional data processing

applications will fail (Bates et al. 2018). Integrated software systems (e.g.

Enterprise Resource Planning) and Electronic Health Records (EHR) enable

information-sharing among different actors and stimulate the use of common

logistics vocabulary. Data aggregation is core and allows for proactive care,

prevention, personalized care and real-time monitoring of quality between

home care, chronic care, hospitals and authorities. In the world of Big Data,

however, security is an important pitfall for digital hospitals of the future. The

large volume of data and interconnectivity raises concerns on privacy,

ownership and cybercrime. Therefore, the European Union adopted a General

Data Protection Regulation (GDPR) to avoid data misuse (Armstrong and

Bywater 2018). GDPR controls the relation between hospital supply chain

processes and privacy concerns.

Technological enablers: Technology will underlie most future trends by

building a sustainable infrastructure for affordable, accessible, high-quality

and patient-centred care. The main challenges are related to solving

interoperability issues and standardizing processes. Technological enablers

such as Artificial Intelligence (AI) and Machine Learning (ML) facilitate

dealing with Big Data, customizing healthcare services and integrating

processes. Furthermore, Internet-of-Things (IoT) and virtual health or

telehealth enhance accessibility of care by providing a continuous connected

infrastructure of medical devices, software applications and health services.

It enhances data aggregation, supports cloud-based computing, and enables

remote clinical monitoring for outpatient services. During the COVID-19

pandemic, telehealth is becoming the new norm to provide long-distance care

by remotely access patients’ medical records and collaborate with other

providers. Although Big Data and technological innovations are the main

drivers of digitalization in healthcare, it is still early days for healthcare

stakeholders to adopt applications due to technical barriers, high costs and

privacy concerns. Table 8-1 gives an overview of technological enablers and

potential applications to shape the future of hospitals.


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Table 8-1. Technological enablers and applications driving the road towards digital

healthcare.

Enabler Application Functionality

AI/ML Logistics robots, Autonomous Guided Vehicles (AGVs)

Food, linen or medication distribution Patient transportation

Chatbots Communication with patients

Risk prediction approaches Risk assessment of (new) equipment and devices

Decision-support systems Operating room programming

IoT/virtual health/ telehealth

Mobile applications (health apps), wearables Teleconference appointment

Remote monitoring of personalized healthcare Live video conferencing for

virtual appointments between patient and doctor

Block chain EHR Accessibility to decentral medical records

Additive manufacturing 3D/4D printing of medical devices

Highly responsive care provision close to patient bed

Hospital architecture: The transition from institution-centric to patient-

centric care asks for a patient-oriented building, whereby spatial qualities

allow for creating a healing environment for patients. The increasing

popularity of remote clinical monitoring holds promising opportunities to

improve patient centeredness because the patient is actively involved in

determining care pathways through shared decision making. This active

participation can also be reflected in the hospital design by introducing

customized patient rooms, smart ergonomic premises, modular lighting and

noise management in order to improve care and well-being of patients as well

as staff. In addition, the hospital design strives towards prevention and early

intervention to move from a ‘sick’ to a ‘health’ care environment. Safety and

environmental friendliness are also considered important when activating the

hospital building.

8.4 Implications for healthcare SCM

A deeper understanding of SCM practices is required to achieve operational excellence

by integrating all tiers of the health system while ensuring qualitative patient care

(Feibert 2017; Melo 2012). Moreover, the transformation to a digitalized healthcare

sector asks for aligning a digital strategy with the organisation’s strategy, people and

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processes. Digitalization and technological advancements of hospital Information

Technology (IT) systems will improve the information flow, enhance connectivity and

integrate services among healthcare partners (e.g. hospitals, patients, blood transfusion

centre, lab, etc.) (Ageron et al. 2018).

The healthcare sector, however, is facing several operational challenges, such as

fragmentation, many stakeholders involved, lack of expertise, growing demand

variability, etc. (see Figure 3-2). In addition, lack of management commitment and

data standards as well as inefficient processes are major obstacles to the

implementation of effective SCM for many hospitals. Further research needs to

address how these challenges can be turned into opportunities, related to the four global

trends – organisation, Big Data, technology and architecture. Although this dissertation

addresses part of the research gaps identified in Table 3-3 by developing OR/OM

techniques to support decision making for logistics operations and process

improvement, much room is left for future research avenues to investigate how the

digitalization trends impact the logistics flow by addressing its unique challenges.

8.4.1 Fragmentation

The logistics responsibility is rather fragmented among different healthcare partners

and policy makers, resulting in ill-coordinated supply chain processes in the ever-

expanding care continuum. Landry and Beaulieu (2013) and Feibert (2017) consider

supply chain integration as an important strategic weapon to improve the coordination

of material and information flows in hospitals. By taking a holistic perspective on

SCM, further research is needed to adopt operations research techniques, focusing on

integrated resource and planning management at different levels of the decision-

making hierarchy (Ageron et al., 2018; Hans et al., 2012; Rakovska & Stratieva, 2018;

Rohleder et al., 2013). Furthermore, Volland et al. (2017) state performance

management in healthcare logistics as a promising research opportunity. In contrast to

the fragmented healthcare supply chain, effective performance management requires

an integrated approach to control the supply chain strategy, implement continuous

improvement programs and improve decision-making capabilities by focusing on Key

Performance Indicators (KPIs) (Maestrini et al., 2017). In this dissertation, we

developed a healthcare logistics performance management framework to evaluate

SCM strategies in order to achieve operational excellence and strive towards supply

chain integration. Rakovska and Stratieva (2018) also stress the importance of internal

and external supply chain integration for improving hospital performance. Recent


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innovations include the transformation from static supply chains to dynamic Digital

Supply Networks (DSN) and virtual supply chain centralization, which enhance

connectivity and responsivity, contain costs, improve supply chain relations and

efficiently use resources (Deloitte, 2018; Feibert, 2017; Vila-Parrish & Simmons,

2013).

8.4.2 Stakeholders

A second challenge relates to the impact of different stakeholders (e.g. providers,

payers, consumers, government) on the healthcare supply chain, as all stakeholders

have individual takes on performance management with for example conflicting goals

for efficiency and responsiveness (De Vries and Huijsman 2011). Misalignment of

incentives across the supply chain is a major obstacle leading to poor hospital

performance (Callender and Grasman 2010; Feibert 2017).

Second, it is challenging to engage different healthcare stakeholders to cooperate and

embrace changes, as they have individual takes on performance management with

possibly conflicting goals to achieve operational excellence(De Vries & Huijsman,

2011). Misalignment of incentives across the supply chain is a major obstacle leading

to poor hospital performance due to silo-based practices (Callender & Grasman, 2010;

Feibert, 2017). The healthcare logistics performance management framework as

developed in this dissertation investigates the multi-dimensional character of the

healthcare supply chain by quantifying the trade-off between four goals – quality, cost,

time and productivity. The Analytic Network Process is applied as a popular Multi-

Criteria Decision Making (MCDM) tool to prioritize KPIs, representing the extent to

which different aspects contribute to hospital performance according to multiple

stakeholders. Moreover, Hans et al. (2012) present a collaborative planning framework

offering a common language for stakeholders on all organizational levels to promote

supply chain integration and implement cost-effective, standardized processes.

Information-sharing is especially important in the expanding care continuum with an

increasing number of outpatients. The digitalization trends are promising to achieve

supply chain integration, though active participation, collaboration and investment by

all healthcare stakeholders is required in shaping this future (Deloitte, 2019). The

human component is often underexplored, though resistance to change is one of the

biggest obstacles to implementing SCM in healthcare. Creating inter-organizational

training environments and expanding skill sets, as well as recruiting, developing and

retaining sustainable and flexible workforce is crucial to build strong relationships and

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respond efficiently to care needs (Ageron et al., 2018; Min, 2017). The HELP project,

as discussed in Section 1.1.3, is an excellent initiative to introduce a new profession of

healthcare logisticians to ensure logistics flows and patient care are consolidated.

8.4.3 Unpredictability

Unpredictability significantly adds to the complexity in the healthcare supply chain

due to variations in both demand and supply processes (Zhong et al., 2017). Data-

driven inventory management can improve hospital performance by updating

forecasting tools, increasing transparency and identifying performance benchmarks

(Alotaibi & Mehmood, 2018).

In the digital hospital era, data plays a key role to enable real-time monitoring of

operations to improve demand accuracy, reduce unnecessary variation and identify

substantial efficiency gains. Massive amounts of data will be generated and new ways

of managing this data, such as AI or ML, are indispensable to respond to the growing

data needs by providing a holistic view of the logistics flow as well as clinical

processes (Kwon et al., 2016). Hence, Big Data predictive analytics is crucial to

support more informed decision-making, streamline logistics processes and it has a

prominent role in improving hospital performance (Wang et al., 2016). The greatest

challenge faced in the healthcare sector today is the problematic nature of missing,

isolated and non-standardized data across different stages in the supply chain. In future

work, hospital networks, supply chain integration, and AI or other smart world systems

are essential to optimize supply chains by implementing consistent hospital IT

systems, standardizing processes and guaranteeing data privacy (Alotaibi &

Mehmood, 2018; Davenport & Kalakota, 2019; Tawalbeh et al., 2016).

8.4.4 Expertise in OR/OM

The impact of logistics in hospitals has been neglected for many years due to poor

understanding of the fundamentals of healthcare SCM and limited expertise in the field

of operations research (Dobrzykowski et al., 2014). The healthcare supply chain is a

complex system requiring a multi-disciplinary approach, combining theories and

transferring knowledge from different research areas (Kwon et al., 2016; Min, 2017).

Therefore, adopting best practices from manufacturing or maintenance operations

provides valuable opportunities for future research as they successfully benefited from

process optimization, performance benchmarking, etc. (Zhong et al., 2017). The


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current body of healthcare SCM literature is rich in describing analytical and

conceptual methodologies focusing on isolated planning/scheduling of operating

theatres, emergency departments or patient appointments. In contrast, healthcare SCM

studies rarely address the holistic impact of inventory control, IT or purchasing (Min,

2017). Moreover, many industrial sectors are exploring the potential of Big Data

analytics (Alotaibi & Mehmood, 2018). Varela and Tjahjono (2014) state that “Major

business players who embrace Big Data as a new paradigm are seemingly offered

endless promises of business transformation and operational efficiency

improvements”. In future work, healthcare SCM studies will benefit greatly from the

massive amounts of data as well as the ability to account for interrelationship between

supply chain operations in order to achieve global optimization (Ranjan, 2014; Wang

et al., 2016). Especially with the increasing popularity of digitalization, healthcare

organizations need to strengthen their supply chain capabilities to reap the benefits of

technological advancements in order to enhance both efficiency and responsivity.

8.4.5 Standardization

Lack of uniformity, missing global identifiers for products/locations and outdated

physician supply preferences have been reported as major challenges to integrate and

streamline healthcare supply chain processes (Dooner, 2014; Feibert, 2017). The great

amount and variety of items held in hospitals are a significant driver of supply chain

costs and complicate activities related to purchasing, inventory and distribution (Melo,

2012). Initiatives for product standardization are a first step towards process

improvement in order to increase coordination and eliminate non-value adding

activities (Min, 2017). In addition, data standardization is a crucial value imperative to

the future trend towards digital hospitals (Kwon et al., 2016; Min, 2017). Digital

technologies support supply chain integration by sharing data, automating processes

and hence improving efficiency. EHR constitute the main source of data in the health

system, where the greatest challenge is creating supply chain data standards to ensure

effective communication and information exchange among various healthcare

stakeholders (Alotaibi & Mehmood, 2018). Moreover, implementing Global

Traceability Standards (e.g. GS1) for healthcare provides consistent clinical and

logistical information (e.g. Global Location Numbers and Global Trade Identification

Numbers), and thus yields important benefits for end-to-end visibility and automated

inventory control (GS1, 2018). Future work should address challenges related to RFID

and barcoding technology in order to improve the quality of barcodes, standardize the

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layout and encourage manufacturers upstream in the supply chain to adopt global

identifiers in the tags.

8.4.6 Automation

Technological interventions are slowly adopted in healthcare logistics processes due

to lack of automation, though technology will have a significant impact on shaping

future hospitals. Automating hospital IT systems, together with data standards,

represent a significant opportunity for accurately measuring healthcare performance

(Böhme et al., 2016; Di Martinelly, 2008; Feibert, 2017; Volland et al., 2017). IT plays

a key role in monitoring daily operations as it enhances accessibility to real-time data

and hence there is much potential for applying operations research techniques and

optimizing SCM (Denton, 2013; Kritchanchai et al., 2018).

Current best practices in healthcare logistics employ barcoding technology since the

benefits of RFID technology in terms of efficiency, visibility, patient security, etc. do

not outweigh the high investment cost (Böhme et al., 2016; Feibert, 2017). In the

future, hospitals require automated data capture using barcodes, RFID or QR

technology to ensure end-to-end supply chain visibility from ordering to bedside

administration. Another future trend examines the role of IoT by configuring the IT

infrastructure and communication architecture as potential enablers for tracing, storing

and analysing data as well as facilitating interoperability among healthcare supply

chain partners (Min, 2017). Moreover, innovations in telehealth, wearable devices and

cloud-based computing platforms have the potential of becoming a key differentiator

for monitoring processes, since healthcare will move away from the hospital to the

home environment and thus necessitates automated and integrated information-sharing

among healthcare partners (Claes et al., 2019; Dinh-Le et al., 2019).

Finally, other technological advancements in future hospitals include automated

transport (e.g. pneumatic tubes, AGVs), automated storage and retrieval systems for

goods, robots for unit dose production and picking, etc. (Feibert, 2017). However, no

single policy exists for implementing all technological interventions. Further work

should investigate which technologies are appropriate under certain circumstances.


271

8.4.7 Other challenges

Another hot topic in healthcare SCM studies is the hospital architecture. The building

aims to embrace the vision of value-based healthcare and to adopt a patient-oriented

approach by creating a healing “health” care environment to encourage active

participation and a strong patient-doctor relationship. Moreover, the services provided

by traditional hospital institutions are pushed towards the community and home

environment which necessitates a change from the conventional single-echelon

institution to a hospital network design model. Integrated Delivery Network (IDN) has

received less attention, though it incorporates several value imperatives into the

healthcare supply chain, such as accessibility to healthcare providers or efficient

distribution of supplies (Landry & Beaulieu, 2013; Min, 2017). The network tends to

reflect a hub-and-spoke model, bridging the gap between inpatient and outpatient care

in respectively a specialized, high-tech hospital and local clinic or at home (see Figure

8-1). Moreover, centralizing logistics services, such as sterile services department,

pharmacy or laboratories, stimulates supply chain integration and standardization in

terms of purchasing, inventory and distribution, while operational costs are reduced

(Abdulsalam et al., 2015). Further work should address outsourcing of hospital supply

chain activities within one network to third parties as a strategic tool (Ageron et al.,

2018; Guimarâes & de Carvalho, 2011).

A relatively new concept is the adoption of block chain technology in healthcare,

which eliminates the need for a centralized third party in distribution networks (Agbo

et al., 2019). With this decentralized architecture, block chain technology allows all

stakeholders to have access to EHR by connecting disparate data silos. Moreover it

improves data security by developing a GDPR-compliant hospital IT system, provides

a transparent supply chain and guarantees patients’ health data ownership. Although a

number of use cases are presented in a healthcare environment (e.g. EHR and

pharmaceutical supply chain management), addressing the challenges of scalability,

interoperability, standardization and security present a rich field for future research.

Furthermore, supplier relationship management greatly contributes to a strategic

benefit by proactively engaging healthcare providers with Group Purchasing

Organizations (GPOs) (Kwon et al., 2016). GPOs help to secure supplies and reduce

costs by leveraging economies of scale and enhancing price transparency through

framework contracts (Burns & Lee, 2008; Jayaraman et al., 2014). Purchasing could

greatly benefit from operations research techniques by developing a systematic

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272

decision-support tool to select appropriate GPOs in order to enhance relationship

management (Min, 2017).

Finally, further work can be undertaken to create a risk management framework for

value-based healthcare. The ASHRM (i.e. American Society for Healthcare Risk

Management) determines basic building blocks constituting the framework, such as

managing uncertainty, maximizing value creation, utilizing data to prioritize risks, and

ensuring safe and trusted care services, when making risk management decisions

(Carroll, 2014). SCM decisions are identified as operational risks resulting from lack

of coordination in internal logistics and clinical processes. Etges et al. (2018) also list

SCM as one of the largest operational concerns in healthcare, presenting a risk to

overall hospital performance.

8.5 Conclusion

Over the next ten years, the hospitals of today will transform into an almost

unrecognizable hospital of the future. This shift is mainly driven by rapidly evolving

technologies, opening up the way towards digitalization in healthcare. Although digital

technologies enhance patient or staff experience, improve care delivery and create

operational efficiencies, hospitals need to align their processes, technology and people

to achieve affordable, accessible, high-quality care (Deloitte 2017).

The supply chain acts as the backbone in navigating the innovation journey towards

the future digital trends in healthcare. This work provides insights into the major

research avenues for future hospitals and aims to highlight how trends of remote

monitoring and patient-centric care affect the logistics processes. In particular,

learning from multi-disciplinary research fields will encourage the future development

of healthcare OR/OM techniques within areas such as supply chain integration,

automation, data analytics, organisation and architecture.

Big Data pave the way to digital hospitals and efficiency gains are realized by

integrating all tiers of the healthcare supply chain. Hospitals will have a smaller role

in the care continuum, representing specialized knowledge hubs in collaboration with

other providers and patients supported by centralized logistics and technological

platforms. Enhanced connectivity and data analytics smoothen information exchange

among healthcare stakeholders, and in turn result in highly responsive supply chains.

Automation through technological enablers and innovations in telehealth, wearable

devices, IoT and AI all have the potential of becoming the engine for monitoring


273

processes and accurately measuring healthcare supply chain performance. Finally,

attention must be given to patients and healthcare professionals’ well-being and human

resource management when shaping the future.

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CHAPTER 9

9 General Conclusion

In an era where “time is money”, operational excellence becomes a prime objective

for various sectors, such as manufacturing, construction or energy. These sectors have

lead the way to cost-efficient operations by adopting effective and integrated Supply

Chain Management (SCM). Recently, the healthcare sector started to recognize the

importance of SCM in the paradigm shift from volume-based to value-based care in

order to improve operational performance while sustaining high-quality patient care.

Four dimensions play a critical role in reforming the healthcare system. According to

this Quadruple Aim strategy hospitals strive towards better outcomes, improved

patient experience, lower costs and enhanced staff satisfaction. In this dissertation, we

mainly focus on the cost and personnel aspect to streamline the internal hospital supply

chain processes.

Hospitals typically carry large amounts of a great variety of items. The logistics

processes that involve storing and distributing these items to point-of-care locations in

the hospital supply chain are of great importance to support patient care services.

However, the medical material costs constitute the second largest hospital expenditure,

after personnel costs. Due to the increasing cost pressures, hospitals definitely need to

do more with less. Aligning clinical, material and information flows is essential for

achieving operational excellence and supply chain integration. From a logistics

perspective, there is a need to understand how the internal supply chain processes are

currently performing. Measuring the performance of the supply chain is fundamental

to identify data-driven process improvements which provide valuable input

information for managerial decision making to the benefit of value-based healthcare.

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In this dissertation, we develop a methodologically rigorous healthcare logistics

performance management framework as a guide for adopting SCM practices in order

to achieve operational excellence. Given the complexity of a healthcare setting, a

robust decision-support tool is required that provides a systematic approach to evaluate

processes, monitor performance and gain actionable insights to effective materials

management. The Operations Research and Operations Management (OR/OM) field

plays a key role in bringing objectivity in decision making, supporting data-driven

process improvement and thus quantifying how logistics contributes to healthcare.

Although OR/OM modelling techniques have been successfully used in industrial

engineering applications to improve performance, adapting these tools to the

healthcare sector is not a simple copy-paste due to the unique challenges inherent to

the internal hospital supply chain. The framework aims to bridge the gap between

theory and practice-based SCM applications by incorporating multiple stakeholders’

perspectives as to comply with possibly conflicting goals when defining operational

excellence. Other barriers to effective healthcare SCM involve lack of standardization

and Information Technology (IT) systems for orchestrating supply chain integration.

Four modules make up the healthcare logistics performance management framework

to support decision making and strive towards operational excellence. We demonstrate

a proof-of-concept to apply industrial engineering techniques, such as SCM concepts,

performance management, Multi-Criteria Decision Making (MCDM) methods and

simulation modelling, for targeting efficiency improvement opportunities in the

internal hospital supply chain. Moreover, the cyclical feature in the framework embeds

feedback loops iterating between the modules in order to ensure SCM is tailored to the

unique healthcare setting. Altogether, the framework uses a closed-loop approach that

enables continuous improvement and allows for customization to specific healthcare

logistics needs.

We initiate the framework development procedure by formulating an answer to the

first research question. In an extensive literature review, the state-of-the-art of

hospital logistics performance management has been described. A comprehensive

methodology is missing for selecting relevant Key Performance Indicators (KPIs) to

measure the performance of the internal hospital supply chain. Chapter 3 addresses

this gap. Several metrics are of interest to different stakeholders, and therefore the KPIs

are assigned to four objectives – quality, time, financial and productivity/organisation

– representing the multi-dimensional character of the internal hospital supply chain.

The framework needs to support decision making by clearly defining and linking the

GENERAL CONCLUSION

277

performance criteria to evaluate efficiency improvements, while taking into account

the organisation’s strategy. MCDM is proposed as a useful OR/OM technique for

determining relationships and prioritizing between performance indicators, since

healthcare SCM decisions involve trade-offs between sometimes conflicting

objectives.

The second research question addresses this prioritization problem using the Analytic

Network Process (ANP). ANP is a popular MCDM technique known as a valuable

analytical tool for supporting decision making related to performance management.

Relative weights are assigned to the indicators by eliciting stakeholder preferences and

SCM interventions are evaluated by trading-off between the KPIs. In Chapter 4, this

prioritization problem is transformed into an ANP-based prototype to provide

guidelines for hospital management to translate strategic or tactical objectives into

KPIs, that make up the definition for operational excellence in healthcare logistics and

thus focus attention on what matters most to healthcare performance. The prototype

only takes into account the expertise of the logistics manager to improve understanding

of the logistics impact to value-based care.

The third module in the framework development procedure aims to provide empirical

evidence for choosing the appropriate logistics policy and determining values for the

parameters by monitoring measurable KPIs and identifying data-driven process

improvements. By combining the ANP-based prototype and Discrete-Event

Simulation (DES) modelling, we provide a theoretically sound basis for the reference

framework to understand the behaviour of the supply chain, visualize improvement

strategies and perform a scenario analysis. The Internal Logistics Efficiency

Performance (ILEP) index is introduced to evaluate logistics policies by quantifying

trade-offs among KPIs. Case studies are chosen as research design to experiment and

gain insights into real-life problems by bridging the gap between theory and practice.

Chapter 5 provides an overview of several case studies focusing on inventory control

and distribution systems in an operating theatre environment in order to answer the

third research question:

From an inventory perspective, the hybrid ANP-DES tool drives policy

decision making by quantifying the main trade-off between service levels and

costs. Classifying inventory items is an essential prerequisite to policy

decision making by distinguishing between inventory hot spots and lower

priority items. No single policy applies to all items, and therefore service

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levels are determined for different item categories acting as a constraint when

identifying the appropriate inventory policy. For critical items, the risk-taking

inventory policy meets very high service levels (i.e. 99,7%) with a cost saving

of up to 37%. The same policy is penalized for non-critical items due to a

10% decrease in service level though similar cost savings could be realized

and the service level requirement is still satisfied. As a consequence, a risk-

averse policy is preferred for controlling non-critical items, which seems

counterintuitive as service level becomes less important when compared to

critical items. Further work can be undertaken to reassign priorities for service

level in the ANP-based prototype depending on the inventory classification.

This in turn will impact the ILEP index and therefore may change the

appropriate inventory policy. Furthermore, more data is needed to

periodically update inventory classification and propose general inventory

parameters for each classification group.

Second, the hybrid tool is illustrated to evaluate internal distribution systems

by determining optimal replenishment policies and surgical case cart flows.

The ILEP index shows a 24% improvement for the Standard replenishment

policy compared to the As-Is situation by eliminating double stock on

replenishment carts and collecting consumption data on the operating room

level. Both the Standard and Copy carts replenishment policies demonstrate

better performance in terms of quality, time and productivity/organisation

compared to As-Is, where the former focuses on a better service and the latter

improves staff productivity. Barcode scanning technology is introduced in

both policies for inventory optimization purposes. In addition, surgical case

carts need to be in place at the right time. Adopting standardization and

centralization initiatives improve distribution performance by increasing

uniformity and balancing workload throughout the supply chain.

Aligning inventory management and distribution systems is essential to

achieve operational excellence by defining parameter values for stock levels,

identifying where to stock and when to replenish, reducing the reverse

logistics flow, increasing uniformity as well as realizing time and cost

savings. The framework guides hospital management to identify areas of low-

hanging fruit to increase efficiency by addressing unique SCM challenges,

such as standardization and stakeholder commitment. Standardization

GENERAL CONCLUSION

279

initiatives are a first step towards supply chain integration by updating

physician preference card management and increasing cost awareness among

hospital stakeholders. The findings show that standardization yields a cost

saving of 30% by rationalizing the surgical product portfolio and educating

surgeons on disposable supply costs. Altogether, a total cost saving of 35%

could be realized by combining the inventory policies and standardization

efforts to streamline the logistics flow to the benefit of value-based

healthcare. The case study findings offer a proof-of-concept that industrial

engineering tools are useful to identify efficiency gains in a healthcare setting

by adopting SCM strategies. Moreover, we prove the generalizability of the

framework by addressing multiple logistics needs and identifying

implementation paths which enhances trust building in the framework. The

framework is not exclusively intended for addressing materials management

problems, but future research may use the reference framework for evaluating

other managerial functions, such as procurement strategies or information

technology systems.

By going through modules 1 to 3, hospital stakeholders gain deeper insights in how

logistics contribute to value-based healthcare by quantifying the impact of SCM

interventions. The final module presents an answer to the fourth research question

by considering a broader perspective to ensure that SCM is tailored to fit the unique

healthcare challenges. The dispersed logistics responsibility among several hospital

departments asks for a multi-level, multi-stakeholder framework validation. Each of

the three modules is verified by incorporating multiple stakeholders’ perspectives.

Stakeholder feedback plays a key role to pave the way for potential efficiency gains in

future logistics applications and generate insights to overcome SCM implementation

barriers in healthcare. Starting from the logistics perspective, the case study findings

allow to increase understanding of SCM, which enhances awareness of logistics and

in turn stimulates stakeholder commitment. Hence, the multi-level, multi-stakeholder

healthcare logistics performance management framework promotes more informed

decision making in order to reduce supply chain fragmentation. In addition, we identify

the need for uniform performance measurement throughout the supply chain which

enables benchmarking and continuous improvement. In this way, the framework

supports knowledge transfer through multi-disciplinary teams and proposes a common

vocabulary for stakeholders on all organisational levels to align patient care and

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logistics processes by having streamlined standards that define operational excellence

and contribute to overall hospital performance.

By providing the answers to the research questions, we gain fruitful insights in

structuring a healthcare logistics performance management framework. Our

framework guides hospital management to identify data requirements for OR/OM

modelling and to monitor the most relevant KPIs for the studied application, and

thereby reducing uncertainty in decision making. Data transparency is a critical factor

that needs to be taken into account in future research avenues in order to have

streamlined data standards, ensure uniform performance measurement and finally

achieve integration in the hospital supply chain. Moreover, data capturing, data

analytics and information-sharing among stakeholders across the healthcare

continuum asks for implementing effective IT systems as potential enablers for holistic

SCM. Chapter 8 presents a future outlook describing the implications of the

digitalization trend in healthcare logistics. Big Data analytics and technological

advancements (e.g. Artificial Intelligence, Internet-of-Things, etc.) have the potential

of becoming the engine for evaluating processes and monitoring healthcare supply

chain performance.

Furthermore, the framework is robust by integrating group decision making. It

stimulates more informed decision making by engaging stakeholders and increasing

knowledge transfer in order to adopt SCM and strive towards operational excellence.

The methodology promotes continuous improvement programs that contribute to the

global interest of the hospital. Finally, the approach is transparent, systematic, flexible

and easy-to-use, and therefore leaves room for applicability in other hospital

departments and on a hospital-wide level by orchestrating supply chain integration. A

roadmap to implement the framework in different contexts is provided in Chapter 7.

Altogether, the merits of the framework lie in the generic nature of the modules as well

as the ability to customize the content of the modules to specific healthcare logistics

applications.

283

APPENDIX

Appendix A – Supermatrix formation following ANP approach

Cluster weights

Quality Cost Productivity

Quality 0,674 0,648 0,558

Cost 0,101 0,230 0,122

Productivity 0,226 0,122 0,320

Weighted supermatrix


Objectives (O) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Inventory

service level

(ISL)

0.218 0.000 0.505 0.539 0.460 0.405 0.350 0.239 0.279 0.331

Inventory

visibility (IV)

0.056 0.066 0.000 0.135 0.088 0.088 0.106 0.239 0.169 0.139

Inventory

accuracy (IA)

0.056 0.146 0.168 0.000 0.201 0.155 0.193 0.080 0.070 0.088

Inventory

criticality (ICr)

0.385 0.537 0.000 0.000 0.000 0.000 0.000 0.000 0.119 0.000

Inventory cost

(ICo)

0.140 0.000 0.020 0.050 0.000 0.000 0.230 0.098 0.000 0.091

Value of

inventory (VoI)

0.047 0.000 0.081 0.050 0.000 0.230 0.000 0.024 0.000 0.030

Inventory

turnover (ITu)

0.016 0.251 0.148 0.169 0.000 0.079 0.066 0.000 0.061 0.040

Inventory

usage (IU)

0.065 0.000 0.056 0.000 0.188 0.028 0.036 0.160 0.000 0.280

Product

standardization

(PS)

0.016 0.000 0.022 0.056 0.063 0.015 0.020 0.160 0.303 0.000

APPENDIX

284

Limit supermatrix


Objectives (O) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Inventory

service level

(ISL) 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.289

Inventory

visibility (IV) 0.106 0.106 0.106 0.106 0.106 0.106 0.106 0.106 0.106 0.106

Inventory

accuracy (IA) 0.123 0.123 0.123 0.123 0.123 0.123 0.123 0.123 0.123 0.123

Inventory

criticality (ICr) 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164

Inventory cost

(ICo) 0.032 0.032 0.032 0.032 0.032 0.032 0.032 0.032 0.032 0.032

Value of

inventory (VoI) 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027

Inventory

turnover (ITu) 0.120 0.120 0.120 0.120 0.120 0.120 0.120 0.120 0.120 0.120

Inventory

usage (IU) 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075

Product

standardization

(PS) 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063

APPENDIX

285

Appendix B – Simulation model

An overview of the demand generation in decentral stock is shown in part 1 and 2,

representing the daily demand generation and how demand is fulfilled from decentral

stock respectively.

In the figure below, the model logic of the decentral inventory replenishment is

displayed.

APPENDIX

286

Appendix C – KPI formulas for inventory problem

KPI Expression

Quality 𝑆𝑒𝑟𝑣𝑖𝑐𝑒 𝑙𝑒𝑣𝑒𝑙 =

𝑎𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 − 𝑔𝑙𝑜𝑏𝑎𝑙 𝑠𝑡𝑜𝑐𝑘𝑜𝑢𝑡𝑠

𝑎𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑

Financial 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑐𝑜𝑠𝑡 = 𝑖𝑡𝑒𝑚 𝑐𝑜𝑠𝑡 + ℎ𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡 + 𝑜𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝑐𝑜𝑠𝑡

𝐼𝑡𝑒𝑚 𝑐𝑜𝑠𝑡 = 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 ∗ 𝑎𝑛𝑛𝑢𝑎𝑙 𝑜𝑟𝑑𝑒𝑟 𝑞𝑢𝑎𝑛𝑡𝑖𝑡𝑦

𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡 = ∑(𝐼�̅� ∗ 0.20 ∗ 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡) + 𝐼𝑐𝑒𝑛𝑡𝑟𝑎𝑙̅̅ ̅̅ ̅̅ ̅̅ ̅ ∗ 0.20 ∗ 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡

𝐺

𝑖=𝐴

𝑂𝑟𝑑𝑒𝑟 𝑐𝑜𝑠𝑡 = 𝑂𝐶𝑐𝑒𝑛𝑡𝑟𝑎𝑙 + 𝑂𝐶𝑑𝑒𝑐𝑒𝑛𝑡𝑟𝑎𝑙

= 5.92€ + 𝑤𝑎𝑔𝑒 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡

𝑜𝑟𝑑𝑒𝑟𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦

*The central order cost of 5.92€ is determined in a study based on time-driven

activity-based costing and includes purchasing/ordering, internal delivery and

invoicing. The decentral order cost is based on the salary of the logistic

employees who replenish the stock. In general, the replenishment cost is

associated with the time and effort of these replenishment activities, and is

assumed to be independent of the number of items ordered (Rosales et al. 2015).

Productivity 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑡𝑢𝑟𝑛𝑜𝑣𝑒𝑟 =

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦

𝑎𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑

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287

Appendix D – KPI formulas for replenishment problem

Quality Quality specifies how well a specific activity has been performed, ensuring that patients

receive care service in a safe manner and that problems such as medical errors are

minimized

Distribution

service level (DSL)

The availability of logistics services to support clinical care

processes

Urgent delivery rate Daily stock-out rate =

∑ (𝑆𝑡𝑜𝑐𝑘𝑜𝑢𝑡𝑖𝑖=454𝑖=1 )

730 𝑑𝑎𝑦𝑠

Additional items

needed

Average replenishment per item =

𝐴𝑣𝑔𝑀𝑎𝑥𝑆𝑡𝑜𝑐𝑘 −𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑎𝑖𝑙𝑦 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑚𝑒𝑛𝑡 𝑖𝑡𝑒𝑚𝑠 𝑝𝑒𝑟 𝑑𝑎𝑦

Delivery accuracy

(DA)

The ability to pick and deliver the correct items and quantities

from storage to point-of-use location

Perfect order

fulfilment

Daily number of incomplete refills =

∑ (𝑀𝑎𝑥𝑆𝑡𝑜𝑐𝑘𝑖 − 𝐼𝑡𝑒𝑚𝑠𝐼𝑛𝑆𝑡𝑜𝑐𝑘𝑖 )𝑖=454𝑖=1

730 𝑑𝑎𝑦𝑠

Centralization

impact (CI)

The ability to locate items only at a central storage room, or also

at decentral storages.

Permanent double

stock

Number of replenishment carts,

containing duplicate of items in

decentral storages

Impact of

centralization

Adjust max stock in decentral locations

Time Time involves the time to complete the logistics operations to ensure that the right items

are at the right place and time

Replenishment

lead time (RLT)

The total amount of time that elapses from the moment an item is

ordered until the item is back on the shelf

Transport time Time to move items to the right place

Replenishing time Time to replenish decentral stock

Scanning time Time to scan items in decentral stock

Preparing time Time to pick requested items from

central stock

Other activities time Time spent on other activities than

replenishing due to interruptions

Replenishment lead

time

= 𝑇𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 + 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑖𝑛𝑔 + 𝑠𝑐𝑎𝑛𝑛𝑖𝑛𝑔 + 𝑝𝑟𝑒𝑝𝑎𝑟𝑖𝑛𝑔 +𝑜𝑡ℎ𝑒𝑟 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠

Response time

(RT)

The ability to deliver items on time, preventing delays in

surgical procedures

On-time delivery Average delivery time = finish time of

replenishing decentral stock

Clinical staff

involvement (CSI)

The amount of time clinical staff is busy with logistics tasks,

rather than their core activities

Logistics employees

involvement

Time spent by logistics employees

= (𝑅𝐿𝑇 − 𝑜𝑡ℎ𝑒𝑟 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠)

APPENDIX

288

Financial Financial indicators identify supply chain cost drivers, such as expenses incurred by

departments for providing services, including direct and overhead costs for inventory

and internal distribution

Distribution cost

(DCo)

Total cost of handling and transporting to move supplies from

storage rooms to point-of-care locations

Replenishing cost Related to replenishment lead time

Personnel cost

(PCo)

The cost related to the time personnel is involved with

logistics activities

Personnel cost Related to logistics employees

involvement

Inventory cost The annual cost of holding inventory at a specific storage

room

Holding cost Average holding cost =

[∑ (𝐼𝑡𝑒𝑚𝑠𝐼𝑛𝑆𝑡𝑜𝑐𝑘𝑖

𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦𝐶𝑜𝑢𝑛𝑡)]𝑖=454

𝑖=1 ∗

𝑈𝑛𝑖𝑡𝐶𝑜𝑠𝑡𝑖 ∗ 0.25

Productivity/

Organisation

Productivity/Organisation involves operational control metrics for logistics

departments used for streamlining processes, reducing costs, facilitating information

flow and enhancing provided care services

Case cart

efficiency (CCE)

The availability and utilization of case carts to provide

surgeons with the required supplies

Not applicable for replenishment process

Delivery

frequency (DF)

The number of visits to decentral storage locations to

deliver or replenish items in these locations

Percentage of items

replenished

Daily percentage of item

replenishment = 𝑇𝑜𝑡𝑎𝑙 𝑖𝑡𝑒𝑚𝑠 −𝐷𝑎𝑖𝑙𝑦 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 𝑖𝑡𝑒𝑚𝑠 𝑖𝑛𝑐𝑙𝑢𝑑𝑒𝑑

Scanning frequency Use of scanner (0/1)

Visits to decentral

locations

Number of opening relay cabins

Standardization

(S)

The ability to simplify workflows between operating rooms

and improve working conditions

Percentage of

scannable items for

replenishment

= 𝑇𝑜𝑡𝑎𝑙 𝑖𝑡𝑒𝑚𝑠 −𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑐𝑎𝑛𝑛𝑎𝑏𝑙𝑒 𝑖𝑡𝑒𝑚𝑠

𝑇𝑜𝑡𝑎𝑙 𝑖𝑡𝑒𝑚𝑠

Personnel

management

(PM)

A measure of how to obtain, use and maintain a satisfied

workforce

Personnel

utilization =

𝑇𝑖𝑚𝑒 𝑏𝑢𝑠𝑦 𝑟𝑒𝑝𝑙𝑒𝑛𝑖𝑠ℎ𝑖𝑛𝑔

480 𝑚𝑖𝑛𝑢𝑡𝑒𝑠

Ergonomics

friendliness

Use of double carts

Workload

distribution

Timeline of logistics employees

interrupted by other activities

APPENDIX

289

Appendix E – ANP weights for surgical case cart distribution problem

ANP weights adapted from Chapter 4.

Distribution objectives Weights KPIs Weights

Quality 0.32 Delivery accuracy 0.17

Centralization 0.17

Time 0.15 Response time 0.10

Financial 0.06 Personnel cost 0.05

Productivity/organisation 0.48 Process standardization 0.29

Personnel utilization 0.22

Response time Weights

Average waiting time 0.23

Average time to finish carts 0.37

Time needed in CSA storage 0.20

Time needed in OKa1-storage 0.20

APPENDIX

290

Appendix F: Example of physician preference list for hepatobiliary surgery

LAP MWA (BT) Kern F

Datum: Zaal: Volgnr: M/V

Aantal PRODUCT LOCATIE PLAATS KAR

BERGING CSA

1 CPT ABD KLEINE LAPAROSCOPIE AGBBA200A

B22C2 ONDERAAN

1 STEEKLAKEN WIT PAPIER 85X210CM A61C1 MAND M BOVEN

1 ASPIRATIEZAK WIT 2L E21D1 MAND M BOVEN

1 LARYNGOSCOOPBLAD MAC 3 H5A MAND M BOVEN

4 GROENE BLOEDAFNAME DOEKJES E50A1 MAND M BOVEN

INDIEN M/V:

V: 1 TUBE 7 TAPERGUARD 18770 E57C1 MAND M BOVEN

M: 1 TUBE 8 TAPERGUARD 18780 E57E1 MAND M BOVEN

1 BEADEMINGSFILTER HOEKIG G18402 E61A1 MAND M BOVEN

1 FLEECE GROEN OK LINNEN MAND M BOVEN

1 BESCHERMHOES VIDEO 8376094-18539 A69D3 MAND L BOVEN

1 OP-TAPE 9X50CM 3550CEA B37E2 MAND L BOVEN

1 BEENHOEZEN 140X79CM 8420CEB B38E3 MAND L BOVEN

2 INSTRUMENTEN ZAK 25X48CM IDE1018L

B45E3 MAND L BOVEN

1 OPERATIEJAS VERSTERKT XLL 150CM S3513CEB

C43A1 MAND L BOVEN

1 SET IRRIGATIE 16.76-5645 D33D1 MAND L BOVEN

1 MEDENA LEIDING M-00335 - 68130 D53D1 MAND L BOVEN

1 BIOPSIEPOTJE 60ML 5601167 D59D2 MAND L BOVEN

APPENDIX

291

1 LAPAROSCOPISCHE GAS FILTER E66F1 MAND L BOVEN

1 LAMPENTOPPEN KERN F / G I1D MAND L

BOVEN

1 NIERBEKKEN C65A1 LEGGER 2

BERGING OKA 1

1 SOLERO MW APPLICATOR 00A1 LEGGER 5

1 XCEL TROCAR 12MM ZONDER MES B12LT

02C1 MAND L BOVEN

1 ANGIOCATH 10GX76MM 382287 05B1 NIERBEKKEN

LEGGER 2

1 TRUCUTNAALD LANG GTZ 1420 05B6 MAND L BOVEN

1 HACDIL 50ML UNI DOSIS MED NIERBEKKEN

LEGGER 2

BERGING KERN F

1 LAP CCE R3 6 - A LEGGER 4

1 VIDEOLAP 5MM 30° ZELF TE NEMEN R3 9 - A LEGGER 4

1 DOOSJE OPEN/TOE R3 10 - A LEGGER 2

1 BLAUWE LAP BIPOLAIRE PINCETTEN TOPAL

R4 4 - B LEGGER 3

1 ECHOPROBE BKM LAPSC NIEUW R4 7 - G/H LEGGER 5

TE CHECKEN VOOR DE INGREEP

3L NACL 0,9% IN KOELKAST

APPENDIX

292

Appendix G: Semi-structured questionnaire

De focus van de vragen zicht zich tot het intern materiaalbeheer (voorraad en

distributie) van wegwerpmaterialen (niet steriele stroom).

1) Algemene informatie

Wat is uw verantwoordelijkheid of taak binnen het departement en/of ziekenhuis?

Wat verstaat u onder logistiek in een ziekenhuis?

Kan u een algemeen beeld schetsen van de huidige interne bevoorrading en distributie

van materialen? Welke strategie wordt gebruikt voor het aanvullen van voorraad (vb.

two-bin)? Hoe en hoe vaak wordt de voorraad geteld/nagekeken? Hoeveel voorraad

wordt er bijgehouden? Hoe vaak wordt voorraad bijbesteld?

Op welke manier kan een beter/efficiënter materiaalbeheer ondersteuning bieden aan

de logistieke noden binnen uw afdeling? En, hoe beïnvloedt dit volgens u de medisch-

gerelateerde taken?

Wat zijn knelpunten of moeilijkheden bij het beheren van logistieke processen

(stockbreuk, opslagruimte, backorders, personeel, financieel, …)?

Gebruikt u KPIs om de prestatie van logistieke processen te meten? Zo ja, kan u deze

KPIs definiëren? En, hoe wordt de data verzameld om de KPIs te meten? En, hoe

frequent worden de KPIs gemeten?

2) Paarsgewijze vergelijkingen

Bij de paarsgewijze vergelijkingen is het de bedoeling dat u aangeeft welke indicator

of objectief de grootste invloed zou kunnen hebben of de efficiëntie van logistieke

processen en hoeveel. De indicatoren of objectieven worden telkens per twee met

elkaar vergeleken.

Welke factoren zijn volgens u het belangrijkste om logistieke processen op te volgen,

efficiënter te maken en om beleidsbeslissingen te maken zodat het hele systeem

verbetert?

Schaal van 1-9: ratio schaal

- Verificatie van indicatoren

APPENDIX

293

Zijn volgens jou alle relevante criteria geïncludeerd in de evaluatie? Zo niet, welk

criterium zou je toevoegen, of verwijderen?

Zijn er factoren die ontbreken in bovengenoemde vragenlijst?

Zijn er factoren die geen invloed hebben in bovengenoemde vragenlijst, en dus

redundant zijn?

- Werking van framework

Hebben de volgende product karakteristieken een impact op wat er belangrijk is in het

opvolgen van de logistieke processen:

Prijs van het product: Zo ja, hoe verandert dit welke aspecten belangrijk zijn

in het logistiek proces? Welke indicatoren worden minder belangrijk of

belangrijker om logistiek efficiënter te maken?

Kriticiteit van het product (door backorder, door impact op operatie, door lead

time): Zo ja, welke indicatoren worden minder belangrijk of belangrijker om

logistiek efficiënter te maken?

Hoe denkt u dat een logistiek framework kan helpen om beleidsbeslissingen te nemen,

zoals bijvoorbeeld outsourcing of producten aankopen in consignatie?

3) Robuustheid framework (enkel OT stakeholders)

De stakeholders worden op de hoogte gebracht van de resultaten van de simulaties. Op

basis hiervan wordt hen gevraagd een score te geven van 1 – 5 om te bepalen wat het

belang is van een indicator met betrekking tot een logistieke verbeteringsmogelijkheid

(vb. scenario).

4) Benchmarking: reële performance score (enkel logistieke stakeholders)

Stakeholders worden gevraagd om op een 5-punten schaal, waarbij 1 ‘strongly

disagree or inadequate’ en 5 ‘strongly agree or adequate’, indicatoren te scoren op de

mate waarin ze momenteel gemeten worden. Op strategisch niveau wordt er ook

gevraagd naar de optimale score om de indicatoren te meten.

APPENDIX

294

Informed consent: Geïnformeerd toestemmingsformulier

Titel van het onderzoeksproject:

Analyse van meerdere stakeholders om de prestatie van interne logistieke processen

binnen de gezondheidszorg te meten.

Naam en statuut van de onderzoeker:

Karen Moons, doctoraatsonderzoeker bij departement werktuigkunde, KU Leuven

1) Ik bevestig dat ik het informatiedocument betreffende het onderzoeksproject

gelezen en begrepen heb en de mogelijkheid heb vragen

te stellen.

2) Ik begrijp dat mijn deelname volledig vrijwillig is en dat

ik op elk moment de kans heb om mij terug te trekken uit

dit onderzoeksproject zonder voorafgaand enige

verklaring te geven.

3) Ik ben akkoord deel te nemen aan het onderzoeksproject

4) Ik ga ermee akkoord dat er audio opnames gemaakt

worden van de interviews: JA - NEEN

5) Ik ga ermee akkoord dat citaten anoniem worden weergegeven in

toekomstige publicatie: JA - NEEN

Naam

(deelnemer):………………………………Handtekening………………………

Karen Moons (onderzoeker) Datum:…………………..Handtekening:………………

Parafeer

295

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LIST OF PUBLICATIONS

Publications in international peer-reviewed journals

Moons, K., Pintelon, L., Waeyenbergh, G. (2016). Optimization of operations by

simulation – a case study at the Red Cross Flanders. American Journal of Industrial

and Business Management, 6(10), 1001-1017.

Moons, K., Waeyenbergh, G., Pintelon, L. (2019). Measuring the logistics

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217.

Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., De Ridder, D. (2019).

Performance indicator selection for operating room supply chains: an application of

ANP. Operations Research for Health Care. Vol 23, December 2019.

Moons, K., Pintelon, L., Jorissen, P., De Ridder, D., Everaerts, W. (2020).

Identification of multi-stakeholder value in medical decision-making. International

Journal of the Analytic Hierarchy Process, vol. 12 (1), 82-103.

Moons, K., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). A Framework for

Operational Excellence in Hospital Logistics: Implementation Roadmap. Health Care

Management Science, Submitted.

Moons, K., Waeyenbergh, G., De Ridder, D., Pintelon, L. (2020). Future outlook:

Implications of digital trends in healthcare logistics. Health and Technology,

Submitted.

Conference proceedings (peer-reviewed)

Moons, K., Waeyenbergh, G., Timmermans, P., De Ridder, D., Pintelon, L. (2020).

Evaluating replenishment systems for disposable supplies at the operating theatre: a

simulation case study. Healthcare Systems Engineering. HCSE 2019. Springer

Proceedings in Mathematics & Statistics, vol. 316.

Moons, K., De Gang, W., Oor, A., Waeyenbergh, G., De Ridder, D., Pintelon, L.

(2020). Measuring the performance of different distribution strategies in the operating

theatre – A simulation case study. International Journal of Production Economics,

Submitted.

LIST OF PUBLICATIONS

322

Presentations/Posters at international conferences

Moons, K., Pintelon, L., Waeyenbergh, G. (2016). Optimization of operations by

simulation – a case study at the Red Cross Flanders. In: 19th International Working

Seminar on Production Economics, Innsbruck, 22-26 February, 2016.

Moons, K., Vandermeulen, E., Waeyenbergh, G., Timmermans, P., De Ridder, D.,

Pintelon, L. (2018). Operating room supply chain management: a simulation case

study. In: 20th International Working Seminar on Production Economics, vol. 1 (283-

294), Innsbruck, 19-23 February, 2018.

Moons, K., Pintelon, L. (2019). Healthcare Logistics research in the CIB research

group at KU Leuven. Séminaire ESSI en logistique hospitalière, 28th March 2019,

Paris-Sénart.

Jorissen, P., Moons, K., Pintelon, L., De Ridder D., Everaerts, W. (2019). PT378 –

Identification of multi-stakeholder value in prostate cancer treatment by application of

Multi-Criteria Decision-Making. European Urology Supplements, 18(1), e2184-

e2185.

Moons, K., De Gang, W., Oor, A., Waeyenbergh, G., De Ridder, D., Pintelon, L.

(2020). Measuring the performance of different distribution strategies in the operating

theatre – A simulation case study. In: 21th International Working Seminar on

Production Economics, vol. 3 (221-244), Innsbruck, 24-28 February, 2020.

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