Disaster Logistics

A study on logistics performance management in disasters

K. Koenen

13/08/2007

Kevin Koenen - 2 - 13/08/2007

Disaster Logistics

A study on logistics performance management in disasters

Core text = 14 997 words / 40 pages

Master Thesis Logistics & Operations Management

Department of Organization & Strategy

Faculty of Economics and Business Administration

Tilburg University

K. Koenen

13/08/2007

Supervisor: Dr. Ir. Cindy M.H. Kuijpers Second reader: Prof. Dr. Ir. Hein A. Fleuren

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Management summary

Disaster logistics is defined as: The planning, implementation and control of all activities

relating to the flow of goods, materials, personnel and the associated information and capital,

from the source (f.e. the supplier or donor) to the final user (f.e. the beneficiaries) in times of

disaster. This task is performed in an uncertain and ad-hoc environment and the ultimate

goal is to perform the task as efficient and cost-effective as possible in order to alleviate the

suffering of weak and vulnerable people in affected areas as soon as possible.

Specific challenges to the disaster logistics function of aid agencies are the aid agency

principles and the concept of the humanitarian space. Moreover, operational challenges, the

complexity of disaster situations, the different stakeholders involved and the collaboration

between them is also a big challenge for disaster logistics. Finally, also learning challenges,

information technology challenges and organizational challenges occur in disaster logistics.

Business sector performance measures and standards in the functional areas of

transportation, warehousing, inventory, purchasing, production and order processing can be

transferred to disaster logistics; however, some measures need adjustments. Similarly,

supply chain comprehensive measures can be applicable to disaster logistics.

In terms of standards setting, the use of internal improvement standards, historical standards

and (non-)competitive benchmarking are the most appropriate ones to be transferred to

disaster logistics. The Sphere-approach to setting standards in aid agencies is useful but

further extension is required in order to be useful for disaster logistics.

The use of scorecard-like measurement systems is also applicable in disaster logistics. The

balanced scorecard from the business sector, together with the Davidson (2006) attempt in

disaster logistics provide a good basis which should be extended even more in the future.

The main benefits for disaster logistics to apply performance management include the

improvement possibility the concept has for disaster logistics as a function, but also for aid

agency operations as a whole. Moreover, performance management can keep track of the

amount of logistical knowledge in the field and thereby limit the learning challenges within

disaster logistics. Another benefit includes the fact that performance management also

means standardization, which helps in improving collaboration and coordination among

stakeholders in disaster logistics.

Also, the use of performance management provides a way for aid agency to objectively proof

their effectiveness and efficiency by means of hard, quantitative data. This first of all tackles

the accountability challenges imposed by donors, but it can also be used to transfer disaster

logistics from a peripheral function to a strategic function within aid agencies.

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Preface

This is my Masters Thesis, which will form the closure of the 2006-2007 program of Logistics

& Operations Management at Tilburg University. The topic is performance management in

disaster logistics, bringing together two different fields: the business world including

conventional logistics and performance management on the one hand, and the humanitarian

world including aid agencies and disaster logistics on the other.

Up until five to ten years ago these two fields did not see any benefit in cooperating: the

business world was too focussed on making money, while the humanitarian world was trying

to save lives and do good at any cost.

In the last ten years, practise has shown that the two distinct fields grew towards each other,

needed each other and actually should learn from each other. The business world is

increasingly focussing on corporate social responsibility and recognizes it can learn from the

complexity associated with disasters. The humanitarian world recognizes that it can learn

from the business world in terms of efficiency and can benefit from the resources the

business world has to offer.

This thesis tries to extend this view within the field of logistics performance management. I

hope people like to read this thesis and afterwards they will share my opinion that the

business world and the humanitarian world should increase their cooperation.

Under the assumption that I survive my defence on August 28th 2007 I do need to thank a

few people for their help and support throughout my seven months of writing. First of all I

would like to thank my friends, roommates and family for supporting me throughout my

studies. Especially my parents deserve a big thanks for giving me the opportunity to study for

five years and for supporting me in everything I did during these five years.

Second, I would like to thank my supervisor, Cindy Kuijpers. Your comments were some

times frustrating for me and sometimes I wondered if I was doing all right. But in the end I

can say that most of the comments you gave were valid and made me think about what I

wrote more critically.

Finally, I would like to thank Krista Kammeraad for being able to discuss our ideas about

disaster logistics, and Mr. Nicolas Romero and Ms. Esther Bosgra of TNT’s ‘Moving the

World’ for providing the background and practical information on the TNT-WFP partnership.

Kevin Koenen,

13/08/2007.

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Table of content

MANAGEMENT SUMMARY 3

PREFACE 4

TABLE OF CONTENT 5

CHAPTER 1 INTRODUCTION 7

Section 1.1 Background 7

Section 1.2 Problem indication 8

Section 1.3 Problem statement 9

Section 1.4 Research questions 9

Section 1.5 Research design 10

Section 1.6 Structure 11

CHAPTER 2 DISASTER LOGISTICS 12

Section 2.1 Introduction 12

Section 2.2 Defining disaster 12

Section 2.3 Defining disaster logistics 14

Section 2.4 Disaster management phases 16

CHAPTER 3 PROCESSES AND CHALLENGES IN DISASTER LOGISTICS 17

Section 3.1 Introduction 17

Section 3.2 Disaster logistics stakeholders 17

Section 3.3 Disaster logistics activities 20

Section 3.4 Disaster logistics flows 21

Section 3.5 Disaster logistics processes 22

Section 3.6 Aid agency principles 24

Section 3.7 Operational challenges 25

Section 3.8 Disaster logistics learning challenges 26

Section 3.9 Other challenges 27

CHAPTER 4 PERFORMANCE MEASUREMENT AND –MANAGEMENT 28

Section 4.1 Introduction 28

Section 4.2 Performance, performance measurement and –management 28

Section 4.3 Logistical performance measures 29

Section 4.4 Standards & benchmarks 30

Section 4.5 Performance management systems 31

CHAPTER 5 DISASTER LOGISTICS PERFORMANCE MANAGEMENT 33

Section 5.1 Introduction 33

Section 5.2 Disaster logistics performance 33

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Section 5.3 Sphere’s minimum standards 33

Section 5.4 Participatory performance management 34

Section 5.5 Davidson’s performance indicators for disaster logistics 35

Section 5.6 Obstacles to performance management in disaster logistics 36

CHAPTER 6 APPLYING BUSINESS SECTOR PERFORMANCE

MANAGEMENT APPROACHES TO DISASTER LOGISTICS 38

Section 6.1 Introduction 38

Section 6.2 Performance measures in disaster logistics 38

Section 6.3 Standards & benchmarks in disaster logistics 39

Section 6.4 Balanced scorecard in disaster logistics 40

Section 6.5 Benefits for disaster logistics 41

CHAPTER 7 CONCLUSIONS, RECOMMENDATIONS & LIMITATIONS 44

Section 7.1 Conclusions 44

Section 7.2 Recommendations 45

Section 7.3 Limitations 46

REFERENCES 47

APPENDIX A DISASTER FIGURES 54

APPENDIX B DISASTER DEFINITIONS 56

Section B.1 Natural disasters 56

Section B.2 Technological disasters 58

Section B.3 Other disasters 59

APPENDIX C IFRC PRINCIPLES 60

APPENDIX D BUSINESS SECTOR PERFORMANCE MEASURES 61

APPENDIX E TNT-WFP KEY PERFORMANCE INDICATORS 68

Section E.1 Key performance indicators 68

Section E.2 Further explanation of the KPI’s 68

Section E.3 Discussion 70

APPENDIX F SPHERE-PROJECT STANDARDS 72

Section F.1 Common standards 72

Section F.2 Minimum standards in water supply, sanitation and hygiene 73

Section F.3 Minimum standards in food security, nutrition and food aid 74

Section F.4 Minimum standards in shelters, settlements & non-food-items 75

Section F.5 Minimum standards in health services 77

APPENDIX G Alphabetical index 79

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Chapter 1 Introduction

Section 1.1 Background

Hurricane Katrina, the Asian Tsunami, the 2005 Pakistan earthquake, the 9-11 world trade

centre drama and the 2005-2006 Malawi food crisis, are just a few disasters which struck the

world during the last few years and there is no reason the believe that these types of

humanitarian crisis’s will decrease. On the contrary, from 1975 until 2006, natural disasters

have increased by 5,8% annually (figure 1.1) and especially the number and impact of

natural disasters has rapidly increased over the last few decades (figure A.1, appendix A).

Moreover, aid donors are hard to find and aid agency budgets are generally low. Therefore

better and more efficient operations are important and aid agencies should invest in

improving its operations in order to cope with the trend of increasing numbers of disasters

(figure A.1 & A.3, appendix A) and the growth trend in numbers of people affected and killed

by disasters (figure A.2 & A.4, appendix A).

Figure 1.1, Time trend of natural disasters 1975-2006 (EM-DAT, 2007)

But why should these aid agencies improve? Where in the organization are the opportunities

to improve? And in what way should these organizations improve? These are all questions

that remain and an answer to these questions could be provided by means of measuring

actual performance, reporting quantitative data and by making informed improvement

decisions based on these reports. So, one area for aid agencies to improve in is performance

management. Especially performance management of logistical performance in disaster

relief efforts, so-called disaster logistics. For now, a suitable definition of disaster logistics is

the management, storage and movement of all goods and information to a disaster area.

This chapter introduces the topic of this thesis more extensively and the main aim of the

research is covered. Section 1.2 provides a description of the ‘problem’, which results in a

problem statement that is formed in section 1.3. From this problem statement several

research questions are derived, which are covered in section 1.4. Then section 1.5 covers

research design and data collection, while section 1.6 discusses the structure of this thesis.

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Section 1.2 Problem indication

As suggested, improvement in disaster logistics is important for the future operations of aid

agencies, moreover performance measurement could help in improving. Several

humanitarian institutions, such as the Fritz Institute and the Humanitarian Practise Network

also suggest this as an area to improve in. Performance management and -measurement

are concepts commonly used in business settings to monitor and improve company

performance. This already highlights one point why it is very interesting to look at

performance measurement and –management in disaster logistics, namely its improvement

potential. Specific areas in which performance measurement can support improvements

include policy- and process improvements within disaster logistics or programmes and

projects aimed at upgrading the disaster logistics function of aid agencies (IFRC, 2007;

Thomas & Kopczak, 2005; Fritz Institute, 2004).

Another reason why it is very interesting to study performance measurement within disaster

logistics is the observation that several stakeholders have an interest in aid agencies and the

services they provide. In the first place, there are the people in need, actually receiving aid,

the so-called beneficiaries. Second, individuals, companies and countries providing money or

donating goods, the donors, also have an interest. Finally, the media are also very interested

in the services aid agencies provide. According to several authors these stakeholders would

like to know how quickly and efficiently aid agencies are able to respond (Davidson, 2006;

Thomas, 2006), what the impact is of all the money spent (Thomas, 2006; Fritz & Thomas,

2004, Macrae et. al., 2002), and that aid agencies are actually reaching the people in need

(Van Wassenhove, 2006). When aid agencies’ performance and more specifically their

logistics performance is accurately measured and quantified these questions of stakeholders

can be clearly answered and supported by actual facts.

So, there seems to be potential for performance measurement and –management within

disaster logistics and there are interesting reasons to study it. But currently several authors

are indicating that aid agencies are lagging behind in both logistics and performance

management. However these authors remain positive and propose a solution: aid agencies

should learn from normal businesses and their logistics divisions. This so-called business

sector and specifically its performance management approaches within logistics are

suggested to have a large value for aid agencies and disaster logistics (CRED, 2007; Van

Wassenhove, 2006; Davidson, 2006; Hofmann, Roberts, Shoham & Harvey, 2004; Fritz &

Thomas, 2004; Thomas & Mizushima, 2005).

Concluding, studying performance management in disaster logistics is a valid and interesting

concept to study. One the one hand it could support improvements within disaster logistics.

One the other hand it could answer difficult questions of stakeholders. But there still is a long

way to go, because currently performance management in disaster logistics lags behind. And

although business sector approaches could help, the question that still remains is how these

approaches could help!

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Section 1.3 Problem statement

From the problem indication, this problem statement was developed:

How can business sector performance management approaches be used to improve disaster

logistics?

The concept of disaster logistics will be further elaborated in the remainder of this thesis, but

it will always be referred to in relation to aid agencies. So whenever disaster logistics is used,

it means disaster logistics for aid agencies involved in relief efforts.

Section 1.4 Research questions

In order to investigate the problem statement these research questions were formed:

What is disaster logistics?

This research question is intended to clarify the concepts of disaster, disaster logistics and

disaster management and forms the basis for covering the other research questions (figure

1.2).

What are the logistical processes and corresponding challenges within disaster logistics?

This second research question will provide the first ‘building block’ for answering the problem

statement (figure 1.2). This research question will result in an overview of the processes,

stakeholders, activities and flows within disaster logistics. Moreover, it will also cover the

principles and challenges associated with disaster logistics.

What are the approaches to logistics performance management in the business sector?

This research question is another ‘building block’ for answering the problem statement (figure

1.2). This research question covers the concept of performance and reviews logistics

performance management within the business sector.

How is disaster logistics performance currently managed?

This research question is the third and final ‘building block’ for answering the problem

statement (figure 1.2). This research question discusses how aid agencies currently manage

and measure their logistical performance and includes a discussion on the concept of

performance and the obstacles to performance management in disaster logistics.

Which business sector performance management approaches can be applied to disaster

logistics and how?

The three ‘building blocks’ are combined in this fourth research question. This research

question results in a discussion on the benefits of applying business sector performance

management to disaster logistics and how it addresses the challenges of disaster logistics

(figure 1.2).

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Section 1.5 Research Design

Sekaran (2003) distinguishes two research purposes. The first is an exploratory study, which

is ‘undertaken when not much is known about a situation at hand, or no information is

available on how similar problems or research issues have been solved in the past’. The

second is a descriptive study, which is ‘undertaken in order to ascertain and be able to

describe the characteristics of the variables of interest in a situation’. The first research

questions of this thesis are descriptive since they are meant to define the concepts used, to

describe processes and challenges of disaster logistics and to describe logistics performance

management. However, the final research question and thus the main problem statement of

this thesis can be labelled as exploratory in nature.

The research method used to tackle the problem statement is the literature review, which

includes the documentation of a comprehensive and critical analysis of relevant work of

business- and management research in the area of specific interest to the writer (Maylor &

Blackmon, 2005; Sekaran, 2003). Since the main research method is the literature review,

the most appropriate data collection method is secondary data collection. This is defined as

‘data gathered through already existing sources, including statistical bulletins, government

publications, (un)published information from within or outside organisations, case studies,

library records, online data, websites, etcetera’ (Sekaran, 2003). The entire list of references

and secondary data sources used are available in the references list at the end of this thesis.

The secondary data sources used are sampled from top-journals in the field logistics and

supply chain management or from books published by world renowned publishers. Next to

the academic resources, more practise oriented data sources are used such as the Fritz

Institute, Humanitarian Practise Network and the forced migration review. These sources

give a more practical insight in the humanitarian sector and the current practise in this field.

Also, on 02-07-2007 an interview was performed with Mr. Nicholas Romero at the TNT

headquarters in Hoofddorp, The Netherlands. Mr. Romero is working at the TNT-WFP

partnership called ‘moving the world’. This interview also provided practical insight into

disaster logistics and aid agencies (WPF in particular).

Reliability refers to the extent to which a measure is without bias and ensures consistent

measurement across time. Moreover it is an indication of stability and consistency with which

the instrument measures the concept and helps to assess the ‘goodness’ of a measure

(Sekaran, 2003). The literature used in this thesis is obtained from various source. First there

are the articles from well respected journals. Because their review process ensures that only

high quality articles are published these journals can be regarded as reliable. However, also

less well known journals and aid agency websites were used as sources. Since these

sources do not have such a strict review process, or because they serve a subjective goal of

the organization, the reliability of these sources could be questioned. This problem is tackled

by means of data triangulation, using different sources to verify claims made by the different

sources.

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Validity can be divided in internal- and external validity. Internal validity refers to the

confidence one can place in the cause-and-effect relationship. External validity is defined as

the extent of generalizability of the results of a study to other settings, people, or events

(Sekaran, 2003). Although aid agencies have extensive experience in disaster logistics, the

researcher himself has not. Given this observation, the researcher still feels that the research

is valid and can be translated to other disaster settings. Still one has to note that the

prescriptions in this thesis are not one-size-fit-all and are still dependent on each specific

disaster situation.

Section 1.6 Structure

The structure of this thesis is quite straightforward, since each research question will be

covered in a separate chapter. First, research question one is covered in chapter two. Then

the three ‘building blocks’ are covered subsequently in chapters three to five. Thereafter, the

‘building blocks’ are combined in chapter six (figure 1.2). This structure ultimately leads to

chapter seven, in which conclusions, recommendations and limitations are covered.

Figure 1.2, Thesis Structure

Because the fields of performance management and disaster logistics use a lot of technical

terms, this thesis introduces every new and important item in an italic font. Thereby, the

reader will know that the term is an important technical term, which is relevant for this thesis.

In appendix G all italic technical terms will be listed in an alphabetical index.

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Chapter 2 Disaster logistics

Section 2.1 Introduction

In this chapter, an important starting point for this thesis will be covered. As the problem

statement deals with disaster logistics, it is very important to accurately define what is meant

when using this term. In order to do this the definition of a disaster should be clear, therefore

section 2.2 will go into this definition. Thereafter section 2.3 will cover the definition of

disaster logistics. Finally, section 2.4 will discuss the different phases within disaster

management.

Section 2.2 Defining disaster

In this section several sources and their definition of disaster will be reviewed, also types and

groups of specific disasters will be treated. Based on this review, a common definition will be

proposed, which will be used throughout the rest of this thesis.

When looking at the definition provided by the dictionary, the word disaster refers to ‘an

occurrence causing widespread destruction and distress; a catastrophe or a grave

misfortune’ (Houghton Mifflin Company, 2006). Aid agencies and organizations involved in

disaster relief efforts, such as the United Nations state that a disaster is ‘a serious disruption

of the functioning of a community or a society causing widespread human, material,

economic or environmental losses which exceed the ability of the affected community or

society to cope using its own resources’ (UN/ISDR, 2007). So, next to the focus on

communities this definition of a disaster also implies that a disaster is only a disaster when a

community cannot handle the consequences on its own.

Another aid agency, the World Health Organization (W.H.O.), maintains a database called

EM-DAT. This EM-DAT database includes data on occurrences and effects of more then

10.000 disasters, which occurred from 1900 to present. For a disaster to be entered into this

database EM-DAT (2007) requires that one of these rather strict criteria should be met:

• 10 or more people should be reported killed because of the disaster (including persons

confirmed as dead and persons missing and presumed dead).

• Minimal 100 people should be reported affected by the disaster (meaning injured,

homeless or requiring assistance).

• The declaration of a state of emergency by local authorities.

• A call for international assistance Samii & van Wassenhove, 2002).

With these definitions in mind, one can think of several types of disaster. Some examples

include: drought, earthquakes, epidemics, extreme temperatures, flood, insect infestation,

slides, volcanic eruption, wild fires, wind storms, industrial accidents and transport accident

(UN/ISDR, 2007; EM-DAT, 2007). For the interested reader, specific definitions of these

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different disaster types are provided in appendix B. The different types of disasters can be

labelled as:

• Natural disasters, disasters caused by natural processes or phenomena,

• Technological disasters, disasters caused by technological-, industrial- or infrastructural

accidents often due to human activities,

• Other disasters, any disaster not being natural or technological, an example includes

people movement such as refugees (UN/ISDR, 2007; IFRC, 2007).

The natural disasters can be further classified in three broad groups being biological-, hydro-

meteorological- and geological disasters. A biological disaster is a disaster with an organic or

biological origin, examples include epidemics and plant- or animal contagion. The hydro-

meteorological disaster is a disaster occurring because of weather and climate related issues.

Some examples include tropical cyclones, extreme temperatures and droughts or floods.

Finally, the geological disasters include all disasters related to earth processes or

phenomena, such as volcanic eruptions and earthquakes (UN/ISDR, 2007). Again, specific

technical definitions of these different disaster groups are provided in appendix B. Classifying

the disaster types as one of the disaster groups identified here, results in table 2.1.

Table 2.1, Disaster groups (EM-DAT, 2007; IFRC, 2007)

The World Health Organization and the United Nations distinguish between natural disasters

and technological disasters. This last group was said to often have a human cause.

Therefore, technological disasters can also be labelled as man-made disasters, a term van

Wassenhove (2006) uses. However, the distinction between natural disasters and man-made,

disasters is not as black-and-white as one might think. Actually it is rather artificial as most

disasters can be a result of natural disasters combined with human vulnerability (House of

Commons, 2006), an example are floods. They are labelled natural disasters, but the reality

is that they are caused by rising water levels, which are a result of global warming, so

actually they are man-made.

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Van Wassenhove (2006) also proposes another way to distinguish disasters; he identifies

slow onset- and sudden onset disasters. Slow onset disasters are disasters from which the

causes have been present and evolving for years, but which are named disasters when

these causes and characteristics become unmanageable and when the resulting outcomes

become too difficult to handle. Examples include famine, poverty and political crisis’s (see

figure 2.2). Sudden onset disasters are disasters, whose exact occurrence cannot be

predicted far in advance, although they are expected to happen someday. These disasters

therefore occur suddenly and unexpectedly and usually involve a large number of people

affected and damage reported. Examples include earthquakes, tornados and terrorist attacks

(see figure 2.2). Combining the two disaster distinctions proposed by van Wassenhove (2006)

results in the matrix of figure 2.2.

Figure 2.2, Explaining disasters (van Wassenhove, 2006)

After reviewing several definitions of disaster, discussing several types of disasters and after

considering several ways to group disaster types, now it is time to conclude this section with

a common definition of disaster, which will be used throughout this thesis. Based on the

literature review of this section, this thesis defines the concept of disaster as:

An event of grave misfortune, distress or destruction, which disrupts the functioning of a

society and requires a state of emergency and (international) assistance because the society

cannot cope using its own resources. This event can be due to natural- or mad-made

reasons and it can occur suddenly or slowly, but it always involves great levels of human-,

material-, economic- or environmental damage and loss.

Section 2.3 Defining disaster logistics

In order to define disaster logistics, a first step is to unravel the traditional definition of the

term logistics. When looking at the dictionary a military definition of logistics appears: ‘the

aspect of military operations that deals with the procurement, distribution, maintenance, and

replacement of materiel and personnel’ (Houghton Mifflin Company, 2006). This definition is

rather similar to the one used by the Department of Defence (2002), who explicitly include

planning and execution of movement into their definition. Although the concept of logistics

originated from the military, today it is mostly used in the business sector.

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Reviewing and combining the definitions used in the business sector results in a definition of

logistics, which includes the planning and execution of procurement, movement and storage

of materials, parts, work-in-progress inventory, finished goods and the associated

information- and capital flows. This work is done throughout the supply chain, from the

source (f.e. the supplier) to the final customer (f.e. the consumer), in a way that is as cost-

effective and profitable as possible (Bowersox, Closs & Cooper, 2002; Christopher, 1998;

Harrison & van Hoek, 2002; Ernst, 2003; van Wassenhove, 2006). According to Ernst (2003)

aid agencies rely on logistics for the same basic reasons as the business sector does for

managing the flow from suppliers (f.e. the donors) to the recipients (f.e. the affected people).

But how can disaster logistics be defined? Several answers to this questions have been

proposed, but a first anecdote about disaster logistics tells us that it is like ‘having the client

from hell: you never know beforehand what they want, when they want it, how much they

want and even where they want it sent’ (Arminas, 2005). Another anecdote about disaster

logistics is provided by John Rickard, Director of Logistics of the I.R.C. who learned of a new

project when the actual purchase requisition appeared on his desk and who received a

vehicle requisition 20 minutes before it was urgently required (Rickard, 2004; Thomas; 2004).

So, disaster logistics is a very uncertain and ad-hoc environment to operate.

A rather extensive definition is proved by Thomas & Kopczak (2005), who state that it is the

process of planning, implementing and controlling the efficient, cost-effective flow and

storage of goods and materials, as well as related information, from the point of origin to the

point of consumption for the purpose of alleviating the suffering of vulnerable people. This

function encompasses a range of activities, including purchasing, production, warehousing

and inventory and transportation. This definition of disaster logistics is rather similar to

traditional business sector definition of logistics, however the definitions also differ somewhat.

To the business sector logistics is performed in order to satisfy the end-customer, the

consumer. With respect to disaster logistics this ‘customer-focus’ is rather odd or as Ernst

(2003) put it: ‘the customers (the people who are assisted) are not generating a ‘voluntary’

demand and hopefully will not generate a ‘repeat purchase’ (Ernst, 2003). Therefore aid

agencies rather speak of aid beneficiaries. Thus, the ultimate goal of disaster logistics is to

‘satisfy’ beneficiaries and to reduce the suffering of weak and vulnerable people in affected

areas as soon as possible.

Concluding this section this thesis defines disaster logistics as:

The planning, implementation and control of all activities relating to the flow of goods,

materials, personnel and the associated information and capital, from the source (f.e. the

supplier or donor) to the final user (f.e. the beneficiaries) in times of disaster. This task is

performed in an uncertain and ad-hoc environment and the ultimate goal is to perform the

task as efficient and cost-effective as possible in order to alleviate the suffering of weak and

vulnerable people in affected areas as soon as possible.

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Section 2.4 Disaster management phases

Disaster logistics is part of the wider concept of disaster management which, among others,

includes preparing for disasters, reducing the risks associated with disasters, adequately

responding to disasters and the rehabilitation after a disaster (IFRC, 2007). Van

Wassenhove (2006) distinguishes between four clear phases within disaster management

(figure 2.3).

First, the mitigation phase, this phase occurs before a disaster actually happens and includes

all actions that prevent disasters from harming humans. For example, avoiding to build

houses on the shoreline in regions prone to tsunamis could (Van Wassenhove, 2006).

Second is the preparedness phase, in which people are prepared as good as possible for the

occurrence of any disaster. Examples here are education about first aid or the

implementation of an early warning system. Of specific interest for disaster logistics could be

the pre-positioning of supplies in warehouses close to disaster-prone areas (Van

Wassenhove, 2006).

When supplies are pre-positioned in the preparedness phase, this has clear advantages for

the third phase, the response phase. This phase includes the actual response in the direct

aftermath of a disaster, this phase includes what Thomas & Kopczak (2005) call aid agency

relief efforts: ‘the emergency food, shelter and services provided in the immediate aftermath

of a natural or man-made disaster’. Here the pre-positioned goods could help in making

responses faster and reaching beneficiaries earlier.

The final phase of disaster management is the rehabilitation phase, which includes the

reconstruction of destroyed infrastructure & housing as well as reconstructing the economic

and social life of an area struck by disaster (Van Wassenhove, 2006). This phase is what

Thomas & Kopczak (2005) name the development activities of aid agencies where the aid

agencies try to provide longer-term aid aimed at creating self-sufficiency and sustainability of

a community.

Figure 2.3, Disaster management phases (van Wassenhove, 2006)

Disaster logistics and the topic of this thesis are concerned with the preparedness- and response

phase of disaster management. The response phase is the biggest component because here the

actual aid is provided and actual performance can be measured. But also the preparedness phase is

important because the performance of aid agencies is also very much affected by the actions taken in

the preparedness phase.

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Chapter 3 Processes and challenges in disaster logistics

Section 3.1 Introduction

In this chapter the first of three ‘building blocks’ is covered: the processes and challenges

that are occurring within disaster logistics. First the stakeholders involved in disaster logistics

will be identified (section 3.2). Then disaster logistics activities are discussed (section 3.3),

followed by disaster logistics flows (section 3.4), continuing with disaster logistics processes

in section 3.5. Section 3.6 is about aid agencies principles, section 3.7 is concerned with

operational challenges, section 3.8 will go into challenges related to learning within disaster

logistics, while section 3.9 will cover technology and organizational challenges within disaster

logistics.

Section 3.2 Disaster logistics stakeholders

Beneficiaries

The single most important stakeholder in the disaster logistics process is the ultimate

beneficiary of the aid provided: the person in need. All other stakeholders in disaster logistics

act because of the needs of these people. In conventional supply chain terminology, this

beneficiary could be labelled the ‘end-customer’ of the entire aid agency ‘supply chain’. This

aid agency supply chain is meant to ‘satisfy the demand’ of the end customer. But as Ernst

(2003) stated this customer focus in disaster logistics is rather odd since the beneficiaries are

not generating a ‘voluntary’ demand and will hopefully not return to the ‘market’ for a ‘repeat

purchase’.

Donor countries

A lot of international aid flows from the world’s richest countries to the poorer and less-

developed countries. For example, the United States and the European Union have

represented roughly 33% and 10% of total aid (in cash, kind and services) in recent years

(IFRC, 2007; Thomas & Kopczak, 2005). There are also less well developed countries, such

as India, who serve both as a donor and as a beneficiary (IFRC, 2007; Thomas & Kopczak,

2005). These donor countries are an important stakeholder in disaster logistics and they

have a strong influence on the process of providing aid. The donations of these countries

and the speed of their reaction to disasters are really important to the timeliness and

effectiveness of relief efforts (IDC, 2006b).

A donor countries’ decision to respond to disasters is mainly based on the aid agency

assessment of unmet needs in the disaster area. But also resources available, attention paid

by other stakeholders (f.e. the media and other governments) and national- and global

politics influence this decision (IDC, 2006b; Samii & van Wassenhove, 2002). An example of

political influence could be that the Netherlands is more inclined to donate to Indonesia

because of the colonial history of the two countries.

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Finally, the donating governments are becoming more demanding: besides donating funds

and goods to aid agencies the governments also want to know what is done with these funds

and goods. They want to see the concrete results and output from their donations, a fact

known as donor accountability (IDC, 2006b; Samii & van Wassenhove, 2002).

Business sector

The business sector is also a large donor to aid agencies. Business sector contributions can

vary from monetary and in-kind donations to profit-oriented activities. An example of these

profit-oriented activities is tendering for a reconstruction contract after a disaster (IDC, 2006a;

IDC, 2006b). Examples of donations in kind include delivery of company services, but also

excesses in inventories (Gustavsson, 2003). An example is TNT’s cooperation with the

World Food Program (WFP). TNT, a multinational mail and express company, is supporting

WFP with so-called 48-hr emergency response teams (Romero, 2007; TNT, 2007; Scott-

Bowden, 2003; Hoffman, 2006).

Besides being a donor, the business sector is also involved as a partner for aid agencies.

Collaboration between the two is increasing and is based on using the skills, expertise and

knowledge available in the business sector to improve operations in disaster relief efforts

(IDC, 2006b; Gustavsson, 2003). An example is Ericsson who was one of the first companies

to cooperate with aid agencies by providing mobile telecommunication in disaster areas

(Scott-Bowden, 2003). Also, TNT provides services such as air transport, transport- and

warehouse coordination and reporting assistance to their WFP partner (Romero, 2007).

A reason for the business sector to donate to- or cooperate with aid agencies is a growing

focus on corporate social responsibility (IDC, 2006a; 2006b). This is a focus on sustainable

development, social- and environmental responsibility in all aspects of a business’ operations

(Habisch, Wegner, Schmidpeter & Jonker, 2005). Another reason for the business sector to

donate is the believe that this improves the company’s reputation together with increased

market knowledge of the disaster area (Arminas, 2005). A final reason is that the business

sector itself can also learn from the challenging circumstances of disaster logistics,

incorporating it into their own supply chains (Van Wassenhove, 2006).

General public

The general public, are also important donors for aid agencies. In fact according to a study,

monetary donations of the general public were the biggest donor to the Indian Ocean

tsunami in 2004 (Reuters, 2005). In addition to monetary donations, the general public often

also makes so-called in-kind donations. These are donations such as second hand clothing

and household items usually gathered by groups of individuals such as religious

organizations or neighbourhood centres. When these donations are not specifically asked for,

aid agencies refer to them as unsolicited donations. These in-kind donations fulfil specific

unmet needs and may be useful; however they may also be inappropriate and even hinder

relief efforts (Romero, 2007). This is a challenge, which will be further covered in section 3.7.

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Aid agencies

Generally speaking there are three categories of aid agencies receiving donations: (1)

agencies operating under United Nations umbrella (f.e. the World Health Organization) (2)

international organization operating as a federation with country subsidiaries (f.e.

International Federation of Red Cross and Red Crescent) and (3) Non-governmental

organizations operating with no affiliation with country governments (f.e. World Vision)

(Thomas & Kopczak, 2005). In order not to complicate matters throughout this thesis all

these categories will be labelled aid agencies.

The goals of these aid agencies include: lobbying and creating awareness of disasters,

policy development and of course the provision of actual aid (IDC, 2006b). Basically, the aid

agencies are the intermediaries between the donors and the beneficiaries, matching the

supply of donated goods with the demands of beneficiaries.

A specific job of aid agencies is to provide reliable needs assessments in the immediate

aftermath of a disaster. This assessment includes an overview of the disaster situation, the

most urgent beneficiary needs and the suggested plan of action. Due to this needs

assessment, donors know what and where they can contribute (IDC, 2006b; IFRC, 2007).

Military

In many disasters the military, but also institutions such as the police and the navy, is a

stakeholder. These institutions collaborate with aid agencies in disaster relief efforts,

although on a much lower scale then the business sector does (Fritz Institute, 2005c). One

reason is that the military operates from different principles then aid agencies do. The

military’s operations are guided by the term ‘force for good’, meaning they will use force in

order to do good. However, aid agency operations are guided from a ‘do-no-harm’

perspective (Wieloch, 2003).

When the military is helping with disaster relief it is either because there are circumstances,

where aid agencies are not able to provide aid, for example during combat situations. Or the

military operates as a sub-contractor of aid agencies. In this case the aid agency has to

coordinate, guide and provide direction to the military, because they view the provision of

disaster relief as a responsibility of aid agencies (Wieloch, 2003).

Media

The role of the media in disaster logistics is to report the occurrence of the disaster and the

relief efforts afterwards. The media’s relation with aid agencies is one, which Van

Wassenhove (2006) describes as a love-hate relationship. First, aid agencies need media

coverage in order to create awareness and attract attention, thereby hopefully generating

more funding (IFRC, 2005; IDC, 2006B). Chopra (2001) indicates that there is indeed a

causal link between the amount of media coverage for a disaster and the amount of

resources and funding donated.

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Second, many aid agencies view media coverage of disasters as rather selective, superficial

and stereotyped (IRC, 2005; Chopra, 2001), focused on what is going wrong with disaster

relief (Ross, 2004). Moreover, since the media is driven by financial imperatives and

audience requirements they tend to focus on unusual disasters which involve considerable

death or destruction thereby ignoring ‘less spectacular’ disasters. Therefore sudden onset

disasters tend to receive more media coverage then slow onset disasters, which results in

more funding for sudden-onset and less for slow-onset disasters (IDC, 2006b; IFRC, 2005).

Beneficiary country government

Beneficiary country governments and the political climate are also important in disaster

logistics. Initially, the beneficiary country government is the stakeholder responsible for

coordinating and organizing disaster relief efforts.

This task is often shared with international aid agencies, because disasters have paralyzed

the government and harmed the employees and structures (EIU, 2005). However, as

Romero (2007) indicated, beneficiary country governments have to take the first step. When

a disaster happens, the country’s government must ask aid agencies for help and give them

access to the country. When this does not happen, aid agencies are not allowed to enter the

country and provide aid.

There are situations in which cooperation between beneficiary country governments and aid

agencies is not very fruitful. Beneficiary countries may, require a different set of rules and

regulations (f.e. import laws) then the aid agencies are used to, thereby making disaster

logistics more difficult (Arminas, 2005).

Section 3.3 Disaster logistics activities

Within disaster logistics several activities are performed. Activities to think about include

order processing, purchasing, production, warehousing, inventory and transportation.

Order processing, in its conventional logistics meaning is concerned with order entry,

communication and invoicing of logistical activities. However, the ‘order’ in disaster logistics

is very different, meaning that disaster logistics order processing activities are also different.

An example of an order processing activity in disaster logistics is the needs assessment

discussed in section 3.2.

The purchasing activities also differ in disaster logistics. The conventional definition includes,

selecting and evaluating (potential) suppliers and buying the inputs for a given organization.

This is also true in disaster logistics, however the purchasing activities might also include

policies to receive the right in-kind donations and to limit the amounts of unsolicited

donations. A concrete example of purchasing within disaster logistics is the establishment of

supply contracts by an aid agency called ‘Save the Children’. This agency has set up deals

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with supplier guaranteeing the supply of essential goods in the event of disaster (Arminas,

2005).

Production is the logistical activity responsible for physically making products that must run

through the logistics process. It is assumed that disaster logistics is mainly performed by aid

agencies and because their core business is providing aid, not producing goods, it is likely

that production is an activity which is less relevant for aid agencies.

Warehousing is about holding inventory. The benefits of holding inventory in warehouses

also includes reducing transportation cost, consolidating shipments to profit from economies

of scale, postponing finale configuration of shipments etcetera (Bowersox et. al., 2002).

These advantages also hold in the context of disaster logistics, for example warehouses

strategically located in a disaster area can greatly enhance the efficiency of the entire

logistics operation (Fritz Institute, 2007). This does actually occur in the TNT-WFP

partnership, as WFP strategically located its five warehouses in Dubai, Ghana, Malaysia,

Panama and Italy (Romero, 2007).

Closely related to warehousing are inventory-related activities, including planning, ordering

and holding inventory. The main reasons to hold inventory include product availability for

future sales, operational support and optimization of transportation or manufacturing

equipment used. The downside of inventory is that it increases costs and thereby usually

limits efficiency. A concrete example of inventory related activities in disaster logistics is the

pre-positioning of first-aid kits in warehouses across the globe in anticipation of disasters

(Fritz Institute, 2004a; 2005a)

Transportation activities include the storage and movement of products from point of origin to

a destination. This can be done by means of different transportation modes such as motor,

rail, air, water and pipeline. In general transportation activities can benefit from two important

principles: economy of scale and economy of scope. These mean that larger shipments or

longer distances can decrease transportation cost per unit, making the activity more efficient

(Bowersox et. al., 2002). These economies and transportation activities also apply to disaster

logistics and probably are the biggest area of interest in disaster logistics today (Fritz Institute,

2007).

Section 3.4 Disaster logistics flows

Within disaster logistics one can distinguish between several different flows. The funding flow,

f.e. the flow of monetary donations, is not the only financial flow that is occurring in disaster

logistics, although it is a critical one. Also other financial flows such as credit terms, payment

schedules and consignment arrangements constitute an important flow in disaster logistics

(van Wassenhove, 2006; Kleindorfer & van Wassenhove, 2004).

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A second flow in disaster logistics is the material flow, representing the flow of physical

products from suppliers or donors to the beneficiaries (van Wassenhove, 2006; Kleindorfer &

van Wassenhove, 2004). Material flows to think about include the provision of food,

medicines and shelter. But also materials for sanitation, water supply and reconstruction are

important examples.

People flow is a third flow in disaster logistics. It includes people moving out of a disaster

area, the so-called displaced people, refugees or asylum seekers (appendix B). But it also

includes people moving into the disaster area. Here one can think about aid agency staff,

medical doctors, and business sector professionals (IFRC, 2007).

A final flow in disaster logistics is the information flow between the different stakeholders

(van Wassenhove, 2006; Kleindorfer & van Wassenhove, 2004). This flow represents all

information necessary to perform disaster logistics and can include information about

beneficiary needs, order tracking and infrastructural conditions, but also the information

donors require, such as aid agency performance information (Thomas & Kopczak, 2005).

Section 3.5 Disaster logistics processes

In section 3.2 several disaster logistics stakeholders were identified. These stakeholders all

have an influence on the processes of disaster logistics. As Tomasini and van Wassenhove

(2004a) indicate, the diversity of stakeholders involved in the disaster logistics process adds

to the complexity of the disaster relief effort.

Because existing figures were either too complex or inappropriate to explain the processes of

disaster logistics, a model was developed. Based on the different flows and stakeholders

within disaster logistics the model tries to explain the processes of disaster logistics more

clearly. In figure 3.1 on the next page, the stakeholders are identified in boxes and the

relationships between them are displayed by arrows, which will be shortly explained.

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Figure 3.1, Disaster logistics processes

Relation donors - aid agencies (A)

This process involves the relationship between the donors and the aid agencies. The

process between these stakeholders involves a bi-directional flow. First there is an

information flow from the aid agency to the donors in which the aid agency signals what

kinds of donations are needed for a given disaster. The response ideally is a material,

people- and financial flow in the other direction involving the donation of both funds and

needed materials to the aid agency. Romero (2007) explained that this initial step in disaster

response can take up to a few weeks. The aid agency will then further handle the distribution

of the donations. Finally, there is an additional information flow between aid agency and

donors, which is of particular interest to this thesis: the information flow providing feedback

and results of disaster logistics performance measurement.

Relation business sector – aid agencies - military (B)

This second process involves the aid agencies as well as the partners in the business sector

and the military. This relation involves aid agencies cooperating with the business sector and

the military to provide disaster relief as efficiently and effectively as possible. Coordination of

activities and communication about progress of the relief effort are important processes here.

These processes have specific challenges that will be covered in section 3.7.

Relation aid agencies - beneficiary country government - beneficiaries (C)

The third process is concerned with the pure distribution of donations to the beneficiaries. A

first flow is an information flow between the beneficiaries and the aid agency, which includes

the needs assessment discussed in section 3.1. Second, the donations received can be

distributed by the aid agencies themselves or as a cooperative effort between the aid agency

and the business sector or the military. However, in all circumstances the donations,

especially the material flow, has to go to the beneficiaries via the beneficiary country

governments. This is a process, which involves specific challenges that will be discussed in

section 3.7.

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Relation donors - aid agencies (D)

Although the desired process of disaster logistics moves via the centralized structure of the

aid agencies, a secondary process is also occurring. This includes the direct flow of

donations from donors to beneficiaries. Because donor countries only provide aid via aid

agencies, this secondary flow of materials is guided by the business sector or the general

public. This process of disaster logistics is largely beyond the control of aid agencies, but it

does provide additional challenges to the process of disaster logistics, which will be covered

in section 3.7.

Media influence (E)

A final issue in disaster logistics is the role of the media. Although it is not a process in itself,

the media has a significant influence on the entire process of disaster logistics. As the model

in figure 3.1 tries to show, the media cover the disaster itself, but also the process of disaster

relief. This coverage influences donor opinions about aid agencies which ultimately guide

donation decisions. This provides challenges to the disaster logistics process, which will be

covered in section 3.7.

Section 3.6 Aid agency principles

Disaster logistics and conventional logistics have similarities as well as differences. One of

these differences are the specific principles aid agencies wish to adhere to. For example, the

International Federation of Red Cross and Red Crescent (IFRC, 2007) always operates

based on seven fundamental principles which they have identified. These principles are the

core of IFRC’s thinking and guide the agency’s operations whenever no specific policy

applies. The seven IFRC principles include (1) Humanity, (2) Impartiality, (3) Neutrality, (4)

Independence, (5) Voluntary service, (6) Unity, (7) Universality. For elaboration of these

seven principles the interested reader is referred to appendix C.

Closely related to the seven IFRC principles is the concept of the humanitarian space. In

figure 3.2 one can see that the humanitarian space is made up of a triangle between the

principles of humanity, neutrality and impartiality. The space in between guides, but also

limits, the ways in which aid agencies perform their task.

Figure 3.2, Humanitarian space (Tomasini & van Wassenhove, 2004b)

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The concept of the humanitarian space represents a calm and tranquil space for aid

agencies to do their job in times of armed- or political conflict. However, there are several

examples that the lines between the humanitarian space these conflicts are rather blurred

(Van Wassenhove, 2006). For example, providing aid to wounded people may mean helping

the aggressive party in an armed conflict. And supplying food may disturb traditional

economies in the disaster area (Van Wassenhove, 2006). So, the boundaries of the

humanitarian space constitute a really fine line and it is a challenge to address beneficiary

needs without comprising the principles aid agencies adhere to.

Section 3.7 Operational challenges

Operational challenges

One challenge related to disaster logistics operations is the destructive nature of most

disasters, which influences the stages of infrastructure. Roads, bridges and airports can be

destroyed, fuel supply may be very difficult to access and transport capacity could be

extremely limited (Thomas & Kopczak, 2005; Aminas, 2005; Gustavsson, 2003). Also, the

sudden increase of trucks, boats and airplanes into a disaster area can cause extreme

congestion, further challenging disaster logistics. An interesting example here is provided by

Simpson (2005), who states that when Iran was hit by an earthquake in 2003 most aid

agency flights were turned away by the authorities because the runways were blocked by

piles of relief supplies and planes being unloaded too slowly.

Another operational challenge is the difference in cultural norms, local conditions, country

values and people’s religious beliefs. Moreover, a multitude of national governments,

regional organizations and local bureaucracies are involved in international disaster logistics,

each with their own set of laws and regulations (Thomas, 2005b; Aminas, 2005).

Also the large amount of unsolicited donations, make disaster logistics much more

challenging then business logistics. For example, these unsolicited donations can cause

bottlenecks in the supply chain because much-needed resources, including personnel and

transportation, are sacrificed to sort through and transport the supplies (Van Wassenhove,

2006). The TNT-WFP partnership also recognizes this challenge and tries to create

awareness of this issue. Together with other logistics service providers such as UPS and

FedEx they try to agree to postpone delivery of these unsolicited goods, first the priority

items (such as food and shelter) have to be delivered (Romero, 2007).

Collaboration & coordination

Section 3.2 identified a large amount of stakeholders involved in disaster relief efforts,

ultimately most of the aid agencies have similar goals. However, although the goals are

similar, collaboration and coordination between aid agencies is rather limited in disaster

logistics (Sowinsky, 2003; Thomas, 2004; Fritz Institute, 2005a). Currently aid agencies

operate primarily on an ad-hoc, immediate needs basis and even compete with each other

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for funding, warehouse space and transport capacity (Romero, 2007; IDC, 2006B; Fritz

Institute, 2005b; Thomas and Kopczak, 2005).

Stakeholder challenges

Stakeholders in disaster logistics can also have different goals. For example, the donor

countries may have different motivations for providing aid then the business sector does.

Also, the role and power of the media in disaster relief efforts is another important variable to

consider in disaster logistics (van Wassenhove, 2006). An example of this media power is

the observation that increased media attention also increases unsolicited donations, thereby

making disaster logistics more challenging (Romero, 2007).

A specific stakeholder challenge which is especially relevant in the context of this thesis is

the increased focus of donors on the efficiency of aid agency response and the ways in

which aid agencies spend their money. Donors would like to know if aid agencies are actually

reaching the people in need. This observation can also be named and increased focus on aid

agency accountability.

Complexity

Finally, logistics in times of disasters is by definition complex, dynamic, unexpected and

unpredictable. Conditions and circumstances are quickly changing and uncertainty is very

large in terms of demand and supply of aid (Gustavsson, 2003; Thomas, 2005a; Van

Wassenhove, 2006). The diversity of challenges, the interactivity among challenges, and the

invisibility and inability to anticipate challenges can all add to this complexity of disaster

logistics (Richardson, 1994).

Section 3.8 Disaster logistics learning challenges

Employee turnover

Trained or experienced aid agency employees tend to leave the aid agency rather soon and

new employees are hard to find. This fact is named employee turnover and is mainly due to

burnouts resulting from stressful and mentally challenging jobs in disaster relief. On average,

each year one out of three employees quits (Thomas, 2004; Van Wassenhove, 2006;

Gustavsson, 2003). A concrete example of the consequences of large numbers of employee

turnover is that in the aftermath of the 2004 Tsunami, 88% of all aid agencies had to

reallocate employees with logistical expertise from other assignments to support the disaster

relief efforts in South-East Asia (Fritz Institute, 2005b).

Lack of logistical knowledge

The aid agency employees working in disaster logistics generally do not have any training or

education in logistics and learned their skills on the job (Gustavsson, 2003; Thomas &

Kopczak, 2005). This challenge is also recognized by the TNT-WFP partnership, Romero

(2007) indicates that logistical knowledge in aid agencies is really operational; people get

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things done, however often not as efficient as it could be. So, the lack of logistical knowledge,

both in the field as well as at senior level, is a challenge in disaster logistics.

Lack of institutional memory

Because of the high amounts of employee turnover and the intensity of the disaster relief

efforts the mistakes made and lessons learned from one disaster are not transferred to the

next (Sowinski, 2003; Thomas, 2004; 2005a; Thomas and Kopczak, 2005; Gustavsson,

2003). Thomas (2005a) calls this challenge a lack of institutional memory. This challenge is

also reinforced by aid agency responses to disasters, which tend to be ad-hoc rather then

strategic. This is named a project-to-project mentality (Van Wassenhove, 2006).

Lack of professionalisation

Thomas (2004) also mentions a lack of professionalisation as a challenge to disaster

logistics. This encompasses inadequate needs assessments (Fritz Institute, 2005), an

absence of performance standards and certification and an underdeveloped system of

performance measurements. These challenges together with the ones covered before add

up to hindering aid agency learning, failing to record operational best-practices and thus

limiting the improvement of disaster logistics performance.

Section 3.9 Other challenges

Information technology challenge

Although information technology and communication systems are widely used in business

logistics, its use in disaster logistics remains relatively underdeveloped according to several

authors (Sowinski, 2003; Thomas, 2005a; Fritz Institute, 2005a; Van Wassenhove, 2006). In

disaster logistics the use of information technology is extremely fragmented. Most of the time,

error sensitive manual processes are still used (Thomas, 2004; Thomas & Kopczak, 2005).

Also the main communication in disaster logistics is still dependent on phone and fax,

resulting in large challenges concerning tracking and tracing of supplies. A reason for this

underdeveloped use of technology is the project-to-project mentality and a lack of funding to

invest in longer term, strategic solutions, such as advanced information technology and

communication systems.

Organizational challenges

An organizational challenge for disaster logistics departments is their quest for recognition in

the overall aid agency. Just as logistics was not considered as a department of interest in the

business sector some twenty to thirty years ago (Sowinsky, 2003; Thomas, 2003), currently

disaster logistics faces the same challenges. Disaster logistics is regarded a support function

meant to execute decisions once they are taken by senior policy makers (Thomas, 2005a).

Because logistics is not involved in structuring operations, understanding between policy

makers and logistics decreases, resulting in tension and a large distance between the two.

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Chapter 4 Performance measurement and -management

Section 4.1 Introduction

Performance, performance measurement and performance management are all rather broad

concepts and people can attach different meanings to these concepts. In order to overcome

this, section 4.2 is intended to define these concepts. Then, in section 4.3, an overview will

be given of the different measures that can be used in organizations. But since measures do

not mean anything by themselves, also standards or benchmarks have to be developed to

compare the measures against. This is what section 4.4 will be covering. Finally, section 4.5

will cover one performance management system used in the business sector. Together,

these sections will form the second ‘building block’ of this thesis.

Section 4.2 Performance, performance measurement and -management

Everybody has an intuitive idea about the meaning of the word performance, however these

meanings differ from person to person. Looking at the dictionary, this ambiguity also remains,

as performance is defined as ‘the way in which someone or something functions’ (Houghton

Mifflin Company, 2006). The different ideas about the concept of performance also occurs

between companies, and even between divisions or individuals within companies (Neely &

Adam, 2001).

Agreement on the concept of performance measurement and its definition is also difficult. For

example the Government Accountability Office defines performance measurement as ‘an

assessment of an organization’s performance, including the measures of productivity,

effectiveness, quality and timeliness’ (CDGP, 2005). Another definition is proposed by Neely

(1998) as ‘the process of quantifying the efficiency and effectiveness of past actions through

acquisition, collation, sorting, analysis, interpretation and dissemination of appropriate data’.

The outcome of performance measurement is usually quantitative however, this quantitative

output does not have a meaning by itself. It does not add any value unless company

managers add a meaning to these outputs. In order for them to do this, it is necessary to

establish performance standards to compare the numbers against.

When performance measures and the standards are known, appropriate management action

can be taken based on them, this is called performance management. Performance

management is the process of using performance measures to monitor, control and direct

the operations of an organization (Bowersox et. al., 2002). Monitoring is about tracking the

performance by periodically reviewing the output of measurement. Control is about taking

appropriate actions when the measurements are below standards. Directing performance

occurs when the measurement system is used as a basis for motivating and rewarding

employees (Bowersox et. al., 2002).

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Performance can be managed at different levels within the supply chain. Hofman (2004)

suggests four main levels of performance management levels.

1. Project level, measuring and managing the performance of a particular logistical project.

2. Organizational level, measuring and managing the performance of a specific organization.

3. Country level, measuring and managing the performance of organizations in a country.

4. Time-frame level, measuring and managing the performance of organizations during a

given time frame (Hofman, 2004).

Section 4.3 Logistical performance measures

The process of logistics consists of several major categories of activities that should be

pursued in order for logistics to function properly. These so-called functional areas provide a

basis for performance measures and were covered in section 3.3, but here examples of

performance measures will be reviewed.

Transport function

The transportation function is concerned with the physical transport of supplies into the firm

as well as transport of end-product out of the firm. In appendix D, figure D.1 an overview is

given of several so-called input-output ratios. On the horizontal axis are the inputs into the

organization, such as labour, facilities, equipment, energy and costs. On the vertical axis

several activities within the transport function are distinguished. An ‘X’ in a cell means that

A.T. Kearney (1984) suggests this ratio as an appropriate performance measure. For

example there is an ‘X’ in the cell labour * loading, meaning that the ratio of labour hours

used in loading over the road trucks can be used to measure transportation performance.

Moreover, appendix D, figure D.2 and D.3 give other examples of transportation measures

divided in a productivity-, utilization- and performance category.

Warehousing function

The warehousing function includes materials handling activities and storage facilities. Again

examples of performance measures include input-output ratios and productivity-, utilization-

and performance measures (figure D.4, D.5 & D6., appendix D).

Purchasing function

The purchasing function is involved with buying the inputs for a given firm. Within this

functional area it is also possible to calculate input-output ratios as a performance measure,

examples are given in figure D.7 of appendix D.

Inventory function

The inventory function is about the financial aspect (cost) and the operational aspect

(efficiency) of holding products in stock. In appendix D, figure D.8 examples of input-output

ratios are shown, figure D.9 and D.10 gives examples of productivity-, utilization- and

performance measures for the inventory function.

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Production function

The production function, a function actually making products, is not always present in every

organization. However it does provide an adequate area for performance measures.

Especially the input-output ratios are very common in this area. Examples of these ratios are

shown in figure D.11, appendix D.

Order processing function

A final functional area is the order processing function involved with order entry,

communication and invoicing of logistical activities. For this area there are also examples of

input-output ratios (figure D.12, appendix D) and productivity-, utilization- and performance

measures (figure D.13 & D.14, appendix D).

Although functional performance measures are a good basis for performance management,

it also has a major drawback. This is the fact that functional performance measures fail to

take the entire logistics process into account and only look at a small part of the entire

process. According to Robeson et. al. (1994) this could result in functions optimizing their

own performance at the expense of total logistics performance. An example could be that the

production function keeps producing in order to optimize the utilization rates, thereby

increasing the amounts of stock and harming the performance of the inventory function.

Since this is an undesirable situation, there is a need for measures reflecting the

performance of the entire process of logistics (Bowersox et. al., 2002).

In a logistics measurement survey performed in 2002 (Harrison & van Hoek, 2002) it

appeared that only 9% of the responding organizations were using performance measures

that spanned the entire process of logistics, the so-called supply chain comprehensive

performance measures. These measures are meant to give an integrated view on logistics

performance, which is consistent across different functions and which can even be

comparable across different firms in the supply chain (Bowersox et. al., 2002). Specific

examples of these measures are provided in figure D.15 and D.16 of appendix D and include

among others: cash-to-cash conversion, dwell time, supply chain response time, internal

defect rates and stock turns.

Section 4.4 Standards & benchmarks

The performance measures discussed in section 4.3, do not mean anything by themselves.

In order for them to have a meaning, the next step in logistics performance management is to

develop performance standards to compare actual performance against (Stock & Lambert,

1987; Slack & Lewis, 2002).

Standards can be established on several different bases, a first way is to develop standards

based on historical data. A drawback of this method is that it is backward looking and that it

may result in continuously focusing on sub-optimal performance (Monczka, Trent & Handfield,

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2005; Slack & Lewis, 2002). A second way is to base standards on internal improvement

goals. The drawback of this method is that it does not take competitor performance into

account (Monczka et. al., 2005; Slack & Lewis, 2002). The third basis for standards is

comparing performance against competitors. This is a promising method but the main

drawback is that generally competitor performance data is hard to obtain (Monczka et. al.,

2005; Slack & Lewis, 2002). The final basis for standards is comparing an organization’s

performance to absolute performance. Meaning that performance is compared against

perfection (Monczka et. al., 2005).

Closely related to the development of performance standards is the concept of benchmarking,

which is defined as ‘the continuous process of measuring products, services, processes,

activities and practices against the company’s best competitors or those companies

renowned as the industry- or functional leaders’ (Robeson et. al., 1994; Camp, 1994;

Monczka et. al., 2005). Basically, benchmarking can also be seen as developing standards

to compare performance against. Four different types of benchmarking can be identified:

1. Internal benchmarking, or performance benchmarking, involves comparing similar

functions within a given company with each other (Robeson et. al., 1994; Camp, 1994;

Van Vliet, 1998; Bowersox et. al., 2002; Slack & Lewis, 2002).

2. Competitive benchmarking is comparing practices and work processes with those of a

company’s main- or best competitors (Robeson et. al., 1994; Camp, 1994; Van Vliet,

1998; Bowersox et. al., 2002; Slack & Lewis, 2002).

3. Functional benchmarking, or non-competitive benchmarking, is comparing work

processes with those of the functional leader in the given function. Most of the time this is

an organization from a completely different industry (Robeson et. al., 1994; Camp, 1994;

Slack & Lewis, 2002).

4. Generic process benchmarking, is comparing the actual process itself, the so-called

generic process. Ultimately, a lot of processes in organizations are similar so they can

provide a basis for performance management (Robeson et. al., 1994; Camp, 1994; Van

Vliet, 1998).

Section 4.5 Performance management systems

Over time, performance measures have been combined into systems meant to manage

organizational performance. These performance management systems vary considerably

and all of them have a unique perspective on performance. They all provide managers with a

different set of lenses through which the organizational performance can be managed,

according to Neely & Adams (2001). In this section one of these performance management

systems will be discussed: the balanced scorecard.

The reason for covering this performance management system is based on literature review.

First, Pun & White (2005) compared a total of ten performance management systems on

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three sets of evaluation criteria, being (1) Dimensions of performance, (2) Performance

measure characteristics and (3) Specifications and requirements of Performance

measurement development. Second, Gijsbrechts (2007) also compared five performance

measurement systems on types, aspects and dimensions of performance. In both of the

papers, the balanced scorecard model was among the top of performance management

systems. Meaning that this system complied with most of the evaluation criteria and was

among the most inclusive performance management systems.

The balanced scorecard approach (Kaplan & Norton, 1996) uses four perspectives to view

an organization from and it includes performance measures based on each of these four

perspectives. The exact perspectives are:

1. Learning and Growth, including employee training and corporate attitudes related to both

individual and corporate self-improvement.

2. Business Process, refers to internal business processes and includes the strategic

management process, but also support processes.

3. Customer, involved with the important area of customer focus and includes measures

related to customer satisfaction.

4. Financial, involved with the traditional performance measures focusing on costs and

benefits (Balanced Scorecard Institute, 2007).

One of the distinguishing features of the balanced scorecard system is that it ‘balances’ the

indicators used. This means that a weight is attached to each specific indicator: the more

important indicators receive a higher weight, the less important indicators receive a lower

weight. Assigning these weights is done by each organization using the balanced scorecard,

therefore it is subjective and there is no one-size-fit-all weighing schedule (Balanced

Scorecard Institute, 2007).

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Chapter 5 Disaster logistics performance management

Section 5.1 Introduction

Chapter 4 dealt with logistics performance management in the business sector, in this

chapter the focus will be on current approaches to disaster logistics performance

management. This is the third and final ‘building block’ of this thesis. First, disaster logistics

performance is considered in section 5.2. Then, section 5.3 is about standards and indicators

in use today, section 5.4 covers the views of aid beneficiaries on performance of aid

agencies and section 5.5 discusses a scorecard for disaster logistics performance

management. Finally section 5.6 covers some important obstacles to performance

management in disaster logistics.

Section 5.2 Disaster logistics performance

Although the definition of disaster logistics is similar to the definition of conventional logistics,

performance may mean different things for the two fields. As Slack (1991) and Harrison &

van Hoek (2002) propose, logistics performance may include five objectives: speed,

dependability, quality, flexibility and cost. Depending on the organization’s strategy one or

two of these objectives prevail. However when looking at the disaster logistics definition,

three performance objectives prevail: speed, flexibility and cost.

In the first 72 hours after a disaster, speed of delivery is crucial, together with flexibility in

handling disaster logistics challenges. At this point in time, cost and efficiency are of less

importance. Later in the disaster management process (see section 2.3), the focus shifts to a

mixture of speed and flexibility while doing it at reasonable cost and as efficient as possible

(van Wassenhove, 2006).

In the next three sections, a review will be given of three initiatives to disaster logistics

performance management, which are currently used by aid agencies: the Sphere project,

URD’s participatory performance management and Davidson’s performance indicators.

Section 5.3 Sphere’s minimum standards

One of the initiatives to performance management in disaster logistics is the Sphere project

(Hofman, 2004). This project was launched by a group of NGOs and the IFRC in 1997. The

aim of the Sphere project is ‘to improve the quality of assistance provided to people affected

by disasters and to enhance the accountability of the humanitarian system in disaster

response’ (Sphere, 2004). In order to achieve this aim, the Sphere handbook was developed,

in which minimum standards and -indicators for disaster response were set out.

These standards have been developed by using aid agency employees. Most of the

standards are not new, but they were adapted and consolidated from existing aid agency

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knowledge and current practice. First, the Sphere handbook (Sphere, 2004) identified

common standards for aid agencies. These include assessment-, response- and evaluation-

standards, which are further elaborated in appendix E.1. These standards in the Sphere

handbook are qualitative in nature and are meant to be applicable in any disaster. Second,

the Sphere handbook (Sphere, 2004) also identifies standards in six specific sectors:

1. Water supply, sanitation and hygiene standards, examples are provided in appendix E.2.

2. Food security standards, examples are provided in appendix E.3.

3. Nutrition standards, examples are provided in appendix E.3.

4. Food aid standards, examples are provided in appendix E.3.

5. Shelter standards, settlements and non-food, examples are provided in appendix E.4

6. Health services standards, examples are provided in appendix E.5.

From these standards, several key indicators were developed to put the standards into

practice. These indicators are measures to the standards and can be qualitative or

quantitative. They are the tool to measure the impact of aid agency relief efforts (Sphere,

2004). It is also stated that without the indicators ‘the standards would be little more than

statements of good intent, difficult to put into practice’ (Sphere, 2004).

Several aid agencies have used the Sphere project and especially the Sphere handbook and

its minimum standards in practise. NGOs such as Oxfam, Care, World Vision and the IFRC

reported positive results (Sphere, 2007). However, these aid agencies also reported

implementation problems of the minimum standards and made suggestions for the extension

of the Sphere standards. A concrete example is the inclusion of the food security standards

in 2004, in the first version of the handbook (Sphere, 2002) these standards were still absent.

Another example of extending the Sphere standards is the inclusion of logistical standards.

Up until 2007 standards like this are not included into the Sphere handbook although disaster

logistics is getting increased attention and is gaining increased strategic relevance in aid

agencies (Thomas, 2004).

Section 5.4 Participatory performance management

Another initiative to performance management in disaster logistics is the quality-project

undertaken by the French NGO URD (URD, 2007). This project measures performance by

means of surveys, research and interviews among aid beneficiaries. This so called

participatory approach to performance management was also applied by the Fritz Institute

(2005) in the aftermath of the Indian Ocean tsunami. Examples of questions asked by the

Fritz Institute (2005) are: in the first 48hours after a disaster which organizations came to

help? Or which services did the aid agencies provide in the first 48hours after a disaster?

The institute stated that surveys and questionnaires offered significant and substantial input

to the management of aid operations. Especially the beneficiary perspectives of the

assistance received across hundreds of affected villages was very helpful in managing

performance (Fritz Institute, 2005).

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The participatory approach also has important drawbacks. One of the most important is the

fact that aid agency employees do not have the statistical knowledge to take valid samples

and to statistically analyze the results of a survey (Romero, 2007; Hofmann, 2004). The

result often is a poorly conducted survey which is not very useful for performance

management. Several authors (Garfield, 2001; Shoham et. al. 2001; SMART, 2002) have

provided examples of such poorly conducted surveys.

Section 5.5 Davidson’s performance indicators for disaster logistics

A third initiative to performance management in disaster logistics is the indicator model as

developed by Davidson (2006). She developed a four indicator model for disaster logistics,

based on the principles of commercial- and military performance measurement, because she

found that disaster logistics faces many of the same issues. The indicators used and the

resulting framework were developed by interviewing IFRC professionals and by using the

data analysis of the 2005 South Asia earthquake (Davidson, 2006).

The resulting set of indicators includes the following four:

1. Appeal coverage, intended to indicate how well the organization is meeting its appeal for

an operation in terms of finding donors and delivering items. Two specific measures were

suggested: (1) Appeal coverage = pledged items / items needed, (2) % of items delivered

= (items delivered on site / items needed) * 100%.

2. Donation-to-delivery-time, intended to measure how long it takes for an item to be

delivered to the destination country after a donor has pledged to donate it. This is

measured by either (1) mean number of days or (2) median number of days.

3. Financial efficiency, intended to incorporate the financial aspect of providing aid. Three

metrics were suggested: (1) relative metric = (Actual cost – budgeted cost)/budgeted cost,

(2) absolute metric = actual cost – budget cost, (3) transportation cost = transport cost /

total cost.

4. Assessment accuracy, is intended to indicate how much the operation’s final budget

changed over time from the original budget. This not only includes the financial changes,

but also changes in quantity of goods needed.

The indicators discussed are combined in a scorecard, an example of which is shown in

figure 5.1. This final scorecard has a few interesting characteristics. First, the scorecard has

a specific column to define quantitative goals for each indicator in the ‘Total Op Target’

column. In chapter 4 it was already stated that setting standards is the critical factor in any

performance management system (Davidson, 2006). Second, the scorecard can be

prepared at different points in time, thereby keeping track of performance over time

(Davidson, 2006). Finally, the scorecard includes a systematic way of defining ‘priority items’

per disaster relief operation (column 3 and 4 in figure 5.1). In this way the scorecard

facilitates communication across the relief operation about what items have priority in

logistics. Moreover, because of the prominent place on the scorecard the aid agency can

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easily monitor and control how quickly and efficiently the priority goods are being delivered to

the field (Davidson, 2006).

Figure 5.1, South Asia earthquake final scorecard (Davidson, 2006)

Section 5.6 Obstacles to performance management in disaster logistics

The initiatives to performance management in disaster logistics suffer from several obstacles.

Examples include the dynamic, dangerous and chaotic environments of disasters, the

difficulties of the operational environment, the need to act quickly in disaster and the intense

influence of the media (Hofmann, 2004; ALNAP, 2002; 2003). All these disaster

characteristics require attention, thereby limiting the resources available to measure

performance.

Moreover, several authors mention that organizational culture in aid agencies is one that

values action over analysis. This organizational culture has to change in order for

performance measurement to be effective (Hofmann, 2004; ALNAP, 2002; 2003; Davidson,

2006). For example aid agencies generally focus on helping people instead of gathering data

and when data is gathered, there usually is no management action when measurements

signal extreme values (Romero, 2007).

Although TNT is pushing the WFP towards using performance measurements, the

partnership also identifies several implementation issues. For example they question who

should be responsible for measurement, they indicate that there is a lack of statistical

knowledge and also reliability and validity of measurements are obstacles to performance

management identified by TNT (Romero, 2007).

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Finally, the use of performance management in disaster logistics potentially creates rather

odd incentives. For example, aid agencies could take fewer risks and stick to operations

which outcomes can be measured easily or operations which outcomes are expected to be

positive (IFRC, 2003). Also, it could result in limited cooperation between aid agencies,

because in that case performance of separate agencies is harder to measure (Hofman, 2004;

IFRC, 2003).

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Chapter 6 Applying business sector performance management approaches to

disaster logistics

Section 6.1 Introduction

In chapter 4 performance management was covered for the business sector. Chapter 5

covered the current state of performance management within disaster logistics. This chapter

is meant to combine these two. The goal of this chapter is to show which business sector

approaches are transferable to disaster logistics. In order to do this, chapter 6 follows the

layout of chapters 4 and 5. Section 6.2 covers performance measures in disaster logistics

while section 6.3 is about standards and benchmarks in disaster logistics. Section 6.4 covers

the balanced scorecard and section 6.5 discusses the benefits of performance management

for disaster logistics.

Section 6.2 Performance measures in disaster logistics

In section 3.3 disaster logistics activities were covered while section 4.3 and appendix D

discussed logistical performance measures. In this section, these two are combined and the

applicability of measurements to disaster logistics is discussed.

Some of the examples given in appendix D are more useful in disaster logistics then others.

For example, the productivity ratios of figures D.1, D.4, D.7, D.8, D.11 and D.12 can easily

be applied to disaster logistics, since the inputs (labour, equipment and overall costs) are

present in conventional logistics as well as disaster logistics.

However, sometimes the suggested measures also have to be adapted. Examples are the

productivity measures of figure D.2 and D.9 which include turnover. Since aid agencies do

not receive money from their ‘customers’, measures based on turnover are inappropriate and

should be adapted to fit disaster logistics. For example turnover could be replaced by total

funds donated when calculating productivity percentages (figure D.2).

Examples of measures, which are probably not applicable to disaster logistics, include the

performance effectiveness measures of figure D.3, D.6, D.10 & D.14, relating actual costs to

budgeted cost. These might be unusable because the uncertain and complex nature of

disasters makes it impossible to set reliable budgets in disasters.

In section 4.2, the supply chain comprehensive measures were covered (figure D.15 & D.16).

For disaster logistics some of these measures could be applicable. Examples are order

delivery lead-time, dwell time and supply chain response time. Others are inappropriate

given the specific nature of disaster logistics and the associated challenges (chapter 3). An

example is the cash-to-cash conversion measure of figure D.16. Given the fact that

beneficiaries in disaster logistics do not pay for the aid they receive, this measure is

inappropriate since it measures the time taken to convert $1 of inventory to $1 of revenue.

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In appendix D several examples of performance measures were given, however this list is

only meant to be illustrative. The examples in appendix D are not meant to be a complete list

of available performance measures. Therefore it is not appropriate to discuss the

transferability of every performance measure available. However, for the figures in appendix

D, each is shortly discussed and the transferability of the measures is briefly covered. This

should give the reader a good idea about the transferability of business sector performance

measures to disaster logistics.

A concrete example of transferring business sector performance measures to disaster

logistics is provided by the TNT-WFP partnership (TNT, 2007b). In appendix E, several key

performance indicators for transportation are identified and further explained by TNT. They

are concerned with utilisation, maintenance and fuel, accidents and overall performance.

Although, in the discussion section (section E.3) TNT argues for monthly data gathering,

feedback sessions and a change of thinking. They also find that there are several difficulties

for WFP to use the key performance indicators (Romero, 2007).

Section 6.3 Standards & benchmarks in disaster logistics

Section 4.4 discussed four ways to set performance measurement standards and it also

covered four types of benchmarking. Section 5.3 covered the Sphere project and its attempt

to specify minimum standards for disaster relief. In this section both are covered and their

applicability to disaster logistics is discussed.

The minimum standards and indicators identified by Sphere are mainly concerned with the

types of aid required as can be seen in section 5.3 and appendix F. Although these

standards are really valuable for aid agencies, Sphere does not identify standards and

indicators for disaster logistics. Therefore, the Sphere project is not very applicable to

disaster logistics at this point. But based on literature review and de findings of this thesis,

the author does see possibilities for the inclusion of Sphere standards and indicators related

to disaster logistics. For example, standards could be developed for the functional areas of

disaster logistics. The specific performance measures discussed could provide a good basis

for this.

The standards set by the Sphere project are developed in cooperation with aid agency

employees and therefore they are internal improvement standards. This implies that internal

improvement standards are applicable in disaster logistics. Another standard or benchmark,

which is applicable in disaster logistics, is comparing disaster logistics performance to the

performance of other aid agencies or other logistics companies. This practice of

(non)competitive benchmarking is applicable since aid agencies can learn a lot from each

other, but also from the performance of conventional logistics organizations.

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The historical standard identified in section 4.4 can also be applied in disaster logistics.

Based on information of past disasters, standards could be set. For example one can set

average standards or worst-case scenario standards for delivery times of relief items.

However, one has to note that this is only useful if standards are set within different disaster-

types (appendix B). For example: applying delivery standards based on past earthquakes on

a man-made- or technological disaster is probably not that useful since circumstances also

differ a lot. The standard setting performed by TNT in the TNT-WPF partnership can be

reviewed in Appendix E. One observation from this example is that a lot of targets are

context specific, depending on disaster area, vehicle types and or local rates.

Section 6.4 Balanced scorecard in disaster logistics

Section 4.5 covered the balanced scorecard, one of the most inclusive performance

management systems. The balanced scorecard ultimately is a strategic management tool,

but section 5.5 showed that the Davidson (2006) approach resembles this system. One of

the main differences between the two is that Davidson (2006) includes measures in several

areas, but the areas are not the same as the four perspectives of the balanced scorecard.

Another difference is that Davidson (2006) does not ‘balance’ the performance measures,

while the balanced scorecard system does propose this (see section 4.5). However,

according to the author the attempt of Davidson (2006) is an interesting one and also the

balance scorecard system has interesting features which should be developed to be

applicable to disaster logistics even more. Especially the focus on different perspectives and

the weighing of performance measures are interesting features for disaster logistics.

However, the differences between disasters and the different stakeholders within disaster

logistics will result in different scorecards for different disasters thereby making it impossible

to draw up one universal disaster logistics scorecard. But to give an example of its

applicability to disaster logistics, the scorecard of figure 6.1 was developed for the transport

function of toilets to a disaster area.

Figure 6.1, first shows the financial perspective including a measure of appeal coverage.

This measure was taken from the IFRC (2007), which has been using this measure from

2000 onwards. The second measure expresses how much of the total available funds is

spend on disaster logistics, while the third measure is obtained from figure D.3. Together

these measures consider overall financial performance of an aid agency and more practical

financial performance of the transport function of toilets.

Second is the learning and growth perspective. This perspective is developed in order to

address the disaster logistics learning challenges, which were covered in section 3.8.

Examples of measures are a measure for staff turnover or measures of logistics staff training.

Institutions such as the Fritz Institute and INSEAD offer disaster management courses and

disaster logistics conferences. Aid agencies could include the learning and growth

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measurements suggested in figure 6.1 in order to monitor and control the amounts of staff

turnover and logistical knowledge levels throughout the aid agency.

Figure 6.1, Disaster logistics balanced scorecard

Perspective Objective Measure Target

Financial

1. Appeal Coverage Meet or exceed funds needed for a disaster ($ of appeal received / $ of appeal requested) * 100% 100%

2. Disaster logistics spend Optimize spend on disaster logistics ($ spend on disaster logistics / $ funds available) * 100% To be set

3. Functional (financial) Efficiency Perform logistics functions as efficient as possible (Actual transport cost / budgeted transport cost) * 100% To be set - below 100%

(Actual transport cost / standard transport cost ) * 100% To be set - below 100%

Learning and growth

1. (Logistics) Staff turnover rate Reduce staff turnover & retain logistical knowledge % of (logistics) staff leaving the aid agency To be set - as low as possible

2. Logistics staff trained Increase disaster logistics knowledge # of people finishing INSEAD disaster management course yearly To be set

# of Fritz Institute humanitarian logistics conferences visited To be set

% of (logistics) staff with disaster logistics degree To be set

Internal business process

1. Delivery lead time Providing needed sanitation within acceptable time frame Donation-to-delivery time of toilets To be set - as low as possible

2. Utilization measure Optimizing capacity utilization transport capacity used / transport capacity at disposal To be set

3. Functional productivity measure Increasing/optimizing functional productivity % of Labour to load a truck with toilets To be set

% of Labour to Line-Haul a truck with toilets To be set

% of Labour to unload a truck with toilets To be set

Customer (Beneficiary)

1. Beneficiary satisfaction Meet or exceed beneficiary expectations% of beneficiaries reporting satisfactory access to toilets

within 24h, 48h, 1 week, 1 monthTo be set

2. Beneficiary access Beneficiary access to toilets 1. % of people having access to toilet To be set - as high as possible

2. # of people per toilet To be set

The third perspective is the internal business process perspective. Here the functional

performance measures discussed in section 4.3 are applicable. In this example the delivery

lead time measure (figure D.15) is included, next to utilization and productivity measures

obtained from figure D.3. The standards for this perspective still have to be set, however the

Sphere project already is a good starting point, especially when minimum standards and

indicators are formed for the functional areas of disaster logistics.

The fourth and final perspective of the example is the customer or beneficiary perspective.

Here the customer service measures of figure D.17-D.19 are applicable. And also the

customer satisfaction approach discussed in section 4.3 and the participatory performance

management approach of section 5.4 are relevant in measuring performance in this

perspective For example; the participatory approach could be used to gather data on

beneficiary satisfaction with the aid offered.

Section 6.5 Benefits for disaster logistics

The past chapters dealt with performance management, disaster logistics and whether the

former is applicable to the latter. However, questions remain unanswered up until now: Why

should aid agencies apply performance management? What are the advantages and

benefits for them?

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This final section is intended to provide an answer to these questions. Here, the benefits of

performance management will be discussed and they will be related to the disaster logistics

challenges covered in chapter 3.

Improve processes

One major benefit of performance management in disaster logistics is the improvement

potential it has for disaster logistics. For example aid agency management can use facts of

actual performance as input for future plans, it can identify and eliminate the causes of bad

performance and it can use the output of measurements for continuous improvement of

processes (Thomas & Kopczak, 2005; Fritz Institute, 2004). So, the use of performance

measurements can enhance the effectiveness of decisions made before, during and after a

relief operation (Douglas-Bate, 2003). Being able to improve operations in disaster logistics

based on facts is a huge benefit of performance management and can clearly help in

addressing the operational challenges identified in section 3.7. For example, although

disasters and disaster logistics will still be complex, managing this complexity will be easier

because aid agency management is better informed.

Logistical knowledge retention

Another benefit of performance management for disaster logistics is closely related to the

former, since it also deals with improving processes. According to the author performance

management can improve learning and knowledge retention in disaster logistic. For example,

according to Davidson (2006) and the Fritz Institute (2005b) performance management can

enable organizations to retain lessons learned and transfer knowledge from one disaster to

the next in a systematic way. Although performance management in disaster logistics does

not limit employee turnover, it does help in addressing the challenges related to a lack of

logistical knowledge and institutional memory (section 3.8).

Standardization

Again, another benefit which is concerned with improving disaster logistics processes is

standardization. Performance management includes setting performance standards, this also

holds in disaster logistics. Standardization is said to increase the effectiveness of people and

facilitate coordination, collaboration and resource sharing in the field (Fritz Institute, 2005b;

Thomas, 2005), which is a clear benefit for disaster logistics. On the one hand

standardization addresses the challenges associated with stakeholders and complexity

(section 3.7) on the other hand standardization also addresses the collaboration and

coordination challenges in disaster logistics (section 3.7).

Accountability

Another benefit of performance management in disaster logistics is that it provides a

quantitative and objective base of actual data showing how aid agencies are performing,

which can be used to indicate that donor contributions are well-spent. This is a benefit for aid

agencies, since they can react to the increasing demand of donors to know how donor-

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money is spent and if aid agencies are operating efficiently. This challenge was named the

accountability challenge in section 3.7.

As aid agencies can show their performance objectively, donors are given an incentive to

continuously support aid agencies (Thomas & Kopczak, 2005). Maybe, this can even result

in a change in the process of aid agency funding from ad-hoc, project-related donations, to

more structural and year-round donations which can be used to improve disaster logistics

and aid agencies structurally in the long-term.

Logistics as strategic function

A final benefit of performance management for disaster logistics is about the role of disaster

logistics within the aid agency. Many people still view disaster logistics as a support function

within aid agencies; others begin to recognise that disaster logistics is actually a strategic

function which adds value. For example Thomas (2004) found that as the professionalisation

and strategic significance of disaster logistics increases, so does the value added by the

humanitarian organisation. Moreover, the Fritz Institute (2005) states that performance

measurements are essential to establishing the strategic position disaster logistics requires.

This final benefit of performance management for disaster logistics is that it addresses the

organizational challenges within aid agencies (section 3.9).

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Chapter 7 Conclusions, recommendations & limitations

Section 7.1 Conclusions

In chapter 1, the problem statement was formed, the research questions were developed and

the structure of the thesis was formed (see figure 7.1). The three ‘building blocks’, chapters 3

– 5, together with chapter 6 led to answering the problem statement:

How can business sector performance management approaches be used to improve disaster

logistics?

Figure 7.1, Research questions and thesis structure

First, chapter 3 showed that the challenges of disaster logistics included operational

challenges, collaboration and coordination challenges, stakeholder challenges, complexity

challenges, logistical learning and knowledge challenges, information technology challenges

and organizational challenges. Second, chapter 4 identified functional logistics performance

measures and supply chain comprehensive performance measures. Moreover standards and

benchmarks were identified and the balanced scorecard system was covered.

Third, chapter 5 covered current performance management in disaster logistics and reviewed

Sphere’s minimum standards, participatory performance management and the Davidson

scorecard. Finally, concluding this thesis chapter 6 combined the three ‘building blocks’ and

showed which performance management approaches are applicable in disaster logistics and

how they can improve disaster logistics. Here, the conclusions of this thesis are outlined:

A first conclusion is that performance management can improve disaster logistics operations

because performance measures quantify logistical performance, compare this with

performance standards and then identifies gaps between actual- and standard performance.

When these are know, aid agency management can determine the cause of the problem and

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identify a solution, thereby tackling operational challenges. All based on objective,

quantifiable performance measures.

A second conclusion is that performance management can address the challenges related to

the lack of knowledge retention in disaster logistics. Quantifying performance and using

performance management can improve disaster logistics because it enables aid agencies to

transfer knowledge from one disaster to the next in a systematic manner.

A third conclusion is that performance management sets standards when measuring

performance. This increases people’s effectiveness and facilitates coordination, collaboration

and resource sharing, thereby addressing several collaboration challenges disaster logistics

faces. This can clearly improve disaster logistics.

The fourth conclusion is that performance management improves disaster logistics because

measuring performance means quantitative, objective data. This data can be used as proof

towards donors that donations are used efficiently, thereby possibly increasing donors or

amounts of funds donated.

The final conclusion is that performance management can improve disaster logistics as a

function in the overall aid agency. Being able to show and improve performance of disaster

logistics means its strategic relevance is also easier to show. When this is done, the

organization challenges in disaster logistics can be addressed, thereby decreasing the

distance between aid agency policy makers and the disaster logistics function.

Section 7.2 Recommendations

A first recommendation for aid agencies and the business sector is to increase cooperation.

There are great learning possibilities when aid agencies and the business sector cooperate.

Not only does cooperation benefit aid agencies, also the business sector can learn from the

complexities and uncertainties associated with disaster logistics. With respect to

performance management, cooperation should be increased in order to find ways to

incorporate a measurement culture in aid agencies.

Second, it is recommended that the Sphere project should include disaster logistics

standards. Up till now, the Sphere handbook has been revised and updated two times. It is

recommended that a next update is made including minimal standards and indicators. This

way aid agencies not only profit from standardization of aid provision, but also from a

standardization of logistics activities and logistics management in times of disaster.

Finally, it is recommended that performance measures and scorecards are used more to

measure and manage performance of disaster logistics. The functional measures discussed

in chapter 6 and the example of appendix E serve as a good basis. Moreover, the balanced

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scorecard could be of assistance, but also the Davidson (2006) attempt serves as a good

basis to improve scorecard use to improve disaster logistics. Section 6.4 and the example of

figure 6.1 showed one of the possibilities to do so.

Besides the practical recommendations, also academic recommendations exist. These are

the recommendations for further research. It is recommended that this thesis is extended in

further research. For example, research into performance measures and –standards could

increase and improve the measurements and standards used.

Also, research could focus more on the implementation of measurements in disaster logistics.

Examples include the practical limitations to measurement and the use of information

technology and software to overcome these practical limitations.

Since measures and standards are developed in performance management, there is an

objective base to compare aid agencies with each other. It is recommended that research is

performed into the possibilities of this comparison. For example, research could look into the

possibilities of certification for aid agencies, in order to show to donors which aid agencies

spend their money efficiently and which do not.

Section 7.3 Limitations

A first limitation is about the usefulness of the conclusions and recommendations across

disasters. Several times throughout this thesis the point was raised that a lot of things in

disaster logistics are really context specific. Although the researcher does not discuss one

specific disaster, the usefulness can be questioned because no concrete, practical examples

can be given that fit a specific disaster. For example there is no prescription of

measurements to use in an earthquake in Southeast Asia or a plane crash in the United

States.

The second limitation is about the fact that the researcher is an academic master’s student

with no background in disaster logistics. This could also be seen as an advantage, because

the researcher is not coloured by his experience in aid agencies, but it is felt that it also limits

this study, because the researcher has no practical knowledge of the context and challenges

of a disaster. The only way in which information about this could be obtained is via aid-

agency websites, publications and the interview with Mr. Romero from the TNT-WFP

partnership.

A final limitation is closely related to the former. As the main data source for this thesis is

secondary data by means of literature review, the claims made throughout this thesis could

be coloured by the researcher’s perceptions of the literature. This limitation is diminished by

means of the interview with Mr. Romero from the TNT-WFP partnership. This primary data

source did indeed verify several claims made throughout this thesis.

Kevin Koenen - 47 - 13/08/2007

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Appendix A Disaster figures

Figure A.1, Total number of natural disasters reported 1900-2006 (EM-DAT, 2007)

Figure A.2, Total number of people reported affected by natural disasters 1900-2006 (EM-DAT, 2007)

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Figure A.3, Total number of technological disasters reported 1900-2006 (EM-DAT, 2007)

Figure A.4, Total number of people reported killed by technological disasters 1900-2006 (Source: EM-DAT, 2007)

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Appendix B Disaster definitions

Section B.1 Natural disasters

Natural hazards: Natural processes or phenomena occurring in the biosphere that may

constitute a damaging event. Natural hazards can be classified by origin namely: geological,

hydro meteorological or biological. Hazardous events can vary in magnitude or intensity,

frequency, duration, area of extent, speed of onset, spatial dispersion and temporal spacing

(UN/ISDR, 2007).

Hydro meteorological hazards: Natural processes or phenomena of atmospheric,

hydrological or oceanographic nature, which may cause the loss of life or injury, property

damage, social and economic disruption or environmental degradation. Hydro meteorological

hazards include: floods, debris and mud floods; tropical cyclones, storm surges,

thunder/hailstorms, rain and wind storms, blizzards and other severe storms; drought,

desertification, wild land fires, temperature extremes, sand or dust storms; permafrost and

snow or ice avalanches. Hydro meteorological hazards can be single, sequential or

combined in their origin and effects (UN/ISDR, 2007).

Geological hazard: Natural earth processes or phenomena that may cause the loss of life or

injury, property damage, social and economic disruption or environmental degradation.

Geological hazard includes internal earth processes or tectonic origin, such as earthquakes,

geological fault activity, tsunamis, volcanic activity and emissions as well as external

processes such as mass movements: landslides, rockslides, rock falls or avalanches,

surfaces collapses, expansive soils and debris or mud flows. Geological hazards can be

single, sequential or combined in their origin and effects (UN/ISDR, 2007).

Biological hazards: Processes of organic origin or those conveyed by biological vectors,

including exposure to pathogenic micro-organisms, toxins and bioactive substances.

Examples include: Outbreaks of epidemic diseases, plant or animal contagion and extensive

infestations (UN/ISDR, 2007).

Drought: Period of deficiency of moisture in the soil such that there is inadequate water

required for plants, animals and human beings (EM-DAT, 2007). In general, drought is

defined as an extended period - a season, a year, several years - of deficient rainfall relative

to the statistical multi-year average for the region. Lack of rainfall leads to inadequate water

required for plants, animals and human beings. A drought leads to other disasters, namely

food insecurity, famine, malnutrition, epidemics and displacement of populations from one

area to another (IFRC, 2007).

Earthquake: Sudden break within the upper layers of the earth, sometimes breaking the

surface, resulting in the vibration of the ground, which where strong enough will cause the

collapse of buildings and destruction of life and property (EM-DAT, 2007; IFRC, 2007).

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Epidemic: Either an unusual increase in the number of cases of an infectious disease, which

already exists in the region or population concerned; or the appearance of an infection

previously absent from a region (in EM-DAT the epidemic disease in included as a disaster

subset) (EM-DAT, 2007). An epidemic is then unusual increase in the number of cases of an

infectious disease which already exists in a certain region or population. It can also refer to

the appearance of a significant number of cases of an infectious disease in a region or

population that is usually free from that disease (IFRC, 2007).

Extreme temperature: Disaster type term used in EM-DAT comprising the two disaster

subsets heat wave: a long lasting period with extremely high surface temperature, and cold

wave: a long lasting period with extremely low surface temperature (EM-DAT, 2007).

Flood: Significant rise of water level in a stream, lake, reservoir or coastal region (EM-DAT,

2007). Flash floods are sudden and extreme volume of water that flow rapidly and cause

inundation Because of its rapid nature flash floods are difficult to forecast and give people

little time to escape or to take food and other essentials with them (IFRC, 2007).

Insect infestation: Pervasive influx and development of insects or parasites affecting humans,

animals, crops and materials (EM-DAT, 2007).

Slide: Disaster type term used in EM-DAT comprising the two disaster subsets avalanche: a

rapid and sudden sliding and flowage of masses of usually unsorted mixtures of

snow/ice/rock material, and landslide: all varieties of slope movement, under the influence of

gravity. More strictly refers to down-slope movement of rock and/or earth masses along one

or several slide surfaces (EM-DAT, 2007).

Volcanic eruption: Discharge (aerially explosive) of fragmentary ejecta, lava and gases from

a volcanic vent (EM-DAT, 2007). Volcanic eruptions happen when lava and gas are

discharged from a volcanic vent (IFRC, 2007).

Wave/surge: Disaster type term used in EM-DAT comprising the two disaster subsets tidal

wave: abrupt rise of tidal water (caused by atmospheric activities) moving rapidly inland from

the mouth of an estuary or from the coast and, tsunami: a series of large waves generated by

sudden displacement of seawater (caused by earthquake, volcanic eruption or submarine

landslide); capable of propagation over large distances and causing a destructive surge on

reaching land (EM-DAT, 2007).

Wild fire: Disaster type term used in EM-DAT comprising the two disaster subsets forest fire:

fires in forest that covers extensive damage. They may start by natural causes such as

volcanic eruptions or lightning, or they may be caused by arsonists or careless smokers, by

those burning wood, or by clearing a forest area and, scrub fire: fires in scrub or bush that

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cover extensive damage. They may start by natural causes such as volcanic eruptions or

lightning, or they may be caused by arsonists or careless smokers, by those burning wood,

or by clearing a forest area (EM-DAT, 2007).

Wind storm: Disaster type term used in EM-DAT comprising the following disaster subsets

cyclone/hurricane/typhoon: large-scale closed circulation system in the atmosphere above

the Indian Ocean and South Pacific (cyclone), the Western Atlantic and East Pacific

(hurricane) or Western Pacific (typhoon), with low barometric pressure and strong winds that

rotate clockwise. Maximum wind speed of 64 knots or more, storm: wind with a speed

between 48 and 55 knots, tornado: violently rotating storm diameter; the most violent

weather phenomenon. It is produced in a very severe thunderstorm and appears as a funnel

cloud extending from the base of a cumulonimbus to the ground, tropical storm: generic term

for a non-frontal synoptic scale cyclone originating over tropical or sub-tropical waters with

organised convection and definite cyclonic surface wind circulation and winter storm: Snow

(blizzard), ice or sleet storm (EM-DAT, 2007). Essentially, cyclones, hurricanes and typhoons

refer to a large scale closed circulation system in the atmosphere which combines low

pressure and strong winds that rotate counter clockwise in the northern hemisphere and

clockwise in the southern hemisphere. The system is referred to as a cyclone in the Indian

Ocean and South Pacific, hurricane in the Western Atlantic and Eastern Pacific and typhoon

in the Western Pacific (IFRC, 2007).

Section B.2 Technological disasters

Technological hazards: Danger originating from technological or industrial accidents,

dangerous procedures, infrastructure failures or certain human activities, which may cause

the loss of life or injury, property damage, social and economic disruption or environmental

degradation. Some examples: industrial pollution, nuclear activities and radioactivity, toxic

wastes, dam failures; transport, industrial or technological accidents (explosions, fires, spills)

(UN/ISDR, 2007). The IFRC (2007) distinguishes technological disasters as either: accidents,

explosions, chemical explosions, (thermo) nuclear explosions, mine explosions, pollution,

acid rain, chemical pollution and atmospheric pollution.

Industrial accident: Disaster type term used in EM-DAT to describe technological accidents

of an industrial nature/involving industrial buildings (f.e. factories). It comprises of a number

of disaster subsets: chemical spill/leak: accident release occurring during the production,

transportation or handling of hazardous chemical substances, explosions: explosions

involving industrial buildings or structures, radiation leakages, collapses: accident involving

the collapse of industrial building or structure, gas leaks from industrial sites, poisoning:

poisoning of atmosphere or water courses due to industrial sources, fires, and other

technological accidents involving industrial sites (EM-DAT, 2007).

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Transport accident: Disaster type term used in EM-DAT to describe technological transport

accidents involving mechanised modes of transport. It comprises of four disaster subsets:

accidents involving aeroplanes, helicopters, airships and balloons, accidents involving sailing

boats, ferries, cruise ships, other boats, accidents involving trains, and accidents involving

motor vehicles on roads and tracks (EM-DAT, 2007).

Miscellaneous accident: Disaster type term used in EM-DAT to describe technological

accidents of a non-industrial or transport nature (f.e. houses). It comprises of a number of

disaster subsets: explosions: explosions involving domestic/non-industrial buildings or

structures, collapses: accident involving the collapse of domestic/non-industrial buildings or

structures, fires, and other miscellaneous accidents involving domestic/non-industrial sites

(EM-DAT, 2007).

Section B.3 Other disasters

Population movement and displaced people: When population movement occurs, it is

important to immediately distinguish whether those moving are asylum seekers, refugees,

migrants or internally displaced people. The distinction is important since support

mechanisms and the legal status of the people can affect the response operation. Refugees

are people moving outside their country of origin - often in mass exodus - for reasons of

conflict and now increasingly, natural disasters. Displaced people who cross a border but are

not accorded refugee status are asylum seekers. Internally displaced persons are persons or

groups of persons who have been forced or obliged to flee or to leave their homes or places

of habitual residence, in particular as a result of or in order to avoid the effects of armed

conflict, situations of generalized violence, violations of human rights or natural or human-

made disasters, and who have not crossed an internationally recognized State border. An

often neglected but substantial moving population is made up of people displaced by factors

other than armed conflict - people forced to move by a natural disaster, economic hardship

and the formation of a new country or changing national boundaries. These people are

migrants (IFRC, 2007).

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Appendix C IFRC principles

The seven IFRC principles (IFRC, 2007) include:

1. Humanity. Refers to protecting and respecting life and preventing and alleviating human

suffering wherever it may be found, while dignity of people should be respected and

protected.

2. Impartiality. Refers to non-discrimination in the provision of aid, which should be guided

solely by recipient needs. Priority is given to the most urgent cases of distress.

Impartiality can also be described in terms of non-subjective aid provision to recipients

(Pictet, 1979).

3. Neutrality. In order to receive confidence of all people, the IFRC shall not take sides in

any political-, racial-, religious-, ideological- or any other type of conflict. And relief should

be provided without bias or affiliation to any party in a conflict.

4. Independence. The IFRC operates autonomous and independent of any local authority,

thereby increasing their impartiality and neutrality.

5. Voluntary service. The IFRC is a voluntary aid agency, with no desire for profit or gain.

6. Unity. In order to foster their unity, the IFRC only allows one Red Cross movement per

country, which should be open to all, a principle closely corresponding to the impartiality

principle.

7. Universality. The worldwide IFRC is based on equality. All members have equal status

and equal responsibilities in providing aid worldwide.

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Appendix D Business sector performance measures

Figure D.1, Transportation productivity ratios (A.T. Kearney, 1984: p. 144)

Transportation

Activities Labour Facilities Equipment Energy

Financial

Investment

Overall

(Cost)

Private fleet over-the-road trucking

- Loading X X

- Line-haul X X X - Unloading X X

- Overall X X X X

Private fleet pickup/delivery trucking

- Pretrip X X - Stem driv ing X X X

- On-route driv ing X X X

- At-stop X X - End-of-trip X X

- Overall X X X X

Transportation management

- Private fleet X - Outside transport X

Discussion: Practically all ratios of figure D.1 can be applied to disaster logistics, because the

inputs labour, equipment, energy and cost are present in both conventional logistics and

disaster logistics. An example is the energy/line-haul ratio, which could be translated into

average fuel consumption per kilometre.

Figure D.2, Transportation productivity measures (Ballou, 1992: p. 786)

Transportation

productivity measure This quarter Last quarter

This quarter

last year

Company

standard

Industry

Average

- Freight costs as a percentage of distribution costs 31% 30% 32% 29% 31%

- Damage- and loss claims as percentage of freight costs 0,5% 0,5% 0,6% 0,5% 0,5%

- Freight costs as a percentage of turnover 0,1% 9,2% 10,2% 9,0% 8,8%

Discussion: the applicability of measures from figure D.2 differs per measure. The first is

applicable, although one needs to know the company standard or industry average to be

useful. The second is not that useful since the ‘customers’ in disaster logistics generally do

not file damage claims. Finally, the third measure has to be adapted in order to be applicable:

turnover should be replaced by total funds available for a given project.

Figure D.3, Transportation effectiveness measures (Gattorna & Walters, 1996: p. 146)

Productivity Utilization Performance

- Weight / distance transported / total

actual transportation costs

- Total transportation capacity used / total transportation capacity owned (or

paid for)

- Actual transportation cost / budgeted

transportation cost

- Stops served / total actual

transportation cost

- Standard transportation cost / actual

transportation cost

- Volume of goods transported to destinations / total actual transportation

cost

- Actual transit times / standard transit times

- Shipments transported to destinations / total actual transportation cost

- Standard weight distance / total actual transportation cost

Transport effectiveness measures

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Discussion: the productivity measures and the utilization measures are both applicable to

disaster logistics. However the second measure in the productivity column seems less

relevant, since the amount of stops served is a measure for routine transport, and this is

obviously not applicable in the uncertain environment of disasters. The third column is

probably not applicable to disaster logistics because of the inclusion of budgeting, which is

pretty difficult in disaster logistics given its uncertainty. However, the measures including

standards could be transferable if appropriate standards were set.

Figure D.4, Warehousing productivity ratios (A.T. Kearney, 1984: p. 195)

Warehousing

Activities Labour Facilities Equipment Energy

Financial

Investment

Overall

(Cost)

Company-operated Warehousing - Receiving X X X X

- Put Away X X X

- Storage X X X

- Replenishment X X X - Order selection X X X

- Checking X X X

- Packing and Marking X X X X - Staging & Order consolidation X X X X

- Shipping X X X X

- Clerical & administration X X X - Overall X X X X X X

Purchased-Outside Warehousing - Storage X

- Handling X

- Consolidation X - Administration X - Overall X

Discussion: As was the case with figure D.1, the ratios proposed here can also be applied,

because the inputs labour, facilities, equipment, energy and cost are present in both

conventional logistics and disaster logistics. An example is the labour/order selection ratio,

which could be translated into labour time per ‘order’ picked. The ratio associated with

financial investment is harder to apply since disaster logistics does not use much permanent

warehouses and per disaster only semi-permanent warehouses are used or warehouse

space is rented.

Figure D.5, Warehousing productivity measures (Ballou, 1992: p. 786)

Warehousing

productivity measure This quarter Last quarter

This quarter

last year

Company

standard

Industry

Average

- % use of warehouse volume 75% 70% 70% 70% 70%

- Processed units per working hour 200 : 1 250 : 1 225 : 1 200 : 1 200 : 1

Discussion: Both of the measures of figure D.5 can be applied to disaster logistics, although

for the second ratio it should be clear which units need to be processed. For example

processing an emergency tent is far more work then processing one blanket. Also the

company standards and/or industry averages need to be set.

Kevin Koenen - 63 - 13/08/2007

Figure D.6, Warehousing effectiveness measures (Gattorna & Walters, 1996: p. 120)

Productivity Utilization Performance

- Dollar value throughput / total warehouse cost

- Actual weight throughput /maximum weight throughput

- Actual total warehouse cost /budgeted warehouse cost

- Weight throughput /

total warehouse cost

- Actual orders throughput /

maximum orders throughput

- Actual weight throughput per total

warehouse cost

- Orders throughput /

total warehouse cost

- Actual lines throughput /

maximum lines throughput

- Actual weight throughput per square

foot / standard weight throughput per square foot

- Lines throughput / total warehouse cost

- Square feet used /square feet available

- Actual orders throughput per total warehouse cost / standard orders

throughput per total warehouse cost

- Units throughput /

total warehouse cost

- Cubic feet used /

cubic feet available

- Actual orders throughput per square

foot / standard orders throughput per

square foot

- Actual lines throughput per total

warehouse cost / standard lines throughput per total warehouse cost

- Actual lines throughput per square foot / standard lines throughput per

square foot

- Actual units throughput per total

warehouse cost / standard units throughput per total warehouse cost

- Actual units throughput per square foot / standard units throughput per

square foot

- Actual throughput cycle time /

standard throughput cycle time

Warehousing effectiveness measures

Discussion: the productivity measures and the utilization measures are both applicable to

disaster logistics. However the second measure in the utilization column seems less relevant,

since the concept of orders is different for disaster logistics. As was the case with figure D.3

the budgeting measure of the third column are not applicable, while the measures including

standards can be applied when appropriate standards are set per disaster context.

Figure D.7, Purchasing productivity ratios (A.T. Kearney, 1984: p. 242)

Purchasing

Activities Labour Equipment

Financial

Investment

Overall

(Cost)

- Sourcing X X

- Procurement X X X

- Cost control X X - Overall X

Discussion: Again, the ratios proposed here can be applied to disaster logistics, because the

inputs labour, equipment and cost are present in both conventional logistics and disaster

logistics. An example is the labour/sourcing ratio, which could be translated into labour time

spend per commodity, when trying to source goods locally in the disaster area.

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Figure D.8, Inventory productivity ratios (A.T. Kearney, 1984: p. 242)

Inventory

Activities Labour Equipment

Financial

Investment

Overall

(Cost)

- Forecasting X X X

- Planning & Budgeting X X X

- Execution & Control X X X - Overall X X

Discussion: the ratios proposed in figure D.8 can be applied to disaster logistics, because the

inputs labour, equipment, investments and cost are present in both conventional logistics and

disaster logistics. An example is the financial investment/overall ratio, which could be

translated into amount of money tied up in aid agency inventory.

Figure D.9, Inventory productivity measures (Ballou, 1992: p. 786)

Inventory

productivity measure This quarter Last quarter

This quarter

last year

Company

standard

Industry

Average

- Inventory circulation frequency 4,5 : 1 4,4 : 1 5,0 : 1 4,7 : 1 6,0 : 1

- Outdated or Obsolete inventory / turnover 0,1 : 1 0,1 : 1 0,3 : 1 0,1 : 1 0,2 : 1

Discussion: The measures suggested in figure D.9 are probably not that useful in disaster

logistics. The first measure is not applicable because of the uncertain environments of

disasters, while this measure is more useful in a more predictable and routine environment.

The second measure includes turnover, which is a concept that is not relevant in disaster

logistics. Replacing turnover with, for example, total funds available might make this measure

more applicable to disaster logistics.

Figure D.10, Inventory effectiveness measures (Gattorna & Walters, 1996: p. 136)

Productivity Utilization Performance

- Total dollar value inventory managed / inventory holding cost

- Stock value / working capital - Actual dollar value of inventory / planned dollar value of inventory

- Total SKUs managed / inventory holding cost

- Stock turns by relevant category - Actual inventory management cost / budgeted inventory management cost

- Total dollar value inventory managed / costs attributable to inventory management activity

Inventory effectiveness measures

Discussion: the productivity- and utilization measures of figure C.10 can be applied to

disaster logistics although inventory remains a rather uncertain activity. For example, for

disasters it is hard to predict when and where they are going to happen, thereby making

holding inventory also a difficult item to measure. Because of the same reasons, the

performance column is hard to apply because these include planning and budgeting.

Kevin Koenen - 65 - 13/08/2007

Figure D.11, Production productivity ratios (A.T. Kearney, 1984: p. 242)

Production

Activities Labour Equipment

Financial

Investment

Overall

(Cost)

- Production planning X X - Production control X X

- Scheduling & Dispatching X X X

- Shop floor data collection X X X - Overall X

Discussion: the ratios proposed in figure D.11 can be applied to disaster logistics, because

the inputs labour, equipment and cost are present in both conventional logistics and disaster

logistics. However, often aid agencies do not produce products themselves; therefore the

relevance for disaster logistics is questionable.

Figure D.12, Order processing productivity ratios (A.T. Kearney, 1984: p. 293)

O rder Processing

Activities Labour

Facilities/

Equipment

W orking

Capital

O verall

(Cost)

O rder processing

- O rder entry/editing X X X X

- Scheduling X X

- O rder/shipping set preparation X X X

- Inv oicing X X X

Custom er com m unication

- O rder m odification X X X

- O rder status inquiries X X X

- Tracing and Expediting X X X

- Error correction X X

- Product inform ation requests X X

Credit and Collection

- Credit checking X X X - Accounts receiv able processing X X X X

Discussion: the performance measures suggested in the order-processing category can be

applied to disaster logistics since the inputs labour, equipment and cost are present in both

conventional logistics and disaster logistics. However, in order to use these measures in

disaster logistics the concept of orders has to be accurately defined, because orders they are

different between conventional- and disaster logistics. For example, invoicing for ‘customer’

orders is not possible in disasters since aid is provided free of charge.

Figure D.13, Order processing productivity measures (Ballou, 1992: p. 786) Order processing

productivity measure This quarter Last quarter

This quarter

last year

Company

standard

Industry

Average

- Processed orders per working hour 50 : 1 45 : 1 55 : 1 50 : 1 50 : 1

- % of orders processed within 24 hours 96% 92% 85% 95% 93%

- Order procession costs per Order € 5,50 € 4,95 € 5,65 € 5,00 -

Discussion: The first and third measures are hard to apply since the concept of orders is

different in disaster logistics; therefore a measure of processed orders per working hour

seems irrelevant. The second measure could be useful, however one has to change orders

in disaster area served for example.

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Figure D.14, Order processing effectiveness measures (Gattorna & Walters, 1996: p. 163)

Productivity Utilization Performance

- Total orders entered / total order entry cost

- Total volume of orders entered / total order entry capacity

- Actual order entry costs / budgeted order entry costs

- Total SKUs entered / total order entry

cost

- Total orders received / total order

entry capacity

- Standard order entry cost allowances

earned / total order entry costs incurred

- Total monetary value of orders

entered / total order entry cost

- Total actual transaction throughput /

maximum transaction capacity

- Actual order set preparation costs /

budgeted order set preparation costs

- Total number order sets prepared / total order set preparation costs

- Total volume of Order sets prepared / Total Order set preparation capacity

- Actual 'other document' preparation costs / budgeted 'other document'

preparation costs

- Total number other transaction

documents prepared / Total cost of

preparing other documents

- Total volume of other transactions

prepared / Total capacity for preparing

other transitions

- Standard order set preparation

allowances earned / total order set

preparation costs incurred

- Total number invoices prepared / total invoice preparation costs

- Total volume of orders received for

preparation / total order set preparation capacity

- Standard 'other document'

preparation allowances earned / total 'other document' preparation costs

- Total monetary value invoiced / Total

invoice preparation costs

- Total volume of invoices processed /

Total invoice processing capacity

- Actual invoice processing costs /

budgeted invoice processing costs

- Total number customer inquiries

handled / total customer communication costs

- Total volume of enquiries handled /

total inquiry handling capacity

- Standard invoice processing cost

allowances earned / Total invoice processing costs incurred

- Actual customer communication cost / budgeted communication cost

- Standard customer communication

cost allowances earned / total actual

costs incurred

Order procession effectiveness measures

Discussion: given the difference in definition of orders for disaster logistics versus

conventional logistics the measures suggested in figure D.14 are hard to apply to disaster

logistics. Moreover the productivity measures including invoicing and or budgeting also seem

irrelevant since they are usually not present, or very hard to perform in disaster logistics.

Figure D.15, Supply chain comprehensive performance measures (Harisson & Van Hoek, 2002: p. 213 - 214)

M easu re E xp lan atio n

- O n tim e in fu ll, ou tboundA m easure of custom er orders fu lf i l led, com ple te and on tim e,

conform ing to spec if ication

- O n tim e in fu ll, inboundA m easure of supplie r deliv eries rece iv ed, com ple te and on tim e,

conf irm ing to spec if ication

- In terna l de fect ratesA m easure of process conform ance and contro l (ra ther then

inspection)

- N ew product in troduction rate A m easure of supply cha in product responsiv eness

- C ost reduction A m easure of usable product and process dev e lopm ent

- S tock turnsA m easure of supply cha in goods f low (th is m easure is usefu l on ly

when app lied to a spec if ic p roducts and the ir supply cha ins)

- O rder to de liv ery lead tim e A m easure of supply cha in process responsiv eness

- F iscal f lex ib il ityA m easure of how easy it is to structure the supp ly chain for

f inanc ia l adv antage

S u p p ly ch ain co m p reh en sive p erfo rm an ce m easu res

Kevin Koenen - 67 - 13/08/2007

Discussion: the first two measures are difficult to apply because ‘on time’ is very difficult to

define in disaster logistics. The fourth measure is also not applicable to disaster logistics

since it is focussed too much on innovation in products, which is not a main concern for

disaster logistics. Cost reduction and order delivery lead-time can be applied to disaster

logistics although one has to consider the difference in the concept of ‘orders’.

Figure D.16, Supply chain comprehensive performance measures (Bowersox et. al., 2002: p. 563 - 566)

Measure Explanation

- Cash-to-Cash conversion

Cash-to-cash cycle time is the time required to convert a dollar spent on inventory into a

dollar collected from sales revenue. = days of supply of inventory + days of accounts receivable outstanding - days of trade accounts payable outstanding.

- Supply chain inventory days of supply

Total finished goods inventory at all plants, duistribution centers, wholesalers, and retailers expressed as the calendar days of sales available.

- Dwell timeInventory dwell time is the ratio of the days inventroy sits idle in the supply chain to the days it is being productively used or positioned.

- On-shelf in-stock percent Simply the percentage of time that a product is available on the shelf in a store.

- Total supply chain costMeasuring the total cost of a supply chain in order to improve with the supply chain as a whole. As long as the total reductions in cost are larger than the cost increase for one supply chain member, the supply chain as a whole is improved.

- Supply chain response timeA theoretical approximation for the amount of time required for a supply chain to recognize a fundamental shoft in marketplace demand, internalize that finding, replan, and adjust output to meet that demand.

Supply chain comprehensive performance measures

Discussion: From figure D.16 dwell time, total supply chain cost and supply chain response

time are applicable measures, since they do not interfere with the disaster logistics process.

The other measurements are not applicable because they interfere with the specific context

of disaster logistics. For instance on-shelf-in-stock is not useable because aid is generally

not provided.

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Appendix E TNT-WFP Key performance indicators

Section E.1 Key performance indicators

Figure E.1, Key vehicle performance indicators utilisation (TNT, 2007b)

Figure E.2, Key vehicle performance indicators maintenance and fuel (TNT, 2007b)

Figure E.3, Key vehicle performance indicators accidents (TNT, 2007b)

Figure E.4, Overall vehicle performance indicator (TNT, 2007b)

Section E.2 Further explanation of the KPI’s

Kilometres travelled

Having a target kilometres to be travelled is going to be very much area specific but a low

number of kilometres travelled in every vehicle is an indication that there is plenty of spare

capacity in the fleet and that it could actually be too large. If in comparing vehicles it is found

that some have considerably lower kilometres travelled than others then that is an indication

of poor vehicle planning and scheduling which would need to be addressed. As a guide four

wheel drive vehicles should be looking to do 2,500 - 3,500 kilometres per month and

motorbikes 750 – 1,250 kilometres per month. The important thing is that all vehicles of the

same type and age are doing roughly the same distance each month (TNT, 2007b).

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Availability %

With a well maintained fleet that is renewed on a timely basis, it is realistic to expect to

achieve 95% vehicle availability. A figure of 100% is not good as it indicates that time is not

been given for planned preventative maintenance. Low availability indicates problems in

maintenance, and that the workshop is performing badly as a well maintained vehicle should

not have to spend a great deal of time in the workshop. It could also indicate that required

documents are not available, such as a road licence. Appropriate vehicles for the work and

terrain, planned preventative maintenance, rigorous quality control and good spare part

availability will significantly improve vehicle availability. High availability in itself does not

equal improved service delivery unless there is a good transport planning and scheduling of

vehicles but is a pre requisite for ensuring that there is sufficient transport at the disposal of

the programmes. A further benefit of high availability is that it usually leads to a lower cost

per kilometre as there are less repair costs (TNT, 2007b).

Utilization %

Utilisation is calculated as a percentage of total number of working hours in a week. The total

numbers of working hours are for each operation different. From 8-6 daily and half a day in

the weekend would mean the total number of working hours a week is 10 x 5 + 4 = 54 hours.

Low utilisation indicates that there is spare capacity in the fleet especially if combined with

low number of kilometres travelled. High Utilisation with low kilometres travelled is often seen

when vehicles are allocated to a person or project rather than being part of a general pool.

This reflects poor planning and scheduling and should be addressed. In circumstances

where one person does need to have a vehicle available to them at all times, the transport

department should consider rotating the vehicle made available to them so that kilometres

travelled and utilisation figures for all vehicles are as close together as possible (TNT, 2007b).

Performance %

This is the single most important indicator as it represents whether we are able to do the

work that we need and want to do. It is the closest indicator to demonstrating the impact of

transport on service delivery. This percentage indicates what percentage of authorised trips

was achieved. As transport is provided to support the work of the programmes the needs

satisfaction target should be 100% however problems will arise and cause the indicator to

drop below 100% without causing major concern. If the performance % is dropping

outsourcing transport, good arrangement for rental vehicles and planning should be looked at

(TNT, 2007b).

Fuel consumption/costs

Fuel consumption varies on engine size, vehicle weight and fuel type. It is also dependent

on the skills of the vehicle operator, the speeds driven, road conditions, the mechanical

condition of the vehicle, and the loads carried. For example the faster a vehicle is being

driven, low on tyre pressure or where gears are poorly used, the higher the fuel consumption

will be. Equally, poor roads, heavily loaded vehicle and use of the air-conditioning will cause

Kevin Koenen - 70 - 13/08/2007

the fuel consumption to be higher. However low figures do not necessarily relate to these

factors and investigations must be made to ensure that fuel is not stolen. Recording the daily

fuel consumption of each vehicle will greatly facilitate this. Same vehicle types of roughly the

same age and travelling on similar terrain can be expected to have similar fuel consumptions

(TNT, 2007b).

It is useful to compare:

• One vehicle to another in a certain month

• One particular vehicle in several months

Figure E.5, Target number of km per litre (TNT, 2007b)

Maintenance costs

Maintenance should be outsourced as much as possible, for preventive maintenance as well

as for repairs. WFP owned workshop should only be established when there is no local

maintenance available. There are three main reasons for this:

• Outsourcing builds local capacity

• Soil pollution

• In-house maintenance is often more expensive then outsourcing maintenance

It is important to keep track of the maintenance and repair costs for each vehicle (TNT,

2007b).

Accident rate

Safety cannot be compromised and therefore there has to be a target of zero accidents and

incidents. If it is ever above zero then a thorough investigation should be carried out.

Accidents can indicate the need for (re)establishing the fleet safety concept within your

organisation (TNT, 2007b).

Section E.3 Discussion

Once performance data exists, it should be measured each month against the KPI targets

set, trends examined and potential for improvements to the fleet identified. At the end of

each month the data for each vehicle can be examined to see if there are any areas that

need any further attention. Local knowledge will then assist in determining the cause of the

problem and in identifying the solution.

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Everybody involved in the transport management system needs to get feedback on

performance and how the information is used. Picking out exemplary performance can be

used just as much as picking out poor performance to stimulate team motivation.

Feedback however needs to go beyond senior management and the transport team. The

real key to improving transport performance is a change in thinking of all people who use

transport. Everyone needs to realise that it is an extremely expensive resource and that its

use is focused on improving the delivery of services that the organisation is responsible for

and that therefore everyone needs to play a role in that. Combining transport requirements

and therefore moving with full vehicles would be one example of how this can be done (TNT,

2007b).

Kevin Koenen - 72 - 13/08/2007

Appendix F SPHERE-project standards

Section F.1 Common standards

Common standard 1: participation

The disaster-affected population actively participates in the assessment, design,

implementation, monitoring and evaluation of the assistance programme (Sphere, 2004).

Common standard 2: initial assessment

Assessments provide an understanding of the disaster situation and a clear analysis of

threats to life, dignity, health and livelihoods to determine, in consultation with the relevant

authorities, whether an external response is required and, if so, the nature of the response

(Sphere, 2004).

Common standard 3: response

A humanitarian response is required in situations where the relevant authorities are unable

and/or unwilling to respond to the protection and assistance needs of the population on the

territory over which they have control, and when assessment and analysis indicate that these

needs are unmet (Sphere, 2004).

Common standard 4: targeting

Humanitarian assistance or services are provided equitably and impartially, based on the

vulnerability and needs of individuals or groups affected by disaster (Sphere, 2004).

Common standard 5: monitoring

The effectiveness of the programme in responding to problems is identified and changes in

the broader context are continually monitored, with a view to improving the programme, or to

phasing it out as required (Sphere, 2004).

Common standard 6: evaluation

There is a systematic and impartial examination of humanitarian action, intended to draw

lessons to improve practice and policy and to enhance accountability (Sphere, 2004).

Common standard 7: aid worker competencies and responsibilities

Aid workers possess appropriate qualifications, attitudes and experience to plan and

effectively implement appropriate programmes (Sphere, 2004).

Common standard 8: supervision, management and support of personnel

Aid workers receive supervision and support to ensure effective implementation of the

humanitarian assistance programme (Sphere, 2004).

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Section F.2 Minimum standards in water supply, sanitation and hygiene

Hygiene promotion standard 1: programme design and implementation

All facilities and resources provided reflect the vulnerabilities, needs and preferences of the

affected population. Users are involved in the management and maintenance of hygiene

facilities where appropriate (Sphere, 2004).

Water supply standard 1: access and water quantity

All people have safe and equitable access to a sufficient quantity of water for drinking,

cooking and personal and domestic hygiene. Public water points are sufficiently close to

households to enable use of the minimum water requirement (Sphere, 2004).

Water supply standard 2: water quality

Water is palatable, and of sufficient quality to be drunk and used for personal and domestic

hygiene without causing significant risk to health (Sphere, 2004).

Water supply standard 3: water use facilities and goods

People have adequate facilities and supplies to collect, store and use sufficient quantities of

water for drinking, cooking and personal hygiene, and to ensure that drinking water remains

safe until it is consumed (Sphere, 2004).

Excreta disposal standard 1: access to, and numbers of toilets

People have adequate numbers of toilets, sufficiently close to their dwellings, to allow them

rapid, safe and acceptable access at all times of the day and night (Sphere, 2004).

Excreta disposal standard 2: design, construction and use of toilets

Toilets are sited, designed, constructed and maintained in such a way as to be comfortable,

hygienic and safe to use (Sphere, 2004).

Vector control standard 1: individual and family protection

All disaster-affected people have the knowledge and the means to protect themselves from

disease and nuisance vectors that are likely to represent a significant risk to health or well-

being (Sphere, 2004).

Vector control standard 2: physical, environmental and chemical protection measures

The numbers of disease vectors that pose a risk to people's health and nuisance vectors that

pose a risk to people's well-being are kept to an acceptable level (Sphere, 2004).

Solid waste management standard 1: collection and disposal

People have an environment that is acceptably uncontaminated by solid waste, including

medical waste, and have the means to dispose of their domestic waste conveniently and

effectively (Sphere, 2004).

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Drainage standard 1: drainage works

People have an environment in which the health and other risks posed by water erosion and

standing water, including storm water, floodwater, domestic wastewater and wastewater from

medical facilities, are minimised (Sphere, 2004).

Section F.3 Minimum standards in food security, nutrition and food aid

Assessment and analysis standard 1: food security

Where people are at risk of food insecurity, programme decisions are based on a

demonstrated understanding of how they normally access food, the impact of the disaster on

current and future food security, and hence the most appropriate response (Sphere, 2004).

Assessment and analysis standard 2: nutrition

Where people are at risk of malnutrition, programme decisions are based on a demonstrated

understanding of the causes, type, degree and extent of malnutrition, and the most

appropriate response (Sphere, 2004).

Food security standard 1: general food security

People have access to adequate and appropriate food and non-food items in a manner that

ensures their survival, prevents erosion of assets and upholds their dignity (Sphere, 2004).

Food security standard 2: primary production

Primary production mechanisms are protected and supported (Sphere, 2004).

Food security standard 3: income and employment

Where income generation and employment are feasible livelihood strategies, people have

access to appropriate income-earning opportunities, which generate fair remuneration and

contribute towards food security without jeopardising the resources on which livelihoods are

based. (Sphere, 2004).

Food security standard 4: access to markets

People's safe access to market goods and services as producers, consumers and traders is

protected and promoted (Sphere, 2004).

General nutrition support standard 1: all groups

The nutritional needs of the population are met. (Sphere, 2004).

General nutrition support standard 2: at-risk groups

The nutritional and support needs of identified at-risk groups are met (Sphere, 2004).

Correction of malnutrition standard 1: moderate malnutrition

Moderate malnutrition is addressed (Sphere, 2004).

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Correction of malnutrition standard 2: severe malnutrition

Severe malnutrition is addressed (Sphere, 2004).

Correction of malnutrition standard 3: micronutrient malnutrition

Micronutrient deficiencies are addressed (Sphere, 2004).

Food aid planning standard 1: ration planning

Rations for general food distributions are designed to bridge the gap between the affected

population's requirements and their own food resources (Sphere, 2004).

Food aid planning standard 2: appropriateness and acceptability

The food items provided are appropriate and acceptable to recipients and can be used

efficiently at the household level (Sphere, 2004).

Food aid planning standard 3: food quality and safety

Food distributed is of appropriate quality and is fit for human consumption (Sphere, 2004).

Food aid management standard 1: food handling

Food is stored, prepared and consumed in a safe and appropriate manner at both household

and community levels (Sphere, 2004).

Food aid management standard 2: supply chain management

Food aid resources (commodities and support funds) are well managed, using transparent

and responsive systems (Sphere, 2004).

Food aid management standard 3: distribution

The method of food distribution is responsive, transparent, equitable and appropriate to local

conditions (Sphere, 2004).

Section F.4 Minimum standards in shelters, settlements & non-food-items

Shelter and settlement standard 1: strategic planning

Existing shelter and settlement solutions are prioritised through the return or hosting of

disaster-affected households, and the security, health, safety and well-being of the affected

population are ensured (Sphere, 2004).

Shelter and settlement standard 2: physical planning

Local physical planning practices are used where possible, enabling safe and secure access

to and use of shelters and essential services and facilities, as well as ensuring appropriate

privacy and separation between individual household shelters (Sphere, 2004).

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Shelter and settlement standard 3: covered living space

People have sufficient covered space to provide dignified accommodation. Essential

household activities can be satisfactorily undertaken, and livelihood support activities can be

pursued as required (Sphere, 2004).

Shelter and settlement standard 4: design

The design of the shelter is acceptable to the affected population and provides sufficient

thermal comfort, fresh air and protection from the climate to ensure their dignity, health,

safety and well-being (Sphere, 2004).

Shelter and settlement standard 5: construction

The construction approach is in accordance with safe local building practices and maximises

local livelihood opportunities (Sphere, 2004).

Shelter and settlement standard 6: environmental impact

The adverse impact on the environment is minimised by the settling of the disaster-affected

households, the material sourcing and construction techniques used (Sphere, 2004).

Non-food items standard 1: clothing and bedding

The people affected by the disaster have sufficient clothing, blankets and bedding to ensure

their dignity, safety and well-being (Sphere, 2004).

Non-food items standard 2: personal hygiene

Each disaster-affected household has access to sufficient soap and other items to ensure

personal hygiene, health, dignity and well-being (Sphere, 2004).

Non-food items standard 3: cooking and eating utensils

Each disaster-affected household has access to cooking and eating utensils (Sphere, 2004).

Non-food items standard 4: stoves, fuel and lighting

Each disaster-affected household has access to communal cooking facilities or a stove and

an accessible supply of fuel for cooking needs and to provide thermal comfort. Each

household also has access to appropriate means of providing sustainable artificial lighting to

ensure personal security (Sphere, 2004).

Non-food items standard 5: tools and equipment

Each disaster-affected household responsible for the construction or maintenance and safe

use of their shelter has access to the necessary tools and equipment (Sphere, 2004).

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Section F.5 Minimum standards in health services

Health systems and infrastructure standard 1: prioritising health services

All people have access to health services that are prioritised to address the main causes of

excess mortality and morbidity (Sphere, 2004).

Health systems and infrastructure standard 2: supporting national and local health systems

Health services are designed to support existing health systems, structures and providers

(Sphere, 2004).

Health systems and infrastructure standard 3: coordination

People have access to health services that are coordinated across agencies and sectors to

achieve maximum impact (Sphere, 2004).

Health systems and infrastructure standard 4: primary health care

Health services are based on relevant primary health care principles (Sphere, 2004).

Health systems and infrastructure standard 5: clinical services

People have access to clinical services that are standardised and follow accepted protocols

and guidelines (Sphere, 2004).

Health systems and infrastructure standard 6: health information systems

The design and development of health services are guided by the ongoing, coordinated

collection, analysis and utilisation of relevant public health data (Sphere, 2004).

Control of communicable diseases standard 1: prevention

People have access to information and services that are designed to prevent the

communicable diseases that contribute most significantly to excess morbidity and mortality

(Sphere, 2004).

Control of communicable diseases standard 2: measles prevention

All children aged 6 months to 15 years have immunity against measles (Sphere, 2004).

Control of communicable diseases standard 3: diagnosis and case management

People have access to effective diagnosis and treatment for those infectious diseases that

contribute most significantly to preventable excess morbidity and mortality (Sphere, 2004).

Control of communicable diseases standard 4: outbreak preparedness

Measures are taken to prepare for and respond to outbreaks of infectious diseases (Sphere,

2004).

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Control of communicable diseases standard 5: outbreak detection, investigation and

response

Outbreaks of communicable diseases are detected, investigated and controlled in a timely

and effective manner (Sphere, 2004).

Control of communicable diseases standard 6: HIV/AIDS

People have access to the minimum package of services to prevent transmission of

HIV/AIDS (Sphere, 2004).

Control of non-communicable diseases standard 1: injury

People have access to appropriate services for the management of injuries (Sphere, 2004).

Control of non-communicable diseases standard 2: reproductive health

People have access to the Minimum Initial Service Package (MISP) to respond to their

reproductive health needs (Sphere, 2004).

Control of non-communicable diseases standard 3: mental and social aspects of health

People have access to social and mental health services to reduce mental health morbidity,

disability and social problems (Sphere, 2004).

Control of non-communicable diseases standard 4: chronic diseases

For populations in which chronic diseases are responsible for a large proportion of mortality,

people have access to essential therapies to prevent death (Sphere, 2004).

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Appendix G Alphabetical index

Accountability (Benefit) 42 Impartiality 24

Aid agency 7, 19 Improve processes (Benefit) 42

Appeal coverage 35 Independence 24

Assessment accuracy 35 Information flow 22

Information technology challenge 27

Benchmarking 31 In-kind donations 18

Beneficiaries 8, 17 Internal benchmarking 31

Beneficiary country government 20 Internal validity 11

Biological disaster 13 Inventory 21

Business Process perspective 32

Business sector 8 Lack of institutional memory (challenge) 27

Lack of logistical knowledge (challenge) 26

Common standards 34 Lack of professionalisation (challenge) 27

Competitive benchmarking 31 Learning and Growth perspective 32

Complexity (challenge) 26 Literature review 10

Control 28 Logistical knowledge retention (Benefit) 42

Corporate social responsibility 18 Logistics as strategic function (Benefit) 43

Customer perspective 32

Man-made disasters 13

Descriptive study 10 Material flow 22

Directing 28 Media 19

Disaster logistics 7 Military 19

Disaster management 16 Mitigation phase 16

Donation-to-delivery-time 35 Monitoring 28

Donors 8

Donor accountability 18 Natural disasters 13

Donor countries 17 Needs assessments 19

Neutrality 24

Employee turnover (challenge) 26 Nutrition standards 34

Exploratory study 10

External validity 11 Operational challenges 25

Order processing 20

Financial efficiency 35 Organizational challenges 27

Financial flow 21 Other disasters 13

Financial perspective 32

Food aid standards 34 People flow 22

Food security standards 34 Performance 28

Functional benchmarking 31 Performance management 7, 28

Funding flow 21 Performance management systems 31

Performance measurement 28

General public 18 Performance standards 28

Generic process benchmarking 31 Preparedness phase 16

Geological disasters 13 Production 21

Project-to-project mentality 27

Health services standards 34 Purchasing 20

Humanitarian space 24

Humanity 24 Rehabilitation phase 16

Hydro-meteorological disaster 13 Reliability 10

Response phase 16

Kevin Koenen - 80 - 13/08/2007

Secondary data 10 Transportation 21

Shelter standards 34

Slow onset disasters 14 Unity 24

Stakeholder challenges 26 Universality 24

Standardization (Benefit) 42 Unsolicited donations 18

Sudden onset disasters 14 Voluntary service 24

Supply chain comprehensive -

performance measures 30 Warehousing 21

Water supply, sanitation and-

hygiene standards 34