European Journal of Operational Research 173 (2006) 211–225

www.elsevier.com/locate/ejor

Production, Manufacturing and Logistics

Modeling the metrics of lean, agile and leagile supply chain:An ANP-based approach

Ashish Agarwal a, Ravi Shankar a,*, M.K. Tiwari b

a Department of Management Studies, Indian Institute of Technology Delhi, HauzKhas, New Delhi 110016, Indiab Department of Manufacturing Engineering, National Institute of Forged and Foundry Technology, Ranchi 834003, India

Received 1 December 2003; accepted 12 December 2004Available online 16 February 2005

Abstract

With the emergence of a business era that embraces �change� as one of its major characteristics, manufacturing suc-cess and survival are becoming more and more difficult to ensure. The emphasis is on adaptability to changes in thebusiness environment and on addressing market and customer needs proactively. Changes in the business environmentdue to varying needs of the customers lead to uncertainty in the decision parameters. Flexibility is needed in the supplychain to counter the uncertainty in the decision parameters. A supply chain adapts the changes if it is flexible and agilein nature. A framework is presented in this paper, which encapsulates the market sensitiveness, process integration,information driver and flexibility measures of supply chain performance. The paper explores the relationship amonglead-time, cost, quality, and service level and the leanness and agility of a case supply chain in fast moving consumergoods business. The paper concludes with the justification of the framework, which analyses the effect of market win-ning criteria and market qualifying criteria on the three types of supply chains: lean, agile and leagile.� 2005 Elsevier B.V. All rights reserved.

Keywords: Agility; Flexibility; Supply chain; Analytic network process

1. Introduction

Enterprises are continuously paying attention inresponding to the customer demand for maintain-

0377-2217/$ - see front matter � 2005 Elsevier B.V. All rights reservdoi:10.1016/j.ejor.2004.12.005

* Corresponding author. Tel.: +91 11 26596421; fax: +91 1126862620/26582037.

E-mail addresses: ashish_ka@yahoo.com (A. Agarwal),ravi1@dms.iitd.ernet.in (R. Shankar), mkt09@hotmail.com(M.K. Tiwari).

ing a competitive advantage over their rivals. Sup-ply Chain Management (SCM) has gainedattention as it focuses on material, informationand cash flows from vendors to customers orvice-versa. A key feature of present day businessis the idea that it is supply chains (SC) thatcompete, not companies (Christopher and Towill,2001), and the success or failure of supply chainsis ultimately determined in the marketplace bythe end consumer. Getting the right product, at

ed.

212 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

the right time to the consumer is not only thelinchpin to competitive success, but also the keyto survival. Hence, customer satisfaction and mar-ket place understanding are critical elements forconsideration when attempting to establish a newSC strategy. Significant interest has been shownin recent years in the idea of ‘‘lean manufactur-ing’’, and the wider concepts of the ‘‘ lean enter-prises’’. The focus of the lean approach hasessentially been on the elimination of waste ormuda. The upsurge of interest in lean manufactur-ing can be traced to the Toyota Production Sys-tems with its focus on the reduction andelimination of waste. Lean is about doing morewith less. Lean concepts work well where demandis relatively stable and hence predictable and wherevariety is low. Conversely, in those contexts wheredemand is volatile and the customer requirementfor variety is high, a much higher level of agilityis required. Leanness may be an element of agilityin certain circumstances, but it will not enable theorganization to meet the precise needs of the cus-tomers more rapidly.

Agility is a business-wide capability thatembraces organizational structures, information

Table 1Comparison of lean, agile, and leagile supply chains

Distinguishing attributes Lean supply chain Agile

Market demand Predictable VolaProduct variety Low HighProduct life cycle Long ShorCustomer drivers Cost LeadProfit margin Low HighDominant costs Physical costs MarkStock out penalties Long term contractual ImmPurchasing policy Buy goods AssigInformation enrichment Highly desirable ObligForecast mechanism Algorithmic ConsTypical products Commodities FashLead time compression Essential EssenEliminate muda Essential DesirRapid reconfiguration Desirable EssenRobustness Arbitrary EssenQuality Market qualifier MarkCost Market winner MarkLead-time Market qualifier MarkService level Market qualifier Mark

Sources: Naylor et al. (1999), Mason-Jones et al. (2000a), Olhager (2

systems, logistics processes and in particular,mindsets (Power et al., 2001; Katayama and Ben-nett, 1999). Agility is being defined as the abilityof an organization to respond rapidly to changesin demand, both in terms of volume and variety(Christopher, 2000). The lean and agile paradigms,though distinctly different, can be and have beencombined within successfully designed and oper-ated total supply chains (Mason-Jones and Towill,1999). The past studies show how the need for agil-ity and leanness depends upon the total supplychain strategy, particularly considering marketknowledge, via information enrichment, and posi-tioning of the de-coupling point. Combining agil-ity and leanness in one SC via the strategic useof a de-coupling point has been termed ‘‘le-agility’’(Naylor et al., 1999). Therefore leagile is the com-bination of the lean and agile paradigms within atotal supply chain strategy by positioning thedecoupling point so as to best suit the need forresponding to a volatile demand down stream yetproviding level scheduling upstream from the mar-ket place (van Hoek et al., 2001). The decouplingpoint is in the material flow streams to which thecustomer orders penetrates (Mason-Jones et al.,

supply chain Leagile supply chain

tile Volatile and unpredictableMedium

t Short-time and availability Service level

Moderateetability costs Both

ediate and volatile No place for stock outn capacity Vendor managed inventoryatory Essentialultative Both/eitherion goods Product as per customer demandtial Desirableable Arbitrarytial Essentialtial Desirableet qualifier Market qualifieret qualifier Market winneret qualifier Market qualifieret winner Market winner

003), Bruce et al. (2004).

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 213

2000a,b; Prince and Kay, 2003). Table 1 illustratesthe comparison of attributes among lean, agile andleagile supply chain.

The present paper presents a framework formodeling performance of lean, agile and leagilesupply chain on the basis interdependent variables.Here performance of SC implies how much the SCis responsive to the needs of the market. Theframework provides an aid to decision makers inanalyzing the variables affecting market sensitive-ness, process integration, information driver andflexibility in lean, agile and leagile supply chainsfor the performance improvement of a case supplychain in fast moving consumer goods (FMCG)business. For this we have adopted Analytic Net-work Process (ANP) approach. By using ANP ina SC context, we can evaluate the influence of var-ious performance dimensions on the specifiedobjectives of SC, such as timely response to meetthe customer demand. It also explicitly considersthe influence of the performance determinants onone another. Since the dimensions and determi-nants of supply chain performance (SCP) have sys-temic characteristics, they may be integrated intoone model. These systemic relationships can moreaccurately portray the true linkages and interde-pendencies of these various determinants (Saaty,1996).

2. Supply chain performance

Supply chain is described as a chain linkingeach element from customer and supplier throughmanufacturing and services so that flow of mate-rial, money and information can be effectivelymanaged to meet the business requirement (Ste-vens, 1989). Most of the companies realize thatin order to evolve an efficient and effective supplychain, SCM needs to be assessed for its perfor-mance (Gunasekaran et al., 2001). Christopherand Towill (2001) have explained the issues relatedto market qualifier and market winner in a supplychain and identified quality, cost, lead-time andservice level as four performance measures. While,service level is the market winner for an agile sup-ply chain, rests are market qualifiers. In case oflean supply quality, lead-time and service level

are the market qualifier and cost is a marketwinner. However, with changed objectives, thequalifier and winner may change positions (Hill,1993). Aspects combining lean and agile featureshave also been explored under the concept of lea-gility (van Hoek, 2000). In the proposed ANPframework market sensitiveness (MS), informa-tion driver (ID), process integration (PI) and flex-ibility (F) have been considered as supply chainperformance (SCP) dimensions by experts of thecase supply chain. These dimensions are importantcharacteristics of agility (Christopher, 2000).

Market sensitiveness involves issues related toquick response to real demand. It is characterizedby six measures (Jayaram et al., 1999; Power et al.,2001; Agarwal and Shankar, 2002a): deliveryspeed (DS), delivery reliability (DR), new productintroduction (NPI), new product developmenttime (NPDT), manufacturing lead-time (MLT)and customer responsiveness (CR). Higher valuesof DS, DR, NPI and CR or lower values of NPDTand MLT would make the supply chain more sen-sitive towards market forces.

Information driver involves making use ofinformation technology to share data betweenbuyers and suppliers. This enables the supplychain to become demand driven. Electronic DataInterchange (EDI), means of information (MOI),such as Internet, data accuracy (DA), etc enablesupply chain partners to act upon the same datawith real time demand.

Another key characteristic of an agile organiza-tion is flexibility (Vickery et al., 1999; Prater et al.,2001; Olhager, 2003). In that respect, the origins ofagility as a business concept lie partially in flexiblemanufacturing systems. Initially it is thought thatthe route to manufacturing flexibility is throughautomation to enable rapid changeovers (i.e. re-duced set-up times) and thus enable a greaterresponsiveness to changes in product mix or vol-ume. Later this idea of manufacturing flexibilityis extended into the wider business context andthe concept of agility as an organizational orienta-tion emerged. The performance dimension flexibil-ity may be broken down into two capabilities: thepromptness with and the degree to which a firmcan adjust its supply chain speed, destinations,and volumes (Prater et al., 2001). The supply chain

214 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

may be broken down into three basic segments:sourcing, manufacturing and delivery. Any firm�ssupply chain agility is determined by how its phys-ical components (i.e. sourcing, manufacturing anddelivery) are configured to incorporate speed andflexibility. As the levels of speed and, more impor-tantly, flexibility increase, the level of supply chainagility increases. The firm can, to a degree, makeup deficiencies in the speed or flexibility of oneof the supply chain parts by excelling in the othertwo. For example, the delivery part of the supplychain may be inherently inflexible, such as is foundin sea transportation (i.e. the speed is low). Supplychain agility may be increased if the firm is able tocompensate for these shortcomings by setting upits inbound logistics (i.e. sourcing) or manufactur-ing operations to be fast or flexible (Olhager et al.,2002). As the speed in outbound logistics is inflex-ible, speed and flexibility in manufacturing andsourcing could help compensate for the slow out-bound transportation.

Shared information between supply chain part-ners can be fully leveraged through process inte-gration (PI). Process integration (PI) meanscollaborative working between buyers and suppli-ers, joint product development, common systemsand shared information (Christopher and Jittner,2000). Collaboration across each partner�s corebusiness processes (CPB), company specific issueson demand side (CDS) such as quality, cost, etcand company specific issues on supply side (CSS)such as buyer–supplier relations, vendor managedinventory, information sharing, etc are the mainenablers of the process integration.

Now we will focus on developing a frameworkfor significant alternative for the performanceimprovement of supply chain.

3. The decision environment

Analytic hierarchy process (AHP) is introducedfor choosing the most suitable alternative, whichfulfils the entire set of objectives in multi-attributedecision-making problem (Wasil and Golden,2003). AHP allows a set of complex issues, to becompared with the importance of each issue rela-tive to its impact on the solution to the problem.

Since the introduction of AHP numerous applica-tions have been published in the literature (Zahedi,1986; Shim, 1989; Kleindorfer and Partovi, 1990;Corner and Corner, 1991, 1995; Ghodsypour andO�Brien, 1998). Analytic Network Process (ANP)is a more general form of AHP, incorporatingfeedback and interdependent relationships amongdecision attributes and alternatives (Saaty, 1996).This provides a more accurate approach for mod-eling complex decision environment (Meade andSarkis, 1999; Lee and Kim, 2000; Agarwal andShankar, 2002b, 2003; Yurdakul, 2003).

We have adopted the ANP-based evaluationframework for the selection of the best alternative(Meade and Sarkis, 1999). The reasons due towhich ANP is selected for this purpose are dueto three facts: (i) analyzing the supply chain per-formance is a multi-criteria decision problem, (ii)many factors, enablers and criteria in decisionenvironment are interdependent on one another,and (iii) some of the criteria, enablers and dimen-sions are subjective due to which a synthetic scorethrough simple weightage method is difficult to ar-rive at. Analytic Hierarchy Process (AHP) is simi-lar to ANP but cannot capture interdependencies(Meade et al., 1997; Meade and Sarkis, 1999).Hierarchical representation is an important com-ponent of ANP, however strict hierarchical struc-ture is not recommended, as is the case withAHP. The ANP technique allows for more com-plex relationships among the decision levels andattributes. The ANP consists of coupling of twophases. The first phase consists of a control hierar-chy of network of criteria and sub-criteria thatcontrol the interactions. The second phase is a net-work of influences among the elements and clus-ters. The network varies from criteria to criteriaand thus different super-matrices of limiting influ-ence are computed for each control criteria. Final-ly, each one of these super-matrices is weighted bythe priority of its control criteria and results aresynthesized through addition for the entire controlcriterion (Saaty, 1996).

A graphical summary of the ANP model and itsdecision environment related to supply chain per-formance is shown in Fig. 1. The overall objectiveis to select the best framework for improving per-formance of the case supply chain.

31

To analyze the Supply Chain performance

Lean supply chain Agile supply chain Leagile supply chain

Supply Chain Performance WeightedIndex

Lead Time Cost Quality Service Level

Market sensitiveness Process integration Information driver Flexibility

Delivery Speed(DS), New productintroduction (NPI),Customerresponsiveness(CR)

Collaboration across eachpartner’s core business process (CPB),Company specific issues ondemand side (CDS),Company specific issues onsupply side(CSS)

Electronic data interchange (EDI),Means of information (MOI),Data accuracy (DA)

Source flexibility (SF), Make flexibility(MF), Delivery flexibility (DF)

CPB

CSD CSS

DS

NPI CR

EDI

MOI DA

SF

MF DF

Supply chain performance determinants

Supply chain performance dimensions

Supply chain performance enablers

Supply chain performance paradigms

Fig. 1. ANP-based framework for Modeling Metrics of Supply Chain Performance.

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 215

4. Deriving the interdependence in supply chain

performance model

The interdependence among different levels insupply chain performance framework have beendeveloped through review of literature on supplychain performance (Naylor et al., 1999; Katayamaand Bennett, 1999; van Hoek, 2000; Christopher,2000; Prater et al., 2001; Aitken et al., 2002; Poweret al., 2001; Stratton and Warburton, 2003; Bruceet al., 2004) and through discussion with experts

from the case supply chain, which incorporatesnetwork of suppliers, manufacturer, distributorsand retailers for fast moving consumer goods(FMCG). These experts have more than ten yearsof experience in the area of purchasing and supplychain management. The group consists of four tofive experts and they are informed about alterna-tive supply chain paradigms. It is believed that ex-perts know relative weights between alternativeparadigms during the process of capturing the rel-ative weights. The case supply chain is involved in

216 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

functional as well as innovative products. Thefunctional products have long product life cyclesand their demand is predictable. The innovativeproducts have short product life cycle and their de-mand is unpredictable. The management of thecase supply chain is not able to decide which sup-ply chain performance criteria should be given pri-ority over other performance criteria. They arealso unable to adopt the proper supply chain strat-egy for their products.

5. Mutual interdependence of enablers

Overall objective of the present work is tomodel performance of three paradigms for a sup-ply chain, which enables it be more flexible inresponding to market demand. Cost, quality, ser-vice level and lead-time are the major determinantsof the proposed framework. These determinantshave dominance over the identified dimensions inthe framework. The impact of one determinanton supply chain performance is affected by theinfluence of the other determinants. Using pairwise comparison matrix with a scale of one to nine,the relative weight of each determinants is ob-tained and given in Table 2. These values havebeen obtained through experts� opinions that areheading the supply chain operation. Enablers ofthe framework are those, which assist in achievingthe controlling dimension of supply chain perfor-mance. Therefore, these are dependent on thedimensions, but there is also some interdepen-dency among enablers, hence the arrows in Fig. 1are shown as arching back to the enablers� decisionlevel. For example enablers under dimension�process integration� are interdependent to somedegree. ANP uniquely captures the interdependen-

Table 2Pair-wise comparison matrix for the relative importance of the determ

Lead-time Cost

Lead-time 1 2.000Cost 0.500 1Quality 0.333 0.500Service level 9.00 4.000

cies at different levels of the control hierarchy aswell as interdependencies that are inherited amongdifferent hierarchies. We would illustrate this as-pect through an example of the case supply chain.This would illustrate interdependencies among dif-ferent enablers under cost determinant.

6. Capture of relative weights obtained through

expert opinion

The relative weights in the pair wise comparisonmatrices of ANP have been obtained through dis-cussion with group of experts of the case supplychain. The group consists of those experts fromthe trading partners of the case supply chain,which have vast experience in the area of supplychain management. For obtaining the relativeweights in Table 2, the research group asked differ-ent questions. A sample question is: ‘‘what is therelative impact on supply chain performance intimely responding to market demand by cost whencost is compared to quality?’’ The answer is 2 on ascale of 1–9 and this is incorporated as second en-try of cost row in Table 2.

Saaty (1980) has suggested a scale of 1–9 forcomparing two components. In the scale of 1–9,1 implies equal impact while 9 implies strongerimpact of row element than column element. Ifexperts feel that column element has stronger im-pact than row element, reciprocal of number from1 to 9 is assigned accordingly (Saaty, 1996).

For obtaining the relative weights in Table 3,the research group asked the question, ‘‘What isthe relative impact on market sensitiveness by ena-bler ‘‘new product introduction (NPI)’’ when com-pare to enabler ‘‘customer responsiveness (CR)’’,for the cost minimization?’’ The answer was 1/3

inants (consistency ratio: 0.016)

Quality Service level

3.000 0.111 0.1622.000 0.250 0.1231 0.125 0.0638.000 1 0.652

Table 3Pair-wise comparison matrix for market sensitiveness (consistency ratio: 0.003)

Cost e-Vector

Market sensitiveness (MS) Delivery speed (DS) New product introduction (NPI) Customer responsiveness (CR)

DS 1 5 2 0.581NPI 0.200 1 0.333 0.110CR 0.5 3.00 1 0.309

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 217

(0.333), which is incorporated as the second entryof NPI row in Table 3.

Experts� opinion is similarly ascertained in allthe tables of ANP framework.

A graphical summary of ANP model and itsdecision environment related to supply chain per-formance (SCP) is shown in Fig. 1.

The overall objective in the ANP approach is toselect a paradigm, which helps in improving theperformance of supply chain. As an illustrationwe have considered four criteria: lead time, cost,quality, and service level.

7. Application of ANP framework

The ANP methodology is applied to the illus-trative supply chain problem as follows:

STEP 1: Model construction and problem

structuringThe top most elements in the hierarchy of

criteria are decomposed into sub criteria and attri-butes. The model development requires identifica-tion of attributes at each level and a definition oftheir inter-relationships. The ultimate objective ofthis hierarchy is to identify alternatives that willbe the significant for improving the performanceof supply chain. We shall evaluate four-supplychain performance hierarchy whose results willbe aggregated in ‘‘supply chain performanceweighted index’’ evaluation step. This form ofanalysis is similar to Saaty�s recommendation ofusing a unique network for benefits, costs, risksand opportunities (BCRO) (Saaty, 1996). Insteadof using the BCRO categories supply chain perfor-mance determinants (lead-time, cost, quality andservice level) are used as the overlying network cat-egories. Cost and quality are important criteria inlean supply chain; lead-time is an important crite-

rion in agile supply chain and service level isan important criterion in leagile supply chain. Inorder to analyze the combined influence of foursupply chain performance determinants on theselection of three alternative paradigms a singleweighted index is calculated, which can prioritizethree alternatives. This weighted index also cap-tures the influence of dimensions and enablers onthe selection process.

STEP 2: Pair-wise comparison matrices between

component/attribute levels

On a scale of one to nine, the decision-makerhas been asked to respond to a series of pair-wisecomparisons with respect to an upper level �con-trol� criterion. These are conducted with respectto their relative importance towards the controlcriterion. In the case of interdependencies, compo-nents within the same level are viewed as control-ling components for each other. Levels may alsobe interdependent.

Through pair-wise comparisons between theapplicable attributes enablers of performancedimension cluster, the weighted priority (e-Vector)is calculated (Saaty, 1996). For example, Table 3presents the comparison matrix for enablers underthe dimension of Market sensitiveness, and controlhierarchy network of the cost.

Similarly, comparison matrices for other ena-blers are prepared and the resultant e-Vectors areimported as forth column in Table 5. For captur-ing the weightages an illustrative question is, �whatis the relative impact on market sensitiveness byattribute enabler, a, when compared to attributeenabler, b, under cost determinant�?

Additional pair-wise comparison matrix is re-quired for the relative importance of each of thedimensions of SCP clusters (MS, PI, ID, and F)on the determinant of SCP level. There will be fourmore matrices, one for each of the determinants.

218 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

This result is presented as second column of Table7.

The final standard pair-wise comparison evalu-ations are required for the relative impacts of eachof the alternative for SCP improvement. The num-ber of pair-wise comparison matrices is dependentof the number of SCP attribute enablers that areincluded in the determinant of the SCP improve-ment hierarchy. There are 12 pair-wise comparisonmatrices are required at this level of relationships.

STEP 3: Pair-wise comparison matrices of

interdependencies

To reflect the interdependencies, in network,pair-wise comparisons among all the attribute ena-blers are conducted. Table 4 illustrates one suchcase.

For brevity the final scores of this and remain-ing matrices are shown in Table 5.

STEP 4: Super matrix formation and analysis

Table 5 shows super matrix M, detailing the re-sults of the relative importance measures for eachof the attribute enablers for the cost determinantof SCP clusters. Since there are 12 pair-wise com-parison matrices, one for each of the interdepen-dent SCP attribute enablers in the cost hierarchy,

Table 4Pair-wise comparison matrix for enablers under market sensi-tiveness, cost and delivery speed

Delivery speed (DS) NPI CR e-Vector

New product introduction (NPI) 1 0.125 0.111Customer responsiveness (CR) 8.00 1 0.889

Table 5Super matrix for cost before convergence

Cost DS NPI CR CPB CDS CSS

DS 0.00 0.333 0.800NPI 0.111 0.00 0.200CR 0.889 0.667 0.00CPB 0 0.889 0.143CDS 0.667 0 0.857CSS 0.333 0.111 0EDIMOIDASFMFDF

there will be 12 non-zero columns in this super ma-trix. Each of the non-zero values in the column insuper matrix M, is the relative importance weightassociated with the interdependently pair-wisecomparison matrices. In this model there are foursuper matrices, one for each of the determinants ofSCP hierarchy networks, which need to beevaluated.

The Super matrix (Table 5) is converged for get-ting a long-term stable set of weights. For thispower of super matrix is raised to an arbitrarilylarge number. In our illustrative example conver-gence is reached at 32nd power. Table 6 illustratesthe value after convergence.

STEP 5: Selection of best alternative

The equation for desirability index, Dia foralternative i and determinant a is defined as(Meade and Sarkis, 1999):

Dia ¼XJ

j¼1

XKja

k¼1

P jaADkjaAI

kjaSikja; ð1Þ

where Pja is the relative importance weight ofdimension jon the determinant �a�, AD

kja is therelative importance weight for attribute enablerk, dimension j and determinant �a� for thedependency (D) relationships between enabler�scomponent levels, AI

kja is the stabilized relativeimportance weight for attribute enabler k of �j �dimension in the determinant �a� for interdepen-dency (I) relationships within the attribute ena-bler�s component level, Sikja is the relative impactof SC alternative paradigm i on SCP enabler k of

EDI MOI DA SF MF DF

0 0.200 0.6670.833 0 0.3330.167 0.800 0

0 0.333 0.8000.111 0 0.2000.889 0.667 0

Table 6Super Matrix for cost after convergence (M32)

Cost DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF

DS 0.41 0.41 0.41NPI 0.14 0.14 0.14CR 0.45 0.45 0.45CPB 0.40 0.40 0.40CDS 0.42 0.42 0.42CSS 0.18 0.18 0.18EDI 0.30 0.30 0.30MOI 0.36 0.36 0.36DA 0.34 0.34 0.34SF 0.41 0.41 0.41MF 0.14 0.14 0.14DF 0.45 0.45 0.45

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 219

dimension of SCP j of SCP hierarchy network a,Kja is the index set of attribute enablers for dimen-sion j of determinant a, J is the index set for thedimension j.

Table 7 shows the calculations for the desirabil-ity indices (Di cost) for alternatives that is based onthe cost control hierarchy by using the weights ob-tained from the pair-wise comparisons of the alter-natives, dimensions and weights of enablers fromthe converged super matrix. These weights areused to calculate a score for the determinant ofSupply chain performance improvement desirabil-ity for each of the alternatives being considered.

The second column in Table 7 presents aboutthe results obtained from step 2, which is enu-

Table 7Supply chain performance desirability index for cost

Dimension # Pja Attribute # ADkja AI

kja S1

MS 0.478 DS 0.581 0.41 0.0.478 NPI 0.110 0.14 0.0.478 CR 0.309 0.45 0.

PI 0.266 CPB 0.467 0.40 0.0.266 CDS 0.376 0.42 0.0.266 CSS 0.157 0.18 0.

ID 0.166 EDI 0.615 0.30 0.0.166 MOI 0.093 0.36 0.0.166 DA 0.292 0.34 0.

F 0.090 SF 0.615 0.41 0.0.090 MF 0.093 0.14 0.0.090 DF 0.292 0.45 0.

Total desirability indices of cost for alternative frameworks

merated based on relative impact of each ofdimensions on cost determinants. The pair-wisecomparison matrix for the relative impact of theattribute enablers on the dimensions of SCP is pre-sented in the fourth column. The values in fifthcolumn are the stable interdependent weights ofattribute enablers obtained through super matrixconvergence. The relative weights of three alterna-tives for each dimension are given in sixth, seventhand eighth columns of Table 7. These weights areobtained by comparing three alternatives for everydimension of supply chain performance. The finalthree columns represent the desirability index(P jaAD

kjaAIkjaSikja) of each alternative for attribute

enablers. For each of the alternatives under cost

S2 S3 Lean Agile Leagile

577 0.160 0.263 0.066 0.018 0.030600 0.144 0.256 0.004 0.001 0.002544 0.110 0.346 0.037 0.007 0.023

579 0.187 0.234 0.029 0.009 0.012548 0.211 0.241 0.023 0.009 0.010490 0.312 0.198 0.004 0.002 0.001

525 0.142 0.334 0.016 0.004 0.010537 0.268 0.195 0.003 0.001 0.001490 0.312 0.198 0.008 0.005 0.003

539 0.297 0.164 0.012 0.007 0.004286 0.143 0.571 0.0003 0.0002 0.001333 0.167 0.500 0.004 0.002 0.006

0.205 0.073 0.097

220 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

determinant, the summation of these results ap-pears in the final row of Table 7. The result showsthat the impact on cost is considered an importantcriterion in lean supply chain (0.205) followed byleagile (0.097) and agile (0.073) supply chain.

STEP 6: Calculation of Supply Chain Perfor-

mance Weighted Index (SPWI)

To complete the analysis supply chain perfor-mance weighted index (SPWI) is determined foreach alternative paradigm. The SPWIi for an alter-native i is the product of the desirability indices(Dia) and the relative importance weights of thedeterminants (Ca) of the SCP.

The results (Table 2) show that the service leveldeterminant (Ca = 0.652) as most important forsupply chain performance improvement. The re-sult indicates that the management of the case sup-ply chain should focus on improving the servicelevel. This result could be due to the competitiveor customer pressure for improving service level.Lead-time (0.162) and cost (0.123) play the nextmost important role but are less important thanservice level.

The final results are shown in Table 8.The Table 8 indicates that for the illustrative

problem the most significant alternative paradigmfor better supply chain performance is leagile sup-ply chain followed by agile supply chain.

8. Sensitivity analysis

Sensitivity analysis is an important concept forthe effective use of any quantitative decision model(Poh and Ang, 1999). In the present work sensitiv-ity analysis is done to find out the changes in theSPWI for lean, agile and leagile supply chain par-

Table 8Supply chain Performance Weighted Index (SPWI) for various altern

Alternatives # Criteria

Lead-time Cost Qual

Weights for criteria: 0.162 0.123 0.063

Lean 0.067 0.205 0.133Agile 0.162 0.073 0.075Leagile 0.106 0.097 0.093Total

adigms with variation in the expert opinion to-wards lead-time with respect to cost, quality andservice level. Overall objective of sensitivity analy-sis is to see the robustness of proposed frameworkdue to variation in the experts� opinion in assign-ing the weights during comparison.

For the case supply chain experts opinion hasbeen sought to analyze the performance of supplychain. Table 8 indicates how the supply chain per-formance weighted indexes (SPWI) for proposedframework of three supply chains varies withchanging priority of lead-time, cost, quality andservice level. When overall objective is to reducelead-time, desirability indices is lower for lean sup-ply chain than agile supply chain. In a strategy tominimize the cost and to improve quality, leansupply chain has the highest desirability indicesamong the three supply chains. In an effort to im-prove service level, desirability indices for leagilesupply chain is slightly higher than agile supplychain. Here it is pertinent to mention that in theuncertain environment desired supply chain per-formance cannot be alone achieved either by leanor by agile supply chain. Lean and agile paradigmsare not mutually exclusive paradigms (Christopherand Towill, 2001), therefore proper combinationof lean and agile (leagile) is required to suit theneed for responding to a volatile demand (Nayloret al., 1999).

In Fig. 2, X-axis represents the relative weightof lead-time as compare to quality. These relativeweights are in the scale of 1/9–9 (Saaty scale). Y-axis represents the normalized value of supplychain performance weighted index (SPWI). Theseweights are obtained using ANP framework,which captures the interdependence among supplychain performance variables. This framework con-

ative frameworks

Calculated weights for alternatives

ity Service level SPWI NORM

0.652

0.081 0.0974 0.3160.099 0.1049 0.3400.109 0.1058 0.343

0.308 1.000

0.280

0.290

0.300

0.310

0.320

0.330

0.340

0.350

0.360

0.111 0.125 0.143 0.167 0.200 0.250 0.333 0.500 0.667 1 2 3 4 5 6 7 8 9

Variation in priority of lead-time with respect to quality

Nor

mal

ized

val

ueLeanAgileLeagile

Fig. 2. Variation in priority of supply chain paradigms with changes in weight assigned to lead-time with respect to quality.

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 221

sists of 117 pair wise comparison matrices. Thepurpose is to analyze the effect of variation inrelative weight assigned to SCP determinants onthe priority level of alternative supply chainparadigms.

In the present ANP framework, experts haveassigned relative weight 3 to lead-time in comparewith quality (XLT/Q) on supply chain performanceimprovement. With this relative weight, SPWI forleagile supply chain is the highest followed by agileand lean supply chain. This implies when the per-ception of experts is more inclined towards lead-time in comparison to quality, they will preferthe supply chain which favors lead-time reduction.Lead-time is an essential metric for leagile andagile supply chains. Here lead-time indicates thetime between raising the demand by customerand receiving the product of his choice. This prior-ity level does not change if XLT/Q lowers from 3 to0.125. This indicates that if experts lower relative

0.250

0.270

0.290

0.310

0.330

0.350

0.370

0.111 0.125 0.143 0.167 0.200 0.250 0.333 0.500 0.667 1.000

Variation in priority of lead-tim

Nor

mal

ized

val

ue

Fig. 3. Variation in priority of supply chain paradigms with cha

importance of lead-time to quality (or give moreimportance to quality as compare to lead-time),priority of leagile supply chain paradigm doesnot change. When XLT/Q is further lowered from0.125 to 0.111, lean supply chain attains top prior-ity followed by leagile supply chain. If weight as-signed to lead-time in comparison to quality isbetween 0.5 and 0.333, policy towards supplychain performance improvement would be combi-nation leanness and agility. This point indicatesthat advantages of both leanness and agility canbe achieved. When the priority weight is further re-duced beyond 0.125, lean supply chain gets toppriority followed by leagile and agile supply chain.

Fig. 3 indicates effect on values of SPWI forlean, agile and leagile supply chains due to varia-tion in the priority weight of lead-time with respectto cost (XLT/C). In the present framework accord-ing to expert�s opinion, XLT/C is 2. When the rela-tive weight XLT/C lies between 0.667 and 3, experts

2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000

e with respect to cost

Lean

Agile

Leagile

nges in weight assigned to lead-time with respect to cost.

222 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

favour strategy for leagile supply chain in meetingthe unpredictable demand. SPWI for agile supplychain improves when experts� opinion deviateand XLT/C varies from 3 to 9. In this situation pri-ority for lean supply chain declines. In the rangebetween 0.667 and 0.111 of XLT/C, experts favorstrategy for cost minimization. In this strategy ex-perts are partially trading off importance of lead-time reduction to cost minimization. Here, the casesupply chain partially looses its agility, which isindicated in the graph (Fig. 3) and the priority le-vel for lean supply chain improves.

In Fig. 4 effect on the values of SPWI for lean,agile, and leagile supply chain due to change in rel-ative weight of lead-time with respect to servicelevel (XLT/SL) is shown.

In proposed ANP framework, XLT/SL is 0.111.At this priority experts favor service level improve-ment. Since service level is the most importantcriteria for leagile and agile supply chain (Nayloret al., 1999), SPWI for leagile supply chain getstop priority at this relative weight followed byagile supply chain (Fig. 4). If the XLT/SL is changedfrom 0.111 to 0.167, SPWI for agile supply chainimproves but leagile supply chain remains at top.When the value of XLT/SL is higher than 0.167, ex-perts relatively consider lead-time more importantthan service level agile supply chain gets top prior-ity followed by leagile and lean supply chain.

The purpose of selecting lead-time, cost, qualityand service level is straightforward. These areorder qualifying and order winning criteria. Withchanges in objective these criteria changes their po-

0.270

0.290

0.310

0.330

0.350

0.370

0.390

0.111 0.125 0.143 0.167 0.200 0.250 0.333 0.500 0.667 1

Varition in priority of lead-time

Nor

mal

ized

val

ue

Fig. 4. Variation in priority of supply chain paradigms with change

sition. Leanness and agility of a supply chain lar-gely depends on these four criteria (Naylor et al.,1999).

9. Discussions

‘‘Agility’’ is needed in less predictable environ-ments where demand is volatile and the require-ment for variety is high (Lee, 2002). ‘‘Lean’’works best in high volume, low variety and pre-dictable environments. Leagility is the combina-tion of the lean and agile paradigm within a totalsupply chain strategy by positioning the de-cou-pling point so as to best suit the need for respond-ing to a volatile demand downstream yet providinglevel scheduling upstream from the de-couplingpoint (Naylor et al., 1999; Bruce et al., 2004).The ANP model proposed in this paper is an aidto supply chain managers in arriving at prudentdecision when the complexities of decision vari-ables and multi-criteria decision environmentmake their decision task quite complicated. ThisANP model is used for selecting appropriate para-digm for improved SC performance of a case com-pany. This could serve as one of the importanttools for taking a strategic decision of this type.The criteria and attributes that are used in themodel focus on the strategy and requirements ofSC performance. The model is capable of takinginto consideration both qualitative and quantita-tive information. Here it is pertinent to discussthe priority values for the determinants, which

2 3 4 5 6 7 8 9

with respect to service level

LeanAgileLeagile

s in weight assigned to lead-time with respect to service level.

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 223

influence the decision of selecting the paradigm forbetter SC performance.

From Table 2, it has been observed that the ser-vice level (0.652) is the most important criteria inthe selection of the framework for the supply chainparadigm. This is followed by lead-time (0.162),cost (0.123) and quality (0.063). For the case sup-ply chain of fast moving consumer goods, the re-sult favors improvement in service level andreduction in lead-time. Cost and quality are lesssupported because improvement in service leveland reduction in lead-time would also help inreducing cost and improving quality. Though theresults do not favor cost and quality, the implica-tion is not straightforward. The lower values forthese two are due to their interdependency onlead-time and service level. For example, a lowvalue of lead-time will lead to lesser waste andquality improvement opportunity. The conversemay not be true. The ANP is capable of handlinginterdependencies of this type. The present deci-sion model provides the priority values in the formof weighted index for different paradigms for im-proved SC performance (Table 8). The final valuesfor supply chain performance weighted index rela-tionship are 0.343 for the leagile, 0.340 for agile,and 0.316 for lean supply chain. For supply chainof the case company, the ANP framework suggeststhat with existing priority levels of supply chainperformance determinants, normalized value ofSPWI for leagile paradigm is slightly higher thanthat of a mere lean or agile paradigm. The highervalue of SPWI for leagile supply chain favorsthe policy for combining the lean and agile ap-proaches. For handling innovative products thecase supply chain should adopt a lean manufactur-ing approach before decoupling point and agileapproach after decoupling point (Olhager, 2003).

Consistency ratio (CR) is calculated for all thepair-wise comparisons to check the inconsistencyin decision-making. In the proposed model CRvaries from 0.002 to 0.19, which is within tolerablelimit (Saaty and Kearns, 1985). An analysis of therobustness of the decision model using sensitivityanalysis is carried out to observe the impact of var-iation in the opinion of decision-makers in assign-ing the weights. Sensitivity analysis indicates thatthe priority levels of SC paradigms do not signifi-

cantly change with variation in the opinion ofdecision-makers in assigning the weights toenablers.

10. Limitations and scope for future work

As compared to analytic hierarchy process(AHP), the analysis using ANP is relatively cum-bersome as in the present work 117 pair-wise com-parison matrices are required. To arriving at therelationship among enablers, it requires long andexhaustive discussion with experts from the casesupply chain. Therefore, the advantages of ANPtechnique could be derived for making strategicdecisions that are vital for the growth and survivalof supply chains.

The values for pair-wise comparisons dependon the knowledge of the decision-makers. There-fore group of decision-makers should includethose experts who understand the implications ofenablers on the supply chain performance in lean,agile and leagile paradigm.

The proposed framework has been developedfor a supply chain in fast moving consumer goods(FMCG) business. Therefore results obtainedfrom the proposed framework cannot begeneralized.

11. Conclusion

Improved supply chain performance impliesthat a supply chain is capable of quickly respond-ing to the variations in the customer demandwith effective cost reduction. Leanness in a supplychain maximizes profits through cost reductionwhile agility maximizes profit through providingexactly what the customer requires. The leagilesupply chain enables the upstream part of thechain to be cost-effective and the downstreampart to achieve high service levels in a volatilemarketplace.

The ANP methodology adopted here arrives at asynthetic score, which may be quite useful for thedecision-makers. The purpose of the present workis to analyze the relative impact of different enablerson three SC paradigms considered for a supply

224 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225

chain. The ANP methodology is a robust multi-attribute decision-making technique for synthesiz-ing the criteria, enablers and dimensions governingthe SC performance. It integrates various criteria,enablers and alternatives in decision model. The ap-proach also captures their relationships and inter-dependencies across and along the hierarchies. Itis effective as both quantitative and qualitativecharacteristics can be considered simultaneouslywithout sacrificing their relationships.

References

Agarwal, A., Shankar, R., 2003. On-line trust building in e-enabled supply chain. Supply Chain Management: AnInternational Journal 8 (4), 324–334.

Agarwal, A., Shankar, R., 2002a. Modeling integration andresponsiveness on a supply chain performance: A systemdynamics approach. International Journal System Dynam-ics and Policy-Making XIV (1&2), 61–83.

Agarwal, A., Shankar, R., 2002b. Analyzing alternatives forimprovement in supply chain performance. Work Study 51(1), 32–37.

Aitken, J., Christopher, M., Towill, D.R., 2002. Understand-ing, implementing and exploiting agility and leanness.International Journal of Logistics: Research and Applica-tions 5 (1), 59–74.

Bruce, M., Daly, L., Towers, N., 2004. Lean or agile: A solutionfor supply chain management in the textiles and clothingindustry? International Journal of Operations and Produc-tion Management 24 (2), 151–170.

Christopher, M., 2000. The agile supply chain, competingin volatile markets. Industrial Marketing Management 29,37–44.

Christopher, M., Jittner, U., 2000. Developing strategic part-nerships in the supply chain: A practitioner perspective.European Journal of Purchasing and Supply Chain Man-agement 6, 117–127.

Christopher, M., Towill, D.R., 2001. An integrated model forthe design of agile supply chains. International Journal ofPhysical Distribution and Logistics Management 31 (4),235–246.

Corner, J.L., Corner, P.D., 1991. Decision analysis applicationsin the operations research literature. Operations Research39, 206–218.

Corner, J.L., Corner, P.D., 1995. Characteristics of decisions indecision analysis practice. Journal of the OperationalResearch Society 46, 304–314.

Ghodsypour, S.H., O�Brien, C., 1998. Decision support systemfor supplier selection using an integrated analytic hierarchyprocess and linear programming. International Journal ofProduction Economics 56–57, 199–212.

Gunasekaran, A., Patel, C., Tirtiroglu, E., 2001. Performancemeasures and metrics in a supply chain environment.

International Journal of Operations and Production Man-agement 21 (1/2), 71–87.

Hill, T., 1993. Manufacturing Strategy: Text and Cases, seconded. Macmillan Press, London.

Jayaram, J., Vickery, S.K., Droge, C., 1999. An empirical studyof time based competition in the North American auto-motive supplier industry. International Journal of Opera-tion and Production Management 19 (10), 1010–1033.

Katayama, H., Bennett, D., 1999. Agility, adaptability andleanness: A comparison of concepts and a study of practice.International Journal of Production Economics 60–61,43–51.

Kleindorfer, P.R., Partovi, F.Y., 1990. Integrating manufac-turing strategy and technology choice. European Journal ofOperational Research 47, 214–224.

Lee, J.W., Kim, S.H., 2000. Using analytic network process andgoal programming for interdependent information systemproject selection. Computers and Operations Research 27,367–382.

Lee, H.L., 2002. Aligning Supply Chain Strategies with ProductUncertainties. California Management Review 44 (3), 105–119.

Mason-Jones, R., Towill, D.R., 1999. Total cycle time com-pression and the agile supply chain. International Journal ofProduction Economics 62, 61–73.

Mason-Jones, R., Naylor, B., Towill, D.R., 2000a. Engineeringthe leagile supply chain. International Journal of AgileManagement Systems 2/1, 54–61.

Mason-Jones, R., Naylor, B., Towill, D.R., 2000b. Lean, agileor leagile? Matching your supply chain to the marketplace.International Journal of Production Research 38/17, 4061–4070.

Meade, L.M., Liles, D.H., Sarkis, J., 1997. Justifying strategicalliances and partnering: A prerequisite for virtual enter-prising. Omega 25 (1), 29–42.

Meade, L., Sarkis, J., 1999. Analyzing organizational projectalternatives for agile manufacturing process: An analyticalnetwork approach. International Journal of ProductionResearch 37 (2), 241–261.

Naylor, J.B., Naim, M.M., Berry, D., 1999. Leagility: Integrat-ing the lean and agile manufacturing paradigms in the totalsupply chain. International Journal of Production Econom-ics 62, 107–118.

Olhager, J., Persson, F., Parborg, B., Rosen, S., 2002. Supplychain impacts at Ericsson––from production units todemand-driven supply units. International Journal of Tech-nology Management 23 (1/2/3), 40–59.

Olhager, J., 2003. Strategic positioning of the order penetrationpoint. International Journal of Production Economics 85,319–329.

Poh, K.L., Ang, B.W., 1999. Transportation fuels and policyfor Singapore: An AHP planning approach. Computer andIndustrial Engineering 37, 507–525.

Power, D.J., Sohal, A.S., Rahman, S., 2001. Critical successfactors in agile supply chain management: An empiricalstudy. International Journal of Physical Distribution andLogistics 31 (4), 247–265.

A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 225

Prater, E., Biehl, M., Smith, M.A., 2001. International supplychain agility––Tradeoffs between flexibility and uncertainty.International Journal of Operations and Production Man-agement 21 (5/6), 823–839.

Prince, J., Kay, J.M., 2003. Combining lean and agile charac-teristics: Creation of virtual groups by enhanced productionflow analysis. International Journal of Production Econom-ics 85, 305–318.

Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York.

Saaty, T.L., 1996. Decision Making with Dependence andFeedback: The Analytic Network Process. RWS Publica-tion, Pittsburgh, PA.

Saaty, T.L., Kearns, K.P., 1985. Analytical Planning. Perg-amon Press, Oxford.

Shim, J.P., 1989. Bibliographical research on the analytichierarchy process. Socio-economic Planning Science 23,161–167.

Stevens, G., 1989. Integrating the supply chain. InternationalJournal of Physical Distribution and Materials Manage-ment 19 (1), 3–8.

Stratton, R., Warburton, R.D.H., 2003. The strategic integra-tion of agile and lean supply. International Journal ofProduction Economics 85, 183–198.

van Hoek, R.I., 2000. The thesis of leagility revisited. Interna-tional Journal of Agile Management Systems 2/3, 196–201.

van Hoek, R.I., Harrison, A., Christopher, M., 2001. Measur-ing agile capabilities in the supply chain. InternationalJournal of Operations and Production Management 21(1/2), 126–147.

Vickery, S., Calantone, R., Droge, C., 1999. Supply chainflexibility: An empirical study. The Journal of Supply ChainManagement 35 (3), 16–24.

Wasil, E., Golden, B., 2003. Celebrating 25 years of AHP-baseddecision making. Computers and Operations Research 30,1419–1420.

Yurdakul, M., 2003. Measuring long-term performance of amanufacturing firm using the Analytic Network Process(ANP) approach. International Journal of ProductionResearch 41 (11), 2501–2529.

Zahedi, F., 1986. The analytic hierarchy process—a survey ofthe method and its applications. Interfaces 16, 96–108.