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Modeling the metrics of lean, agile and leagile supply chain ...

  1. 1. European Journal of Operational Research 173 (2006) 211–225 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, India b Department of Manufacturing Engineering, National Institute of Forged and Foundry Technology, Ranchi 834003, India Received 1 December 2003; accepted 12 December 2004 Available online 16 February 2005 Abstract With the emergence of a business era that embraces ÔchangeÕ 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 the business environment and on addressing market and customer needs proactively. Changes in the business environment due to varying needs of the customers lead to uncertainty in the decision parameters. Flexibility is needed in the supply chain to counter the uncertainty in the decision parameters. A supply chain adapts the changes if it is flexible and agile in 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 among lead-time, cost, quality, and service level and the leanness and agility of a case supply chain in fast moving consumer goods 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. Ó 2005 Elsevier B.V. All rights reserved. Keywords: Agility; Flexibility; Supply chain; Analytic network process 1. Introduction ing a competitive advantage over their rivals. Sup- ply Chain Management (SCM) has gained Enterprises are continuously paying attention in attention as it focuses on material, information responding to the customer demand for maintain- and cash flows from vendors to customers or vice-versa. A key feature of present day business * is the idea that it is supply chains (SC) that Corresponding author. Tel.: +91 11 26596421; fax: +91 11 compete, not companies (Christopher and Towill, 26862620/26582037. E-mail addresses: (A. Agarwal), 2001), and the success or failure of supply chains (R. Shankar), is ultimately determined in the marketplace by (M.K. Tiwari). the end consumer. Getting the right product, at 0377-2217/$ - see front matter Ó 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ejor.2004.12.005
  2. 2. 212 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 the right time to the consumer is not only the systems, logistics processes and in particular, linchpin to competitive success, but also the key mindsets (Power et al., 2001; Katayama and Ben- to survival. Hence, customer satisfaction and mar- nett, 1999). Agility is being defined as the ability ket place understanding are critical elements for of an organization to respond rapidly to changes consideration when attempting to establish a new in demand, both in terms of volume and variety SC strategy. Significant interest has been shown (Christopher, 2000). The lean and agile paradigms, in recent years in the idea of ‘‘lean manufactur- though distinctly different, can be and have been ing’’, and the wider concepts of the ‘‘ lean enter- combined within successfully designed and oper- prises’’. The focus of the lean approach has ated total supply chains (Mason-Jones and Towill, essentially been on the elimination of waste or 1999). The past studies show how the need for agil- muda. The upsurge of interest in lean manufactur- ity and leanness depends upon the total supply ing can be traced to the Toyota Production Sys- chain strategy, particularly considering market tems with its focus on the reduction and knowledge, via information enrichment, and posi- elimination of waste. Lean is about doing more tioning of the de-coupling point. Combining agil- with less. Lean concepts work well where demand ity and leanness in one SC via the strategic use is relatively stable and hence predictable and where of a de-coupling point has been termed ‘‘le-agility’’ variety is low. Conversely, in those contexts where (Naylor et al., 1999). Therefore leagile is the com- demand is volatile and the customer requirement bination of the lean and agile paradigms within a for variety is high, a much higher level of agility total supply chain strategy by positioning the is required. Leanness may be an element of agility decoupling point so as to best suit the need for in certain circumstances, but it will not enable the responding to a volatile demand down stream yet organization to meet the precise needs of the cus- providing level scheduling upstream from the mar- tomers more rapidly. ket place (van Hoek et al., 2001). The decoupling Agility is a business-wide capability that point is in the material flow streams to which the embraces organizational structures, information customer orders penetrates (Mason-Jones et al., Table 1 Comparison of lean, agile, and leagile supply chains Distinguishing attributes Lean supply chain Agile supply chain Leagile supply chain Market demand Predictable Volatile Volatile and unpredictable Product variety Low High Medium Product life cycle Long Short Short Customer drivers Cost Lead-time and availability Service level Profit margin Low High Moderate Dominant costs Physical costs Marketability costs Both Stock out penalties Long term contractual Immediate and volatile No place for stock out Purchasing policy Buy goods Assign capacity Vendor managed inventory Information enrichment Highly desirable Obligatory Essential Forecast mechanism Algorithmic Consultative Both/either Typical products Commodities Fashion goods Product as per customer demand Lead time compression Essential Essential Desirable Eliminate muda Essential Desirable Arbitrary Rapid reconfiguration Desirable Essential Essential Robustness Arbitrary Essential Desirable Quality Market qualifier Market qualifier Market qualifier Cost Market winner Market qualifier Market winner Lead-time Market qualifier Market qualifier Market qualifier Service level Market qualifier Market winner Market winner Sources: Naylor et al. (1999), Mason-Jones et al. (2000a), Olhager (2003), Bruce et al. (2004).
  3. 3. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 213 2000a,b; Prince and Kay, 2003). Table 1 illustrates are the market qualifier and cost is a market the comparison of attributes among lean, agile and winner. However, with changed objectives, the leagile supply chain. qualifier and winner may change positions (Hill, The present paper presents a framework for 1993). Aspects combining lean and agile features modeling performance of lean, agile and leagile have also been explored under the concept of lea- supply chain on the basis interdependent variables. gility (van Hoek, 2000). In the proposed ANP Here performance of SC implies how much the SC framework market sensitiveness (MS), informa- is responsive to the needs of the market. The tion driver (ID), process integration (PI) and flex- framework provides an aid to decision makers in ibility (F) have been considered as supply chain analyzing the variables affecting market sensitive- performance (SCP) dimensions by experts of the ness, process integration, information driver and case supply chain. These dimensions are important flexibility in lean, agile and leagile supply chains characteristics of agility (Christopher, 2000). for the performance improvement of a case supply Market sensitiveness involves issues related to chain in fast moving consumer goods (FMCG) quick response to real demand. It is characterized business. For this we have adopted Analytic Net- by six measures (Jayaram et al., 1999; Power et al., work Process (ANP) approach. By using ANP in 2001; Agarwal and Shankar, 2002a): delivery a SC context, we can evaluate the influence of var- speed (DS), delivery reliability (DR), new product ious performance dimensions on the specified introduction (NPI), new product development objectives of SC, such as timely response to meet time (NPDT), manufacturing lead-time (MLT) the customer demand. It also explicitly considers and customer responsiveness (CR). Higher values the influence of the performance determinants on of DS, DR, NPI and CR or lower values of NPDT one another. Since the dimensions and determi- and MLT would make the supply chain more sen- nants of supply chain performance (SCP) have sys- sitive towards market forces. temic characteristics, they may be integrated into Information driver involves making use of one model. These systemic relationships can more information technology to share data between accurately portray the true linkages and interde- buyers and suppliers. This enables the supply pendencies of these various determinants (Saaty, chain to become demand driven. Electronic Data 1996). Interchange (EDI), means of information (MOI), such as Internet, data accuracy (DA), etc enable supply chain partners to act upon the same data 2. Supply chain performance with real time demand. Another key characteristic of an agile organiza- Supply chain is described as a chain linking tion is flexibility (Vickery et al., 1999; Prater et al., each element from customer and supplier through 2001; Olhager, 2003). In that respect, the origins of manufacturing and services so that flow of mate- agility as a business concept lie partially in flexible rial, money and information can be effectively manufacturing systems. Initially it is thought that managed to meet the business requirement (Ste- the route to manufacturing flexibility is through vens, 1989). Most of the companies realize that automation to enable rapid changeovers (i.e. re- in order to evolve an efficient and effective supply duced set-up times) and thus enable a greater chain, SCM needs to be assessed for its perfor- responsiveness to changes in product mix or vol- mance (Gunasekaran et al., 2001). Christopher ume. Later this idea of manufacturing flexibility and Towill (2001) have explained the issues related is extended into the wider business context and to market qualifier and market winner in a supply the concept of agility as an organizational orienta- chain and identified quality, cost, lead-time and tion emerged. The performance dimension flexibil- service level as four performance measures. While, ity may be broken down into two capabilities: the service level is the market winner for an agile sup- promptness with and the degree to which a firm ply chain, rests are market qualifiers. In case of can adjust its supply chain speed, destinations, lean supply quality, lead-time and service level and volumes (Prater et al., 2001). The supply chain
  4. 4. 214 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 may be broken down into three basic segments: Since the introduction of AHP numerous applica- sourcing, manufacturing and delivery. Any firmÕs tions have been published in the literature (Zahedi, supply chain agility is determined by how its phys- 1986; Shim, 1989; Kleindorfer and Partovi, 1990; ical components (i.e. sourcing, manufacturing and Corner and Corner, 1991, 1995; Ghodsypour and delivery) are configured to incorporate speed and OÕBrien, 1998). Analytic Network Process (ANP) flexibility. As the levels of speed and, more impor- is a more general form of AHP, incorporating tantly, flexibility increase, the level of supply chain feedback and interdependent relationships among agility increases. The firm can, to a degree, make decision attributes and alternatives (Saaty, 1996). up deficiencies in the speed or flexibility of one This provides a more accurate approach for mod- of the supply chain parts by excelling in the other eling complex decision environment (Meade and two. For example, the delivery part of the supply Sarkis, 1999; Lee and Kim, 2000; Agarwal and chain may be inherently inflexible, such as is found Shankar, 2002b, 2003; Yurdakul, 2003). in sea transportation (i.e. the speed is low). Supply We have adopted the ANP-based evaluation chain agility may be increased if the firm is able to framework for the selection of the best alternative compensate for these shortcomings by setting up (Meade and Sarkis, 1999). The reasons due to its inbound logistics (i.e. sourcing) or manufactur- which ANP is selected for this purpose are due ing operations to be fast or flexible (Olhager et al., to three facts: (i) analyzing the supply chain per- 2002). As the speed in outbound logistics is inflex- formance is a multi-criteria decision problem, (ii) ible, speed and flexibility in manufacturing and many factors, enablers and criteria in decision sourcing could help compensate for the slow out- environment are interdependent on one another, bound transportation. and (iii) some of the criteria, enablers and dimen- Shared information between supply chain part- sions are subjective due to which a synthetic score ners can be fully leveraged through process inte- through simple weightage method is difficult to ar- gration (PI). Process integration (PI) means rive at. Analytic Hierarchy Process (AHP) is simi- collaborative working between buyers and suppli- lar to ANP but cannot capture interdependencies ers, joint product development, common systems (Meade et al., 1997; Meade and Sarkis, 1999). and shared information (Christopher and Jittner, Hierarchical representation is an important com- 2000). Collaboration across each partnerÕs core ponent of ANP, however strict hierarchical struc- business processes (CPB), company specific issues ture is not recommended, as is the case with on demand side (CDS) such as quality, cost, etc AHP. The ANP technique allows for more com- and company specific issues on supply side (CSS) plex relationships among the decision levels and such as buyer–supplier relations, vendor managed attributes. The ANP consists of coupling of two inventory, information sharing, etc are the main phases. The first phase consists of a control hierar- enablers of the process integration. chy of network of criteria and sub-criteria that Now we will focus on developing a framework control the interactions. The second phase is a net- for significant alternative for the performance work of influences among the elements and clus- improvement of supply chain. ters. The network varies from criteria to criteria and thus different super-matrices of limiting influ- ence are computed for each control criteria. Final- 3. The decision environment ly, each one of these super-matrices is weighted by the priority of its control criteria and results are Analytic hierarchy process (AHP) is introduced synthesized through addition for the entire control for choosing the most suitable alternative, which criterion (Saaty, 1996). fulfils the entire set of objectives in multi-attribute A graphical summary of the ANP model and its decision-making problem (Wasil and Golden, decision environment related to supply chain per- 2003). AHP allows a set of complex issues, to be formance is shown in Fig. 1. The overall objective compared with the importance of each issue rela- is to select the best framework for improving per- tive to its impact on the solution to the problem. formance of the case supply chain.
  5. 5. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 215 To analyze the Supply Chain performance Supply Chain Performance Weighted Index Supply chain performance determinants Lead Time Cost Quality Service Level Supply chain performance dimensions Market sensitiveness Process integration Information driver Flexibility Supply chain performance enablers Collaboration across each Delivery Speed Source flexibility partner’s core business process Electronic data interchange (DS), (SF), (CPB), (EDI), New product Means of information Make flexibility Company specific issues on introduction (NPI), (MOI), (MF), demand side (CDS), Customer Data accuracy (DA) Delivery flexibility Company specific issues on responsiveness(CR) supply side(CSS) (DF) DS CPB SF EDI NPI CR CSD CSS MOI DA MF DF Supply chain performance paradigms Lean supply chain Agile supply chain 31 Leagile supply chain Fig. 1. ANP-based framework for Modeling Metrics of Supply Chain Performance. 4. Deriving the interdependence in supply chain from the case supply chain, which incorporates performance model network of suppliers, manufacturer, distributors and retailers for fast moving consumer goods The interdependence among different levels in (FMCG). These experts have more than ten years supply chain performance framework have been of experience in the area of purchasing and supply developed through review of literature on supply chain management. The group consists of four to chain performance (Naylor et al., 1999; Katayama five experts and they are informed about alterna- and Bennett, 1999; van Hoek, 2000; Christopher, tive supply chain paradigms. It is believed that ex- 2000; Prater et al., 2001; Aitken et al., 2002; Power perts know relative weights between alternative et al., 2001; Stratton and Warburton, 2003; Bruce paradigms during the process of capturing the rel- et al., 2004) and through discussion with experts ative weights. The case supply chain is involved in
  6. 6. 216 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 functional as well as innovative products. The cies at different levels of the control hierarchy as functional products have long product life cycles well as interdependencies that are inherited among and their demand is predictable. The innovative different hierarchies. We would illustrate this as- products have short product life cycle and their de- pect through an example of the case supply chain. mand is unpredictable. The management of the This would illustrate interdependencies among dif- case supply chain is not able to decide which sup- ferent enablers under cost determinant. ply chain performance criteria should be given pri- ority over other performance criteria. They are also unable to adopt the proper supply chain strat- 6. Capture of relative weights obtained through egy for their products. expert opinion The relative weights in the pair wise comparison 5. Mutual interdependence of enablers matrices of ANP have been obtained through dis- cussion with group of experts of the case supply Overall objective of the present work is to chain. The group consists of those experts from model performance of three paradigms for a sup- the trading partners of the case supply chain, ply chain, which enables it be more flexible in which have vast experience in the area of supply responding to market demand. Cost, quality, ser- chain management. For obtaining the relative vice level and lead-time are the major determinants weights in Table 2, the research group asked differ- of the proposed framework. These determinants ent questions. A sample question is: ‘‘what is the have dominance over the identified dimensions in relative impact on supply chain performance in the framework. The impact of one determinant timely responding to market demand by cost when on supply chain performance is affected by the cost is compared to quality?’’ The answer is 2 on a influence of the other determinants. Using pair scale of 1–9 and this is incorporated as second en- wise comparison matrix with a scale of one to nine, try of cost row in Table 2. the relative weight of each determinants is ob- Saaty (1980) has suggested a scale of 1–9 for tained and given in Table 2. These values have comparing two components. In the scale of 1–9, been obtained through expertsÕ opinions that are 1 implies equal impact while 9 implies stronger heading the supply chain operation. Enablers of impact of row element than column element. If the framework are those, which assist in achieving experts feel that column element has stronger im- the controlling dimension of supply chain perfor- pact than row element, reciprocal of number from mance. Therefore, these are dependent on the 1 to 9 is assigned accordingly (Saaty, 1996). dimensions, but there is also some interdepen- For obtaining the relative weights in Table 3, dency among enablers, hence the arrows in Fig. 1 the research group asked the question, ‘‘What is are shown as arching back to the enablersÕ decision the relative impact on market sensitiveness by ena- level. For example enablers under dimension bler ‘‘new product introduction (NPI)’’ when com- Ôprocess integrationÕ are interdependent to some pare to enabler ‘‘customer responsiveness (CR)’’, degree. ANP uniquely captures the interdependen- for the cost minimization?’’ The answer was 1/3 Table 2 Pair-wise comparison matrix for the relative importance of the determinants (consistency ratio: 0.016) Lead-time Cost Quality Service level Lead-time 1 2.000 3.000 0.111 0.162 Cost 0.500 1 2.000 0.250 0.123 Quality 0.333 0.500 1 0.125 0.063 Service level 9.00 4.000 8.000 1 0.652
  7. 7. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 217 Table 3 Pair-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 0.581 NPI 0.200 1 0.333 0.110 CR 0.5 3.00 1 0.309 (0.333), which is incorporated as the second entry rion in agile supply chain and service level is of NPI row in Table 3. an important criterion in leagile supply chain. In ExpertsÕ opinion is similarly ascertained in all order to analyze the combined influence of four the tables of ANP framework. supply chain performance determinants on the A graphical summary of ANP model and its selection of three alternative paradigms a single decision environment related to supply chain per- weighted index is calculated, which can prioritize formance (SCP) is shown in Fig. 1. three alternatives. This weighted index also cap- The overall objective in the ANP approach is to tures the influence of dimensions and enablers on select a paradigm, which helps in improving the the selection process. performance of supply chain. As an illustration STEP 2: Pair-wise comparison matrices between we have considered four criteria: lead time, cost, component/attribute levels quality, and service level. On a scale of one to nine, the decision-maker has been asked to respond to a series of pair-wise comparisons with respect to an upper level Ôcon- 7. Application of ANP framework trolÕ criterion. These are conducted with respect to their relative importance towards the control The ANP methodology is applied to the illus- criterion. In the case of interdependencies, compo- trative supply chain problem as follows: nents within the same level are viewed as control- STEP 1: Model construction and problem ling components for each other. Levels may also structuring be interdependent. The top most elements in the hierarchy of Through pair-wise comparisons between the criteria are decomposed into sub criteria and attri- applicable attributes enablers of performance butes. The model development requires identifica- dimension cluster, the weighted priority (e-Vector) tion of attributes at each level and a definition of is calculated (Saaty, 1996). For example, Table 3 their inter-relationships. The ultimate objective of presents the comparison matrix for enablers under this hierarchy is to identify alternatives that will the dimension of Market sensitiveness, and control be the significant for improving the performance hierarchy network of the cost. of supply chain. We shall evaluate four-supply Similarly, comparison matrices for other ena- chain performance hierarchy whose results will blers are prepared and the resultant e-Vectors are be aggregated in ‘‘supply chain performance imported as forth column in Table 5. For captur- weighted index’’ evaluation step. This form of ing the weightages an illustrative question is, Ôwhat analysis is similar to SaatyÕs recommendation of is the relative impact on market sensitiveness by using a unique network for benefits, costs, risks attribute enabler, a, when compared to attribute and opportunities (BCRO) (Saaty, 1996). Instead enabler, b, under cost determinantÕ? of using the BCRO categories supply chain perfor- Additional pair-wise comparison matrix is re- mance determinants (lead-time, cost, quality and quired for the relative importance of each of the service level) are used as the overlying network cat- dimensions of SCP clusters (MS, PI, ID, and F) egories. Cost and quality are important criteria in on the determinant of SCP level. There will be four lean supply chain; lead-time is an important crite- more matrices, one for each of the determinants.
  8. 8. 218 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 This result is presented as second column of Table there will be 12 non-zero columns in this super ma- 7. trix. Each of the non-zero values in the column in The final standard pair-wise comparison evalu- super matrix M, is the relative importance weight ations are required for the relative impacts of each associated with the interdependently pair-wise of the alternative for SCP improvement. The num- comparison matrices. In this model there are four ber of pair-wise comparison matrices is dependent super matrices, one for each of the determinants of of the number of SCP attribute enablers that are SCP hierarchy networks, which need to be included in the determinant of the SCP improve- evaluated. ment hierarchy. There are 12 pair-wise comparison The Super matrix (Table 5) is converged for get- matrices are required at this level of relationships. ting a long-term stable set of weights. For this STEP 3: Pair-wise comparison matrices of power of super matrix is raised to an arbitrarily interdependencies large number. In our illustrative example conver- To reflect the interdependencies, in network, gence is reached at 32nd power. Table 6 illustrates pair-wise comparisons among all the attribute ena- the value after convergence. blers are conducted. Table 4 illustrates one such STEP 5: Selection of best alternative case. The equation for desirability index, Dia for For brevity the final scores of this and remain- alternative i and determinant a is defined as ing matrices are shown in Table 5. (Meade and Sarkis, 1999): STEP 4: Super matrix formation and analysis XX J Kja Table 5 shows super matrix M, detailing the re- Dia ¼ P ja AD AI S ikja ; kja kja ð1Þ sults of the relative importance measures for each j¼1 k¼1 of the attribute enablers for the cost determinant where Pja is the relative importance weight of of SCP clusters. Since there are 12 pair-wise com- dimension jon the determinant ÔaÕ, AD is the kja parison matrices, one for each of the interdepen- relative importance weight for attribute enabler dent SCP attribute enablers in the cost hierarchy, k, dimension j and determinant ÔaÕ for the dependency (D) relationships between enablerÕs Table 4 component levels, AI is the stabilized relative kja Pair-wise comparison matrix for enablers under market sensi- importance weight for attribute enabler k of ÔjÕ tiveness, cost and delivery speed dimension in the determinant ÔaÕ for interdepen- Delivery speed (DS) NPI CR e-Vector dency (I) relationships within the attribute ena- New product introduction (NPI) 1 0.125 0.111 blerÕs component level, Sikja is the relative impact Customer responsiveness (CR) 8.00 1 0.889 of SC alternative paradigm i on SCP enabler k of Table 5 Super matrix for cost before convergence Cost DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF DS 0.00 0.333 0.800 NPI 0.111 0.00 0.200 CR 0.889 0.667 0.00 CPB 0 0.889 0.143 CDS 0.667 0 0.857 CSS 0.333 0.111 0 EDI 0 0.200 0.667 MOI 0.833 0 0.333 DA 0.167 0.800 0 SF 0 0.333 0.800 MF 0.111 0 0.200 DF 0.889 0.667 0
  9. 9. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 219 Table 6 Super Matrix for cost after convergence (M32) Cost DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF DS 0.41 0.41 0.41 NPI 0.14 0.14 0.14 CR 0.45 0.45 0.45 CPB 0.40 0.40 0.40 CDS 0.42 0.42 0.42 CSS 0.18 0.18 0.18 EDI 0.30 0.30 0.30 MOI 0.36 0.36 0.36 DA 0.34 0.34 0.34 SF 0.41 0.41 0.41 MF 0.14 0.14 0.14 DF 0.45 0.45 0.45 dimension of SCP j of SCP hierarchy network a, merated based on relative impact of each of Kja is the index set of attribute enablers for dimen- dimensions on cost determinants. The pair-wise sion j of determinant a, J is the index set for the comparison matrix for the relative impact of the dimension j. attribute enablers on the dimensions of SCP is pre- Table 7 shows the calculations for the desirabil- sented in the fourth column. The values in fifth ity indices (Di cost) for alternatives that is based on column are the stable interdependent weights of the cost control hierarchy by using the weights ob- attribute enablers obtained through super matrix tained from the pair-wise comparisons of the alter- convergence. The relative weights of three alterna- natives, dimensions and weights of enablers from tives for each dimension are given in sixth, seventh the converged super matrix. These weights are and eighth columns of Table 7. These weights are used to calculate a score for the determinant of obtained by comparing three alternatives for every Supply chain performance improvement desirabil- dimension of supply chain performance. The final ity for each of the alternatives being considered. three columns represent the desirability index The second column in Table 7 presents about (P ja AD AI S ikja ) of each alternative for attribute kja kja the results obtained from step 2, which is enu- enablers. For each of the alternatives under cost Table 7 Supply chain performance desirability index for cost Dimension # Pja Attribute # AD kja AI kja S1 S2 S3 Lean Agile Leagile MS 0.478 DS 0.581 0.41 0.577 0.160 0.263 0.066 0.018 0.030 0.478 NPI 0.110 0.14 0.600 0.144 0.256 0.004 0.001 0.002 0.478 CR 0.309 0.45 0.544 0.110 0.346 0.037 0.007 0.023 PI 0.266 CPB 0.467 0.40 0.579 0.187 0.234 0.029 0.009 0.012 0.266 CDS 0.376 0.42 0.548 0.211 0.241 0.023 0.009 0.010 0.266 CSS 0.157 0.18 0.490 0.312 0.198 0.004 0.002 0.001 ID 0.166 EDI 0.615 0.30 0.525 0.142 0.334 0.016 0.004 0.010 0.166 MOI 0.093 0.36 0.537 0.268 0.195 0.003 0.001 0.001 0.166 DA 0.292 0.34 0.490 0.312 0.198 0.008 0.005 0.003 F 0.090 SF 0.615 0.41 0.539 0.297 0.164 0.012 0.007 0.004 0.090 MF 0.093 0.14 0.286 0.143 0.571 0.0003 0.0002 0.001 0.090 DF 0.292 0.45 0.333 0.167 0.500 0.004 0.002 0.006 Total desirability indices of cost for alternative frameworks 0.205 0.073 0.097
  10. 10. 220 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 determinant, the summation of these results ap- adigms with variation in the expert opinion to- pears in the final row of Table 7. The result shows wards lead-time with respect to cost, quality and that the impact on cost is considered an important service level. Overall objective of sensitivity analy- criterion in lean supply chain (0.205) followed by sis is to see the robustness of proposed framework leagile (0.097) and agile (0.073) supply chain. due to variation in the expertsÕ opinion in assign- STEP 6: Calculation of Supply Chain Perfor- ing the weights during comparison. mance Weighted Index (SPWI) For the case supply chain experts opinion has To complete the analysis supply chain perfor- been sought to analyze the performance of supply mance weighted index (SPWI) is determined for chain. Table 8 indicates how the supply chain per- each alternative paradigm. The SPWIi for an alter- formance weighted indexes (SPWI) for proposed native i is the product of the desirability indices framework of three supply chains varies with (Dia) and the relative importance weights of the changing priority of lead-time, cost, quality and determinants (Ca) of the SCP. service level. When overall objective is to reduce The results (Table 2) show that the service level lead-time, desirability indices is lower for lean sup- determinant (Ca = 0.652) as most important for ply chain than agile supply chain. In a strategy to supply chain performance improvement. The re- minimize the cost and to improve quality, lean sult indicates that the management of the case sup- supply chain has the highest desirability indices ply chain should focus on improving the service among the three supply chains. In an effort to im- level. This result could be due to the competitive prove service level, desirability indices for leagile or customer pressure for improving service level. supply chain is slightly higher than agile supply Lead-time (0.162) and cost (0.123) play the next chain. Here it is pertinent to mention that in the most important role but are less important than uncertain environment desired supply chain per- service level. formance cannot be alone achieved either by lean The final results are shown in Table 8. or by agile supply chain. Lean and agile paradigms The Table 8 indicates that for the illustrative are not mutually exclusive paradigms (Christopher problem the most significant alternative paradigm and Towill, 2001), therefore proper combination for better supply chain performance is leagile sup- of lean and agile (leagile) is required to suit the ply chain followed by agile supply chain. need for responding to a volatile demand (Naylor et al., 1999). In Fig. 2, X-axis represents the relative weight 8. Sensitivity analysis of lead-time as compare to quality. These relative weights are in the scale of 1/9–9 (Saaty scale). Y- Sensitivity analysis is an important concept for axis represents the normalized value of supply the effective use of any quantitative decision model chain performance weighted index (SPWI). These (Poh and Ang, 1999). In the present work sensitiv- weights are obtained using ANP framework, ity analysis is done to find out the changes in the which captures the interdependence among supply SPWI for lean, agile and leagile supply chain par- chain performance variables. This framework con- Table 8 Supply chain Performance Weighted Index (SPWI) for various alternative frameworks Alternatives # Criteria Calculated weights for alternatives Lead-time Cost Quality Service level SPWI NORM Weights for criteria: 0.162 0.123 0.063 0.652 Lean 0.067 0.205 0.133 0.081 0.0974 0.316 Agile 0.162 0.073 0.075 0.099 0.1049 0.340 Leagile 0.106 0.097 0.093 0.109 0.1058 0.343 Total 0.308 1.000
  11. 11. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 221 0.360 Lean 0.350 Agile Normalized value 0.340 Leagile 0.330 0.320 0.310 0.300 0.290 0.280 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 Fig. 2. Variation in priority of supply chain paradigms with changes in weight assigned to lead-time with respect to quality. sists of 117 pair wise comparison matrices. The importance of lead-time to quality (or give more purpose is to analyze the effect of variation in importance to quality as compare to lead-time), relative weight assigned to SCP determinants on priority of leagile supply chain paradigm does the priority level of alternative supply chain not change. When XLT/Q is further lowered from paradigms. 0.125 to 0.111, lean supply chain attains top prior- In the present ANP framework, experts have ity followed by leagile supply chain. If weight as- assigned relative weight 3 to lead-time in compare signed to lead-time in comparison to quality is with quality (XLT/Q) on supply chain performance between 0.5 and 0.333, policy towards supply improvement. With this relative weight, SPWI for chain performance improvement would be combi- leagile supply chain is the highest followed by agile nation leanness and agility. This point indicates and lean supply chain. This implies when the per- that advantages of both leanness and agility can ception of experts is more inclined towards lead- be achieved. When the priority weight is further re- time in comparison to quality, they will prefer duced beyond 0.125, lean supply chain gets top the supply chain which favors lead-time reduction. priority followed by leagile and agile supply chain. Lead-time is an essential metric for leagile and Fig. 3 indicates effect on values of SPWI for agile supply chains. Here lead-time indicates the lean, agile and leagile supply chains due to varia- time between raising the demand by customer tion in the priority weight of lead-time with respect and receiving the product of his choice. This prior- to cost (XLT/C). In the present framework accord- ity level does not change if XLT/Q lowers from 3 to ing to expertÕs opinion, XLT/C is 2. When the rela- 0.125. This indicates that if experts lower relative tive weight XLT/C lies between 0.667 and 3, experts 0.370 Lean Agile 0.350 Leagile Normalized value 0.330 0.310 0.290 0.270 0.250 0.111 0.125 0.143 0.167 0.200 0.250 0.333 0.500 0.667 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 Variation in priority of lead-time with respect to cost Fig. 3. Variation in priority of supply chain paradigms with changes in weight assigned to lead-time with respect to cost.
  12. 12. 222 A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 favour strategy for leagile supply chain in meeting sition. Leanness and agility of a supply chain lar- the unpredictable demand. SPWI for agile supply gely depends on these four criteria (Naylor et al., chain improves when expertsÕ opinion deviate 1999). and XLT/C varies from 3 to 9. In this situation pri- ority for lean supply chain declines. In the range between 0.667 and 0.111 of XLT/C, experts favor 9. Discussions strategy for cost minimization. In this strategy ex- perts are partially trading off importance of lead- ‘‘Agility’’ is needed in less predictable environ- time reduction to cost minimization. Here, the case ments where demand is volatile and the require- supply chain partially looses its agility, which is ment for variety is high (Lee, 2002). ‘‘Lean’’ indicated in the graph (Fig. 3) and the priority le- works best in high volume, low variety and pre- vel for lean supply chain improves. dictable environments. Leagility is the combina- In Fig. 4 effect on the values of SPWI for lean, tion of the lean and agile paradigm within a total agile, and leagile supply chain due to change in rel- supply chain strategy by positioning the de-cou- ative weight of lead-time with respect to service pling point so as to best suit the need for respond- level (XLT/SL) is shown. ing to a volatile demand downstream yet providing In proposed ANP framework, XLT/SL is 0.111. level scheduling upstream from the de-coupling At this priority experts favor service level improve- point (Naylor et al., 1999; Bruce et al., 2004). ment. Since service level is the most important The ANP model proposed in this paper is an aid criteria for leagile and agile supply chain (Naylor to supply chain managers in arriving at prudent et al., 1999), SPWI for leagile supply chain gets decision when the complexities of decision vari- top priority at this relative weight followed by ables and multi-criteria decision environment agile supply chain (Fig. 4). If the XLT/SL is changed make their decision task quite complicated. This from 0.111 to 0.167, SPWI for agile supply chain ANP model is used for selecting appropriate para- improves but leagile supply chain remains at top. digm for improved SC performance of a case com- When the value of XLT/SL is higher than 0.167, ex- pany. This could serve as one of the important perts relatively consider lead-time more important tools for taking a strategic decision of this type. than service level agile supply chain gets top prior- The criteria and attributes that are used in the ity followed by leagile and lean supply chain. model focus on the strategy and requirements of The purpose of selecting lead-time, cost, quality SC performance. The model is capable of taking and service level is straightforward. These are into consideration both qualitative and quantita- order qualifying and order winning criteria. With tive information. Here it is pertinent to discuss changes in objective these criteria changes their po- the priority values for the determinants, which 0.390 Lean Agile 0.370 Leagile Normalized value 0.350 0.330 0.310 0.290 0.270 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 Varition in priority of lead-time with respect to service level Fig. 4. Variation in priority of supply chain paradigms with changes in weight assigned to lead-time with respect to service level.
  13. 13. A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 223 influence the decision of selecting the paradigm for cantly change with variation in the opinion of better SC performance. decision-makers in assigning the weights to From Table 2, it has been observed that the ser- enablers. vice level (0.652) is the most important criteria in the selection of the framework for the supply chain paradigm. This is followed by lead-time (0.162), 10. Limitations and scope for future work cost (0.123) and quality (0.063). For the case sup- ply chain of fast moving consumer goods, the re- As compared to analytic hierarchy process sult favors improvement in service level and (AHP), the analysis using ANP is relatively cum- reduction in lead-time. Cost and quality are less bersome as in the present work 117 pair-wise com- supported because improvement in service level parison matrices are required. To arriving at the and reduction in lead-time would also help in relationship among enablers, it requires long and reducing cost and improving quality. Though the exhaustive discussion with experts from the case results do not favor cost and quality, the implica- supply chain. Therefore, the advantages of ANP tion is not straightforward. The lower values for technique could be derived for making strategic these two are due to their interdependency on decisions that are vital for the growth and survival lead-time and service level. For example, a low of supply chains. value of lead-time will lead to lesser waste and The values for pair-wise comparisons depend quality improvement opportunity. The converse on the knowledge of the decision-makers. There- may not be true. The ANP is capable of handling fore group of decision-makers should include interdependencies of this type. The present deci- those experts who understand the implications of sion model provides the priority values in the form enablers on the supply chain performance in lean, of weighted index for different paradigms for im- agile and leagile paradigm. proved SC performance (Table 8). The final values The proposed framework has been developed for supply chain performance weighted index rela- for a supply chain in fast moving consumer goods tionship are 0.343 for the leagile, 0.340 for agile, (FMCG) business. Therefore results obtained and 0.316 for lean supply chain. For supply chain from the proposed framework cannot be of the case company, the ANP framework suggests generalized. that with existing priority levels of supply chain performance determinants, normalized value of SPWI for leagile paradigm is slightly higher than 11. Conclusion that of a mere lean or agile paradigm. The higher value of SPWI for leagile supply chain favors Improved supply chain performance implies the policy for combining the lean and agile ap- that a supply chain is capable of quickly respond- proaches. For handling innovative products the ing to the variations in the customer demand case supply chain should adopt a lean manufactur- with effective cost reduction. Leanness in a supply ing approach before decoupling point and agile chain maximizes profits through cost reduction approach after decoupling point (Olhager, 2003). while agility maximizes profit through providing Consistency ratio (CR) is calculated for all the exactly what the customer requires. The leagile pair-wise comparisons to check the inconsistency supply chain enables the upstream part of the in decision-making. In the proposed model CR chain to be cost-effective and the downstream varies from 0.002 to 0.19, which is within tolerable part to achieve high service levels in a volatile limit (Saaty and Kearns, 1985). An analysis of the marketplace. robustness of the decision model using sensitivity The ANP methodology adopted here arrives at a analysis is carried out to observe the impact of var- synthetic score, which may be quite useful for the iation in the opinion of decision-makers in assign- decision-makers. The purpose of the present work ing the weights. Sensitivity analysis indicates that is to analyze the relative impact of different enablers the priority levels of SC paradigms do not signifi- on three SC paradigms considered for a supply
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