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Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach
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 b
a,*
, M.K. Tiwari
b
a Department of Management Studies, Indian Institute of Technology Delhi, HauzKhas, New Delhi 110016, India 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 success 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 winning 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 Enterprises are continuously paying attention in responding to the customer demand for maintain*
Corresponding author. Tel.: +91 11 26596421; fax: +91 11 26862620/26582037. E-mail addresses: [email protected] (A. Agarwal), [email protected] (R. Shankar), [email protected] (M.K. Tiwari).
ing a competitive advantage over their rivals. Supply Chain Management (SCM) has gained attention as it focuses on material, information 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 compete, not companies (Christopher and Towill, 2001), and the success or failure of supply chains is ultimately determined in the marketplace by 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
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the right time to the consumer is not only the linchpin to competitive success, but also the key to survival. Hence, customer satisfaction and market place understanding are critical elements for consideration when attempting to establish a new SC strategy. Significant interest has been shown in recent years in the idea of ‘‘lean manufacturing’’, and the wider concepts of the ‘‘ lean enterprises’’. The focus of the lean approach has essentially been on the elimination of waste or muda. The upsurge of interest in lean manufacturing can be traced to the Toyota Production Systems with its focus on the reduction and elimination of waste. Lean is about doing more with less. Lean concepts work well where demand is relatively stable and hence predictable and where variety is low. Conversely, in those contexts where demand is volatile and the customer requirement for variety is high, a much higher level of agility is required. Leanness may be an element of agility in certain circumstances, but it will not enable the organization to meet the precise needs of the customers more rapidly. Agility is a business-wide capability that embraces organizational structures, information
systems, logistics processes and in particular, mindsets (Power et al., 2001; Katayama and Bennett, 1999). Agility is being defined as the ability of an organization to respond rapidly to changes in demand, both in terms of volume and variety (Christopher, 2000). The lean and agile paradigms, though distinctly different, can be and have been combined within successfully designed and operated total supply chains (Mason-Jones and Towill, 1999). The past studies show how the need for agility and leanness depends upon the total supply chain strategy, particularly considering market knowledge, via information enrichment, and positioning of the de-coupling point. Combining agility and leanness in one SC via the strategic use of a de-coupling point has been termed ‘‘le-agility’’ (Naylor et al., 1999). Therefore leagile is the combination of the lean and agile paradigms within a total supply chain strategy by positioning the decoupling point so as to best suit the need for responding to a volatile demand down stream yet providing level scheduling upstream from the market place (van Hoek et al., 2001). The decoupling point is in the material flow streams to which the 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 Product variety Product life cycle Customer drivers Profit margin Dominant costs Stock out penalties Purchasing policy Information enrichment Forecast mechanism Typical products Lead time compression Eliminate muda Rapid reconfiguration Robustness Quality Cost Lead-time Service level
Predictable Low Long Cost Low Physical costs Long term contractual Buy goods Highly desirable Algorithmic Commodities Essential Essential Desirable Arbitrary Market qualifier Market winner Market qualifier Market qualifier
Volatile High Short Lead-time and availability High Marketability costs Immediate and volatile Assign capacity Obligatory Consultative Fashion goods Essential Desirable Essential Essential Market qualifier Market qualifier Market qualifier Market winner
Volatile and unpredictable Medium Short Service level Moderate Both No place for stock out Vendor managed inventory Essential Both/either Product as per customer demand Desirable Arbitrary Essential Desirable Market qualifier Market winner Market qualifier Market winner
Sources: Naylor et al. (1999), Mason-Jones et al. (2000a), Olhager (2003), Bruce et al. (2004).
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2000a,b; Prince and Kay, 2003). Table 1 illustrates the comparison of attributes among lean, agile and leagile supply chain. The present paper presents a framework for modeling performance of lean, agile and leagile supply chain on the basis interdependent variables. Here performance of SC implies how much the SC is responsive to the needs of the market. The framework provides an aid to decision makers in analyzing the variables affecting market sensitiveness, process integration, information driver and flexibility in lean, agile and leagile supply chains for the performance improvement of a case supply chain in fast moving consumer goods (FMCG) business. For this we have adopted Analytic Network Process (ANP) approach. By using ANP in a SC context, we can evaluate the influence of various performance dimensions on the specified objectives of SC, such as timely response to meet the customer demand. It also explicitly considers the influence of the performance determinants on one another. Since the dimensions and determinants of supply chain performance (SCP) have systemic characteristics, they may be integrated into one model. These systemic relationships can more accurately portray the true linkages and interdependencies of these various determinants (Saaty, 1996).
2. Supply chain performance Supply chain is described as a chain linking each element from customer and supplier through manufacturing and services so that flow of material, money and information can be effectively managed to meet the business requirement (Stevens, 1989). Most of the companies realize that in order to evolve an efficient and effective supply chain, SCM needs to be assessed for its performance (Gunasekaran et al., 2001). Christopher and Towill (2001) have explained the issues related to market qualifier and market winner in a supply chain and identified quality, cost, lead-time and service level as four performance measures. While, service level is the market winner for an agile supply chain, rests are market qualifiers. In case of lean supply quality, lead-time and service level
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are the market qualifier and cost is a market winner. However, with changed objectives, the qualifier and winner may change positions (Hill, 1993). Aspects combining lean and agile features have also been explored under the concept of leagility (van Hoek, 2000). In the proposed ANP framework market sensitiveness (MS), information driver (ID), process integration (PI) and flexibility (F) have been considered as supply chain performance (SCP) dimensions by experts of the case supply chain. These dimensions are important characteristics of agility (Christopher, 2000). Market sensitiveness involves issues related to quick response to real demand. It is characterized by six measures (Jayaram et al., 1999; Power et al., 2001; Agarwal and Shankar, 2002a): delivery speed (DS), delivery reliability (DR), new product introduction (NPI), new product development time (NPDT), manufacturing lead-time (MLT) and customer responsiveness (CR). Higher values of DS, DR, NPI and CR or lower values of NPDT and MLT would make the supply chain more sensitive towards market forces. Information driver involves making use of information technology to share data between buyers and suppliers. This enables the supply chain to become demand driven. Electronic Data Interchange (EDI), means of information (MOI), such as Internet, data accuracy (DA), etc enable supply chain partners to act upon the same data with real time demand. Another key characteristic of an agile organization is flexibility (Vickery et al., 1999; Prater et al., 2001; Olhager, 2003). In that respect, the origins of agility as a business concept lie partially in flexible manufacturing systems. Initially it is thought that the route to manufacturing flexibility is through automation to enable rapid changeovers (i.e. reduced set-up times) and thus enable a greater responsiveness to changes in product mix or volume. Later this idea of manufacturing flexibility is extended into the wider business context and the concept of agility as an organizational orientation emerged. The performance dimension flexibility may be broken down into two capabilities: the promptness with and the degree to which a firm can adjust its supply chain speed, destinations, and volumes (Prater et al., 2001). The supply chain
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may be broken down into three basic segments: sourcing, manufacturing and delivery. Any firmÕs supply chain agility is determined by how its physical components (i.e. sourcing, manufacturing and delivery) are configured to incorporate speed and flexibility. As the levels of speed and, more importantly, flexibility increase, the level of supply chain agility increases. The firm can, to a degree, make up deficiencies in the speed or flexibility of one of the supply chain parts by excelling in the other two. For example, the delivery part of the supply chain may be inherently inflexible, such as is found in sea transportation (i.e. the speed is low). Supply chain agility may be increased if the firm is able to compensate for these shortcomings by setting up its inbound logistics (i.e. sourcing) or manufacturing operations to be fast or flexible (Olhager et al., 2002). As the speed in outbound logistics is inflexible, speed and flexibility in manufacturing and sourcing could help compensate for the slow outbound transportation. Shared information between supply chain partners can be fully leveraged through process integration (PI). Process integration (PI) means collaborative working between buyers and suppliers, joint product development, common systems and shared information (Christopher and Jittner, 2000). Collaboration across each partnerÕs core business processes (CPB), company specific issues on demand side (CDS) such as quality, cost, etc and company specific issues on supply side (CSS) such as buyer–supplier relations, vendor managed inventory, information sharing, etc are the main enablers of the process integration. Now we will focus on developing a framework for significant alternative for the performance improvement of supply chain.
3. The decision environment Analytic hierarchy process (AHP) is introduced for choosing the most suitable alternative, which fulfils the entire set of objectives in multi-attribute decision-making problem (Wasil and Golden, 2003). AHP allows a set of complex issues, to be compared with the importance of each issue relative to its impact on the solution to the problem.
Since the introduction of AHP numerous applications have been published in the literature (Zahedi, 1986; Shim, 1989; Kleindorfer and Partovi, 1990; Corner and Corner, 1991, 1995; Ghodsypour and OÕBrien, 1998). Analytic Network Process (ANP) is a more general form of AHP, incorporating feedback and interdependent relationships among decision attributes and alternatives (Saaty, 1996). This provides a more accurate approach for modeling complex decision environment (Meade and Sarkis, 1999; Lee and Kim, 2000; Agarwal and Shankar, 2002b, 2003; Yurdakul, 2003). We have adopted the ANP-based evaluation framework for the selection of the best alternative (Meade and Sarkis, 1999). The reasons due to which ANP is selected for this purpose are due to three facts: (i) analyzing the supply chain performance is a multi-criteria decision problem, (ii) many factors, enablers and criteria in decision environment are interdependent on one another, and (iii) some of the criteria, enablers and dimensions are subjective due to which a synthetic score through simple weightage method is difficult to arrive at. Analytic Hierarchy Process (AHP) is similar to ANP but cannot capture interdependencies (Meade et al., 1997; Meade and Sarkis, 1999). Hierarchical representation is an important component of ANP, however strict hierarchical structure is not recommended, as is the case with AHP. The ANP technique allows for more complex relationships among the decision levels and attributes. The ANP consists of coupling of two phases. The first phase consists of a control hierarchy of network of criteria and sub-criteria that control the interactions. The second phase is a network of influences among the elements and clusters. The network varies from criteria to criteria and thus different super-matrices of limiting influence are computed for each control criteria. Finally, each one of these super-matrices is weighted by the priority of its control criteria and results are synthesized through addition for the entire control criterion (Saaty, 1996). A graphical summary of the ANP model and its decision environment related to supply chain performance is shown in Fig. 1. The overall objective is to select the best framework for improving performance of the case supply chain.
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To analyze the Supply Chain performance
Supply Chain Performance Weighted Index Supply chain performance determinants Cost
Lead Time
Service Level
Quality
Supply chain performance dimensions
Market sensitiveness
Process integration
Information driver
Flexibility
Supply chain performance enablers
Delivery Speed (DS), New product introduction (NPI), Customer responsiveness(CR)
Collaboration across each partner’s core business process (CPB), Company specific issues on demand side (CDS), Company specific issues on supply side(CSS)
DS NPI
Electronic data interchange (EDI), Means of information (MOI), Data accuracy (DA)
CPB
CR
CSD
Source flexibility (SF), Make flexibility (MF), Delivery flexibility (DF)
SF
EDI
CSS
MOI
DA
MF
DF
Supply chain performance paradigms
Lean supply chain
Agile supply 31 chain
Leagile supply chain
Fig. 1. ANP-based framework for Modeling Metrics of Supply Chain Performance.
4. Deriving the interdependence in supply chain performance model The interdependence among different levels in supply chain performance framework have been developed through review of literature on supply chain performance (Naylor et al., 1999; Katayama and Bennett, 1999; van Hoek, 2000; Christopher, 2000; Prater et al., 2001; Aitken et al., 2002; Power et al., 2001; Stratton and Warburton, 2003; Bruce et al., 2004) and through discussion with experts
from the case supply chain, which incorporates network of suppliers, manufacturer, distributors and retailers for fast moving consumer goods (FMCG). These experts have more than ten years of experience in the area of purchasing and supply chain management. The group consists of four to five experts and they are informed about alternative supply chain paradigms. It is believed that experts know relative weights between alternative paradigms during the process of capturing the relative weights. The case supply chain is involved in
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functional as well as innovative products. The functional products have long product life cycles and their demand is predictable. The innovative products have short product life cycle and their demand is unpredictable. The management of the case supply chain is not able to decide which supply chain performance criteria should be given priority over other performance criteria. They are also unable to adopt the proper supply chain strategy for their products.
5. Mutual interdependence of enablers Overall objective of the present work is to model performance of three paradigms for a supply chain, which enables it be more flexible in responding to market demand. Cost, quality, service level and lead-time are the major determinants of the proposed framework. These determinants have dominance over the identified dimensions in the framework. The impact of one determinant on supply chain performance is affected by the influence of the other determinants. Using pair wise comparison matrix with a scale of one to nine, the relative weight of each determinants is obtained and given in Table 2. These values have been obtained through expertsÕ opinions that are heading the supply chain operation. Enablers of the framework are those, which assist in achieving the controlling dimension of supply chain performance. Therefore, these are dependent on the dimensions, but there is also some interdependency among enablers, hence the arrows in Fig. 1 are shown as arching back to the enablersÕ decision level. For example enablers under dimension Ôprocess integrationÕ are interdependent to some degree. ANP uniquely captures the interdependen-
cies at different levels of the control hierarchy as well as interdependencies that are inherited among different hierarchies. We would illustrate this aspect through an example of the case supply chain. This would illustrate interdependencies among different enablers under cost determinant.
6. Capture of relative weights obtained through expert opinion The relative weights in the pair wise comparison matrices of ANP have been obtained through discussion with group of experts of the case supply chain. The group consists of those experts from the trading partners of the case supply chain, which have vast experience in the area of supply chain management. For obtaining the relative weights in Table 2, the research group asked different questions. A sample question is: ‘‘what is the relative impact on supply chain performance in timely responding to market demand by cost when cost is compared to quality?’’ The answer is 2 on a scale of 1–9 and this is incorporated as second entry of cost row in Table 2. Saaty (1980) has suggested a scale of 1–9 for comparing two components. In the scale of 1–9, 1 implies equal impact while 9 implies stronger impact of row element than column element. If experts feel that column element has stronger impact than row element, reciprocal of number from 1 to 9 is assigned accordingly (Saaty, 1996). For obtaining the relative weights in Table 3, the research group asked the question, ‘‘What is the relative impact on market sensitiveness by enabler ‘‘new product introduction (NPI)’’ when compare to enabler ‘‘customer responsiveness (CR)’’, 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
Cost
Quality
Service level
1 0.500 0.333 9.00
2.000 1 0.500 4.000
3.000 2.000 1 8.000
0.111 0.250 0.125 1
0.162 0.123 0.063 0.652
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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 NPI CR
1 0.200 0.5
5 1 3.00
2 0.333 1
(0.333), which is incorporated as the second entry of NPI row in Table 3. ExpertsÕ opinion is similarly ascertained in all the tables of ANP framework. A graphical summary of ANP model and its decision environment related to supply chain performance (SCP) is shown in Fig. 1. The overall objective in the ANP approach is to select a paradigm, which helps in improving the performance of supply chain. As an illustration we have considered four criteria: lead time, cost, quality, and service level.
7. Application of ANP framework The ANP methodology is applied to the illustrative supply chain problem as follows: STEP 1: Model construction and problem structuring The top most elements in the hierarchy of criteria are decomposed into sub criteria and attributes. The model development requires identification of attributes at each level and a definition of their inter-relationships. The ultimate objective of this hierarchy is to identify alternatives that will be the significant for improving the performance of supply chain. We shall evaluate four-supply chain performance hierarchy whose results will be aggregated in ‘‘supply chain performance weighted index’’ evaluation step. This form of analysis is similar to SaatyÕs recommendation of using a unique network for benefits, costs, risks and opportunities (BCRO) (Saaty, 1996). Instead of using the BCRO categories supply chain performance determinants (lead-time, cost, quality and service level) are used as the overlying network categories. Cost and quality are important criteria in lean supply chain; lead-time is an important crite-
0.581 0.110 0.309
rion in agile supply chain and service level is an important criterion in leagile supply chain. In order to analyze the combined influence of four supply chain performance determinants on the selection of three alternative paradigms a single weighted index is calculated, which can prioritize three alternatives. This weighted index also captures the influence of dimensions and enablers on the selection process. STEP 2: Pair-wise comparison matrices between component/attribute levels 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 ÔcontrolÕ criterion. These are conducted with respect to their relative importance towards the control criterion. In the case of interdependencies, components within the same level are viewed as controlling components for each other. Levels may also be interdependent. Through pair-wise comparisons between the applicable attributes enablers of performance dimension cluster, the weighted priority (e-Vector) is calculated (Saaty, 1996). For example, Table 3 presents the comparison matrix for enablers under the dimension of Market sensitiveness, and control hierarchy network of the cost. Similarly, comparison matrices for other enablers are prepared and the resultant e-Vectors are imported as forth column in Table 5. For capturing the weightages an illustrative question is, Ôwhat is the relative impact on market sensitiveness by attribute enabler, a, when compared to attribute enabler, b, under cost determinantÕ? Additional pair-wise comparison matrix is required for the relative importance of each of the dimensions of SCP clusters (MS, PI, ID, and F) on the determinant of SCP level. There will be four more matrices, one for each of the determinants.
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there will be 12 non-zero columns in this super matrix. Each of the non-zero values in the column in super matrix M, is the relative importance weight associated with the interdependently pair-wise comparison matrices. In this model there are four super matrices, one for each of the determinants of SCP hierarchy networks, which need to be evaluated. The Super matrix (Table 5) is converged for getting a long-term stable set of weights. For this power of super matrix is raised to an arbitrarily large number. In our illustrative example convergence is reached at 32nd power. Table 6 illustrates the value after convergence. STEP 5: Selection of best alternative The equation for desirability index, Dia for alternative i and determinant a is defined as (Meade and Sarkis, 1999):
This result is presented as second column of Table 7. The final standard pair-wise comparison evaluations are required for the relative impacts of each of the alternative for SCP improvement. The number of pair-wise comparison matrices is dependent of the number of SCP attribute enablers that are included in the determinant of the SCP improvement hierarchy. There are 12 pair-wise comparison matrices 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 enablers are conducted. Table 4 illustrates one such case. For brevity the final scores of this and remaining matrices are shown in Table 5. STEP 4: Super matrix formation and analysis Table 5 shows super matrix M, detailing the results of the relative importance measures for each of the attribute enablers for the cost determinant of SCP clusters. Since there are 12 pair-wise comparison matrices, one for each of the interdependent SCP attribute enablers in the cost hierarchy,
Dia ¼
j¼1
NPI
CR
e-Vector
New product introduction (NPI) Customer responsiveness (CR)
1 8.00
0.125 1
0.111 0.889
I P ja AD kja Akja S ikja ;
ð1Þ
k¼1
where Pja is the relative importance weight of dimension jon the determinant ÔaÕ, AD kja is the relative importance weight for attribute enabler k, dimension j and determinant ÔaÕ for the dependency (D) relationships between enablerÕs component levels, AIkja is the stabilized relative importance weight for attribute enabler k of ÔjÕ dimension in the determinant ÔaÕ for interdependency (I) relationships within the attribute enablerÕs component level, Sikja is the relative impact of SC alternative paradigm i on SCP enabler k of
Table 4 Pair-wise comparison matrix for enablers under market sensitiveness, cost and delivery speed Delivery speed (DS)
Kja J X X
Table 5 Super matrix for cost before convergence Cost
DS
NPI
CR
DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF
0.00 0.111 0.889
0.333 0.00 0.667
0.800 0.200 0.00
CPB
CDS
CSS
0 0.667 0.333
0.889 0 0.111
0.143 0.857 0
EDI
MOI
DA
0 0.833 0.167
0.200 0 0.800
0.667 0.333 0
SF
MF
DF
0 0.111 0.889
0.333 0 0.667
0.800 0.200 0
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Table 6 Super Matrix for cost after convergence (M32) Cost
DS
NPI
CR
DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF
0.41 0.14 0.45
0.41 0.14 0.45
0.41 0.14 0.45
CPB
CDS
CSS
0.40 0.42 0.18
0.40 0.42 0.18
0.40 0.42 0.18
dimension of SCP j of SCP hierarchy network a, Kja is the index set of attribute enablers for dimension j of determinant a, J is the index set for the dimension j. Table 7 shows the calculations for the desirability indices (Di cost) for alternatives that is based on the cost control hierarchy by using the weights obtained from the pair-wise comparisons of the alternatives, dimensions and weights of enablers from the converged super matrix. These weights are used to calculate a score for the determinant of Supply chain performance improvement desirability for each of the alternatives being considered. The second column in Table 7 presents about the results obtained from step 2, which is enu-
EDI
MOI
DA
0.30 0.36 0.34
0.30 0.36 0.34
0.30 0.36 0.34
SF
MF
DF
0.41 0.14 0.45
0.41 0.14 0.45
0.41 0.14 0.45
merated based on relative impact of each of dimensions on cost determinants. The pair-wise comparison matrix for the relative impact of the attribute enablers on the dimensions of SCP is presented in the fourth column. The values in fifth column are the stable interdependent weights of attribute enablers obtained through super matrix convergence. The relative weights of three alternatives for each dimension are given in sixth, seventh and eighth columns of Table 7. These weights are obtained by comparing three alternatives for every dimension of supply chain performance. The final three columns represent the desirability index I (P ja AD kja Akja S ikja ) of each alternative for attribute enablers. For each of the alternatives under cost
Table 7 Supply chain performance desirability index for cost Dimension #
Pja
Attribute #
AD kja
AIkja
S1
S2
S3
Lean
Agile
Leagile
MS
0.478 0.478 0.478
DS NPI CR
0.581 0.110 0.309
0.41 0.14 0.45
0.577 0.600 0.544
0.160 0.144 0.110
0.263 0.256 0.346
0.066 0.004 0.037
0.018 0.001 0.007
0.030 0.002 0.023
PI
0.266 0.266 0.266
CPB CDS CSS
0.467 0.376 0.157
0.40 0.42 0.18
0.579 0.548 0.490
0.187 0.211 0.312
0.234 0.241 0.198
0.029 0.023 0.004
0.009 0.009 0.002
0.012 0.010 0.001
ID
0.166 0.166 0.166
EDI MOI DA
0.615 0.093 0.292
0.30 0.36 0.34
0.525 0.537 0.490
0.142 0.268 0.312
0.334 0.195 0.198
0.016 0.003 0.008
0.004 0.001 0.005
0.010 0.001 0.003
SF 0.615 0.41 MF 0.093 0.14 DF 0.292 0.45 cost for alternative frameworks
0.539 0.286 0.333
0.297 0.143 0.167
0.164 0.571 0.500
0.012 0.0003 0.004 0.205
0.007 0.0002 0.002 0.073
0.004 0.001 0.006 0.097
F
0.090 0.090 0.090 Total desirability indices of
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determinant, the summation of these results appears in the final row of Table 7. The result shows that the impact on cost is considered an important criterion in lean supply chain (0.205) followed by leagile (0.097) and agile (0.073) supply chain. STEP 6: Calculation of Supply Chain Performance Weighted Index (SPWI) To complete the analysis supply chain performance weighted index (SPWI) is determined for each alternative paradigm. The SPWIi for an alternative i is the product of the desirability indices (Dia) and the relative importance weights of the determinants (Ca) of the SCP. The results (Table 2) show that the service level determinant (Ca = 0.652) as most important for supply chain performance improvement. The result indicates that the management of the case supply chain should focus on improving the service level. This result could be due to the competitive or customer pressure for improving service level. Lead-time (0.162) and cost (0.123) play the next most important role but are less important than service level. The final results are shown in Table 8. The Table 8 indicates that for the illustrative problem the most significant alternative paradigm for better supply chain performance is leagile supply chain followed by agile supply chain.
8. Sensitivity analysis Sensitivity analysis is an important concept for the effective use of any quantitative decision model (Poh and Ang, 1999). In the present work sensitivity analysis is done to find out the changes in the SPWI for lean, agile and leagile supply chain par-
adigms with variation in the expert opinion towards lead-time with respect to cost, quality and service level. Overall objective of sensitivity analysis is to see the robustness of proposed framework due to variation in the expertsÕ opinion in assigning the weights during comparison. For the case supply chain experts opinion has been sought to analyze the performance of supply chain. Table 8 indicates how the supply chain performance weighted indexes (SPWI) for proposed framework of three supply chains varies with changing priority of lead-time, cost, quality and service level. When overall objective is to reduce lead-time, desirability indices is lower for lean supply chain than agile supply chain. In a strategy to minimize the cost and to improve quality, lean supply chain has the highest desirability indices among the three supply chains. In an effort to improve service level, desirability indices for leagile supply chain is slightly higher than agile supply chain. Here it is pertinent to mention that in the uncertain environment desired supply chain performance cannot be alone achieved either by lean or by agile supply chain. Lean and agile paradigms are not mutually exclusive paradigms (Christopher and Towill, 2001), therefore proper combination of lean and agile (leagile) is required to suit the need for responding to a volatile demand (Naylor et al., 1999). In Fig. 2, X-axis represents the relative weight of lead-time as compare to quality. These relative weights are in the scale of 1/9–9 (Saaty scale). Yaxis represents the normalized value of supply chain performance weighted index (SPWI). These weights are obtained using ANP framework, which captures the interdependence among supply chain performance variables. This framework con-
Table 8 Supply chain Performance Weighted Index (SPWI) for various alternative frameworks Alternatives #
Lean Agile Leagile Total
Criteria
Calculated weights for alternatives
Lead-time
Cost
Quality
Service level
Weights for criteria: 0.162
0.123
0.063
0.652
0.067 0.162 0.106
0.205 0.073 0.097
0.133 0.075 0.093
0.081 0.099 0.109
SPWI
NORM
0.0974 0.1049 0.1058 0.308
0.316 0.340 0.343 1.000
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Lean
0.350
Normalized value
221
Agile
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 purpose is to analyze the effect of variation in relative weight assigned to SCP determinants on the priority level of alternative supply chain paradigms. In the present ANP framework, experts have assigned relative weight 3 to lead-time in compare with quality (XLT/Q) on supply chain performance improvement. With this relative weight, SPWI for leagile supply chain is the highest followed by agile and lean supply chain. This implies when the perception of experts is more inclined towards leadtime in comparison to quality, they will prefer the supply chain which favors lead-time reduction. Lead-time is an essential metric for leagile and agile supply chains. Here lead-time indicates the time between raising the demand by customer and receiving the product of his choice. This priority level does not change if XLT/Q lowers from 3 to 0.125. This indicates that if experts lower relative
importance of lead-time to quality (or give more importance to quality as compare to lead-time), priority of leagile supply chain paradigm does not change. When XLT/Q is further lowered from 0.125 to 0.111, lean supply chain attains top priority followed by leagile supply chain. If weight assigned to lead-time in comparison to quality is between 0.5 and 0.333, policy towards supply chain performance improvement would be combination leanness and agility. This point indicates that advantages of both leanness and agility can be achieved. When the priority weight is further reduced beyond 0.125, lean supply chain gets top priority followed by leagile and agile supply chain. Fig. 3 indicates effect on values of SPWI for lean, agile and leagile supply chains due to variation in the priority weight of lead-time with respect to cost (XLT/C). In the present framework according to expertÕs opinion, XLT/C is 2. When the relative weight XLT/C lies between 0.667 and 3, experts
0.370
Lean Agile
Normalized value
0.350
Leagile 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.
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favour strategy for leagile supply chain in meeting the unpredictable demand. SPWI for agile supply chain improves when expertsÕ opinion deviate and XLT/C varies from 3 to 9. In this situation priority for lean supply chain declines. In the range between 0.667 and 0.111 of XLT/C, experts favor strategy for cost minimization. In this strategy experts are partially trading off importance of leadtime reduction to cost minimization. Here, the case supply chain partially looses its agility, which is indicated in the graph (Fig. 3) and the priority level 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 relative weight of lead-time with respect to service level (XLT/SL) is shown. In proposed ANP framework, XLT/SL is 0.111. At this priority experts favor service level improvement. Since service level is the most important criteria for leagile and agile supply chain (Naylor et al., 1999), SPWI for leagile supply chain gets top priority at this relative weight followed by agile supply chain (Fig. 4). If the XLT/SL is changed from 0.111 to 0.167, SPWI for agile supply chain improves but leagile supply chain remains at top. When the value of XLT/SL is higher than 0.167, experts relatively consider lead-time more important than service level agile supply chain gets top priority followed by leagile and lean supply chain. The purpose of selecting lead-time, cost, quality and service level is straightforward. These are order qualifying and order winning criteria. With changes in objective these criteria changes their po-
sition. Leanness and agility of a supply chain largely depends on these four criteria (Naylor et al., 1999).
9. Discussions ‘‘Agility’’ is needed in less predictable environments where demand is volatile and the requirement for variety is high (Lee, 2002). ‘‘Lean’’ works best in high volume, low variety and predictable environments. Leagility is the combination of the lean and agile paradigm within a total supply chain strategy by positioning the de-coupling point so as to best suit the need for responding to a volatile demand downstream yet providing level scheduling upstream from the de-coupling point (Naylor et al., 1999; Bruce et al., 2004). The ANP model proposed in this paper is an aid to supply chain managers in arriving at prudent decision when the complexities of decision variables and multi-criteria decision environment make their decision task quite complicated. This ANP model is used for selecting appropriate paradigm for improved SC performance of a case company. This could serve as one of the important tools for taking a strategic decision of this type. The criteria and attributes that are used in the model focus on the strategy and requirements of SC performance. The model is capable of taking into consideration both qualitative and quantitative information. Here it is pertinent to discuss the priority values for the determinants, which
0.390
Lean Agile
Normalized value
0.370
Leagile
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
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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.
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influence the decision of selecting the paradigm for better SC performance. From Table 2, it has been observed that the service 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), cost (0.123) and quality (0.063). For the case supply chain of fast moving consumer goods, the result favors improvement in service level and reduction in lead-time. Cost and quality are less supported because improvement in service level and reduction in lead-time would also help in reducing cost and improving quality. Though the results do not favor cost and quality, the implication is not straightforward. The lower values for these two are due to their interdependency on lead-time and service level. For example, a low value of lead-time will lead to lesser waste and quality improvement opportunity. The converse may not be true. The ANP is capable of handling interdependencies of this type. The present decision model provides the priority values in the form of weighted index for different paradigms for improved SC performance (Table 8). The final values for supply chain performance weighted index relationship are 0.343 for the leagile, 0.340 for agile, and 0.316 for lean supply chain. For supply chain of the case company, the ANP framework suggests that with existing priority levels of supply chain performance determinants, normalized value of SPWI for leagile paradigm is slightly higher than that of a mere lean or agile paradigm. The higher value of SPWI for leagile supply chain favors the policy for combining the lean and agile approaches. For handling innovative products the case supply chain should adopt a lean manufacturing approach before decoupling point and agile approach after decoupling point (Olhager, 2003). Consistency ratio (CR) is calculated for all the pair-wise comparisons to check the inconsistency in decision-making. In the proposed model CR varies from 0.002 to 0.19, which is within tolerable limit (Saaty and Kearns, 1985). An analysis of the robustness of the decision model using sensitivity analysis is carried out to observe the impact of variation in the opinion of decision-makers in assigning the weights. Sensitivity analysis indicates that the priority levels of SC paradigms do not signifi-
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cantly change with variation in the opinion of decision-makers in assigning the weights to enablers.
10. Limitations and scope for future work As compared to analytic hierarchy process (AHP), the analysis using ANP is relatively cumbersome as in the present work 117 pair-wise comparison matrices are required. To arriving at the relationship among enablers, it requires long and exhaustive discussion with experts from the case supply chain. Therefore, the advantages of ANP technique could be derived for making strategic decisions that are vital for the growth and survival of supply chains. The values for pair-wise comparisons depend on the knowledge of the decision-makers. Therefore group of decision-makers should include those experts who understand the implications of enablers on the supply chain performance in lean, agile and leagile paradigm. The proposed framework has been developed for a supply chain in fast moving consumer goods (FMCG) business. Therefore results obtained from the proposed framework cannot be generalized.
11. Conclusion Improved supply chain performance implies that a supply chain is capable of quickly responding to the variations in the customer demand with effective cost reduction. Leanness in a supply chain maximizes profits through cost reduction while agility maximizes profit through providing exactly what the customer requires. The leagile supply chain enables the upstream part of the chain to be cost-effective and the downstream part to achieve high service levels in a volatile marketplace. The ANP methodology adopted here arrives at a synthetic score, which may be quite useful for the decision-makers. The purpose of the present work is to analyze the relative impact of different enablers on three SC paradigms considered for a supply
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chain. The ANP methodology is a robust multiattribute decision-making technique for synthesizing the criteria, enablers and dimensions governing the SC performance. It integrates various criteria, enablers and alternatives in decision model. The approach also captures their relationships and interdependencies across and along the hierarchies. It is effective as both quantitative and qualitative characteristics can be considered simultaneously without sacrificing their relationships. References Agarwal, A., Shankar, R., 2003. On-line trust building in eenabled supply chain. Supply Chain Management: An International Journal 8 (4), 324–334. Agarwal, A., Shankar, R., 2002a. Modeling integration and responsiveness on a supply chain performance: A system dynamics approach. International Journal System Dynamics and Policy-Making XIV (1&2), 61–83. Agarwal, A., Shankar, R., 2002b. Analyzing alternatives for improvement in supply chain performance. Work Study 51 (1), 32–37. Aitken, J., Christopher, M., Towill, D.R., 2002. Understanding, implementing and exploiting agility and leanness. International Journal of Logistics: Research and Applications 5 (1), 59–74. Bruce, M., Daly, L., Towers, N., 2004. Lean or agile: A solution for supply chain management in the textiles and clothing industry? International Journal of Operations and Production Management 24 (2), 151–170. Christopher, M., 2000. The agile supply chain, competing in volatile markets. Industrial Marketing Management 29, 37–44. Christopher, M., Jittner, U., 2000. Developing strategic partnerships in the supply chain: A practitioner perspective. European Journal of Purchasing and Supply Chain Management 6, 117–127. Christopher, M., Towill, D.R., 2001. An integrated model for the design of agile supply chains. International Journal of Physical Distribution and Logistics Management 31 (4), 235–246. Corner, J.L., Corner, P.D., 1991. Decision analysis applications in the operations research literature. Operations Research 39, 206–218. Corner, J.L., Corner, P.D., 1995. Characteristics of decisions in decision analysis practice. Journal of the Operational Research Society 46, 304–314. Ghodsypour, S.H., OÕBrien, C., 1998. Decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics 56–57, 199–212. Gunasekaran, A., Patel, C., Tirtiroglu, E., 2001. Performance measures and metrics in a supply chain environment.
International Journal of Operations and Production Management 21 (1/2), 71–87. Hill, T., 1993. Manufacturing Strategy: Text and Cases, second ed. Macmillan Press, London. Jayaram, J., Vickery, S.K., Droge, C., 1999. An empirical study of time based competition in the North American automotive supplier industry. International Journal of Operation and Production Management 19 (10), 1010–1033. Katayama, H., Bennett, D., 1999. Agility, adaptability and leanness: 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 manufacturing strategy and technology choice. European Journal of Operational Research 47, 214–224. Lee, J.W., Kim, S.H., 2000. Using analytic network process and goal programming for interdependent information system project selection. Computers and Operations Research 27, 367–382. Lee, H.L., 2002. Aligning Supply Chain Strategies with Product Uncertainties. California Management Review 44 (3), 105– 119. Mason-Jones, R., Towill, D.R., 1999. Total cycle time compression and the agile supply chain. International Journal of Production Economics 62, 61–73. Mason-Jones, R., Naylor, B., Towill, D.R., 2000a. Engineering the leagile supply chain. International Journal of Agile Management Systems 2/1, 54–61. Mason-Jones, R., Naylor, B., Towill, D.R., 2000b. Lean, agile or 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 strategic alliances and partnering: A prerequisite for virtual enterprising. Omega 25 (1), 29–42. Meade, L., Sarkis, J., 1999. Analyzing organizational project alternatives for agile manufacturing process: An analytical network approach. International Journal of Production Research 37 (2), 241–261. Naylor, J.B., Naim, M.M., Berry, D., 1999. Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of Production Economics 62, 107–118. Olhager, J., Persson, F., Parborg, B., Rosen, S., 2002. Supply chain impacts at Ericsson––from production units to demand-driven supply units. International Journal of Technology Management 23 (1/2/3), 40–59. Olhager, J., 2003. Strategic positioning of the order penetration point. International Journal of Production Economics 85, 319–329. Poh, K.L., Ang, B.W., 1999. Transportation fuels and policy for Singapore: An AHP planning approach. Computer and Industrial Engineering 37, 507–525. Power, D.J., Sohal, A.S., Rahman, S., 2001. Critical success factors in agile supply chain management: An empirical study. International Journal of Physical Distribution and Logistics 31 (4), 247–265.
A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 Prater, E., Biehl, M., Smith, M.A., 2001. International supply chain agility––Tradeoffs between flexibility and uncertainty. International Journal of Operations and Production Management 21 (5/6), 823–839. Prince, J., Kay, J.M., 2003. Combining lean and agile characteristics: Creation of virtual groups by enhanced production flow analysis. International Journal of Production Economics 85, 305–318. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGrawHill, New York. Saaty, T.L., 1996. Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publication, Pittsburgh, PA. Saaty, T.L., Kearns, K.P., 1985. Analytical Planning. Pergamon Press, Oxford. Shim, J.P., 1989. Bibliographical research on the analytic hierarchy process. Socio-economic Planning Science 23, 161–167. Stevens, G., 1989. Integrating the supply chain. International Journal of Physical Distribution and Materials Management 19 (1), 3–8.
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Stratton, R., Warburton, R.D.H., 2003. The strategic integration of agile and lean supply. International Journal of Production Economics 85, 183–198. van Hoek, R.I., 2000. The thesis of leagility revisited. International Journal of Agile Management Systems 2/3, 196–201. van Hoek, R.I., Harrison, A., Christopher, M., 2001. Measuring agile capabilities in the supply chain. International Journal of Operations and Production Management 21 (1/2), 126–147. Vickery, S., Calantone, R., Droge, C., 1999. Supply chain flexibility: An empirical study. The Journal of Supply Chain Management 35 (3), 16–24. Wasil, E., Golden, B., 2003. Celebrating 25 years of AHP-based decision making. Computers and Operations Research 30, 1419–1420. Yurdakul, M., 2003. Measuring long-term performance of a manufacturing firm using the Analytic Network Process (ANP) approach. International Journal of Production Research 41 (11), 2501–2529. Zahedi, F., 1986. The analytic hierarchy process—a survey of the method and its applications. Interfaces 16, 96–108.
Production, Manufacturing and Logistics
Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach Ashish Agarwal a, Ravi Shankar b
a,*
, M.K. Tiwari
b
a Department of Management Studies, Indian Institute of Technology Delhi, HauzKhas, New Delhi 110016, India 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 success 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 winning 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 Enterprises are continuously paying attention in responding to the customer demand for maintain*
Corresponding author. Tel.: +91 11 26596421; fax: +91 11 26862620/26582037. E-mail addresses: [email protected] (A. Agarwal), [email protected] (R. Shankar), [email protected] (M.K. Tiwari).
ing a competitive advantage over their rivals. Supply Chain Management (SCM) has gained attention as it focuses on material, information 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 compete, not companies (Christopher and Towill, 2001), and the success or failure of supply chains is ultimately determined in the marketplace by 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
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the right time to the consumer is not only the linchpin to competitive success, but also the key to survival. Hence, customer satisfaction and market place understanding are critical elements for consideration when attempting to establish a new SC strategy. Significant interest has been shown in recent years in the idea of ‘‘lean manufacturing’’, and the wider concepts of the ‘‘ lean enterprises’’. The focus of the lean approach has essentially been on the elimination of waste or muda. The upsurge of interest in lean manufacturing can be traced to the Toyota Production Systems with its focus on the reduction and elimination of waste. Lean is about doing more with less. Lean concepts work well where demand is relatively stable and hence predictable and where variety is low. Conversely, in those contexts where demand is volatile and the customer requirement for variety is high, a much higher level of agility is required. Leanness may be an element of agility in certain circumstances, but it will not enable the organization to meet the precise needs of the customers more rapidly. Agility is a business-wide capability that embraces organizational structures, information
systems, logistics processes and in particular, mindsets (Power et al., 2001; Katayama and Bennett, 1999). Agility is being defined as the ability of an organization to respond rapidly to changes in demand, both in terms of volume and variety (Christopher, 2000). The lean and agile paradigms, though distinctly different, can be and have been combined within successfully designed and operated total supply chains (Mason-Jones and Towill, 1999). The past studies show how the need for agility and leanness depends upon the total supply chain strategy, particularly considering market knowledge, via information enrichment, and positioning of the de-coupling point. Combining agility and leanness in one SC via the strategic use of a de-coupling point has been termed ‘‘le-agility’’ (Naylor et al., 1999). Therefore leagile is the combination of the lean and agile paradigms within a total supply chain strategy by positioning the decoupling point so as to best suit the need for responding to a volatile demand down stream yet providing level scheduling upstream from the market place (van Hoek et al., 2001). The decoupling point is in the material flow streams to which the 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 Product variety Product life cycle Customer drivers Profit margin Dominant costs Stock out penalties Purchasing policy Information enrichment Forecast mechanism Typical products Lead time compression Eliminate muda Rapid reconfiguration Robustness Quality Cost Lead-time Service level
Predictable Low Long Cost Low Physical costs Long term contractual Buy goods Highly desirable Algorithmic Commodities Essential Essential Desirable Arbitrary Market qualifier Market winner Market qualifier Market qualifier
Volatile High Short Lead-time and availability High Marketability costs Immediate and volatile Assign capacity Obligatory Consultative Fashion goods Essential Desirable Essential Essential Market qualifier Market qualifier Market qualifier Market winner
Volatile and unpredictable Medium Short Service level Moderate Both No place for stock out Vendor managed inventory Essential Both/either Product as per customer demand Desirable Arbitrary Essential Desirable Market qualifier Market winner Market qualifier Market winner
Sources: Naylor et al. (1999), Mason-Jones et al. (2000a), Olhager (2003), Bruce et al. (2004).
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2000a,b; Prince and Kay, 2003). Table 1 illustrates the comparison of attributes among lean, agile and leagile supply chain. The present paper presents a framework for modeling performance of lean, agile and leagile supply chain on the basis interdependent variables. Here performance of SC implies how much the SC is responsive to the needs of the market. The framework provides an aid to decision makers in analyzing the variables affecting market sensitiveness, process integration, information driver and flexibility in lean, agile and leagile supply chains for the performance improvement of a case supply chain in fast moving consumer goods (FMCG) business. For this we have adopted Analytic Network Process (ANP) approach. By using ANP in a SC context, we can evaluate the influence of various performance dimensions on the specified objectives of SC, such as timely response to meet the customer demand. It also explicitly considers the influence of the performance determinants on one another. Since the dimensions and determinants of supply chain performance (SCP) have systemic characteristics, they may be integrated into one model. These systemic relationships can more accurately portray the true linkages and interdependencies of these various determinants (Saaty, 1996).
2. Supply chain performance Supply chain is described as a chain linking each element from customer and supplier through manufacturing and services so that flow of material, money and information can be effectively managed to meet the business requirement (Stevens, 1989). Most of the companies realize that in order to evolve an efficient and effective supply chain, SCM needs to be assessed for its performance (Gunasekaran et al., 2001). Christopher and Towill (2001) have explained the issues related to market qualifier and market winner in a supply chain and identified quality, cost, lead-time and service level as four performance measures. While, service level is the market winner for an agile supply chain, rests are market qualifiers. In case of lean supply quality, lead-time and service level
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are the market qualifier and cost is a market winner. However, with changed objectives, the qualifier and winner may change positions (Hill, 1993). Aspects combining lean and agile features have also been explored under the concept of leagility (van Hoek, 2000). In the proposed ANP framework market sensitiveness (MS), information driver (ID), process integration (PI) and flexibility (F) have been considered as supply chain performance (SCP) dimensions by experts of the case supply chain. These dimensions are important characteristics of agility (Christopher, 2000). Market sensitiveness involves issues related to quick response to real demand. It is characterized by six measures (Jayaram et al., 1999; Power et al., 2001; Agarwal and Shankar, 2002a): delivery speed (DS), delivery reliability (DR), new product introduction (NPI), new product development time (NPDT), manufacturing lead-time (MLT) and customer responsiveness (CR). Higher values of DS, DR, NPI and CR or lower values of NPDT and MLT would make the supply chain more sensitive towards market forces. Information driver involves making use of information technology to share data between buyers and suppliers. This enables the supply chain to become demand driven. Electronic Data Interchange (EDI), means of information (MOI), such as Internet, data accuracy (DA), etc enable supply chain partners to act upon the same data with real time demand. Another key characteristic of an agile organization is flexibility (Vickery et al., 1999; Prater et al., 2001; Olhager, 2003). In that respect, the origins of agility as a business concept lie partially in flexible manufacturing systems. Initially it is thought that the route to manufacturing flexibility is through automation to enable rapid changeovers (i.e. reduced set-up times) and thus enable a greater responsiveness to changes in product mix or volume. Later this idea of manufacturing flexibility is extended into the wider business context and the concept of agility as an organizational orientation emerged. The performance dimension flexibility may be broken down into two capabilities: the promptness with and the degree to which a firm can adjust its supply chain speed, destinations, and volumes (Prater et al., 2001). The supply chain
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may be broken down into three basic segments: sourcing, manufacturing and delivery. Any firmÕs supply chain agility is determined by how its physical components (i.e. sourcing, manufacturing and delivery) are configured to incorporate speed and flexibility. As the levels of speed and, more importantly, flexibility increase, the level of supply chain agility increases. The firm can, to a degree, make up deficiencies in the speed or flexibility of one of the supply chain parts by excelling in the other two. For example, the delivery part of the supply chain may be inherently inflexible, such as is found in sea transportation (i.e. the speed is low). Supply chain agility may be increased if the firm is able to compensate for these shortcomings by setting up its inbound logistics (i.e. sourcing) or manufacturing operations to be fast or flexible (Olhager et al., 2002). As the speed in outbound logistics is inflexible, speed and flexibility in manufacturing and sourcing could help compensate for the slow outbound transportation. Shared information between supply chain partners can be fully leveraged through process integration (PI). Process integration (PI) means collaborative working between buyers and suppliers, joint product development, common systems and shared information (Christopher and Jittner, 2000). Collaboration across each partnerÕs core business processes (CPB), company specific issues on demand side (CDS) such as quality, cost, etc and company specific issues on supply side (CSS) such as buyer–supplier relations, vendor managed inventory, information sharing, etc are the main enablers of the process integration. Now we will focus on developing a framework for significant alternative for the performance improvement of supply chain.
3. The decision environment Analytic hierarchy process (AHP) is introduced for choosing the most suitable alternative, which fulfils the entire set of objectives in multi-attribute decision-making problem (Wasil and Golden, 2003). AHP allows a set of complex issues, to be compared with the importance of each issue relative to its impact on the solution to the problem.
Since the introduction of AHP numerous applications have been published in the literature (Zahedi, 1986; Shim, 1989; Kleindorfer and Partovi, 1990; Corner and Corner, 1991, 1995; Ghodsypour and OÕBrien, 1998). Analytic Network Process (ANP) is a more general form of AHP, incorporating feedback and interdependent relationships among decision attributes and alternatives (Saaty, 1996). This provides a more accurate approach for modeling complex decision environment (Meade and Sarkis, 1999; Lee and Kim, 2000; Agarwal and Shankar, 2002b, 2003; Yurdakul, 2003). We have adopted the ANP-based evaluation framework for the selection of the best alternative (Meade and Sarkis, 1999). The reasons due to which ANP is selected for this purpose are due to three facts: (i) analyzing the supply chain performance is a multi-criteria decision problem, (ii) many factors, enablers and criteria in decision environment are interdependent on one another, and (iii) some of the criteria, enablers and dimensions are subjective due to which a synthetic score through simple weightage method is difficult to arrive at. Analytic Hierarchy Process (AHP) is similar to ANP but cannot capture interdependencies (Meade et al., 1997; Meade and Sarkis, 1999). Hierarchical representation is an important component of ANP, however strict hierarchical structure is not recommended, as is the case with AHP. The ANP technique allows for more complex relationships among the decision levels and attributes. The ANP consists of coupling of two phases. The first phase consists of a control hierarchy of network of criteria and sub-criteria that control the interactions. The second phase is a network of influences among the elements and clusters. The network varies from criteria to criteria and thus different super-matrices of limiting influence are computed for each control criteria. Finally, each one of these super-matrices is weighted by the priority of its control criteria and results are synthesized through addition for the entire control criterion (Saaty, 1996). A graphical summary of the ANP model and its decision environment related to supply chain performance is shown in Fig. 1. The overall objective is to select the best framework for improving performance of the case supply chain.
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To analyze the Supply Chain performance
Supply Chain Performance Weighted Index Supply chain performance determinants Cost
Lead Time
Service Level
Quality
Supply chain performance dimensions
Market sensitiveness
Process integration
Information driver
Flexibility
Supply chain performance enablers
Delivery Speed (DS), New product introduction (NPI), Customer responsiveness(CR)
Collaboration across each partner’s core business process (CPB), Company specific issues on demand side (CDS), Company specific issues on supply side(CSS)
DS NPI
Electronic data interchange (EDI), Means of information (MOI), Data accuracy (DA)
CPB
CR
CSD
Source flexibility (SF), Make flexibility (MF), Delivery flexibility (DF)
SF
EDI
CSS
MOI
DA
MF
DF
Supply chain performance paradigms
Lean supply chain
Agile supply 31 chain
Leagile supply chain
Fig. 1. ANP-based framework for Modeling Metrics of Supply Chain Performance.
4. Deriving the interdependence in supply chain performance model The interdependence among different levels in supply chain performance framework have been developed through review of literature on supply chain performance (Naylor et al., 1999; Katayama and Bennett, 1999; van Hoek, 2000; Christopher, 2000; Prater et al., 2001; Aitken et al., 2002; Power et al., 2001; Stratton and Warburton, 2003; Bruce et al., 2004) and through discussion with experts
from the case supply chain, which incorporates network of suppliers, manufacturer, distributors and retailers for fast moving consumer goods (FMCG). These experts have more than ten years of experience in the area of purchasing and supply chain management. The group consists of four to five experts and they are informed about alternative supply chain paradigms. It is believed that experts know relative weights between alternative paradigms during the process of capturing the relative weights. The case supply chain is involved in
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functional as well as innovative products. The functional products have long product life cycles and their demand is predictable. The innovative products have short product life cycle and their demand is unpredictable. The management of the case supply chain is not able to decide which supply chain performance criteria should be given priority over other performance criteria. They are also unable to adopt the proper supply chain strategy for their products.
5. Mutual interdependence of enablers Overall objective of the present work is to model performance of three paradigms for a supply chain, which enables it be more flexible in responding to market demand. Cost, quality, service level and lead-time are the major determinants of the proposed framework. These determinants have dominance over the identified dimensions in the framework. The impact of one determinant on supply chain performance is affected by the influence of the other determinants. Using pair wise comparison matrix with a scale of one to nine, the relative weight of each determinants is obtained and given in Table 2. These values have been obtained through expertsÕ opinions that are heading the supply chain operation. Enablers of the framework are those, which assist in achieving the controlling dimension of supply chain performance. Therefore, these are dependent on the dimensions, but there is also some interdependency among enablers, hence the arrows in Fig. 1 are shown as arching back to the enablersÕ decision level. For example enablers under dimension Ôprocess integrationÕ are interdependent to some degree. ANP uniquely captures the interdependen-
cies at different levels of the control hierarchy as well as interdependencies that are inherited among different hierarchies. We would illustrate this aspect through an example of the case supply chain. This would illustrate interdependencies among different enablers under cost determinant.
6. Capture of relative weights obtained through expert opinion The relative weights in the pair wise comparison matrices of ANP have been obtained through discussion with group of experts of the case supply chain. The group consists of those experts from the trading partners of the case supply chain, which have vast experience in the area of supply chain management. For obtaining the relative weights in Table 2, the research group asked different questions. A sample question is: ‘‘what is the relative impact on supply chain performance in timely responding to market demand by cost when cost is compared to quality?’’ The answer is 2 on a scale of 1–9 and this is incorporated as second entry of cost row in Table 2. Saaty (1980) has suggested a scale of 1–9 for comparing two components. In the scale of 1–9, 1 implies equal impact while 9 implies stronger impact of row element than column element. If experts feel that column element has stronger impact than row element, reciprocal of number from 1 to 9 is assigned accordingly (Saaty, 1996). For obtaining the relative weights in Table 3, the research group asked the question, ‘‘What is the relative impact on market sensitiveness by enabler ‘‘new product introduction (NPI)’’ when compare to enabler ‘‘customer responsiveness (CR)’’, 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
Cost
Quality
Service level
1 0.500 0.333 9.00
2.000 1 0.500 4.000
3.000 2.000 1 8.000
0.111 0.250 0.125 1
0.162 0.123 0.063 0.652
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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 NPI CR
1 0.200 0.5
5 1 3.00
2 0.333 1
(0.333), which is incorporated as the second entry of NPI row in Table 3. ExpertsÕ opinion is similarly ascertained in all the tables of ANP framework. A graphical summary of ANP model and its decision environment related to supply chain performance (SCP) is shown in Fig. 1. The overall objective in the ANP approach is to select a paradigm, which helps in improving the performance of supply chain. As an illustration we have considered four criteria: lead time, cost, quality, and service level.
7. Application of ANP framework The ANP methodology is applied to the illustrative supply chain problem as follows: STEP 1: Model construction and problem structuring The top most elements in the hierarchy of criteria are decomposed into sub criteria and attributes. The model development requires identification of attributes at each level and a definition of their inter-relationships. The ultimate objective of this hierarchy is to identify alternatives that will be the significant for improving the performance of supply chain. We shall evaluate four-supply chain performance hierarchy whose results will be aggregated in ‘‘supply chain performance weighted index’’ evaluation step. This form of analysis is similar to SaatyÕs recommendation of using a unique network for benefits, costs, risks and opportunities (BCRO) (Saaty, 1996). Instead of using the BCRO categories supply chain performance determinants (lead-time, cost, quality and service level) are used as the overlying network categories. Cost and quality are important criteria in lean supply chain; lead-time is an important crite-
0.581 0.110 0.309
rion in agile supply chain and service level is an important criterion in leagile supply chain. In order to analyze the combined influence of four supply chain performance determinants on the selection of three alternative paradigms a single weighted index is calculated, which can prioritize three alternatives. This weighted index also captures the influence of dimensions and enablers on the selection process. STEP 2: Pair-wise comparison matrices between component/attribute levels 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 ÔcontrolÕ criterion. These are conducted with respect to their relative importance towards the control criterion. In the case of interdependencies, components within the same level are viewed as controlling components for each other. Levels may also be interdependent. Through pair-wise comparisons between the applicable attributes enablers of performance dimension cluster, the weighted priority (e-Vector) is calculated (Saaty, 1996). For example, Table 3 presents the comparison matrix for enablers under the dimension of Market sensitiveness, and control hierarchy network of the cost. Similarly, comparison matrices for other enablers are prepared and the resultant e-Vectors are imported as forth column in Table 5. For capturing the weightages an illustrative question is, Ôwhat is the relative impact on market sensitiveness by attribute enabler, a, when compared to attribute enabler, b, under cost determinantÕ? Additional pair-wise comparison matrix is required for the relative importance of each of the dimensions of SCP clusters (MS, PI, ID, and F) on the determinant of SCP level. There will be four more matrices, one for each of the determinants.
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there will be 12 non-zero columns in this super matrix. Each of the non-zero values in the column in super matrix M, is the relative importance weight associated with the interdependently pair-wise comparison matrices. In this model there are four super matrices, one for each of the determinants of SCP hierarchy networks, which need to be evaluated. The Super matrix (Table 5) is converged for getting a long-term stable set of weights. For this power of super matrix is raised to an arbitrarily large number. In our illustrative example convergence is reached at 32nd power. Table 6 illustrates the value after convergence. STEP 5: Selection of best alternative The equation for desirability index, Dia for alternative i and determinant a is defined as (Meade and Sarkis, 1999):
This result is presented as second column of Table 7. The final standard pair-wise comparison evaluations are required for the relative impacts of each of the alternative for SCP improvement. The number of pair-wise comparison matrices is dependent of the number of SCP attribute enablers that are included in the determinant of the SCP improvement hierarchy. There are 12 pair-wise comparison matrices 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 enablers are conducted. Table 4 illustrates one such case. For brevity the final scores of this and remaining matrices are shown in Table 5. STEP 4: Super matrix formation and analysis Table 5 shows super matrix M, detailing the results of the relative importance measures for each of the attribute enablers for the cost determinant of SCP clusters. Since there are 12 pair-wise comparison matrices, one for each of the interdependent SCP attribute enablers in the cost hierarchy,
Dia ¼
j¼1
NPI
CR
e-Vector
New product introduction (NPI) Customer responsiveness (CR)
1 8.00
0.125 1
0.111 0.889
I P ja AD kja Akja S ikja ;
ð1Þ
k¼1
where Pja is the relative importance weight of dimension jon the determinant ÔaÕ, AD kja is the relative importance weight for attribute enabler k, dimension j and determinant ÔaÕ for the dependency (D) relationships between enablerÕs component levels, AIkja is the stabilized relative importance weight for attribute enabler k of ÔjÕ dimension in the determinant ÔaÕ for interdependency (I) relationships within the attribute enablerÕs component level, Sikja is the relative impact of SC alternative paradigm i on SCP enabler k of
Table 4 Pair-wise comparison matrix for enablers under market sensitiveness, cost and delivery speed Delivery speed (DS)
Kja J X X
Table 5 Super matrix for cost before convergence Cost
DS
NPI
CR
DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF
0.00 0.111 0.889
0.333 0.00 0.667
0.800 0.200 0.00
CPB
CDS
CSS
0 0.667 0.333
0.889 0 0.111
0.143 0.857 0
EDI
MOI
DA
0 0.833 0.167
0.200 0 0.800
0.667 0.333 0
SF
MF
DF
0 0.111 0.889
0.333 0 0.667
0.800 0.200 0
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Table 6 Super Matrix for cost after convergence (M32) Cost
DS
NPI
CR
DS NPI CR CPB CDS CSS EDI MOI DA SF MF DF
0.41 0.14 0.45
0.41 0.14 0.45
0.41 0.14 0.45
CPB
CDS
CSS
0.40 0.42 0.18
0.40 0.42 0.18
0.40 0.42 0.18
dimension of SCP j of SCP hierarchy network a, Kja is the index set of attribute enablers for dimension j of determinant a, J is the index set for the dimension j. Table 7 shows the calculations for the desirability indices (Di cost) for alternatives that is based on the cost control hierarchy by using the weights obtained from the pair-wise comparisons of the alternatives, dimensions and weights of enablers from the converged super matrix. These weights are used to calculate a score for the determinant of Supply chain performance improvement desirability for each of the alternatives being considered. The second column in Table 7 presents about the results obtained from step 2, which is enu-
EDI
MOI
DA
0.30 0.36 0.34
0.30 0.36 0.34
0.30 0.36 0.34
SF
MF
DF
0.41 0.14 0.45
0.41 0.14 0.45
0.41 0.14 0.45
merated based on relative impact of each of dimensions on cost determinants. The pair-wise comparison matrix for the relative impact of the attribute enablers on the dimensions of SCP is presented in the fourth column. The values in fifth column are the stable interdependent weights of attribute enablers obtained through super matrix convergence. The relative weights of three alternatives for each dimension are given in sixth, seventh and eighth columns of Table 7. These weights are obtained by comparing three alternatives for every dimension of supply chain performance. The final three columns represent the desirability index I (P ja AD kja Akja S ikja ) of each alternative for attribute enablers. For each of the alternatives under cost
Table 7 Supply chain performance desirability index for cost Dimension #
Pja
Attribute #
AD kja
AIkja
S1
S2
S3
Lean
Agile
Leagile
MS
0.478 0.478 0.478
DS NPI CR
0.581 0.110 0.309
0.41 0.14 0.45
0.577 0.600 0.544
0.160 0.144 0.110
0.263 0.256 0.346
0.066 0.004 0.037
0.018 0.001 0.007
0.030 0.002 0.023
PI
0.266 0.266 0.266
CPB CDS CSS
0.467 0.376 0.157
0.40 0.42 0.18
0.579 0.548 0.490
0.187 0.211 0.312
0.234 0.241 0.198
0.029 0.023 0.004
0.009 0.009 0.002
0.012 0.010 0.001
ID
0.166 0.166 0.166
EDI MOI DA
0.615 0.093 0.292
0.30 0.36 0.34
0.525 0.537 0.490
0.142 0.268 0.312
0.334 0.195 0.198
0.016 0.003 0.008
0.004 0.001 0.005
0.010 0.001 0.003
SF 0.615 0.41 MF 0.093 0.14 DF 0.292 0.45 cost for alternative frameworks
0.539 0.286 0.333
0.297 0.143 0.167
0.164 0.571 0.500
0.012 0.0003 0.004 0.205
0.007 0.0002 0.002 0.073
0.004 0.001 0.006 0.097
F
0.090 0.090 0.090 Total desirability indices of
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determinant, the summation of these results appears in the final row of Table 7. The result shows that the impact on cost is considered an important criterion in lean supply chain (0.205) followed by leagile (0.097) and agile (0.073) supply chain. STEP 6: Calculation of Supply Chain Performance Weighted Index (SPWI) To complete the analysis supply chain performance weighted index (SPWI) is determined for each alternative paradigm. The SPWIi for an alternative i is the product of the desirability indices (Dia) and the relative importance weights of the determinants (Ca) of the SCP. The results (Table 2) show that the service level determinant (Ca = 0.652) as most important for supply chain performance improvement. The result indicates that the management of the case supply chain should focus on improving the service level. This result could be due to the competitive or customer pressure for improving service level. Lead-time (0.162) and cost (0.123) play the next most important role but are less important than service level. The final results are shown in Table 8. The Table 8 indicates that for the illustrative problem the most significant alternative paradigm for better supply chain performance is leagile supply chain followed by agile supply chain.
8. Sensitivity analysis Sensitivity analysis is an important concept for the effective use of any quantitative decision model (Poh and Ang, 1999). In the present work sensitivity analysis is done to find out the changes in the SPWI for lean, agile and leagile supply chain par-
adigms with variation in the expert opinion towards lead-time with respect to cost, quality and service level. Overall objective of sensitivity analysis is to see the robustness of proposed framework due to variation in the expertsÕ opinion in assigning the weights during comparison. For the case supply chain experts opinion has been sought to analyze the performance of supply chain. Table 8 indicates how the supply chain performance weighted indexes (SPWI) for proposed framework of three supply chains varies with changing priority of lead-time, cost, quality and service level. When overall objective is to reduce lead-time, desirability indices is lower for lean supply chain than agile supply chain. In a strategy to minimize the cost and to improve quality, lean supply chain has the highest desirability indices among the three supply chains. In an effort to improve service level, desirability indices for leagile supply chain is slightly higher than agile supply chain. Here it is pertinent to mention that in the uncertain environment desired supply chain performance cannot be alone achieved either by lean or by agile supply chain. Lean and agile paradigms are not mutually exclusive paradigms (Christopher and Towill, 2001), therefore proper combination of lean and agile (leagile) is required to suit the need for responding to a volatile demand (Naylor et al., 1999). In Fig. 2, X-axis represents the relative weight of lead-time as compare to quality. These relative weights are in the scale of 1/9–9 (Saaty scale). Yaxis represents the normalized value of supply chain performance weighted index (SPWI). These weights are obtained using ANP framework, which captures the interdependence among supply chain performance variables. This framework con-
Table 8 Supply chain Performance Weighted Index (SPWI) for various alternative frameworks Alternatives #
Lean Agile Leagile Total
Criteria
Calculated weights for alternatives
Lead-time
Cost
Quality
Service level
Weights for criteria: 0.162
0.123
0.063
0.652
0.067 0.162 0.106
0.205 0.073 0.097
0.133 0.075 0.093
0.081 0.099 0.109
SPWI
NORM
0.0974 0.1049 0.1058 0.308
0.316 0.340 0.343 1.000
A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225 0.360
Lean
0.350
Normalized value
221
Agile
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 purpose is to analyze the effect of variation in relative weight assigned to SCP determinants on the priority level of alternative supply chain paradigms. In the present ANP framework, experts have assigned relative weight 3 to lead-time in compare with quality (XLT/Q) on supply chain performance improvement. With this relative weight, SPWI for leagile supply chain is the highest followed by agile and lean supply chain. This implies when the perception of experts is more inclined towards leadtime in comparison to quality, they will prefer the supply chain which favors lead-time reduction. Lead-time is an essential metric for leagile and agile supply chains. Here lead-time indicates the time between raising the demand by customer and receiving the product of his choice. This priority level does not change if XLT/Q lowers from 3 to 0.125. This indicates that if experts lower relative
importance of lead-time to quality (or give more importance to quality as compare to lead-time), priority of leagile supply chain paradigm does not change. When XLT/Q is further lowered from 0.125 to 0.111, lean supply chain attains top priority followed by leagile supply chain. If weight assigned to lead-time in comparison to quality is between 0.5 and 0.333, policy towards supply chain performance improvement would be combination leanness and agility. This point indicates that advantages of both leanness and agility can be achieved. When the priority weight is further reduced beyond 0.125, lean supply chain gets top priority followed by leagile and agile supply chain. Fig. 3 indicates effect on values of SPWI for lean, agile and leagile supply chains due to variation in the priority weight of lead-time with respect to cost (XLT/C). In the present framework according to expertÕs opinion, XLT/C is 2. When the relative weight XLT/C lies between 0.667 and 3, experts
0.370
Lean Agile
Normalized value
0.350
Leagile 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.
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favour strategy for leagile supply chain in meeting the unpredictable demand. SPWI for agile supply chain improves when expertsÕ opinion deviate and XLT/C varies from 3 to 9. In this situation priority for lean supply chain declines. In the range between 0.667 and 0.111 of XLT/C, experts favor strategy for cost minimization. In this strategy experts are partially trading off importance of leadtime reduction to cost minimization. Here, the case supply chain partially looses its agility, which is indicated in the graph (Fig. 3) and the priority level 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 relative weight of lead-time with respect to service level (XLT/SL) is shown. In proposed ANP framework, XLT/SL is 0.111. At this priority experts favor service level improvement. Since service level is the most important criteria for leagile and agile supply chain (Naylor et al., 1999), SPWI for leagile supply chain gets top priority at this relative weight followed by agile supply chain (Fig. 4). If the XLT/SL is changed from 0.111 to 0.167, SPWI for agile supply chain improves but leagile supply chain remains at top. When the value of XLT/SL is higher than 0.167, experts relatively consider lead-time more important than service level agile supply chain gets top priority followed by leagile and lean supply chain. The purpose of selecting lead-time, cost, quality and service level is straightforward. These are order qualifying and order winning criteria. With changes in objective these criteria changes their po-
sition. Leanness and agility of a supply chain largely depends on these four criteria (Naylor et al., 1999).
9. Discussions ‘‘Agility’’ is needed in less predictable environments where demand is volatile and the requirement for variety is high (Lee, 2002). ‘‘Lean’’ works best in high volume, low variety and predictable environments. Leagility is the combination of the lean and agile paradigm within a total supply chain strategy by positioning the de-coupling point so as to best suit the need for responding to a volatile demand downstream yet providing level scheduling upstream from the de-coupling point (Naylor et al., 1999; Bruce et al., 2004). The ANP model proposed in this paper is an aid to supply chain managers in arriving at prudent decision when the complexities of decision variables and multi-criteria decision environment make their decision task quite complicated. This ANP model is used for selecting appropriate paradigm for improved SC performance of a case company. This could serve as one of the important tools for taking a strategic decision of this type. The criteria and attributes that are used in the model focus on the strategy and requirements of SC performance. The model is capable of taking into consideration both qualitative and quantitative information. Here it is pertinent to discuss the priority values for the determinants, which
0.390
Lean Agile
Normalized value
0.370
Leagile
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.
A. Agarwal et al. / European Journal of Operational Research 173 (2006) 211–225
influence the decision of selecting the paradigm for better SC performance. From Table 2, it has been observed that the service 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), cost (0.123) and quality (0.063). For the case supply chain of fast moving consumer goods, the result favors improvement in service level and reduction in lead-time. Cost and quality are less supported because improvement in service level and reduction in lead-time would also help in reducing cost and improving quality. Though the results do not favor cost and quality, the implication is not straightforward. The lower values for these two are due to their interdependency on lead-time and service level. For example, a low value of lead-time will lead to lesser waste and quality improvement opportunity. The converse may not be true. The ANP is capable of handling interdependencies of this type. The present decision model provides the priority values in the form of weighted index for different paradigms for improved SC performance (Table 8). The final values for supply chain performance weighted index relationship are 0.343 for the leagile, 0.340 for agile, and 0.316 for lean supply chain. For supply chain of the case company, the ANP framework suggests that with existing priority levels of supply chain performance determinants, normalized value of SPWI for leagile paradigm is slightly higher than that of a mere lean or agile paradigm. The higher value of SPWI for leagile supply chain favors the policy for combining the lean and agile approaches. For handling innovative products the case supply chain should adopt a lean manufacturing approach before decoupling point and agile approach after decoupling point (Olhager, 2003). Consistency ratio (CR) is calculated for all the pair-wise comparisons to check the inconsistency in decision-making. In the proposed model CR varies from 0.002 to 0.19, which is within tolerable limit (Saaty and Kearns, 1985). An analysis of the robustness of the decision model using sensitivity analysis is carried out to observe the impact of variation in the opinion of decision-makers in assigning the weights. Sensitivity analysis indicates that the priority levels of SC paradigms do not signifi-
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cantly change with variation in the opinion of decision-makers in assigning the weights to enablers.
10. Limitations and scope for future work As compared to analytic hierarchy process (AHP), the analysis using ANP is relatively cumbersome as in the present work 117 pair-wise comparison matrices are required. To arriving at the relationship among enablers, it requires long and exhaustive discussion with experts from the case supply chain. Therefore, the advantages of ANP technique could be derived for making strategic decisions that are vital for the growth and survival of supply chains. The values for pair-wise comparisons depend on the knowledge of the decision-makers. Therefore group of decision-makers should include those experts who understand the implications of enablers on the supply chain performance in lean, agile and leagile paradigm. The proposed framework has been developed for a supply chain in fast moving consumer goods (FMCG) business. Therefore results obtained from the proposed framework cannot be generalized.
11. Conclusion Improved supply chain performance implies that a supply chain is capable of quickly responding to the variations in the customer demand with effective cost reduction. Leanness in a supply chain maximizes profits through cost reduction while agility maximizes profit through providing exactly what the customer requires. The leagile supply chain enables the upstream part of the chain to be cost-effective and the downstream part to achieve high service levels in a volatile marketplace. The ANP methodology adopted here arrives at a synthetic score, which may be quite useful for the decision-makers. The purpose of the present work is to analyze the relative impact of different enablers on three SC paradigms considered for a supply
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