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Prioritising the alternatives for flexibility in supplychainsRajesh Kumar Singha & Milind Kumar Sharmab

a Department of Operations and Supply Chain Management, Indian Institute of ForeignTrade, IIFT Bhawan, B-21 Qutab Institutional Area, New Delhi-110016, India.b Faculty of Engineering & Architecture, Department of Production & Industrial Engineering,M.B.M. Engineering College, J.N.V. University, Jodhpur-342011 (Rajasthan), India.Published online: 09 May 2013.

To cite this article: Rajesh Kumar Singh & Milind Kumar Sharma (2014) Prioritising the alternatives for flexibility in supplychains, Production Planning & Control: The Management of Operations, 25:2, 176-192, DOI: 10.1080/09537287.2013.782951

To link to this article: http://dx.doi.org/10.1080/09537287.2013.782951

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Prioritising the alternatives for flexibility in supply chains

Rajesh Kumar Singha and Milind Kumar Sharmab*

aDepartment of Operations and Supply Chain Management, Indian Institute of Foreign Trade, IIFT Bhawan, B-21 Qutab InstitutionalArea, New Delhi 110016, India; bFaculty of Engineering & Architecture, Department of Production & Industrial Engineering,

M.B.M. Engineering College, J.N.V. University, Jodhpur 342011 (Rajasthan), India

(Received 15 April 2012; final version received 25 February 2013)

In the era of globalisation, requirements of customer are changing very fast. Product life cycle is shortening.Organisations are under pressure to reduce cost, delivery time, improve reliability of product by changing theirprocess continuously. Supply chain management has become integral part of strategy for most of the organisations inmeeting these challenges. Success of supply chain depends on effective strategy for improving coordination amongthe members to make it more responsive for market needs by optimising available resources. In this context, supplychain needs to be flexible. Based on the literature, it is observed that overall flexibility of supply chain depends uponsuppliers’ flexibility, manufacturing flexibility and customers’ flexibility. Other sub-factors affecting flexibility may beproduct and process design, capacity planning, logistic management, suppliers’ capabilities and nature of customers.This study has tried to prioritise flexibility alternatives by analytical network process approach. Input for this analysisis based on four Indian case studies, which are briefly described in the paper. These organisations had been veryactive in improving flexibility of their supply chains. Findings of the study report that organisations should give toppriority for improving manufacturing flexibility followed by customers’ and suppliers’ flexibility.

Keywords: supply chain; flexibility; performance; case study; analytic network process

1. Introduction

A supply chain is a series of units that transforms rawmaterials into finished products and delivers the productsto customers (Mabert and Venkataramanan 1998). Someof the units in a chain are located inside a single organisa-tion’s borders, while others cross such borders in complexand evolving ways. Wu and O’Grady (2004) definedsupply chain as a system through which organisationsdeliver their products and services to their customers.Effectively managing supply chains is vital to organisa-tional success. Indeed, there is a growing recognition thatmodern competition is being fought ‘supply chain vs.supply chain’ rather than ‘firm vs. firm’ (Ketchen andGuinipero 2004).

A supply chain is a network of facilities anddistribution entities (suppliers, manufacturers, distributors,retailers) that performs the functions of procurement ofraw materials, transformation of raw materials into inter-mediate and finished products and distribution of finishedproducts to customers. A supply chain is typically charac-terised by a forward flow of materials and a backward flowof information. Recently, enterprises have shown agrowing interest for flexible supply chain management(SCM). Flexibility in SCM can lead to lower production

cost, inventory cost and transportation cost and improvedcustomer service throughout all the stages that areinvolved in the chain. Various alternative methods havebeen proposed for modelling supply chains. According toBeamon (1998), they can be grouped into four categories:deterministic models where all the parameters are known,stochastic models where at least one parameter is unknownbut follows a probabilistic distribution, economic game-theoretic models and models based on simulation, whichevaluate the performance of various supply chain strate-gies. The majority of these models are steady-state modelsbased on average performance or steady-state conditions.Sharma and Bhagwat (2007) developed a balancedscorecard to evaluate the performance of SCM. A multi-criterion objective model is also suggested using inte-grated analytical hierarchy process-pre-emptive goalprogramming based approaches for performanceevaluation of SCM (Bhagwat and Sharma 2007, 2009).However, static models are insufficient when dealing withthe dynamic characteristics of the supply chain system dueto frequent demand fluctuations, lead-time delays, salesforecasting, etc. Due to the complex nature of supplychains; having various activities encompassing multiplefunctions and organisations (Arshinder and Deshmukh

*Corresponding author. Email: milindksharma@rediffmail.com

Production Planning & Control, 2014Vol. 25, No. 2, 176–192, http://dx.doi.org/10.1080/09537287.2013.782951

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2008), such models does not provide long-term solutionsto address problems such as the bullwhip effect in supplychains. Today, supply chains need to move towards effi-cient business units with a unified system and centralisedcontrol while supply chain members acting in a decentra-lised manner (Zhu, Gavirneni, and Kapuscinski 2010).

For any supply chain to become successful it requiresseveral decisions referring to the supply chain networkconfiguration, flow of information, product and opera-tions. All these decisions broadly divided under threecategories depending upon the nature and frequency ofeach decision, namely strategic, tactical, and operationallevel (Gunasekarana, Patel, and McGaugheyc 2004;Bhagwat, Chan, and Sharma 2009). At all the decisionlevels, key element is the flexibility, which paves theway for optimum delivery results in supply chains. Flexi-bility is the ability to adjust to changes in product mix,production volume, or design (Russell and Taylor 2009).As the supply chain extends beyond the enterprise, sup-ply chain flexibility (SCF) must also extend beyond onefirm’s internal flexibility and agility. Agile supply chainis mostly about volatile demands, high product variety,short product life cycles, short lead time, high productavailability and high profit margin (Soni and Kodali2010). Some researchers (Hudson, Lean, and Smart2001; Gunasekarana, Patel, and McGaugheyc 2004;Folan and Browne 2005; Chan, Chan, and Qi 2006;Shepherd and Günter 2006) identified SCM measures/metrics for performance measurement and proposed dif-ferent frameworks. All these research have suggested acomprehensive framework for measuring performance ofsupply chains from different perspectives highlighting onvarious performance metrics including flexibility.However, in this research an attempt is made to focusexclusively on prioritising alternatives for flexibility insupply chain. Next section of the paper will discussliterature review, followed by research methodology,development of model, results and concluding remarks.

2. Literature review

Flexibility is defined as the ability of a system to adaptto external changes, while maintaining satisfactorysystem performance. In supply chains, flexibility includesthe ability to produce a wide variety of products, tointroduce new product and modify existing ones quickly,and to respond to customer needs. To meet the demandfor variety, many firms widen their product ranges,increasing not only revenues but also operationalinefficiencies (Brun and Pero 2012). With growth inoutsourcing, many managers now realise that, as firmssuccessfully streamline their own operation, the nextopportunity for improvement needs to manage and inte-grate the whole value chain from raw material providerto final consumer (Stevenson and Spring 2007). A small

variation in demand of the product in the supply chaincan result in larger variations at the subsequent upstreammembers (e.g. distributor or manufacturer) due to aphenomenon known as the bullwhip effect. Flexibilityprovides an effective parameter for characterising thebehaviour of asynchronous supply chains. A highly flexi-ble relationship is one in which there is little fluctuationin the procurement price under different supply condi-tions (Abdel-Malek and Das 2003). Golden and Powell(1999) define flexibility as the capacity to adapt acrossfour dimensions: temporal, range, intention and focus.Flexibility measures a system’s ability to accommodatevolume and schedule fluctuations from suppliers, manu-facturers, and customers. Vickery et al. (2003) proposedthe dimensions of SCF such as product, volume, launch,access and target market.

Wadhwa, Saxena, and Chan (2008) proposed a modelfor understanding the impact of flexibility on the cost-based performance of a dynamic supply chain. Yang,Lin, and Sheu (2007) gave the relationship between sup-plier collaboration and manufacturing flexibility in themotherboard industry. Garavelli (2003) extended the con-cept of SCF for dynamic and complex environment.Much of the existing research has a limited definition ofSCF and describes flexibility simply as a reactive meansto cope with uncertainty (Stevenson and Spring 2007).

2.1. Flexibility configurations for the SCM

SCF represents the capability of supply chains torespond to unanticipated changes in customer needs andcompetitor actions (Yi, Ngai, and Moon 2011). It is theability to change with little penalty in time, effort, costor performance (Upton 1994; Stecke and Raman 1995).It can be considered as a crucial weapon to increasecompetitiveness in such a complex and turbulent market-place. Flexibility becomes particularly relevant when thewhole supply chain is considered, consisting of a net-work of supply, production, and delivering firms. In thiscase, many sources of uncertainty have to be handled,such as market demand; supplier lead time, product qual-ity and information delay (Giannoccaro 2003). Flexibilityallows switching production among different plants andsuppliers, so that management can cope with internaland external variability. Wadhwa, John, and Gandhi(2002) highlighted the role of flexibility in the supplychains using a demo model of a supply chain. In aglobal scenario, not only manufacturing, but also logis-tics can be an important source of competitive advantagein maintaining flexibility, since material flows stronglyaffect business performance. The production orderassignments to the plants and the organisation of trans-ports are then critical decisional factors that can decreasethe performance of a wide range of products (Albino,Izzo, and Kühtz 2002). A strategy of coordination of the

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different production plants is required to address thechallenge of several markets (Bhatnagar, Sohal, and Mil-len 1999). To coordinate the production network, it isnecessary to analyse the flexibility of the system compo-nents and their relationships, in order to evaluate theirimpact on the whole system.

As commonly shared in the literature on manufactur-ing systems, flexibility is a complex and multidimen-sional concept, difficult to summarise. Flexibility reflectsthe ability of a system to properly and rapidly respond tochanges, coming from inside as well as outside of thesystem. Referring to the several papers which haveproposed useful taxonomies, different aspects of flexibil-ity can be outlined, such as functional aspects, i.e. flexi-bility in operations, marketing, logistics, etc. (Lynch andCross 1991), hierarchical aspects, that is flexibility atshop, plant or company level (Koste, Malhotra, and Shar-ma 2004), measurement aspects, focused on global flexi-bility measures versus context specific ones (Chen andChung 1996), strategic aspects, centred on the strategicrelevance of flexibility, time horizon aspects, e.g. long-term vs. short-term flexibility. From an operational per-spective, however, the most interesting aspect of flexibil-ity is probably the one concerning the object of change,that is flexibility of product, mix, volume, etc. (Wahab,Wu, and Lee 2008). Flexibility is the ability of a manu-facturing system to respond cost-effectively and rapidlyto changing production needs and requirements (Malhotraand MacKelprang 2010). Abdel-Malek and Das (2003)define SCF as the robustness of the buyer supplier rela-tionship under changing supply conditions. The analysis,based on the supply chain literature, reveals that theunpredictable dynamics of the supply chain can arisefrom a variety of internal and external sources, includingsuppliers, operating systems, customers and competitors(Yi, Ngai, and Moon 2011). Hence, the analysis of theSCF involves the consideration of the flexibility of thesupply chain components and their relationships, in orderto evaluate their impact on the whole system.

2.2. Agility and flexibility in supply chain

Modern days supply chains are consequence of advancesin information technology coupled with more efficienttransportation networks. This enables the establishmentof better communication between buyers and suppliersand the more cost effective movement of smaller loadsin shorter times. Managers must still plan to accommo-date the uncertainties and variations that characterise theproduct demand process. These uncertainties range fromdampened customer behaviour to process quality prob-lems. Decision makers must therefore cautiously evaluatepotential suppliers and make intelligent choices. Theunderlying assumption of a good supply chain is thatbuyers and suppliers are willing to accommodate the

uncertainties and variations in each other’s businesses. Ifthese issues are not appropriately addressed during theestablishment of the relationship, it will ultimately leadto poor performance and conflict. Ideally, one wouldwant a supplier that provides the buyer with the neededflexibility to appropriately adjust their supply process asdemand conditions change.

In a supply chain, a buyer and a supplier enter intoto a relationship whereby the supplier provides the buyerwith needed quantities of a component, sub-assembly orproduct at fixed intervals. The supply quantity maychange as demand conditions changes. According toLang, Chiang, and Lan (2009), selection of appropriatesuppliers in SCM strategy is a challenging issue becauseit requires battery of evaluation criteria/attributes, whichare characterised with complexity, elusiveness, anduncertainty in nature. Chan, Bhagwat, and Wadhwa(2009) present a simulation study on suppliers’ flexibil-ity level in relation to information system automationlevel of the supply chain and physical characteristics ofthe flexible suppliers. In an asynchronous chain, thesupply quantities and intervals are different for eachrelationship pair in the supply chain, while in a synchro-nous chain they are the same. The underlying premiseof a supply chain relationship is that it is long term andformulated to protect the interests of both parties. Understeady conditions, these relationships work well, but asmarket uncertainty increases they need to be wellanalysed to ensure that the needed flexibility is in-built.The need to accommodate uncertainty in the supplyprocess raises the issue of flexibility. Abdel-Malek andDas (2003) define manufacturing flexibility as the abilityof a system or facility to adjust to changes in its internalor external environment. Wathne and Heide (2004)argued that supplier involvement enhances a manufac-turer’s responsiveness to downstream customer changes.Further, this adjustment must occur with little penalty intime, effort or operational performance. We thereforedefine SCF as the elasticity of the buyer–supplier rela-tionship under changing supply conditions. Past studiesindicate that order quantities and supply lead times arethe two most common changes, which occur in supplychains, and are most often the cause of buyer–suppliergrievance. Since a buyer is not always able to predictdownstream conditions, it will often issue procurementorders that are for a smaller quantity than normal and/orrequest a shorter supply lead time than normal. In aninflexible relationship, a supplier will only accept theseorders at a much higher unit price. In a survey of manu-facturing managers, Abdel-Malek and Das (2003)observed that over 90% of the respondents emphasisedthat manufacturing flexibility was a key component oftheir strategy to maintain competitiveness. This validatesthe need for an effective model to track and evaluateSCF.

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Tachizawa and Gimenez (2009) defined flexibility insupply chains as the ability of the purchasing function torespond in a timely and cost-effective manner to chang-ing requirements of purchased components, in terms ofvolume, mix and delivery date. Purchasing managersneed to evaluate periodically supplier performance inorder to retain those suppliers who meet their require-ments in terms of several performance criteria. Six attri-butes frequently used as performance criteria areidentified and used in a study by Mummalaneni, Dubas,and Chaos (1996). These attributes are as follows: on-time delivery, quality, price/cost targets, professionalism,responsiveness to customer needs and long term relation-ships with the purchasing company. At least two of theseare directly addressed in this paper.

Quality and service considerations tend to dominateprice and delivery criteria. Verma and Pullman (1998),on the other hand, pointed out that although managerssay that quality is the most important attribute for thesupplier, their actual supplier choice is based largely oncost and delivery performance. Furthermore, the impor-tance placed on the different attributes was found to varylargely in accordance with the differing cultural aspectsof society. Petroni and Braglia (2000) provided an alter-native methodology to aid purchasing managers in iden-tifying and selecting suppliers.

In the area of demand uncertainty and supply chains,Jung, Ahn, and Rhee (1999) focused on the capacity util-isation in flexible facilities, and appropriate demandmanagement strategies. They found that a supplier whofaces a smaller demand with high variation would investmore in flexible facilities. They also found that when thelot size is increased it mitigates the correlation of pur-chase orders. Additionally, they found that vendorswhose facilities are flexible would prefer frequent orderswith the smaller lots only when market demands are neg-atively correlated. Different issues related to SCM areinventory levels, quality, information sharing, number ofsuppliers, cycle times, commitment, and relationship.Narasimhan and Das (2000) observed that for a companyto compete through flexibility, the sourcing or supplypractices are quite important. They examine the role ofsourcing practices on achieving manufacturing flexibility,as well as the implication of the relationship betweenmanufacturing flexibility and sourcing. It should benoted that cross-functional teaming was found to detractfrom manufacturing flexibility. Delivery and volume flex-ibilities were found to benefit from both supplier respon-siveness to delivery changes as well as supplierinvolvement in product design. Flexibility plays a majorrole in the performance of supply chains. Swamidass andNewell (1987) in a study confirmed that flexibilityimproved performance in uncertain environments. Flexi-bility is an adaptive response to environmental uncer-tainty. Generic strategies for success in flexibility

implementation should focus on uncertainty adaptation,uncertainty redefinition, and uncertainty reduction.

2.3. Metrics for flexibility

SCF is a complex, dynamic and multifaceted conceptviewed from a strategic and customer perspective (Moreand Babu 2009). Many supply chains compete byresponding quickly to the unique needs of differentcustomers. Both manufacturing and service firms candemonstrate flexibility. Manufactures distinguish amongseveral types of flexibility, including: mix flexibility, orthe ability to produce a wide range of different products;changeover design flexibility, or the ability to beginproduction of new product with minimal delay; designflexibility, or the ability to change the design of aproduct to accommodate specific customers; volume flex-ibility or the ability to produce whatever volume thecustomer needs. Different types of flexibility may requiredifferent operations and supply chain solutions. Firmsmust decide which types of flexibility are important totheir customers and adjust their operations and supplychain efforts accordingly. Flexibility has become particu-larly valuable in new product development.

Flexibility has a variety of dimensions attached withit. In the context of supply chain, flexibility needs to bedefined by identifying its measures clearly (Arshinder2012). Flexibility related measures, for example, design,process, mix flexibility etc. are usually used to measurethe supply chain performance.

In the background of the above literature review, thispaper attempts to propose a model to prioritise alterna-tives for SCF to accommodate various issues such assupplier, customer and manufacturer and their variousparameters that affect the SCF. For the sake of simplic-ity, a three-stage supply chain is considered. The actorsof the supply chain are named as customers, manufactur-ers and suppliers. To conduct the study, a case studymethodology is used, which is followed by an analyticalnetwork process (ANP) methodology in order to modeland prioritise flexibility issues in supply chains. In theliterature, there are some studies addressed to evaluatethe effect of flexibility configurations on supply chainplanning performance, such as lost sales (e.g. Albino,Izzo, and Kühtz 2002; Giannoccaro 2003). However,these studies do not clearly throw light on prioritisingvarious flexibilities used in supply chains to enable themto enhance their competitiveness.

3. Research methodology

For this study, qualitative research using multiple casestudy methodology is used in four ancillary automotivecompanies. This data collection technique was chosenfor three main reasons (Eisenhardt 1989). Firstly, the

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research was explorative as there is a lack of research onthe topic studied. Secondly, the case studies were consid-ered very useful for revealing possible contingencyeffects and for finding empirically grounded explanationsfor them. Finally, case studies have proven to be one ofthe most powerful research methods, particularly indevelopment theory (Voss, Tsikriktsis, and Frohlich2002). One of the major limitations of case research isthe limited generalisability of the findings (Eisenhardt1989). Four companies from different industrial contextsand backgrounds were chosen for this study in order toachieve a fairly generalisable set of results. The datawere collected by visiting the companies and interview-ing entrepreneurs and managers at different organisa-tional levels. Company documents and interviews withcompany consultants were used to collect additionalinformation and to better understand the data gathered.The interview protocol was dynamically adjusted tomaximise insights into the themes that emerged duringthe interviews (Eisenhardt 1989). The case studies weretested for construct validity and internal validity. Con-struct validity is the extent to which we establish correctoperational measures for the concepts being studied.To ensure construct validity, authors have looked formultiple sources of evidence for each of the importantelements in the propositions, using the important tech-nique of triangulation. Use of multiple – informants anduse of archival data helped authors crosscheck pertinentinformation and verify the reliability of data obtained. Todemonstrate the internal validity, the authors recordedevidence of other factors that might be alternative expla-nations for the observed patterns. Internal validity is theextent to which we can establish a casual relationship,whereby certain conditions are shown to lead to otherconditions, as distinguished from spurious relationships.Sites selected for the study are maximally different onimportant dimensions. The results obtained in case stud-ies are analysed using a mathematical technique ANPdeveloped by Saaty (1996). Saaty (1980) first proposedanalytic hierarchy process (AHP). AHP must satisfy thecharacteristic of independence among the criteria beforeit can proceed to decision making. However, given theproblems encountered in reality, a dependent and feed-back relationship will usually be generated among theevaluation criteria and such an interdependent relation-ship usually becomes more complex with the change inscope and depth of the decision-making problems.Therefore, Saaty (1996) developed a new analysismethod – ANP that simultaneously takes into accountsboth the relationships of feedback and dependence, anddeveloped the ANP. The merits of AHP in groupdecision-making are as follows: (i) both tangibles andintangibles, individual values, and shared values can beincluded in the decision process; (ii) the discussion ina group can be focused on objectives rather than on

alternatives; (iii) the discussion can be structured so thatevery factor relevant to the decision is considered; and(iv) in a structured analysis, the discussion continuesuntil relevant information from each individual memberin the group is considered and a consensus is achieved.

In addition to these merits of AHP, the ANP providesa more generalised model in decision-making withoutmaking assumptions about the independency of thehigher level elements from lower level ones and also ofthe elements within their own level. A two-way arrowamong different levels of attributes may graphicallyrepresent the interdependencies in an ANP model. Ifinterdependencies are present within the same level ofanalysis, a looped arc may be used to represent suchinterdependencies. Generally ANP is used in the cases ofstrategic decision-making especially where the decisionmaking-process costs more than the outcome of thedecision. As for the case of flexibility measurement inthe supply chain, the decision is strategic and willbroadly effect the operations of not just one, but manyorganisations linked in the supply chain network. Thus,the investment in making a decision that would pro-foundly effect the operation of the supply chain clearlyrequires intensive and robust managerial analysis. InANP, one important consideration in the effectivenessand efficiency of the decision framework begins at themodelling stages. The model and the various dependen-cies will determine the amount of effort required toarrive at a solution. This effort includes input fromdecision-makers as well as the mathematical approach tosolve the problem. Hence, it can be said that ANP meth-odology is a robust multiattribute decision makingtechnique. The Super Decisions Version 1.4.2. softwareis used for using ANP model in this study.

3.1. Case studies

Four companies were chosen for this study. Brief profileof these companies is given as below.

Company 1A1 Ltd. was established in October 2004. A1 Ltd. manu-factures auto parts. A1 Ltd. is spread in 6000 squaremetres area with covered area of 3000square meters.Total employees working in A1 Ltd. are about 1500.Main product of A1 Ltd. is axle, supplied to differentcustomers. Major customers of A1 Ltd. are Bajaj autopvt. Ltd. Suzuki motorcycle India pvt. Ltd. Honda motor-cycle and scooter India (Pvt.) Ltd. Hero motocorp Ltd.Machine tools used by A1 Ltd. are turning centers,machining center, forging centers, hobbing m/cs, and heattreatment furnace. It operates in four shifts from 8 am to4.30 pm (1st shift), 4 pm–12.30 am (2nd shift), 12–8.30am (3rd shift) and 9 am–5 pm general shift. According tocompany vision statement, its goals include maintaining

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leadership in the Indian automobile parts manufacturingindustry, creating customer delight. This organisation isaggressively working to align its operations along with itssupply chain partners for improving overall SCF.

Company 2A2 Ltd. was established in 1979 and its growth has beengenerated by a paramount concern to provide result-ori-ented services to customer’s specific needs. It isISO9001, QS 9000 and TS16949 certified company. It isan umbrella company of a reputed group. It offers singlewindow service through integrated facilities of AdvancedDesign Center with several seats of CAD/CAM/CAE,coupled with Mould flow analysis, Modern Tool Roomwith latest CNC machines from Europe and Japan withthe capability of making moulds and dies up to 4 tonsand injection moulding facilities to make in house plasticcomponents as small as 1 g and up to as big as 4000 g.

The company is supplier to the Automotive, Electron-ics and White Goods Industries. Main Customers of theorganisation are General Motors, Ford, Delphi, Suzuki,Daewoo, Panasonic, Xerox, Canon, Whirlpool, LG Elec-tronics, Gillette, Electrolux. It has export markets in Eur-ope and America. Primary objective of the organisation isto produce plastic components of global excellence, pre-cision, durability and elegance conforming to end productrequirements. This company is member of Automotivecomponent manufacturers association of India, plasticexport promotion council, Indo-German chamber of com-merce, PHD (progress harmony development) chamber ofcommerce and industry, Federation of Indian chambers ofcommerce and industry, India trade promotion organisa-tion. The element of flexibility, coupled with fastresponses, innovation, quality consciousness and respon-sibility towards the customers, has enabled the companyto always keep its business partners happy and satisfiedall over the world. This organisation wants to improve itsperformance in terms of cost, quality and delivery timeby improving coordination in the supply chain.

Company 3A3 Ltd. is family run company, which started its opera-tion in 1966 with vision of becoming a supplier of QualityMachined Parts for the Original Equipment manufactur-ers. In 1977 with the coming of Majestic Auto Ltd. a unitof Hero Group, there was a strong need of good QualityParts Suppliers. The company becomes an Automaticchoice for this company due to its commitment towardsquality and on time delivery. Today, the company is aleading Vendor of components for two and four-wheelerOEMS in India like Hero-Honda, Honda, Maruti, TVS,Bajaj, LML, Kirloskar with market share 40% and currentyear growth in all respect is targeted to be 25% higherthan the previous year. The company’s products includeparts of electricals, Nuts, Washers and Bolts apart from

two and four wheeler components and Bright Bars ofvarious sizes and shapes. The company has got ISO9001:2000 Certification from BVQI (bureau veritas qual-ity international), which shows the company’s commit-ment towards quality.

One of the biggest advantages that the companyenjoys is the wide variety of machines. As a result, thecustomer can get a wide variety of products from thiscompany. It supply pipe assemblies, hose assemblies,tubings, multi layer hoses, stainer assemblies, mouldedcomponents, pipes, and so on for all kinds of worldwideautomotives. For this improving flexibility across thesupply chain is top priority.

Company 4A4 Ltd. was established in 1989. It is manufacturinghorns for all kind of automobiles with present marketshare of 37% in OEM’s. Present products include Diskhorn of various diameters like 65, 70, 82 and 95mm. Fur-ther diversification into various new products like buzzer,pressure horns, etc is also being done. Sales turnover oforganisation in 2010–011 was Rs. 80 Crore. It has gotcertificates like QS9000, TS16949, and ISO14001. It hasgot technical collaboration with largest number of hornmanufacturing European organisation. Major customersare Bajaj, Ford, GM, Honda, LML, Maruti, M and M,Suzuki Indonesia, Telco, etc. Vision of this organisationis to become best suppliers of horns in Asia, to be theno-1 quality horn manufacturer in India, to achievelevelised production for 100% on time delivery. Toachieve these goals, this organisation is giving focus onimproving flexibility of its supply chain.

Data collected from the above case studies aremodelled and analysed by ANP technique using SuperDecision Software.

3.2. Super Decision Software

Development of the ANP model is based on the study ofsupply chain performance measures. Proposed ANPmodel incorporates main supply chain performance con-cerns. Before presenting the details of the ANP modelproposed, a brief introduction of the software used todevelop the model is given. The software used is calledas the Super Decisions Version 1.4.2. It was developedby Saaty (2003). The software implements the ANPapproach. In Figure 1, an abstract representation of anANP model is given. There are clusters, nodesand arrows that define the model in Super Decisions.The clusters hold a group of nodes. Nodes represent thedecision elements. Arrows, on the other hand, define thedependencies among nodes. Two-directional arrows showtwo-way interdepencies among elements of the network.Figure 1 defines the clusters, nodes and theirrelationships with each other; it shows how the Super

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Decisions Software represent them. As seen in Figure 1there are dependencies between nodes in differentclusters as well as nodes in the same cluster. If there aredependencies among nodes in different clusters, SuperDecisions software generates an arrow between clusters(Figure 1). That is called the ‘Outer dependence’. Ifthere are dependencies among nodes in the same cluster,the software represents it by attaching an arrow from acluster to itself (Figure 1(b)). This is called the ‘Innerdependence’. Note that, in an AHP model, there are noinner-dependencies (Saaty 1980). The pairwise compari-sons are carried out depending on the lines connectingnodes and clusters.

The following are the general steps used fordeveloping the ANP model for evaluating the alternativeconnection types using the Super Decisions Software:

(1) Generate clusters representing the alternative con-nection types and the important aspects of supplychain performance measures.

(2) Generate nodes (i.e. the decision criteria that areused to evaluate the alternative and place them inappropriate clusters.

(a)

(b)

Figure 1. Abstract views of a Super Decisions model.

Figure 2. ANP model of flexibility measurement in SCM.

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(3) Develop dependencies among criteria and thealternatives.

(4) Do pairwise comparisons.(5) Solve the model using solution module of the

Super Decisions Software.(6) Discuss the results.(7) Give the conclusion.

4. Model and dependency

In this section study will try to develop the ANP modeland will do pair wise comparisons.

4.1. ANP model

Firstly the clusters are developed. There are four clustersin this model. The first cluster is ‘Alternatives’ which is

Table 2. Description of clusters and nodes.

Clusters Nodes

C1: Alternatives E1.1 Manufacturing flexibility: The in house flexibility of the firm to change as per the design changesE1.2 Supplier flexibility: The ability of the supplier to change according to the requirements of design

requirements within minimum time (Garavelli 2003)E1.3 Customer flexibility: The total options available to the customer at the time of purchasing the product

C2: Manufacturingissues

E2.1 Product/Process Design: The ability of the design and production department to change as per thedemands of the market

E2.2 Capacity Planning: The role of planning department to plan the new targets according to the capabilityof the man, material, machines availability in the plant

E2.3 Logistics and Inventory control: the logistics and inventory control role at the time of varying demandsof orders

E2.4 Inspection and Quality control: The role of inspection and quality department to provide the serviceefficiently according to the varying requirements of the departments (Gunasekarana, Patel, andMcGaugheyc 2004)

E2.5 Financial planning and analysis: The financial planning and investment analysis done before investing inthe big project. The financial suitability decides the project will be affordable at commercial level or not

C3: Supplier issues E3.1 Quantity provided: The total quantity provided in minimum time to satisfy the requirements of the plantE3.2 Cost: The competence of the cost provided by the supplier.E3.3 Quality: The ability of the supplier to provide required quality product to the firmE3.4 Reliability: The reliability by which the supplier giving the order, it can be Judged by past experience.

The old suppliers get the importance in this areaC4: Customer issues E4.1 Nature of customer: The nature of customer depends upon the class of the customer. The customer who

is interested in buying Mercedes will not buy the low cost car, and customer who is interested in buyingany low cost car will not even think about Mercedes or any other high segment car

E4.2 Type of customer: The individual, private firm, institute, trust, government, cooperative body, publicsector, bank all these are type of customers whose attitude is differ from one to one

E4.3 Quantity: The total consumption of the product by the customer

Table 1. Table representing dependency for the ANP model.

Wrt) E 1.1 E 1.2 E 1.3 E 2.1 E 2.2 E 2.3 E 2.4 E 2.5 E 3.1 E 3.2 E 3.3 E 3.4 E 4.1 E 4.2 E 4.3

E 1.1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1E 1.2 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1E 1.3 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1E 2.1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1E 2.2 1 1 1 1 0 1 0 1 1 0 0 1 0 0 1E 2.3 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1E 2.4 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1E 2.5 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1E 3.1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1E 3.2 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1E 3.3 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1E 3.4 1 1 1 0 0 0 1 0 1 1 1 0 0 0 0E 4.1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0E 4.2 1 1 1 1 1 1 0 1 0 1 0 0 0 0 1E 4.3 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0

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Supply chain performance (flexibility) enablers. It consistof three nodes, that is, ‘Supplier’s flexibility’, ‘Manufac-turing flexibility’ and ‘Customer’s flexibility’. In thedesign of supply chain, focus is on these alternatives.The second cluster is ‘Manufacturing Issues’ which

Table 3. List of tables.

S. No. With respect to node Cluster

1 Customer flexibility (C.F.) Customer issue2 Customer flexibility (C.F.) Manufacturing issue3 Customer flexibility (C.F.) Supplier issue4 Manufacturing flexibility (M.F.) Customer issue5 Manufacturing flexibility (M.F.) Manufacturing issue6 Manufacturing flexibility (M.F.) Supplier issue7 Supplier flexibility (S.F.) Customer issue8 Supplier flexibility (S.F.) Manufacturing issue9 Supplier flexibility (S.F.) Supplier issue10 Nature of customers Alternatives11 Nature of customers Manufacturing issue12 Quantity Alternatives13 Quantity Manufacturing issue14 Quantity Supplier issue15 Type of customers Alternatives16 Type of customers Manufacturing issue17 Capacity planning Alternatives18 Capacity planning Manufacturing issue19 Capacity planning Supplier issue20 Financial planning & analysis Alternatives21 Financial planning & analysis Customer issue22 Financial planning & analysis Manufacturing issue23 Financial planning & analysis Supplier issue24 Inspection & quality control Alternatives25 Inspection & quality control Customer issue26 Inspection & quality control Manufacturing issue27 Inspection & quality control Supplier issue28 Logistics & inventory control Alternatives29 Logistics & inventory control Manufacturing issue30 Product/process design Customer issue31 Product/process design Manufacturing issue32 Cost Customer issue33 Cost Manufacturing issue34 Cost Supplier issue35 Quality Alternatives36 Quality Customer issue37 Quality Manufacturing issue38 Quality Supplier issue39 Quantity provided Alternatives40 Quantity provided Customer issue41 Quantity provided Manufacturing issue42 Quantity provided Supplier issue43 Reliability Alternatives44 Reliability Supplier issue

Table 4. Cluster pairwise comparison.

Inconsistency Manufacturing issues Supplier issues

Customer issues ← 6.0 ← 4.0Manufacturing issues ↑ 4.0

Table 5. Node pairwise comparison.

InconsistencyInspection andquality control

Logistics andinventorycontrol

Product/ProcessDesign

Capacityplanning

← 7.0 ← 6.0 ← 7.0

Inspection andqualitycontrol

↑ 3.0 ← 1.0

Logistics andinventorycontrol

← 4.0

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includes five nodes that is, ‘Product/Process Design’,‘Capacity Planning’, ‘Logistic and Inventory control’,‘Inspection and Quality Control’ and ‘Financial Planningand analysis’. Similarly other two clusters are also devel-oped, that is ‘Supplier Issues’ and ‘Customer Issues’.These clusters are interdependent with each other. Thisdependency is denoted by arrows in between clusters.Arrows show the dependency of the clusters for exampleX→Y means that the nodes of cluster Y are depends onnodes of cluster X. Those clusters that have no arrowinput are source clusters and which have no arrow leavesare known as sink clusters and those which have arrowboth enter and exit are known as transient cluster. Someclusters have loop that connect them to themselves arecalled interdependent clusters. Model for present study isshown in Figure 2. In this model three clusters are inter-dependent, that is, ‘manufacturing issues’, ‘supplierissues’ and ‘customer issues’. Table 1 shows the interde-pendence relationship drawn from the case studies andfinally Figure 2 shows the proposed model for SCF mea-sure based on the results obtained from Table 1. Clusters,nodes and their descriptions are described in Table 2.

The ANP model is completed by developing the con-nections among cluster and nodes. These connectionsdetermine how the pairwise comparisons are to be madein the network. The objective of the model is to prioritiesthe alternatives for SCF. Connections are determined andpresented using the binary values in Table 1. The criteriain the rows of Table 1 are evaluated with respect to crite-ria in the columns of Table 1. The table contains values of0s and 1s. The 1s in the column of Table 1 determinewhich criteria in the rows are pairwise compared withrespect to that column. For example, 1s in the column ofE1.1 mean E2.1 through E4.3 are pairwise compared withrespect to E1.1 (i.e. Suppliers Flexibility.).

4.2. Pairwise comparisons in the ANP model

According to the connections developed in the model, allpairwise comparisons are done. ANP uses a verbal scaledeveloped by Saaty (1980), which enables the experts toincorporate subjectivity and experience. The comparison

is based on expert’s opinion (Saaty 1980). In the presentstudy the decision on pairwise comparisons are made onthe basis of majority of the opinion observed during thecase studies. For the proposed model a series of pairwisecomparison matrix is developed. There are four tablesfor cluster comparisons and 44 tables for node compari-sons as described in Table 3. Due to space constraintonly one cluster pairwise comparison table is shown asTable 4, that is, ‘cluster comparison for alternatives’ andone for node pairwise comparison table is shown asTable 5, that is, ‘comparison with respect to Financialplanning and analysis node in manufacturing issues’.Table 6 shows the priorities and the inconsistancy indexfor Table 5. In the same way, other 44 tables can also begenerated by software.

Each pairwise comparison matrix has an associatedpriority vector or vector of weights (Saaty 1996). Thesepriorities are derived from the pairwise comparisons andentered in the unweighted supermatrix. There are threesupermatrices associated with each network: theUnweighted Supermatrix, the Weighted Supermatrix andthe Limit Supermatrix (Saaty 1996). Supermatrices arearranged with the clusters in alphabetical order acrossthe top and down the left side, and with the elementswithin each cluster in alphabetical order across the topand down the left side. The unweighted supermatrixcontains the local priorities derived from the pairwisecomparisons throughout the network. The weightedsupermatrix is obtained by multiplying all the elementsin a component of the unweighted supermatrix by thecorresponding cluster weight.

ANP uses supermatrix to deal with the relationshipof feedback and interdependence among the criteria. Ifno interdependent relationship exists among the criteria,the pairwise comparison value would be 0. In contrast, ifan interdependent and feedback relationship existsamong the criteria, then such value would no longer be0 and an unweighted supermatrix M will be obtained. Ifthe matrix does not conform to the principle of columnstochastic, the decision maker can provide the weights toadjust it into a supermatrix that conforms to the principleof column stochastic, and it will become a weighted

Table 6. Priorities.

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Table

7.Unw

eigh

tedsuperm

atrix.

C.F.

M.F.

S.F.

Nature�

Quantity

Type

of�

Capacit�

Financi�

Inspect�

Log

isti�

Produ

ct�

Cost

Quality

Quantity

�Reliabi�

C.F.

0.00

000

0.00

000

0.00

000

0.79

573

0.08

522

0.75

825

0.07

325

0.07

037

0.08

130

0.06

111

0.00

000

0.33

333

0.75

376

0.77

659

0.78

377

M.F.

0.00

000

0.00

000

0.00

000

0.07

895

0.64

422

0.09

051

0.67

080

0.77

659

0.78

377

0.76

263

0.00

000

0.33

333

0.08

274

0.07

037

0.13

493

S.F.

0.00

000

0.00

000

0.00

000

0.12

532

0.27

056

0.15

125

0.25

596

0.15

304

0.13

493

0.17

626

0.00

000

0.33

333

0.16

350

0.15

304

0.08

130

Nature�

0.57

822

0.06

039

0.09

585

0.00

000

0.00

000

0.00

000

0.00

000

0.09

855

0.68

698

0.00

000

0.07

235

0.16

667

0.00

000

0.12

503

0.00

000

Quantity

0.26

267

0.72

969

0.59

567

0.00

000

0.00

000

1.00

000

1.00

000

0.74

501

0.12

654

1.00

000

0.64

244

0.83

333

0.16

667

0.87

497

0.00

000

Typ

eof

�0.15

910

0.20

992

0.30

848

0.00

000

0.00

000

0.00

000

0.00

000

0.15

644

0.18

648

0.00

000

0.28

521

0.00

000

0.83

333

0.00

000

0.00

000

Capacit�

0.19

690

0.54

965

0.44

151

0.05

163

0.48

695

0.04

362

0.00

000

0.67

032

0.12

099

0.76

924

0.63

957

0.00

000

0.00

000

0.62

950

0.00

000

Financi�

0.17

798

0.03

878

0.09

321

0.42

888

0.13

946

0.28

607

0.19

981

0.00

000

0.1149

20.08

400

0.05

822

0.77

317

0.00

000

0.05

218

0.00

000

Inspect�

0.26

732

0.07

834

0.04

247

0.13

576

0.07

052

0.00

000

0.00

000

0.07

186

0.00

000

0.00

000

0.08

938

0.13

916

0.85

714

0.06

967

1.00

000

Log

istic�

0.12

687

0.12

946

0.13

806

0.06

683

0.30

308

0.06

231

0.68

334

0.18

975

0.00

000

0.00

000

0.21

284

0.00

000

0.00

000

0.18

276

0.00

000

Produ

ct�

0.23

093

0.20

377

0.28

476

0.31

689

0.00

000

0.60

799

0.1168

50.06

806

0.76

409

0.14

676

0.00

000

0.08

767

0.14

286

0.06

589

0.00

000

Cost

0.29

498

0.1114

50.22

518

0.00

000

0.00

000

1.00

000

0.00

000

0.87

500

0.00

000

0.00

000

0.00

000

0.00

000

0.06

337

0.71

849

0.07

895

Quality

0.09

638

0.26

233

0.10

889

1.00

000

0.00

000

0.00

000

0.00

000

0.00

000

0.78

539

0.00

000

0.00

000

0.74

291

0.00

000

0.22

749

0.79

573

Quantit�

0.07

146

0.55

176

0.6119

60.00

000

0.88

889

0.00

000

0.87

500

0.12

500

0.14

882

1.00

000

0.00

000

0.18

671

0.16

697

0.00

000

0.12

532

Reliabi�

0.53

717

0.07

446

0.05

398

0.00

000

0.11111

0.00

000

0.12

500

0.00

000

0.06

579

0.00

000

0.00

000

0.07

038

0.76

966

0.05

402

0.00

000

Table

8.Weigh

tedsuperm

atrix.

C.F.

M.F.

S.F.

Nature�

Quantity

Type

of�

Capacit�

Financi�

Inspect�

Log

isti�

Produ

ct�

Cost

Quality

Quantit�

Reliabi�

C.F.

0.00

000

0.00

000

0.00

000

0.03

421

0.00

366

0.02

832

0.00

243

0.00

233

0.00

270

0.00

203

0.00

000

0.0112

30.02

539

0.02

616

0.02

855

.F.

0.00

000

0.00

000

0.00

000

0.00

339

0.02

769

0.00

338

0.02

225

0.02

576

0.02

600

0.02

530

0.00

000

0.0112

30.00

279

0.00

237

0.00

491

S.F.

0.00

000

0.00

000

0.00

000

0.00

539

0.0116

30.00

565

0.00

849

0.00

508

0.00

448

0.00

585

0.00

000

0.0112

30.00

551

0.00

516

0.00

296

Nature�

0.39

419

0.04

117

0.06

534

0.00

000

0.00

000

0.00

000

0.00

000

0.00

771

0.05

375

0.00

000

0.00

756

0.01

250

0.00

000

0.00

938

0.00

000

Quantity

0.17

907

0.49

745

0.40

608

0.00

000

0.00

000

0.13

108

0.07

824

0.05

829

0.00

990

0.07

824

0.06

713

0.06

251

0.01

250

0.06

564

0.00

000

Typ

eof

�0.10

847

0.14

311

0.21

030

0.00

000

0.00

000

0.00

000

0.00

000

0.01

224

0.01

459

0.00

000

0.02

980

0.00

000

0.06

251

0.00

000

0.00

000

Capacit�

0.01

613

0.04

503

0.03

617

0.03

832

0.36

138

0.02

813

0.00

000

0.44

943

0.08

112

0.51

576

0.57

274

0.00

000

0.00

000

0.12

527

0.00

000

Financi�

0.01

458

0.00

318

0.00

764

0.31

828

0.10

349

0.18

447

0.13

397

0.00

000

0.07

705

0.05

632

0.05

213

0.15

386

0.00

000

0.01

038

0.00

000

Inspect�

0.02

190

0.00

642

0.00

348

0.10

075

0.05

234

0.00

000

0.00

000

0.04

818

0.00

000

0.00

000

0.08

004

0.02

769

0.17

057

0.01

386

0.21

514

Log

istic�

0.01

040

0.01

061

0.0113

10.04

960

0.22

492

0.04

018

0.45

816

0.12

723

0.00

000

0.00

000

0.19

059

0.00

000

0.00

000

0.03

637

0.00

000

Produ

ct�

0.01

892

0.01

670

0.02

333

0.23

517

0.00

000

0.39

207

0.07

834

0.04

564

0.51

231

0.98

400

0.00

000

0.01

745

0.02

843

0.01

311

0.00

000

Cost

0.06

972

0.02

634

0.05

322

0.00

000

0.00

000

0.18

672

0.00

000

0.19

085

0.00

000

0.00

000

0.00

000

0.00

000

0.04

387

0.49

741

0.05

909

Quality

0.02

278

0.06

200

0.02

573

0.21

488

0.00

000

0.00

000

0.00

000

0.00

000

0.17

130

0.00

000

0.00

000

0.51

431

0.00

000

0.15

749

0.59

556

Quantit�

0.01

689

0.13

040

0.14

463

0.00

000

0.19

101

0.00

000

0.19

085

0.02

726

0.03

246

0.21

811

0.00

000

0.12

926

0.1155

90.00

000

0.09

380

Reliabi�

0.12

696

0.01

760

0.01

276

0.00

000

0.02

388

0.00

000

0.02

726

0.00

000

0.01

435

0.00

000

0.00

000

0.04

873

0.53

283

0.03

740

0.00

000

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supermatrix M. We then get the limited weightedsupermatrix M and allow for gradual convergence of theinterdependent relationship to obtain the accurate relativeweights among the criteria.

M� ¼ limk!1

Mk

Final results of the ANP model obtained from thesoftware are directly shown in Tables 7–10.

4.3. Sensitivity analysis

This section provides insights into the sensitivity analysisof the proposed ANP model of flexibility measurementin SCM.

Figure 3 shows that priorities of different alternativeswill change with the change in the priority of customerflexibility. So the proposed model is sensitive about cus-tomer flexibility. It is sensitive about other alternativesalso. However, it is not sensitive about any node, asshown in Figure 4; the priorities of alternatives remainsconstant as these are not varying with the change in pri-ority of any node.

Figure 5 shows the sensitivity analysis of the modelabout three nodes and indicates that the priorities of thealternatives remain constant with the change in the prior-ities of nodes. From the above results of the sensitivityanalysis, it can be concluded that the proposed model isnot sensitive about the nodes, where as it shows sensitiv-ity about alternatives of the model.

5. Result and discussion

Tables 7–10 are the output directly obtained from thesoftware, each table has its own significance. Table 7gives the unweighted priorities which is converged toTable 8 which gives weighted priorities. The Table 9 isobtained by limiting the weighted matrix that is,fM� ¼ limk!1 Mkg. Table 10 shows that the sequenceof importance of the alternatives as manufacturingflexibility, customer flexibility and supplier flexibilityrespectively. In supermatrix, there are some zero whichindicate that corresponding row criteria does not haveany relation with its column criteria. Higher the valuemeans more the priority of row criteria over columncriteria and vise versa.

From the results, it is observed that the manufactur-ing flexibility is most important parameter of the SCFfollowed by customer and supplier flexibility. It meanssupply chain should work to focus on the requirementsof manufacturing flexibility by building infrastructureand technical capability in order to make supply chainsflexible from the manufacturing perspective. This obser-vation is found in line with the results of the Rush andTa

ble9.

Lim

itmatrix.

C.F.

M.F.

S.F.

Nature�

Quantity

Typ

eof�

Capacit�

Financi�

Inspect�

Log

isti�

Produ

ct�

Cost

Quality

Quantit�

Reliabi�

C.F.

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

0.01119

M.F.

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

0.01

373

S.F.

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

0.00

600

Nature�

0.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

50.0114

5Quantity

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

0.05

954

Type

of�

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

0.01

576

Capacit�

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

0.17

503

Financi�

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

0.06

497

Inspect�

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

0.05

539

Log

istic�

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

0.12

123

Produ

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0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

0.07

272

Cost

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

0.08

147

Quality

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

0.12

069

Quantit�

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

0.10

972

Reliabi�

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

0.08

111

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Hoffman 1992 and some of the important benefits ofmanufacturing flexibility are listed in Table 11.

Another interesting observation obtained from themodel is that customer flexibility has been given moreimportance than the supplier flexibility. This result con-firms the observation of Bhagwat and Sharma (2007),(2009) multi-criteria objective model for SCM perfor-mance evaluation, in which customer perspective wasrated as second most important perspective after the finan-cial perspective. There is also interrelationship betweenthe supplier and manufacturer requirements which shouldmatch for customer satisfaction and long term businesssurvival. The different strategies are required to fulfil thedifferent supply chain requirements, which can beachieved by using better management techniques,improved processes that can reduce the cost and improvethe quality and delivery of the product and services. Fur-ther, the results of sensitivity analysis show that the sug-gested model is sensitive about alternatives and hardlyshows any sensitivity about the nodes of the model.

Table 10. Synthesized priorities table.

Table 11. Some of the achieved benefits of a FlexibleManufacturing System (Rush, Hoffman, and Bessant 1992).

Performancemeasure

Effect onperformancemeasure

Proportion of respondingcompanies reporting effect

(%)

Lead time 30–60%saving

42

Throughout 60–70%increase

65

Inventory Over 70%reduction

100

Utilisation 40–400%improvement

39

Set-up times 50–90%reductions

39

Quality Improved 64Responsiveness

to demandIncreased 87

Figure 3. Sensitivity analysis.

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6. Concluding remarks and implications of the study

Flexibility cannot be studied on the aggregate level(i.e. high vs. low level). For example, constantly changingcustomer demand would lead to short product lifecycles

and creates a great degree of uncertainty in a firm’s opera-tions. This type of uncertainty, in turn, drives firms todevelop new-product flexibility but not volume flexibility.From the perspective of the supply chain, suppliers clearlyplay an important role in moving goods through the whole

Figure 4. Sensitivity analysis.

Figure 5. Sensitivity analysis.

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chain in less time and at lower cost. For instance, Dellspeeds up production and delivery cycle time by perform-ing several collaborative activities with suppliers, such assharing demand information and pre-reserving factorycapacity. The major finding of this study is that the manu-facturing flexibility is the most important parameter of theSCF followed by customer and supplier flexibility.

As the proposed model solves the problem as perthe priority of the goals suggested by the ANP, itmakes the solution very sensitive to the variations inthe weighting procedures. If, the pairwise comparisonsof the ANP model is not made correctly, then theweights will be generated incorrectly, which directlyaffect the outcome of the model. This is the majorlimitation observed of this work. Hence, it is sug-gested to give ranks judiciously while comparing thedifferent criteria and alternatives pair-wise in the ANPmethodology. Further, as these analysis and findingsare based on only automotive supplier companiesoperational in northern part of India, it necessitatescaution in interpreting the results. As the companiesselected for the study are typical of developing coun-try business, the findings of this research may notreadily extensible to the companies of differentregions and of different sizes. Future research couldexamine these results using a different sample set indifferent country settings. Future studies could alsovalidate these findings by replicating this study withmore types of enterprises. All companies chosen forthis study are based on single-country context, andhence, further additional research is recommended toexamine if the findings could be extended to compa-nies in other developing/developed nations. Secondly,the present study focused only on the operations ofancilliary original equipment supplying units andfurther research is required targeting the large compa-nies to examine whether the results are the same ordifferent.

The article has contributed to important issues ofSCF theory and practices. These can be summarised asbelow.

• This paper contributes to flexibility evaluation ofsupply chains. It points out the importance of keyplayers in the flexibility measurement of SCM,and the nature of roles they need to play.

• The paper provides insights to prioritise flexibilitiesin supply chain metrics using ANP methodology.

• It further helps firms to focus on the most criti-cal flexibility measures while giving them thetop priority based on the results of the ANPanalysis in SCM performance measurement.

• The ANP sensitivity analysis also providesimportant insights into prioritising flexibility insupply chains.

• This paper also overcomes shortcomings ofpreviously proposed a multi-objective goalprogramming model based on the results of theAHP analysis for SCM performance measurement.

AcknowledgementsAuthors thank anonymous reviewers for their constructive andhelpful comments for improving quality and content of thepaper.

Notes on contributorsDr Rajesh K. Singh is Associate Professor atIndian Institute of Foreign Trade (IIFT),Delhi, India. His research interest includesCompetitiveness Strategies, Small BusinessManagement, Quality Management andSupply chain management. He has about 80research papers published in reputedinternational/national journals and conferences.He has published research papers in journals

such as Industrial Management Data Systems, SingaporeManagement Review, International Journal of Productivity andPerformance Management, Benchmarking: An InternationalJournal, Journal of Modelling in Management, CompetitivenessReview: An International Business Journal, Global Journal ofFlexible Systems and Management, International Journalsof Productivity and Quality Management, International Journalof Services and Operations Management, International Journal ofAutomotive Industry and Management, International Journal ofLogistic Systems Management, IIMB Management Review andProductivity Promotion. He is also on editorial board of somereputed journals.

Milind Kumar Sharma has taught manysubjects related to production & industrialengineering and operations management.Prior to joining the Department ofProduction & Industrial Engineering, M.B.M. Engineering College, J.N.V. University,Jodhpur in 1998, he has served in industryfor four years. He has been awardedresearch projects under the SERC fast track

scheme for young scientist by Department of Science &Technology (DST), Career Award for Young TeacherScheme by the All India Council for Technical Education(AICTE) and University Grants Commission (UGC), NewDelhi, India. His areas of research interests includemanagement information system, performance measurement,supply chain management and small business development. Hehas published research papers in Production Planning andControl, Computers & Industrial Engineering, InternationalJournal of Productivity and Quality Management, Journal ofManufacturing Technology Management, International Journalof Globalization and Small Business, International Journal ofEnterprise Network Management, Measuring BusinessExcellence.

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