6.supply chain 2

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6.supply chain 2

  1. 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htmBIJ18,6 Supply chain collaboration performance metrics: a conceptual framework856 Usha Ramanathan Newcastle Business School, Northumbria University, Newcastle upon Tyne, UK Angappa Gunasekaran Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts, North Dartmouth, Massachusetts, USA, and Nachiappan Subramanian Department of Mechanical Engineering, Thiagarajar College of Engineering, Madurai, India Abstract Purpose – Successful implementation of supply chain collaboration (SCC) by Wal-Mart has encouraged many manufacturing companies, such as Procter & Gamble, Hewlett-Packard Co, and West Marine Products Inc., to initiate collaboration. Subsequently, collaboration between suppliers and retailers has become a common practice in many recent supply chains. However, measuring the benefits of collaboration is still a big challenge. Based on supply chain literature and practice, this paper aims to propose a conceptual framework and a standard set of metrics to evaluate the performance of SCC. Design/methodology/approach – The authors discuss two case studies to validate the proposed model. The case study discussions are appropriate to understand the usage of different performance metrics in initial and advanced stages of collaboration. Findings – From the case study it is recognized that the collaborating members in the supply chain are not able to visualise all possible benefits of collaboration. To surmount this issue, the paper proposes a framework to study the performance of companies involved in initial and advanced stages of collaboration. Originality/value – The classification suggested in this paper on different stages of collaboration and related metrics can guide researchers and practitioners in manufacturing companies to evaluate the performance of SCC. Keywords Collaboration, Performance metrics, Supply chain, Supply chain management, Manufacturing industries Paper type Research paper 1. Introduction Supply chain involves raw material and component suppliers, manufacturers, distributors, and retailers until the finished products reach end customers. It has been generally agreed that the performance of the entire supply chain could be improved through collaboration (Barratt and Oliveira, 2001; Seifert, 2003). The literature reveals that businesses have beenBenchmarking: An International collaborating in general for several decades in many different forms for varied purposes.Journal Some of the purposes of collaboration are to improve overall business performance,Vol. 18 No. 6, 2011pp. 856-872 reduce cost, increase profit, and improve forecast accuracy (McIvor et al., 2003; McCarthyq Emerald Group Publishing Limited and Golicic, 2002; Matchette and Seikel, 2004). Lucrative benefits of collaboration can1463-5771DOI 10.1108/14635771111180734 encourage many supply chain members to initiate the process of collaboration.
  2. 2. In general, businesses with similar objectives work closer to achieve excellence in common Supply chainsupply chain processes such as planning, forecasting, and replenishment. The extent and performanceintensity of collaboration may vary greatly based on business objectives, which in turndecide the success of supply chain collaboration (SCC) (Larsen et al., 2003, ECR Europe, metrics2002). Owing to cost involved in initiating collaboration, sometimes SCC will be moreviable to suppliers than buyers (Chen et al., 2007) or more viable to buyers than suppliers(Dong and Xu, 2002). Hence, in the process of SCC, each business needs to weigh their 857current scenario with past and future. This may include periodic review of performanceof collaboration using a standard set of metrics. Periodic reviews can help improvecollaboration agreement with other supply chain members regularly. In the literature of SCC, many performance measures have been suggested includingcost, benefits such as profit, lead time, customer satisfaction, inventory, forecast accuracy,etc. (Chang et al., 2007; Kim and Oh, 2005; Angerhofer and Angelides, 2006; Simatupangand Sridharan, 2005). Majority of supply chain metrics in the literature are measures ofinternal performance of a firm (Lambert and Pohlen, 2001; Barratt, 2004). If information onperformance of supply chain is shared with other partners, then it could possibly improvethe overall efficiency of the supply chain. Simatupang and Sridharan (2004a, b) haveproposed a collaborative performance system consisting of three cycles with respect tocollaborative enablers to improve operational performances. On reviewing the literature ofSCC, we have identified that the performance metrics for SCC were not given adequateimportance as compared to general supply chain performance. We have also found that there is no specific set of metrics readily available to supplychain members to measure their performance in SCC at pilot and advanced stages ofcollaboration. Hence, we believe that identifying the key metrics to measure theperformance of SCC from suppliers’ or buyers’ viewpoint is indispensable. Therefore, in thispaper, we propose a conceptual framework to measure performance of SCC at initial andadvanced stages of partnership. In line with supply chain literature of collaboration andperformance measurement we have developed a conceptual model for performance metrics. The major objective of this paper is to suggest a specific set of metrics at early andadvanced stages of collaboration. To facilitate this study, we have attempted tounderstand the current status of collaboration in SC through literature review. Then wehave conducted two case studies as this approach will be appropriate to have an in-depthknowledge on the selected cases in achieving our research objective (Yin, 1994). For casestudy, we have considered two manufacturing companies at two different stages ofcollaboration. One of the case study companies is practicing “collaborative planningforecasting and replenishment (CPFR)” for the past four years, whereas the othercompany is recently involved in CPFR. Choice of these two cases has been instrumentalin relating our literature findings to match with initial and advanced stages ofcollaboration. Further in this article SCC with respect to literature review refers to acombination of different supply chain practices, whereas in the context of case study,SCC is specific to CPFR practice. This paper starts with an introduction to role of collaboration in supply chain and areview of literature related to performance metrics of SCC. Section 3 proposes variousfunctional drivers and enhancers for constructing SCC. This section also describesa conceptual framework on performance metrics of SCC. Section 4 describes andanalyses the SCC at two case companies that practice collaboration at two differentstages. The paper concludes with some scope for future research.
  3. 3. BIJ 2. SCC and performance18,6 2.1 Role of collaboration in supply chain In order to improve supply chain processes and to gain support from other supply chain partners, several supply chain management practices such as vendor managed inventory (VMI), efficient consumer response (ECR), continuous replenishment (CR), and electronic data interchange have been suggested in the literature. In VMI (developed in858 the mid-1980s) the customer’s inventory policy and replenishment process are managed by vendor or supplier. However, VMI’s supply chain visibility has not been found powerful enough to avoid the bullwhip effect completely (Barratt and Oliveira, 2001). Here, bullwhip effect refers to amplification of demand fluctuations from downstream to upstream in supply chain. This drawback of VMI has been successfully modified in the later versions in different sectors and the derived versions are termed as ECR, CR, etc. Ever increasing supply chain demands have led to the invention of CPFR (introduced in late 1990s), another supply chain management practice, which incorporates planning, forecasting, and replenishment under a single framework (Fliedner, 2003). CPFR has been introduced as a pilot project between Wal-Mart and Warner-Lambert in the mid-1990s aiming to develop a supply chain responsive to customer demand. CPFR is a new collaborative business perspective that combines the intelligence of multiple trading partners in the planning and fulfilment of customer demand by linking sales and marketing information (VICS, 2002). The CPFR framework encourages all partners to share sales, inventory, forecast, and all related information to improve forecast accuracy (VICS, 2002). Such information sharing believes to avoid bullwhip effect (Lee et al., 2000; Cachon and Fisher, 2000). This information exchange is made possible through advanced technology in many retail sectors (for example, Wal-Mart’s electronic point of sale data is made available to all its collaborating partners). Quality of information being exchanged among SC partners’ influences the supply chain processes and forecast accuracy (Forslund and Jonsson, 2007). Some of the benefits of SCC such as cost reduction, inventory reduction and forecast accuracy are revealed through many case studies (Smaros, 2007; Danese, 2007) and some mathematical models (Lee et al., 2000; Aviv, 2007); but the indicators for measuring the benefits of collaborations are not clear and precise. We have endeavoured to group the performance metrics of SCC, identified from the literature, in the next section. 2.2 Performance metrics for SCC The primary objective of initiating collaboration in any supply chain is to enhance the overall performance of supply chain and this can be achieved through the collective effort of all supply chain members (Angerhofer and Angelides, 2006). Barratt (2004) identified managing change, cross-functional activities, process alignment, joint decision making, and supply chain metrics as essential elements for successful SCC. In these five elements, the first two are related to initial front-end agreement among SC members and their involvement in SCC. Power sharing and leadership issues are also included in the front-end agreements. Whilst, supply chain processes and joint decision making are commonly used in all type of SCC, the supply chain metrics are different for inter- and intra-– organizational collaborations. Internal and logistics performance measures are also discussed in recent literatures of SCC. In this paper, we have tried to identify all possible performance metrics from the literature of SCC with respect
  4. 4. to different stages of collaboration. In this attempt, first we have checked the motive of Supply chainSCC as this will indirectly indicate SC processes to be evaluated. performance Like-minded people or businesses with similar objectives come closer to form agroup. One or more of them take a leading role in initiating formal collaboration. Then, metricsinterested supply chain members make front-end agreement (VICS, 2002). Topmanagement decides cross-functional activities and involvement of variousdepartments in collaboration at functional/operational and strategic level (Ireland and 859Crum, 2005). Performance at this stage of collaboration is measured through operationalefficiency and risk/return ratio. Hence, business strategy is been considered as one of themetrics as it measures the functional capability of the SC member for varying marketdemand (Akkermans et al., 1999; SCOR model). Although CPFR suggests equalopportunities to the SC members in collaboration, this is not reflected in practice. Hence,the order of dominance and decision sharing create a win- win or win- or lose-losesituation in SCC (Kim and Oh, 2005). Partnership revival or inclusion is considered incase of unexpected loss/profit or reduction in profit. As various processes of supplychain (namely planning, forecasting, production, and replenishment) have impact oncost, profit, inventory levels, stock outs and resource measures, these measures havebeen deemed important by many academics and practitioners (Angerhofer andAngelides, 2006; Gunasekaran et al., 2001). Table I lists the measures of SC from theliterature. Supply chain models developed after inception of CPFR incorporated someimprovement to the original CPFR framework by measuring its performance EssentialRole of SCC elements for SCC Performance metrics AuthorsCollaborative Cross-functional Business strategies (functional Akkermans et al. (1999) andplanning and activities capabilities), processes SCC (2001) – SCOR modelproduction, (operational efficiencies), stakedecision making holders view (risk/return ratio) SCC leadership Order of dominance and decision Kim and Oh (2005), and power sharing Simatupang and Sridharan sharing (2004a,b), and Aviv (2007) Process Cost, profit, excess inventory, Beamon (1999), Lambert alignment stock-out, resource measure and Pohlen (2001), Dong and Chen (2005), and Emmet and Crocker (2006)Information Joint decision Impact of information quality on McCarthy and Golicicsharing, making forecasting (2002), Forslund andforecasting Information Jonsson (2007),decision making sharing and Raghunathan (2001), and forecasting Chang et al. (2007) Managing Reliability, reactivity/flexibility Forme et al. (2007), changes Angerhofer and Angelides (external and (2006), and Barratt and internal) Oliveira (2001)Replenishment, Internal and Inventory and stock position, Cachon (2001); Ettl et al. Table I.decision making logistics stock out, lead time, internal (2000), Aviv (2007), Simchi- Supply chain performance service rate, cross-functional Levi and Zhao (2005), and performance metric and capability, logistics efficiency Chen and Paulraj (2004) its correlation with SCC
  5. 5. BIJ and identifying areas of improvement. Stank et al. (2001) and Rowat (2006) attempted to18,6 relate internal and external collaboration with logistical service performance. McCarthy and Golicic (2002) used responsiveness along with other basic measures – cost and revenue. Chang et al. (2007) claimed that “Augmented CPFR” (an improved model with third party information) is a better model with improved forecast accuracy and inventory. In a recent literature on SCC, capacity utilization and supply chain flexibility860 have also been considered as measures of performance (Angerhofer and Angelides, 2006 and Aviv, 2007). In the literature, flexibility, and reactivity are used as synonyms to represent ability of the supply chain in adapting to the changes (Forme et al., 2007; Angerhofer and Angelides, 2006; Barratt and Oliveira, 2001). Normally, only the changes internal to an organization have been considered for this purpose. Responsiveness of the SCC is another metric that has not been discussed adequately in the literature. In recent years, information exchange has become integral part of SCC processes and hence it also needs to be measured periodically. Though quality of information is important (Forslund and Jonsson, 2007; Forme et al., 2007), use of technology for improving quality of information has not been adequately stressed in the literature. If one could measure the responsiveness of SC on timely information (timely information to act upon), this will measure the importance of information exchange in SCC to a larger extent. Measures of responsiveness and flexibility can reflect a wider perspective of supply chain performance incorporating suppliers and buyers. Hence, comprehensive view of performance metrics of SCC need to involve all the metrics mentioned in Table I along with a few elemental measures such as managing change (use of technology), sharing performance metrics with customer (responsiveness), and sharing performance metrics with suppliers (flexibility). While, flexibility measures the ability of adapting to the changes effectively with available resources, responsiveness can measure the response of the supply chain for any unexpected changes in demand. Responsiveness is usually related with innovative products or products with short lead time which decides the level of collaboration needed (Lee, 2002). Recently, many companies have started giving more emphasis on the use of information technology and hence IT has become an integral part of SCC (VICS, 2002). For example, use of barcode and radio frequency identification technology in the retail sector helps to track point of sale, which in turn makes supply chain more responsive (Ireland and Crum, 2005). Such technological advancement makes communication between retailer and manufacturer easier. Hence, in this paper we have included the use of technology as one of the performance metrics of SCC. We augment the metrics suggested in the literature with three other important measures namely flexibility responsiveness and technology in our comprehensive view of performance metrics of SCC (Figure 1). Applying all the above measures identified from the literature into a single model to evaluate a SCC will be a complicate task. However, the objective of SCC and front-end agreements between SC partners can help to decide on which measures need to be used. To our knowledge, none of the models listed in Table I has discussed performance metrics at different stages of SCC. Hence, in this paper, we have attempted to align all the identified performance measures at two different stages of SCC. In this line, we have developed a conceptual framework on performance measurement of SCC.
  6. 6. Technology Supply chain performance metrics Supplier Manufacturer Retailer Flexible Responsive 861 Cost, profit, stock-out, and resource measure , Business strategies (functional capabilities), processes (operational efficiencies), stake holders view (risk/ return ratio), Impact of information quality on Figure 1. forecasting, Order of dominance and decision sharing, Inventory & stock position, stock out, lead time, internal service rate or cross functional capability, logistics efficiency, Reliability, reactivity Comprehensive view of performance metrics Metrics from the literature of SCC3. Conceptual framework for SCC and related metricsSCC transforms the partnership from narrower perspective of intra-organizational levelto wider perspective of inter-organizational level (Barratt, 2004). This also incorporatesall or many personnel in strategic- tactical- and operational level. Long-term businessplan is generally decided at strategic level, short-term planning and forecasting is madeat tactical level and day-to-day operations are planned and executed in operationallevel. Performance measurement will be complete only if it is conducted at all these threelevels (Gunasekaran et al., 2001, 2004). Generally, all the companies practicing SCC initially test their performance undercollaboration in a pilot stage. Successful pilot stage may facilitate in furthercollaboration (Cassivi, 2006). This is evident from several cases such as Wal-Mart andProcter and Gamble, and also through our case study analysis of two manufacturingfirms, discussed in the next section. The companies need to have different set ofperformance metrics specific to their stage of collaboration. At the same time, the stageof collaboration is decided by various elements. The elements which form the basis forinitiation of collaboration are common business objectives and supply chain processesand can be termed as functional drivers. Other elements such as degree of involvement(joint decision making), use of technology (managing change) and incentive sharing,which enhance or support the collaboration can be classified as enhancers. We feel thatSCC has two distinct stages – pilot stage when the initial attempts are made to testSCC, and advanced stage when all the partners are convinced of SCC and are fullycommitted. Accordingly, the metrics to measure performance of SCC should bedifferent for pilot and advanced stages. Measuring functional drivers can givecomprehensive idea on performance of SCC at pilot stage. If the company had otherbusiness goals of achieving responsive supply chain for changing demands, it mighthave enhancers in collaboration and related metrics. Measuring functional drivers andenhancers collectively will represent the performance of SCC in its advanced stage. Theessential elements of SCC suggested by Barratt (2004) serve as a backbone forproposing this conceptual framework and related metrics.3.1 Metrics to measure “functional drivers”As mentioned earlier, functional drivers of SCC include business objectives andSC processes. Business objectives, such as financial and operational, are main factors toSCC. Supply chain members who intend to establish their business are keen inidentifying partners with similar objectives to have long-term collaboration.
  7. 7. BIJ As the first step for collaboration, the companies form a front-end agreement; this needs18,6 to be reviewed periodically for any changes and can be measured through cost-benefit analysis. Supply chain processes in CPFR framework are divided into four main stages namely planning, forecasting, production, and replenishment (VICS, 2002). But in the recent years, handling product returns has also become one of the foremost reasons for SCC862 (Lambert and Cooper, 2000). Hence, we have included “return” as one more stage in the supply chain processes. These supply chain processes can be measured through different possible measures suitable to the adopting company. Some of the suggested metrics in the literature are capacity utilization, adherence to plan, inventory, stock-outs, and feedback on returns (Aviv, 2007; Cachon and Fisher, 2000). The feedback from retailers will be one of the effective measures, as it provides opportunity for manufacturer to improve the product quality or avoid future error or improve sales based upon feedbacks of returned items. The flexibility, which measures the efficiency of SCC with upstream members (suppliers), can be measured through timely delivery of raw material, availability of material at the time of production on urgent orders, and service rate. 3.2 Metrics to measure “enhancers” It is generally agreed that collaboration among supply chain members is built encompassing their business objectives. When the top management support more collaboration the company will establish collaboration with more partners and may invest more on SCC. Hence, degree of involvement is the first enhancer of SCC. Degree to which supply chain partners involve in collaboration is captured through investment on collaboration and sharing decision making. A great deal of business is based on the information sharing and proper use of data. Accelerated information sharing among all supply chain will increase the reliability of the order generation (VICS, 2002). Improved forecast accuracy is another motivating factor of SCC. Achieving forecasting accuracy is mainly through information sharing among members of SCC. Quality of information adds more value to the process of forecasting and hence it needs to be measured periodically. Improved forecasting accuracy will be an indicator of effective information exchange. If technology is used for exchanging information, its efficiency can be measured through accessibility of information by supply chain members. Based on this, any business can make decisions on investment on technology. Incentive sharing is another important enhancer of SCC, which attracts more members in collaboration and hence incentive sharing agreement needs periodic revision. Regular contacts among members of SCC and feedback on performance of supply chain will help to revive incentive sharing agreement. Responsiveness, which measures the efficiency of SCC with changing demand in downstream (retailers), could be measured through product availability. 3.3 Conceptual framework for the whole SCC Every company taking part in SCC needs to decide on the performance metrics on functional drivers and/or enhancers to track its success. The conceptual model developed based on the above discussions is shown in Figure 2. The desired metrics essential for measuring SCC is listed out in Figure 2 under categories functional
  8. 8. Supply chain Functional Drivers performance Processes Plan, Forecast, Produce, Business objectives Metrics to measure the performance of SCC metrics Financial & Operational Replenish and Return Measuring Functional drivers - Front end agreements (mutual agreements) - Business strategy (Profit and loss) Initiate Collaboration (Initial stage) - Processes (production, forecast accuracy, replenishment and handling of returned products) 863 - Capacity utilization (production efficiency) - Adherence to plan (plan vs. actual) - Availability of material (resource planning efficiency) Manufacturer - Inventory (Stock outs /Excess) (Evaluator) - Service rate (Product lead time measure) Strategic - Feedback Supplier Retailer Measuring Enhancers Tactical - Decision making sharing (involvement of partners, Operational involvement in information exchange and forecasting) - Investment on communication technologies (support and financial measure) - Use of technology (communication, information exchange & forecasting) Support Collaboration (advanced stage) - Information sharing &communication (Frequency and access) Degree of Information sharing, forecasting - Information quality (accuracy) Incentive - Forecasting involvement and technology - Product availability - Feedback Enhancers Overall effectiveness of SCC Responsiveness + Flexibility + Technical excellence Figure 2. Proposed metrics for SCC frameworkdrivers and enhancers. In addition, measures on responsiveness, flexibility andtechnical excellence can help the company to assess the overall effectiveness of SCC.Based on this assessment, further changes to the collaboration can be incorporated ifneeded.4. SCC in practice – case study observationsCollaboration and its suitability with the retail sector have been rigorously examinedby numerous researchers (Smaros, 2007; Holweg et al., 2005; Rowat, 2006). In the recentliterature, design for SCC is suggested by Simatupang and Sridharan (2008). But,research on performance metrics suitable to manufacturing companies is still in itsinfancy. This paper studies the performance metrics used in a packaging firm at theirinitial (pilot) stages of collaboration. Case of textile company has been used to analyzethe use of metrics in advanced stage of collaboration. The choice of a case is importantas it explores the research question (Eisenhardt, 1989; Yin, 1994) namely the metrics tomeasure performance of SCC. In this research, case studies aim to understand SCC and performance metrics usedat various stages of collaboration. Case 1 is a packaging firm has been involved in SCCwith their downstream members for the past 18 months to control inventory and toavoid obsolescence. Case 2 is a textile company initiated collaboration before four yearsand has well established SCC with their buyers mainly for promotional sales andforecasting. Although, both these companies are in SCC, the level of collaboration isdifferent and hence their practice on performance measurement is also different.
  9. 9. BIJ We have conducted case studies in two stages. The first stage has been intended to18,6 study existing SCC and assess its reliability. The second stage of case study is mainly for the purpose of understanding the metrics used in SCC. Interviews and frequent visits are the methods adapted to perform the above case studies. Interviews have been conducted with dependable officers responsible for collaborative relationship among partners, information exchange, forecasting, and operations. A few interviews have864 also been conducted with decision makers. The first author has visited the company several times in the span of two years in order to observe the changes in current collaborative arrangement in comparison with the sales and order data. Initially, Nvivo tool has been used to analyse the interview transcripts. Brief description of the case companies will help readers to understand the SCC in practice. 4.1 Description of case 1 Company background. The packaging company (Case 1) considered for this case was established in 1966. In its early years, Case 1 produced waterproof packaging materials and gradually expanded its production base to produce flexible intermediate bulk containers (FIBC). In the local market, Case 1 is the first manufacturer introducing FIBC and has nearly 50 percent market share. After 1996, the company has started to export its products to many international companies in petrochemical industry, mineral industry, dyes industry, and selected products in pharmaceutical industry. Case 1 has maintained quality and durability of the packaging material by treating it with ultra violet (UV) radiation. The company’s global operation requires them to have partnership with their supply chain partners to survive in the competitive international business. Supply chain at Case 1 before collaboration. Raw material suppliers to packaging industry are available in plenty and hence competition to become a partner in supply chain is very fierce. Though many raw material suppliers are available, the company prefers to have collaboration with a few local markets. In this case, supplier-manufacturer collaboration is simple and straight forward. As the company has been maintaining a good relationship with their clients, supply chain members exchanged information related to inventory and demand. The company builds their demand forecast based on those information from SC members and resulted in poor forecast accuracy. This has promoted the company to focus on information accuracy and related problems. Owing to lack of formal agreement among SC members, the information accuracy has always been uncertain. Without clear vision on incentive of SCC, no supply chain member has been committed for success of supply chain performance. As a consequence, forecasted demand from downstream member is 25-30 percent higher than the actual orders. The company essentially produces to order, though it also produces a limited amount to stock. About 50 percent of the basic common production process used to be completed based on initial forecast made through available information. As a result, the company has been facing a problem of excess inventory of finished and unfinished products. Recently, Case 1 has realized the importance of collaborative agreement to improve the information quality and accuracy. New government environmental regulation has forced the company to make use of raw materials and UV treatment of bags. This has necessitated the company to upgrade their products or to sell their product quickly before implementation of new sales regulation. Ultimately the company has incurred a loss at the end of 2006.
  10. 10. As-is scenario. In the beginning of the year 2007, the top management of the Supply chaincompany engaged in formal supply collaboration to revive its performance. The performancecompany has adopted vertical collaboration with suppliers and customers as part oftheir external collaboration and also has maintained internal collaboration among metricsvarious departments. The company has adopted a transparent profit sharing policy forSCC and also assured timely delivery for their clients. These two features of SCC havehelped them to get committed involvement of other members. Decision on profit 865sharing has been bound to the duration of collaboration and proportion of share in SCactivities. Front-end agreement among SC partners has clearly mentioned the role ofeach member in SCC. The company has incorporated 40 percent of their clients incollaboration in its pilot stage of SCC. Partners with similar business objectives andwith further interest in future collaboration have worked together. At the same time,the company has not invested much on information technology in its pilot stage of SCC.Most of Case 1’s communication with their customers has been carried out throughiMail Server (iMail is one of the advanced recent communication technology that workswell even in the presence of other servers such as e-mail server, SMTP, POP3, andIMAP). The company has used information from other partners to make their demandforecast. This has been fundamental in minimizing forecast errors. Periodically, thecompany has measured performance of collaboration through simple measures suchas cost, profit, timely delivery of goods to customers, inventory level, and forecastaccuracy. The above given information on various performance metrics of SCC inCase 1 and their purposes are further detailed in Table II. At the end of the next 12 months (end of 2007), the company has achieved 20 percentinventory reduction and 10 percent overall cost reduction. Improved forecast accuracyhas helped the company on production plan and expansion. Case 1 has reduced theirsafety stock level to 10 percent of expected demand as against its earlier 30 percent.4.2 Description of case 2Company background. Case 2 is a leading textile manufacturing and exporting firmlocated in the main lands of Asia. Case 2 exports to various countries across the globe.Customized products are embroidered dress materials with exclusive design, andmade-to-measure finished cushions, pillows, and curtains. Standard products areembroidered material with multiple repeated designs and curtain materials. Thecompany generally follows make-to-order strategy for its exports and local business ofcustomized products. A small part of the business (standard products to local markets)follows make-to-stock strategy with very limited stock that minimizes inventory andobsolescence cost. Like Case 1, Case 2 also has a strong uninterrupted supplier base forraw materials. In order to compete with ever growing challenges, the company hasbeen involved in SCC with other downstream members. Supply chain at Case 2 at initial stage of collaboration. Like any other company, Case 2has intended to improve inventory and reduce obsolescence and hence it has involvedin SCC with their suppliers and buyers. Its collaboration with suppliers signifiesa confirmation of availability of material/resources at the time of production. Initialcollaboration with buyers has been very successful to the company in terms of profit.Case 2 measures their performance every month and analyzes the area of improvement.Accordingly, at the end of every year (for the first two years) the company revives theirfront-end agreement with customers. Except the measure of handling product returns,
  11. 11. BIJ Metrics in use18,6 Purpose Desired metrics Case 1 Case 2 Initial stage Initiate and maintain collaboration Front-end agreements x x Business objective (financial) Business strategy (profit or cost) x x866 Supply chain process and business Processes processes On time production – x Forecast accuracy x x Timely replenishment x x Handling product returns – – Production process Capacity utilization – x Planning execution Adherence to plan – x Supplier collaboration Availability of material on time – x Inventory control Inventory (stock outs/excess) x x Production/replenishment Service rate – x Improvement of SCC Feedback – x Advanced stage Investment decision in Technology Use of technology – Future involvement in Decision making sharing collaboration x Investment in the state-of-the-art Investment on technologies (IT and technologies communication) x Improve SC processes and Information sharing No collaboration collaboration x Improve forecast accuracy and SC Information quality (accuracy) processes –Table II. Improve forecast accuracy Forecasting xPurpose of desired Improve inventory position Product availability xmetrics in SCC for case Improvement of SCC Feedback xcompanies Efficient use of SCC Managing change of whole SCC x all the other measures suggested in our conceptual framework have been measured by the company during their initial period of SCC. On success of initial SCC, the company intends – to involve in further collaboration with long-term agreements and to engage in advanced collaboration. As-is scenario of Case 2. In the advanced collaboration, the company involves all SCC members into information sharing and collaborative forecasting. Transparent and timely information has helped them to arrive at a single forecast figure which improved the forecast accuracy. As production and resource planning are directly linked to this single forecast figure, the company has reported improved product availability and adherence to production plan. Case 2’s investment on information technology and communication devises has helped them to secure exclusive network for receiving and sending information on sales, inventory and production processes. This has effected in considerable reduction of logistics difficulties during the time of replenishment. The company expects to be benefited more from SCC and related metrics. The measures of performance of SCC in Case 2 and their purposes are given in Table II.
  12. 12. 4.3 Possible scenario with advanced SCC and related metrics Supply chainAlthough Case 1 was successful in terms of controlling inventory and related cost, the performancetop management was not sure on further benefits of CPFR as performance metricswere not clear to them. In its pilot stage of collaboration, Case 1 aimed to improve their metricsinventory to avoid loss. In this stage, the company must check their efficiency in SCCthrough the list of metrics given under “measures of functional drivers”. But Case 1used only four performance metrics, namely forecast accuracy, inventory level, timely 867replenishment, and cost, during their pilot stage of SCC. We have suggested our proposed conceptual framework of performance metrics toidentify the performance of SCC. The first result after implementing the suggestedframework for performance metrics, the company has reported that they could identifytheir strength and weakness in SCC under evaluation of each metrics. After calculation,Case 1 officials have confirmed that they are in a good position after SCC and henceintended to continue further collaboration with most of the existing partners. Theyhave also considered revising front-end agreement with some of the SCC members. Thecompany has also showed their interest in adopting our proposed metrics for SCCframework as their standard measure. When the company moves to the advanced stage of collaboration, they need tomeasure the effectiveness of enhancers. Collective consideration of functional driversand enhancers will help the company to identify its areas of improvement. Thisexercise should be repeated periodically to review the front-end agreement oncollaboration. The cost-benefit analysis of both the companies at the end of 2007 has encouragedthem to invest more on SCC. Hence, in the next stage of collaboration, Case 1 hasdecided to invest more on technology to gain access to their clients’ data on real timebasis. They have believed that this could improve quality and visibility of information.So the company has decided to have set of metrics as given in Table II to measureperformance of SCC at its second stage. However, Case 2 had a well established basiccollaboration and now they are in an advanced stage of collaboration. Substantial benefit of SCC has encouraged Case 2 to involve in further collaborationat its next stage. They have also shown interest in exploring the suggested performancemetrics in the advanced stage of collaboration. The company has measured almost allthe measures suggested in our framework. “Product returns” have not been includedin the inventory and hence product has not been realigned. In the advanced stage “use oftechnology” and “quality of information” have not been measured. But later during ourdiscussion, the company has understood the importance of these two measures in theirdecision making. The performance of overall SCC through responsiveness, flexibilityand technical excellence for managing changes is another metric that has been viewedimportant by the case company to improve their SCC. Table II represents the list ofmeasures currently being used by the companies for measuring their performancein SCC. This table also lists the desired set of metrics at pilot and advanced stages ofcollaboration. By comparing these two columns of desired metrics and metrics in usein the Table II, it is clear that the company (Case 2) that aims to have advancecollaboration use more number of metrics than Case 1 that practices pilot stage ofcollaboration. However, before establishing further collaboration, Case 1 has beenadvised to measure all the desired metrics to evaluate their SCC performance. Thisapproach can be used as basic guidelines by any firm that is interested in SCC
  13. 13. BIJ to measure its performance. Based on the level of collaboration, the top management can18,6 choose the metrics to evaluate its benefits of SCC. 5. Conclusion and scope for future research In this paper, we have identified several performance metrics from the existing literature and through two case studies. We have proposed a set of metrics to measure SCC at its868 initial (pilot stage) and advanced stages. We have suggested including flexibility, responsiveness and use of technology as important measures in comprehensive view of performance metrics of SCC. While, flexibility measures the ability of adapting to the changes effectively with available resources, responsiveness can measure the response of the supply chain for any unexpected changes in demand. Evaluating the collaboration at the time of initiation is suggested through measurement of functional drivers. Tracking the benefits of collaborative arrangement by measuring enhancers would be ideal for decision makers to revisit their agreement on SCC. While analysing the case of packaging firm, we have identified that the technology is not necessarily a key obstacle but effective communication is vital. Proper uses of technology, flexibility, and responsiveness have been considered as important criteria for successful SCC by the case companies. Measures of evaluating these three SCC criteria are termed as overall performance metrics in the conceptual framework. Another important observation from the case analysis is that ample availability of raw material supply or suppliers will engage manufacturers in simple collaboration with their suppliers mainly for on time material availability. Meanwhile they try to establish strong collaboration with their buyers in order to improve the product sale, inventory control, etc. Incentive alignment in collaboration will be beneficial to all partners involved. One of the observations about utility of production facilities reveals that the support from suppliers helps to provide raw material on time to make use of the production capacity to its maximum. Meanwhile, relationship with buyers does have an indirect impact on production capacity utilization and planning as job allocation is based on demand. Both the case companies did not have close relationship with its suppliers compared to buyers. Further research is indeed necessary to identify the impact of closer partnership with suppliers. Manufacturers with high degree of collaboration may or may not perform well. But consistent intervention and necessary changes as required by the system will aid to improve the performance. In case of no improvement in the performance, the collaboration can be withdrawn or revamped with new set up. This case study reveals that the manufacture-to-order type of business requires more support from their buyers than their suppliers to exchange information, to improve forecasting accuracy, to avoid inventory and also to achieve overall performance in the supply chain. The same kind of research can be extended to manufacture-to-stock business or assemble-to-order type of businesses. Detailed survey-based analysis is also essential to validate the above framework in future and to standardise for various sectors other than manufacturing. The case study did not consider number of suppliers as an important factor due to the availability of sufficient suppliers and their readiness to serve. The main reason for such attitude is products from packaging industry have got more life and have more opportunity to sell in the other market’s before their value got eroded. But collaborative relationship with suppliers will help to reduce excess raw material inventory. By the way of allotting incentive, manufacturer can involve supplier in SCC.
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  16. 16. SCC (2001), Supply-Chain Operations Reference-Model V5.0, Supply-Chain Council, Atlanta, GA. Supply chainSimchi-Levi, D. and Zhao, Y. (2005), “Safety stock positioning in supply chains with stochastic performance lead times”, Manufacturing & Service Operations Management, Vol. 7, pp. 295-318.Seifert, D. (2003), Collaborative Planning, Forecasting and Replenishment: How to Create a Supply metrics Chain Advantage, AMACOM, Saranac Lake, NY.Simatupang, T.M. and Sridharan, R. (2004a), “A benchmarking scheme for supply chain collaboration”, Benchmarking: An international Journal, Vol. 11 No. 1, pp. 9-29. 871Simatupang, T.M. and Sridharan, R. (2004b), “Benchmarking supply chain collaboration: an empirical study”, Benchmarking: An international Journal, Vol. 11 No. 5, pp. 484-503.Simatupang, T.M. and Sridharan, R. (2005), “The collaboration index: a measure for supply chain collaboration”, International Journal of Physical Distribution & Logistics Management, Vol. 35 No. 1, pp. 44-62.Simatupang, T.M. and Sridharan, R. (2008), “Design for supply chain collaboration”, Business Process Management Journal, Vol. 14 No. 3, pp. 401-18.Smaros, J. (2007), “Forecasting collaboration in the European grocery sector: observations from a case study”, Journal of Operations Management., Vol. 25 No. 3, pp. 702-16.Stank, T.P., Keller, S.B. and Daugherty, P.J. (2001), “Supply chain collaboration and logistical service performance”, Journal of Business Logistics, Vol. 22 No. 1, pp. 29-48.VICS (2002), CPFR Guidelines, Voluntary Inter-industry Commerce Standards, available at: www.cpfr.org (accessed January 2007)Yin, R.K. (1994), Case Study Research: Design and Methods, Applied Social Research Methods Series, 2nd ed., Vol. 5, Sage, London.About the authorsDr Usha Ramanathan is a Senior Lecturer in Logistics and Supply Chain Management inNewcastle Business School, Northumbria University, UK. Her research interest includes supplychain collaboration, collaborative planning forecasting and replenishment (CPFR), value ofinformation sharing and forecasting, structural equation modeling, simulation, AHP andSERVQUAL. She has published in leading journals such as International Journal of ProductionEconomics, Expert Systems with Applications and Omega: The International Journal ofManagement Science. Dr Angappa Gunasekaran is a Professor in, and the Chairperson of, the Department of Decisionand Information Sciences at the Charlton College of Business, University of Massachusetts,Dartmouth. He teaches undergraduate and graduate courses in operations management andmanagement science. He has over 190 articles published in 40 different peer-reviewed journals,has presented about 50 papers and published over 50 articles in conferences, and has given anumber of invited talks in about 20 countries. Dr Gunasekaran is on the editorial board ofover 20 journals. He is the editor of several journals in the field of operations managementand information systems. Dr Gunasekaran is currently interested in researching informationtechnology/systems evaluation, performance measures and metrics in new economy,technology management, logistics and supply chain management. He actively serves on severaluniversity committees. He is also the Director of the Business Innovation Research Center (BIRC). Dr Nachiappan Subramanian is an Associate Professor at Thiagarajar College ofEngineering, Madurai, India. Nachiappan (Nachi) has published over 75 refereed papers whichinclude journal articles and international conference papers. Currently, he is on the editorialboard of the International Journal of Integrated Supply Management and International Journal ofApplied Industrial Engineering. He also serves as a reviewer for many leading operations
  17. 17. BIJ and supply chain management journals. In September 2011 he is joining as an associate professor in operations management at the University of Nottingham Ningbo, China. Previously,18,6 Nachi conducted his post-doctoral research at University of Nottingham, UK, under BOYSCAST fellowship program and received the Australian Endeavour Research Fellowship Award to conduct research on complexity, risks and low-cost country sourcing (with special reference to India). His research interests are supply chain operations, modeling and analysis of manufacturing systems, sustainable supplier selection, low-cost country sourcing, supply chain872 complexity and resilience and reverse logistics. Nachiappan Subramanian is the corresponding author and can be contacted at: spnmech@tce.edu To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints

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