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  • 1. Industrial Management & Data SystemsEmerald Article: Collaborative performance measurement in supply chainDimitris Papakiriakopoulos, Katerina PramatariArticle information:To cite this document: Dimitris Papakiriakopoulos, Katerina Pramatari, (2010),"Collaborative performance measurement in supplychain", Industrial Management & Data Systems, Vol. 110 Iss: 9 pp. 1297 - 1318Permanent link to this document:http://dx.doi.org/10.1108/02635571011087400Downloaded on: 14-07-2012References: This document contains references to 71 other documentsTo copy this document: permissions@emeraldinsight.comThis document has been downloaded 1871 times since 2010. *Users who downloaded this Article also downloaded: *Charles Inskip, Andy MacFarlane, Pauline Rafferty, (2010),"Organising music for movies", Aslib Proceedings, Vol. 62 Iss: 4 pp.489 - 501http://dx.doi.org/10.1108/00012531011074726Laura C. Engel, John Holford, Helena Pimlott-Wilson, (2010),"Effectiveness, inequality and ethos in three English schools",International Journal of Sociology and Social Policy, Vol. 30 Iss: 3 pp. 140 - 154http://dx.doi.org/10.1108/01443331011033337Aryati Bakri, Peter Willett, (2011),"Computer science research in Malaysia: a bibliometric analysis", Aslib Proceedings, Vol. 63Iss: 2 pp. 321 - 335http://dx.doi.org/10.1108/00012531111135727Access to this document was granted through an Emerald subscription provided by INDIAN INSTITUTE OF MANAGEMENT AT LUCKNOWFor Authors:If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service.Information about how to choose which publication to write for and submission guidelines are available for all. Please visitwww.emeraldinsight.com/authors for more information.About Emerald www.emeraldinsight.comWith over forty years experience, Emerald Group Publishing is a leading independent publisher of global research with impact inbusiness, society, public policy and education. In total, Emerald publishes over 275 journals and more than 130 book series, aswell as an extensive range of online products and services. Emerald is both COUNTER 3 and TRANSFER compliant. The organization isa partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archivepreservation. *Related content and download information correct at time of download.
  • 2. The current issue and full text archive of this journal is available at www.emeraldinsight.com/0263-5577.htm Collaborative Collaborative performance performance measurement in supply chain measurement Dimitris Papakiriakopoulos and Katerina Pramatari ELTRUN, Department of Management Science and Technology, 1297 Athens University of Economics and Business, Athens, Greece Received 11 February 2010Abstract Revised 10 April 2010Purpose – The objective of this paper is to demonstrate the challenges when developing a common Accepted 19 June 2010performance measurement system (PMS) in the context of a collaborative supply chain.Design/methodology/approach – The paper utilizes qualitative and quantitative data from a casestudy. The qualitative data refer to the assessment of collaborative performance measures based oninterviews with experts, while the quantitative data demonstrate the use of two performance measuresin a collaborative supply chain network.Findings – The development of a collaborative PMS is a challenging task. Through the systematicstudy of two significant performance measures for a supply chain, it was found that the one could notbe supported due to reliability restrictions, while the other requires the development of a complexinformation system. Based on these, a discussion of specific challenges follows.Research limitations/implications – The paper has the general case study limitations.Practical implications – Companies operating in supply chain networks need to synchronizeexisting business processes and data before the design of a new PMS. Selecting the measures and themeasurement method is not a trivial task. Important challenges reveal when dealing with, underlyingdata, business processes and the evaluation method of a PMS in supply chains.Originality/value – The management control function usually focuses on the design anddevelopment of PMSs for a single organization. Limited knowledge exists when more than twocompanies require the development of a PMS for a jointly agreed business process.Keywords Supply chain management, Performance measurement (quality), Inventory, PartnershipPaper type Research paper1. IntroductionThe design and development of performance measurement systems (PMSs) is part of themanagement control function (Simons, 2000). The field attracts the interest ofcross-discipline researchers and includes several methods and tools, which areincreasing due to the lack of an accepted, uniform applicable and consolidated theory(Otley, 1999). Management control has the constant need to capture the efficiency andthe effectiveness of a company, and performance measurement is the actual and concreteinstrument to cover this need (Eccles, 1991). In this paper, we examine the employmentof a common PMS in the context of a complex organizational setting, namely acollaborative network, as this is formed by a supply chain in the fast moving consumergoods industry. The supply chain environment calls for collaboration between supply chain partners,who often establish strong relationships with each other. In such a complex setting, Industrial Management & Datathe quest for performance is still an open issue (Fawcett et al., 2008). Systems Vol. 110 No. 9, 2010Performance-measurement concepts and tools have been proposed to cover pp. 1297-1318management control needs for a single company (Kaplan and Norton, 1995; Anderson q Emerald Group Publishing Limited 0263-5577and Young, 1999; Otley, 1999). The integrative philosophy of supply chain management DOI 10.1108/02635571011087400
  • 3. IMDS eliminates the boundaries of the single firm and puts emphasis on the effectiveness of the110,9 supply chain as a whole (Bowersox and Closs, 1996; Chan et al., 2003). Relevant research efforts in measuring performance of supply chains focus either on the identification of significant performance metrics (Gunasekaran et al., 2001; Lambert and Pohlen, 2001; Hofman, 2004) or on the examination of the collaborative success of the supply chain (Corsten and Kumar, 2005; Fawcett et al., 2008). The idea of a common PMS was1298 suggested by Holmberg (2000), who identified the fragmented measurement activities of a Swedish home furnishing business supply chain and proposed the use of systems thinking when developing PMSs. The importance of the topic has been recently recognized by Busi and Bititci (2006), who have indicated collaborative performance measurement as an issue for further research. The objective of this paper is to demonstrate the challenges when developing a common PMS in the context of a collaborative supply chain, enabled by information sharing practices between supplier and retailer. In doing so, we studied the collaborative process of store ordering and shelf replenishment. Based on the analysis of user requirements and interviews with experts, we concluded a set of performance measures to be maintained by the collaborative platform. To limit the scope of research, we further investigated two crucial measures (inventory level and product availability), because they: . reflect the operational results of the replenishment process; . require the involvement of all the trading partners; and . are highly innovative and do not contradict with existing performance measures. The lessons learnt when developing these measures range from technical inefficiencies to core management control functions of the business processes. In Section 2 of the paper, we briefly present the background literature of the performance-measurement field, addressing the pertinent research streams, the types of performance measures, and the role of IT and summarize relevant initiatives and case studies. The Section 3 describes the research methodology and the steps undertaken in order to design a collaborative PMS. In order to focus on the realistic application and use of the system, a case study of an existing collaborative supply network is described in Section 4, where two important performance measures are examined in detail, followed by the identified challenges. Finally, Section 5 concludes the paper with the study’s limitations and thoughts for further research. 2. Performance measurement in supply chain Performance measurement is the process of quantifying the effectiveness and efficiency of action (Neely et al., 1995). The instrument that regularly supports the performance-measurement process is referred to as PMS. A PMS maintains various metrics (performance measures) that are used for different purposes, like supporting decision making and management control, evaluating the results, motivating people, stimulating learning, improving coordination and communication (Neely et al., 1995; Simons, 2000). A performance measure is information delivered to the management function, evaluating the efficiency and the effectiveness of a process, resource or an outcome. Most of the studies in the area argue that a PMS should contain financial and non-financial metrics (Kaplan and Norton, 1995).
  • 4. Few performance frameworks have been proposed like activity-based costing Collaborative(Anderson and Young, 1999), the balanced scorecard (Kaplan and Norton, 1995) and performanceperformance prism (Neely et al., 2002), to facilitate the design of a PMS. Designing PMSsis a widely discussed issue and many researchers have examined important aspects like measurementthe linkage of strategy with the measures, balancing internal with external measures,mapping measures to processes, etc. (Kaplan and Norton, 1995; Bourne et al., 2000;Neely et al., 2002). The need to extend the knowledge around PMSs from the boundaries 1299of a single firm to the level of supply chain has been suggested early in the pertinentliterature (Van Hoek, 1998; Beamon, 1999). Supply chain management is a multidisciplinary field and it is addressed from manydifferent perspectives. Otto and Kotzab (2003) through desk research identified systemdynamics, operation research, logistics, marketing, organizational theory and strategyas relevant scientific fields to performance measurement in supply chains. Thesefindings are in line with the suggestions of Neely et al. (1995) who proposed that a PMSshould incorporate different perspectives, because they are of equal importance from amanagement perspective. The existence of different perspectives blurs the decisionregarding what it is (or not) significant to measure in a supply chain, thus a growing, yetimportant, number of performance measures has been suggested in the literature. At the end of the 1990s, most of the measures suggested in the area of supply chainmanagement were focusing on the performance of the logistics and distributionnetworks. Undoubtedly, measures related to the inventory cost or lead time areimportant, but provide limited and inadequate view when the level of discussion refersto complex supply chain settings. According to Van Hoek (1998), the scope ofperformance measurement in a supply chain needs to be holistic. A similar suggestion isalso provided by other scholars, who agree that an integrated approach needs to beadopted when measuring performance in a supply chain (Bititci et al., 2000; Lambert andPohlen, 2001). Beamon (1999) claimed that appropriate measures in supply chainmanagement fall into three categories, namely resources, output and flexibility.Gunasekaran et al. (2001) argue that performance measures should be identified intodifferent levels according to the decision-making process, thus the suggested measuresare strategic, tactical and operational. De Toni and Tonchia (2001) suggested thatfinancial and non-financial measures should be considered. In a synthetic and importantstudy, Gunasekaran and Kobu (2007) reviewed the pertinent literature and a number ofcases. They identified 46 different performance measures, addressing the performanceof a supply chain. They remarked that almost 50 percent of the suggested performancemeasures are related to internal business processes (internal view) of a supply chain andthe remaining 50 percent refer to the customer (external view) of the supply chain.Making the choice between the internal and the external view of a supply chain is alsoassociated to finding the right balance between operational efficiency and customerresponsiveness (Fisher, 1997). Other research efforts adopt a specific performance measurement framework(e.g. balanced scorecard) and suggest other sets of measures. For example, Kleijnen andSmits (2003) used balanced scorecard and through simulation they examined howperformance metrics react with environmental and managerial control factors. In thesame direction, Brewer and Speh (2000) followed the framework of balanced scorecardto measure supply chain performance. Gunasekaran et al. (2004) proposed a frameworkfor performance measurement in the supply chain, incorporating several
  • 5. IMDS performance measures, like variances against budget, human resource productivity,110,9 quality of delivered goods, etc. Depending on the supply chain activities and processes, measures from all many different perspectives are found in their suggested framework, which was further empirically validated. Most of the studies related to measuring performance in supply chains discuss what to measure and provide valuable information and guidelines for the design of the PMS.1300 Folan and Browne (2005) reviewed the available recommendations and frameworks in the area of performance measurement and identified more than 30 propositions regarding how to build a PMS. Within the growing literature of recommendations, guidelines, performance measurement frameworks and suggested measures, little attention has been paid on case studies that would enable the validation and extraction of knowledge regarding the implementation and use of a PMS in the real environment. Hudson et al. (2001) surveyed the use of PMSs in small and medium enterprises and found substantial implementation barriers. The problems faced during the implementation of the balanced scorecard in a single firm are also reported in Ahn’s (2001) work. Bourne et al. (2003) were among the first who explicitly argued that the research stream of performance measurement is at the stage of identifying difficulties and pitfalls to be avoided based on practitioner experience. The need to bridge the gap between theory and practice has motivated the study of implementation issues of PMSs and frameworks (Lohman et al., 2004; Wagner and Kaufmann, 2004; Fernandes et al., 2006; Searcy et al., 2008). These studies point out the usefulness of adopting a specific performance measurement framework, but they also highlight important issues during the implementation. For example, Lohman et al. (2004) suggest that data uniformity is crucial, since different teams in the supply chain are the users of the single PMS. The discussion of implementing a PMS shifts the focus from the strategic/managerial perspective of performance, to the operational use and usefulness of an information system. The advocate work of Holmberg (2000) identified that systems thinking has an important role when developing PMSs in supply chains. In the same direction, Beamon (1999) suggested that a “system” of performance measures is required for accurate measurement of the supply chain. The implementation of a PMS addresses questions like which are the relevant data, how do available data support the selected performance measures, who has access to the measures, how is a measure linked to a corrective action, etc. Simatupang and Shidharan (2003) propose that the members of the supply chain should jointly agree on a PMS. Moreover, they suggest a generic process to measure performance in supply chains with the following steps: (1) design the PMS; (2) facilitate measurement though the utilization of a common information sharing and resource-allocation system; (3) provide incentives to the members of the supply chain; and (4) intensify performance, which addresses system’s maintenance though comparing and modifying performance measures. The aforementioned steps are highly related with a systems thinking approach, because they take into account how performance measurement affects decisions and participation of the members of the supply chain and in addition address the issue
  • 6. of maintaining the system. The approach of Bourne et al. (2000) is also based on Collaborativesystems thinking, as they identify three different evolving stages for a PMS, namely performancedesign, implementation and maintenance. Speakman et al. (1998) argue that collaboration is a dominant approach in supply measurementchain management aimed to gain benefits and share results among the trading partners.Indeed, several researchers have reported that collaboration, enabled by informationsharing, can increase the performance of a supply chain (Cachon and Fisher, 2000; 1301Lee et al., 2000; Croson and Donohue, 2003; Pramatari and Miliotis, 2008). However, theimpact on supply chain performance also depends on the kind of information shared, thefrequency of sharing and the relationship between the trading partners (Kehoe andBoughton, 2001), making questionable whether collaboration achieves the expectedresults. Existing collaboration practices in supply chains, facilitated by informationsharing, have not yet examined the performance systematically, implying the absence ofa collaborative PMS. Most of the studies examining the impact of information sharing onsupply chain performance utilize simulation models (Chen et al., 2007), numerical andexperimental data analysis (Fu and Piplani, 2004), and surveys (Akintoye et al., 2000). In conclusion, the development and maintenance of a collaborative PMS has not beendiscussed in the pertinent literature. Moreover, the research community has longstressed the importance of case studies as a consolidation tool between existing theoryand practice (Lohman et al., 2004; Hudson et al., 2001). To this end, we argue that thechallenges to build a common PMS includes managerial and technological barriers thatsupply chain trading partners need to overcome. Based on these, the contribution of thiswork is summarized as follows: . It studies performance measurement in the supply chain based on a real case setting. . It focuses on the implementation issues and challenges of the PMS. . It refers to a collaborative supply chain with daily information sharing activities in the downstream of the supply chain.3. Methodology3.1 Research methodOur empirical research has been facilitated through case study research. We have,specifically, selected an existing collaboration network comprising major productsuppliers and a retail chain. The selection of this setting was done in order to meet therequirements of collaboration, namely trust and commitment between the tradingpartners (Speakman et al., 1998; Saura et al., 2009). The collaborating members havejointly developed a new store ordering and replenishment business process in order toalign their strategic plans and provide increased service level to the end consumers, thusthe information sharing is mainly conducted in the downstream of the supply chain.Moreover, the daily information sharing between the participants is facilitated by theinternet and web technologies. After few years of operation, managers were not aware about the results achievedthrough this collaborative network. The main barrier has been the absence of a PMSmeasuring collaboration success (Law et al., 2009; Fawcett et al., 2008). Thus, on onehand, the management function had designed and jointly implemented a collaborativebusiness process and, on the other, it was not able to fully evaluate the impact
  • 7. IMDS of the process. The decision to develop a common PMS has been facilitated by the110,9 suggestions of Simatupang and Shidharan (2003) regarding the jointly agreement on what to measure and how to measure. Approaching a common agreement between multiple trading partners, regarding what to measure, few iterations are required (Figure 1). The scope of the collaboration and the respective PMS has been defined around the1302 store replenishment process and the business needs associated with it. This further guided the review of the literature and identification of other relevant studies (Van Hoek, 1998; Beamon and Ware, 1998; Lambert and Cooper, 2000). Special attention has been paid to other collaborative planning, forecasting and replenishment cases within the retail industry, because the selected case has been based on this framework (Holmstrom et al., 2002; Seifert, 2002; Fliedner, 2003). The result was a set of candidate performance measures that were found relevant to implement by all the trading partners. The presentation of the candidate measures to the experts paved the ground for in-depth interviews and acted as the base to exchange ideas for refinement of the measures. At a later step, the performance measures were examined under two perspectives: (1) whether a common measurement method is acceptable by the trading partners; and (2) whether the available data of the collaborative network can support the performance measures. The final step dealt with the implementation and evaluation of the performance measures. Intentionally, all types of performance measures (e.g. financial and non-financial) were included. In the following sections, we present in more depth the two of the selected performance measures, inventory level and product availability, in order to demonstrate the challenges encountered during the development and evaluation of the PMS. Scope of the collaborative processes Relevant literature Candidate performance measures Review by experts Examine feasibility Agree for a common Available data supports No measurement method performance measures No Yes The measure could be supported Yes Implement the performance measuresFigure 1.The researchmethod followed Evaluate performance measures
  • 8. 3.2 Data availability CollaborativeThrough the collaborative platform supporting the store-replenishment process, we performancehad access to various sources of data. Below an indicative list of the available data ispresented: measurement . Point-of-sales data are the collection describing the sales of the store on a daily basis. . Orders data describe the requests placed from the store to the Central Warehouse 1303 (CWH) of the retail chain, depicting the products the store wants to replenish. . Deliveries data are the response of the CWH to the store, showing which products and how many items are delivered compared to what has been ordered. . Promotion plan is a calendar of the in-store promotion activities planned by the retailer and the supplier in collaboration for every store. . Product assortment is the list of the active products currently available at a specific store. Additionally, this file has information regarding the delivery method of a product (delivered by the CWH or direct store delivery (DSD) by the product supplier). . Physical store audits are based on physical visits of researchers to the store in order to spontaneously monitor Product Availability of selected products on the store shelves.4. Empirical work4.1 The case settingThe collaborative supply chain of the case comprises three major product suppliers (twomultinational and one national) currently offering more than 1,000 different products inthe market. The retail chain has four CWHs and approximately 200 geographicallydisperse retail stores located in Greece. The collaboration was initiated by the productsuppliers, who wanted to increase the visibility in the supply chain and acquire thebenefits of information sharing. The long-time trading activities between the membersof the supply chain ensured the sharing of common goals and beliefs for the Greekmarket, a high level of trust in the relationship and finally that the requirements ofcollaboration are met. Although two of the product suppliers are main competitorswithin important product categories (e.g. detergents and hair care) their collaborationwith the same retailer did not present any competitive threat (Figure 2). The decisions made in collaboration are: . What items does a store need to replenish and in what quantities? . What are the expected sales (forecasts) per product? . Should the products be replenished by the retailer’s CWH or directly by the supplier? . Which is the recommenced product mix for each store? . Which products to promote and in which stores?The ordering decisions require the daily information sharing of various sourcessuch as: POS data, store assortment, promotion activities, etc. The role of informationtechnology is essential in the collaboration since large amounts of data need
  • 9. IMDS Collaboration platform110,9 Direct store delivery1304 Retailer distribution center (DC)Figure 2.The structure of the Product Backroom Shelfcollaborative network suppliers Store to be processes timely and accurately and delivered in a usable manner both to suppliers and to the store managers. A startup company, following the software-as-a-service business model, developed and operated the collaboration platform. One of the service extensions was the development of a PMS, as performance measurement is a necessary tool for successful management (Phusavat et al., 2009). Managers from all the trading partners expressed their interest in examining the performance according to the objective of the collaboration, which has been “to offer high service level to consumers by efficiently handling the store ordering process enhanced by information sharing capabilities”. The design of the PMS should be linked to the collaboration strategy, in order to: . demonstrate the results achieved through the new store ordering and replenishment process and . stimulate learning on the product suppliers’ side wishing to evaluate the effect of collaboration and examine the possibility of expanding the collaboration process to other retail chains. Moreover, the adoption of a specific performance measurement framework (e.g. balanced scorecard) was found to be complex and costly, at least at the initial stage, due to the narrow and structured scope of the collaboration. Therefore, it was decided to study a limited number of performance measures focusing on the specific collaboration process and on the responsiveness to the consumer. Relevant works in PMS design have provided guidelines for the PMS of our case. Table I depicts how existing suggestions in the literature have affected some design options of the specific PMS. Based on discussions with the managers, we examined their motives to join this collaboration effort and reconfigure the store ordering and replenishment process. In particular, the trading partners had the following issues: . Inventory levels at the stores were not optimized, implying either overstocking or out-of-stock situations, and the participants expected that they would be able to handle the problem through collaboration.
  • 10. CollaborativeSuggested in the literature Impact on the design of the PMS performanceReflect strategic alignment (Eccles, 1991; Understand the strategy of the collaboration as the measurementKaplan and Norton, 1995; Bititci et al., 2000) tradeoff between effectiveness and responsiveness Focus on the results of collaboration Exclude financial performance measures at the initial stage 1305Monitor critical activities (Azzone et al., 1991; Focus on the store-replenishment processNeely et al., 1995) Link of problematic areas with specific performance measures Focus at the store level of the supply chainMeasure product delivery from supplier to The integrative view of the supply chain makes thecustomer (Dixon et al., 1990) consumer as the only customer Focus at the store level of the supply chainProvide measures that all members could Use collaboration platform to share performance Table I.understand (Dixon et al., 1990) measures in a daily base Utilizing literatureFocus on measures that customer can see Develop performance measure that reflects the service suggestions in the PMS(Kaplan and Norton, 1995) level of the collaboration to the consumer design of the case . High forecast inaccuracies mean that demand forecasting could not be used for most of the products. . Product shelf unavailability (also referred to as out-of-shelf (OOS)) has recently emerged as one of the most important problems in the retail sector affecting revenue streams as well as consumer satisfaction. . Imperfect orders address a “weak” connection between the retailer’s central distribution center (DC) and he stores, implying that the DC is not able to cover the stores’ demand.Creating performance measures relevant to the problematic areas leverages thecommitment of managers to participate in the design of the PMS. Table II linksProblematic area Interested partner Candidate performance measuresInventory levels Supplier-retailer Inventory (Beamon, 1999) Fill rate (Kleijnen and Smits, 2003) Backorder/stockout (Beamon, 1999) Stockout probability (Beamon, 1999)Forecast accuracy Supplier Forecast accuracy (Gunasekaran et al., 2004; Fisher, 1997; Hadaya and Cassivi, 2007)Product shelf availability Supplier-retailer Flexibility of service system to meet customer needs (Gunasekaran et al., 2004) Point of consumption product availability (Neely et al., 1995)Imperfect orders Retailer Delivery reliability performance (Gunasekaran et al., 2004) Response delay (Kleijnen and Smits, 2003) Table II. Reliability (Neely et al., 1995) Problems identified and Deliverability (Neely et al., 1995) the associated On-time deliveries (Neely et al., 1995) performance measures
  • 11. IMDS the identified problematic areas with a set of candidate performance measures, as found110,9 in the pertinent literature, and the partner mostly interested in the issue. From a system architecture perspective, the proposed PMS is located at the top of the collaboration platform in order to have access to all the available information and be visible to the respective trading partners. The PMS was developed as a reporting tool based on an integrative view of the shared data sources. Depending on the1306 employed measurement method, we distinguish the performance measures into two categories: (1) Single performance measures are derived directly by the data sources, are deterministic in nature and can be expressed though simple formulas. (2) Composite performance measures extend the available data sources with other parameters (e.g. probabilities, loss functions, etc.). Studies in performance system design do not usually include the calculation method for each suggested performance measure. From a system perspective, this is a major drawback, because the linkage between the performance measure and the available data is missing. In our case, defining each performance measure was necessary in order to proceed with the implementation of the PMS. All measures are defined at the store and product level. Table III provides information for the implementation of the selected performance measures. In order to demonstrate the challenges associated with the development of a common PMS, the measures selected for in-depth investigation are inventory level and product availability. The reasons for selecting these two measures are: . they are the most critical measures in respect to the store replenishment process; . they measure problematic areas identified by both the retailer and supplier (Table II); . they increase supply chain visibility for the product supplier, because they offer a view at store level on a daily basis; and . the former is examined as a single performance measure and the later as a composite one. Performance measure Type Data used Description Frequency Inventory level Single Sales Number of items existing in the store Daily Deliveries for a certain product Forecast Composite Forecast plans The difference between the expected Weekly accuracy Sales sales and the observed sales. In-store promotion Seasonal and promotion activities amplification of the sales are taken into account Product Composite Sales Describes if a product is available on Daily availability Inventory levels the shelf of a store or notTable III. Product assortmentA view of the Promotion activitiesperformance Imperfect Single Orders Examines if the items and quantity Dailymeasures used orders Deliveries delivered meet the order of the store
  • 12. The next sections describe the challenges encountered during the development and Collaborativeevaluation of the two performance measures. performance4.2 Measuring inventory at the store measurementThe inventory level of a product in a store is related with the product availability. On onehand, if a retail store has enough product quantity stored in the backroom to face thefuture consumer demand, at least within the lead time of the store replenishment cycle, 1307then the possibility of stock out is minimized. On the other hand, ordering and stockinglarge amounts of a specific product would lead to overstocking situations. It would be unrealistic to count the inventory level at the end of each day for all theproducts in a store. The approach to use available information and subtract a product’ssales from the delivered quantities in order to determine the inventory level has highinaccuracy. According to Kang and Gershwin (2005), it is very difficult to maintain perfectinventory records at the store level due to various sources of error (e.g. shoplifting, damageof the products during the transportation, delays in information sharing, etc.). In thespecific case, we found that inventory inaccurate records negatively affect the reliability ofinventory level as a store performance measure. More specifically, in order to evaluate the accuracy of the inventory level measure, weselected nine representative stores and thoroughly examined the consistency betweensales and deliveries for all the products for a six-month period. Depending on thedelivery method, we classified the available products into three categories: (1) The CWH category includes the products that are delivered to the store through the retail CWH, i.e. retail DC, on a regular basis. (2) The second category is labeled DSD and includes the products delivered to the store directly by the supplier. (3) The last category (CWH/DSD) includes the products which do not meet any of the above classes. These products are replenished by both the CWH and the product supplier, in a mixture that is not known in advance, and it is subject to factors like demand fluctuations, stockout at the CWH, imperfect orders, etc.Table IV illustrates the distribution among the three classes. On one hand, Store 1 isthe largest store examined, having over 5,000 different products in its assortment,while on the other hand the smallest store (Store 9) merchandises approximately 1,500different products. Most of the products (approximately 48 percent) are delivered Delivery methodStore CWH DSD CWH/DSD Number of productsStore 1 2,291 (45.66%) 2,006 (39.98%) 721 (14.37%) 5,018Store 2 1,459 (42.44%) 1,491 (43.37%) 488 (14.19%) 3,438Store 3 2,169 (49.46%) 1,728 (39.41%) 488 (11.13%) 4,385Store 4 2,533 (50.15%) 1,838 (36.39%) 680 (13.46%) 5,051Store 5 1,813 (44.18%) 1,783 (43.45%) 508 (12.38%) 4,104Store 6 1,065 (41.50%) 1,177 (45.87%) 324 (12.63%) 2,566 Table IV.Store 7 1,771 (43.14%) 1,770 (43.12%) 564 (13.74%) 4,105 Classification of productsStore 8 1,780 (44.82%) 1,721 (43.34%) 470 (11.84%) 3,971 based on the sales andStore 9 735 (47.88%) 599 (39.02%) 201 (13.09%) 1,535 inventory records
  • 13. IMDS through the CWH, because the ordering cost is lower compared to the cost of the DSD.110,9 The number of products delivered directly to the store varies between 35 and 45 percent, depending on the store size. The remaining products (that are neither CWH nor DSD) are classified under the CWH/DSD label. On average, this class represents 14 percent of products. The collaboration platform shares on a daily basis data from the retail chain,1308 including deliveries and sales data. This means that the transactions referring to the CWH products are timely available through the platform and, thus, in the following, we look only at CWH products. We assume that the total delivered quantity (Q) should always exceed the observed sales (S) for a given product and store. Consequently, we adopt the holding inventory formula to calculate the inventory level as a store performance measure. By definition, holding inventory is non-negative and expressed by the following formula: Holding Inventory ¼ Q 2 S $ 0 ð1Þ The percentage of records that equation (1) is violated has been examined using the available data (POS and deliveries data). As Table V presents, around 9 percent of the records has a negative value for the holding inventory. This “unexpected” phenomenon is caused by deliveries occurring in the store and not monitored on time or at all by the information system. On the other extreme, the overstocked products are around 5 percent, according to the available records, which is significantly high for retail business. The inventory measure, relying on the available information, provides unrealistic results (negative values and high percent of overstocking items), which does not reflect the exact situation of the daily store inventory. However, we noticed that the holding inventory value for few product categories with long life cycle, low-priced products and small promotion activity (e.g. snacks and pasta) could be correctly estimated. Other categories, more expensive, with high promotion activity and frequent product introductions (e.g. detergents and shampoo) are on the other extreme, and it is very unlike to have a reliable performance measure. Real life events distort the available information regarding the inventory levels. Having only 50 percent of the available records in the area of the normal levels of holding inventory is an important barrier for the development of a widely acceptable performance measure in the specific case. Additionally, the variability of the holding Negative holding Very low-holding Low-holding Normal levels of inventory inventory inventory holding inventory Overstocking (%) (%) (%) (%) (%) Store 1 10.78 9.38 16.37 58.57 4.90 Store 2 10.56 14.05 28.10 43.98 3.31 Store 3 7.70 13.32 25.68 48.07 5.23 Store 4 13.42 11.69 18.56 48.91 7.42 Store 5 7.7 10.98 26.14 50.07 5.64 Store 6 9.11 15.21 34.18 36.76 4.74Table V. Store 7 6.32 12.59 30.89 44.77 5.43Inaccurate records of the Store 8 8.15 12.75 27.81 45.63 5.67holding inventory Store 9 14.55 16.60 32.65 33.76 2.44
  • 14. inventory changes between the stores. To this end, the development of a performance Collaborativemeasure related to inventory level at the store could not be supported due to information performancequality restrictions. Possible options to gather a more realistic inventory view could bebased on the following: measurement . employment of multiple inventory measurement methods for different stores and product categories; . use of probabilistic models to calculate inventory shrinkages; and 1309 . use of radio-frequency identification (RFID) technology.4.3 Measuring product availability at the storeThe term product availability implies that a product is accessible by the consumer on theshelf of a retail outlet. However, empirical research has shown that it is not unusualthat the product is not on the shelf when a consumer is looking for it, leading to lost salesand decreasing consumer loyalty. According to Gruen et al. (2002), the OOS rate is closeto 8.3 percent worldwide, which is considered very high given that an acceptable level(determined by suppliers and retailers) would be less than 2 percent. Additionally, OOSrates of promoted items are much higher, affecting the promotional effectiveness(Gruen et al., 2002). In our case, the suppliers and the retail chain have found such aperformance measure directly related to the objective of collaboration, since it depictsthe responsiveness of the collaborative supply chain towards the end customer. Currently, the measurement of OOS is utilized through physical store audits,conducted by the retailer or the product suppliers. However, the high cost of measuringproduct availability and the dynamically changing states of the shelves are the majorbarriers for acquiring timely information and understanding the problem in detail.Company Alpha, as the owner of the collaboration platform had decided to implementa method for the evaluation of product availability at the store utilizing the availabledata. After thorough examination of the available computational methods,a sophisticated heuristic rule-based method was proposed in order to automaticallydetect products missing from the shelf. The method has been based on knowledgeengineering principles and more than 100 different rules developed through adata-mining process (Papakiriakopoulos et al., 2009). Using the same rules on a dailybasis and for all the stores of the retail chain, it is possible to detect products missingfrom the shelf. A sample of rules used for the automatic detection of OOS productsis depicted in Table VI. Products merchandizedRuleID Rule body Accuracy (%)Rule 21 (LastPosDays $ 3) AND (day ¼ ‘Wednesday’) AND (StoreSize ¼ ‘Large’) AND (SD_DailyPosAvg # 2.82) AND (FastMovingIdx . 0.76) 0.82 0.4Rule 43 (LastPosDays . 6) AND (SD_PosAvg . 7.9) AND (day ¼ ‘Tuesday’) 0.42 0.1 Table VI.Rule 47 (TypeOfProducts ¼ ‘ADV’) AND (posavg . 1.9) Indicative rules used for AND (Last_Order . 12) AND detecting products (Mean_Order_quantity , 6) 0.91 0.01 missing from the shelf
  • 15. IMDS Although the rules have been found to accurately detect OOS products, they have an important drawback, because they cannot cover all the different cases of products110,9 missing from the shelf. For example, Rule 21 characterizes as OOS the products that have not sold for the last three days (LastPosDays . ¼ 3), the day of detection is Wednesday (day ¼ “Wednesday”), the area of interest is only the large stores of the retail chain (Store_Size ¼ “Large”), the standard deviation of sales only for Wednesday1310 should be low (SD_DailyPosAvg , ¼ 2.82) and finally the products are fast-moving items (FastMovingIdx . 0.76). This rule has relatively high detection accuracy (82 percent) but refers only to a small proportion of the total OOS occurring daily in the store. Thus, on one hand, the collaborative network acquired an accurate mechanism for measuring product availability; on the other hand, the mechanism only partially monitors the products merchandized by the store. However, a linear correlation has been found between the OOS rate and the number of products the system detects as OOS per day. The higher OOS rate a store has, the more OOS alarms it gets. Table VII presents the number of products that are active in a store, the average OOS rate, as estimated by physical store audits, and the average number of products the detection mechanism reports as OOS per day. As expected, stores with a greater OOS problem receive relatively higher counts by the detection mechanism. The performance measure for estimating product availability was found very interesting by the participants, but the measurement method employed was rather complicated. Based on the available data of the collaboration platform, an intelligent information system was designed and developed but this had limited detection capabilities for the products missing from the shelf. Nevertheless, the employed mechanism correctly detected the retail stores encountering the biggest OOS problem, thus offering a reasonable and uniform method to product suppliers and the retailer to agree on the stores having the lower product availability and to start planning corrective actions. 4.4 Implications and discussion The available case setting revealed some aspects in the area of performance measurement. The need for a universal framework for selecting performance measures in supply chains, as identified in the presented case, validates Beamon and Ware’s (1998) prior research. The challenges identified through the effort to build a common PMS could be grouped into three broad categories namely: (1) data management; (2) business-process management; and (3) collaboration. Real world Detection mechanism Products monitored Average OOS rate (%) Average daily alarmed products Store 1 4.548 8.91 78 Store 2 3.401 11.70 94Table VII. Store 3 3.120 12.12 115Relationship between Store 4 4.079 8.53 82OOS and alarms by the Store 5 4.634 9.60 103detection mechanism Store 6 2.870 12.47 117
  • 16. Data management. Data management includes all the actions performed during the life Collaborativecycle of shared data. While the importance of information sharing has been recognized in performancethe literature (Yu et al., 2001), in practice information quality is a major obstacle.Information quality has been studied in the context of planning supply chain activities measurementand some researchers have expressed the opinion that information quality is positivelyrelated with the performance of the supply chain (Petersen et al., 2005; Simchi-Levi et al.,2008). Previous studies in the upstream supply chain have stressed the benefits of timely 1311(Bourland et al., 1996; Karaesmen et al., 2004) and complete (Chu and Lee, 2006)information sharing. In our case, we examined a single performance measure (inventorylevel at the store), at the downstream supply chain. The data sources (sales anddeliveries) were considered as timely and complete. Sales data were collected though thePOS scanning infrastructure of the retail stores and the deliveries data were provided bythe warehouse management system that controls the operations of the retail DC.However, their mix failed to accurately estimate the inventory levels at the store, due toshrinkage and other factors. The role of new technologies, and in particular RFIDtechnology, could be a key enabler to improve information quality in an automatedmanner (Kelepouris et al., 2007; Lee and Park, 2008). The provision of inaccurate performance measures is associated with incorrectdecisions. Managers utilize performance measures to quickly identify areas ofimprovement (Neely et al., 1995), therefore the provision of unreliable performanceinformation, would eventually initiate unnecessary corrective actions and as aconsequence the managers lose trust in the PMS. The role of trust is tightly linked withperformance in inter-organizational settings (Zaheer et al., 1998), thus the quality ofinformation provided by the PMS could hinder the performance if neither reliabilitychecks nor quality control processes are considered during the implementation. Business-process management. The examined performance measures did not onlyreflect the quality of the shared data, but also support decisions on business processesthat are not part of collaboration. For example, the shelf layout is subject to the categorymanagement business process, which is not covered by the collaboration platformpresented in our case. Hence, the performance measure of product availabilitysignificantly depends on decisions made during the category management process.The complex nature of supply chain operations is an important challenge to overcomewhen implementing such PMSs (Fawcett et al., 2008). Following a “divide and conquer”approach, as suggested by systems thinking, to implement the PMS is contradictorywith the integrative philosophy of managing a supply chain. Nevertheless, it was foundto be a good managerial learning process, because it motivated managers to re-evaluatethe scope of collaboration and identify existing business processes that need to besupported by the collaboration platform. To this end, the development of themeasurement system itself can enhance the collaborative strategic management processby challenging the assumptions and the existing strategy (Bourne et al., 2000), providinggrowth prospects though continuous improvement programs. Prior works have stressedthe importance of performance measurement to motivate people and stimulate learningin the organizations (Kaplan and Norton, 1995). In the presented case, the lessonsacquired through performance measurement made collaborating partners re-evaluatethe collaboration objectives, shift the focus from the timely information sharing toinformation quality and re-examine the scope of collaboration through the incorporationof new business processes.
  • 17. IMDS Collaboration. Most of the benefits identified on the product supplier side derived110,9 from the practice of information sharing. Based on collaboration and information sharing, the trading partners reported better performance of decisions in the store- replenishment process (Lee and Whang, 1999). The availability of daily OOS alarms to suppliers per retail store has been considered as valuable information since it facilitates supply chain visibility and allows for rapid corrective actions. From the retailer’s1312 perspective, this information is useful only because it provides a uniform approach to estimate the true OOS rate, thus is utilized as a benchmarking process for the stores. We believe that this is the main opportunity when developing common PMSs: while every partner activates its individual mechanisms and identifies areas of improvement in a different way, all of them share the common objective of collaboration. Collaboration in the supply chain is a key enabling factor for the implementation of a PMS. Bourne (2005) examined 16 different performance measurement cases at different levels of design and implementation. His findings suggest that the top management support is a key factor to proceed with the implementation phase, although in his study, only two cases finally managed to implement performance measures. The initial definition of collaboration (Speakman et al., 1998) implies the commitment of the trading partners, thus it is expected that a PMS referring to a collaboration effort is more likely to be implemented, as happened to our case. It is also important that non-financial performance measures are more likely to be part of the collaborative PMS for the next two reasons: (1) Financial measures are difficult to be agreed and designed because the resources are common and the cost centers are different for the trading partners. (2) Most of the managers want to identify the alignment between the jointly agreed objectives of collaboration and the results achieved. Common implementation problems, as already discussed in the literature, have been found in the presented case. Lack of a structured development process of the PMS (Hudson et al., 2001) and increased effort to collect data and support composite performance measures (Ahn, 2001) have been barriers to the implementation effort. However, resistance to measurement efforts (Bourne et al., 2000) and top management commitment (Neely et al., 1995) have not been substantial problems to the implementation of the presented PMS. At the early 1980s, a PMS was close to the budgetary control and aligned with accounting procedures (Traditional PMS). Ten years later, the management thinking approach broadened the view of performance measurement and initiated the discussion regarding strategic alignment of measuring performance, improvement though measurement, focus on the quality, etc. The role of supply chain management enabled by information technology allowed the discussion about an integrative approach on more complex structures and how to manage them though performance measurement (supply chain PMS). We could say that the case presented in this paper discusses what we could call a collaborative PMS, making performance measurement a central issue in managing collaboration (collaborative PMS). Table VIII summarizes the difference among the three classes of PMS. 5. Conclusions This paper discusses the development of a PMS in a collaborative context. Although companies in supply chain networks have the constant need to measure performance,
  • 18. Collaborative Traditional PMS Supply chain PMS Collaborative PMS performanceMeasuring Identify performance Identify performance Measures derived from the measurement measures dimensions according to the objective of collaboration supply chain structureNumber of Variable number of Increased number of Limited number ofmeasures performance measures performance measures to performance measures 1313 cover all the dimensionsMeasures Bias towards financial Focus on financial and non- Focus on non-financialusage measures financial measures measuresMotives to Measure to improve Measure to understand, Measure to understand themeasure identify areas and improve success of collaborationApproach Accounting Management thinking Systems thinking System thinkingData Significant effort to Very significant effort to Information sharing enabledmanagement identify and gather data identify and gather data by technologyData issues Gather available data Integrate available data Information quality Table VIII.Scope Firm Firm and trading partners Collaborating partners Classification of PMSsthe corresponding systems are in practice isolated. The existing knowledge in the areaof performance measurement needs to be extended to cover the needs of a supply chain,where collaboration and information sharing practices integrate the participatingcompanies into a single and integrative unit. The challenges we found during the development of a common PMS derive from datamanagement, business process management and collaboration issues. The field is openin the identification of further challenges, opportunities and barriers. The proper useof IT is essential in the development of a common PMS, but we argue that the mostimportant issues are context specific and related to the practical implementation. There are several limitations in this study; many are associated with the data usedfrom the collaboration platform and others with the selected case setting. The firstproblem relates to the gap between the available data and the business processessupported by the collaboration network. Although the store-replenishment process iscommon for all the trading partners, the data used to support the selected performancemeasures have been found to be restrictive against the requirements of a common PMS,due to the complex nature of the setting (e.g. many participants, different informationsources, etc.). Here, we can argue that the measures examined were in the area of afeasible solution for the specific case. Nevertheless, the challenges we have faced can berelevant to similar cases as well, where companies collaborate and share information toaccomplish a specific business objective and not to build a common PMS, which isusually underestimated as a management function. The second problem deals with the limitations posed by examining only twoperformance measures. The examination of other performance measures might have ledto slightly different results. As with any case study, the findings cannot easily begeneralized to other empirical settings of relevant industrial sectors (e.g. pharmaceutics,cosmetics, etc.). Since these industries are sharing the same supply chain managementprinciples, though, it is likely that they face similar challenges when developing a PMS.However, further investigation is required and a cross-industry comparative researchmight reveal a set of common challenges.
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  • 23. IMDS Further reading110,9 Slack, N. (1991), The Manufacturing Advantage, Mercury Books, London. About the authors Dimitris Papakiriakopoulos holds a BSc in Informatics and MSc in Information Systems from Athens University of Economics and Business (AUEB), and a PhD in Information Systems and1318 Artificial Intelligence also from AUEB. He is a Senior Research Officer at the ELTRUN Research Centre at AUEB. He has extensive research experience, having been involved in various research European projects for the last ten years and has more than 15 publications in scientific journals and international conferences. His research interests are on the area of machine learning methods, supply chain management and performance improvement though the intervention of technology. Dimitris Papakiriakopoulos is the corresponding author and can be contacted at: dpap@aueb.gr Katerina Pramatari is an Assistant Professor at the Department of Management Science and Technology of the AUEB. She holds a BSc in Informatics and MSc in Information Systems from AUEB, and a PhD in Information Systems and Supply Chain Management also from AUEB. She has won both business and academic distinctions and has been granted eight state and school scholarships. Her research and teaching areas are supply and demand chain collaboration, traceability and RFID, e-procurement, e-business integration and electronic services. She has published more than 60 papers in edited books, international conferences and scientific journals, including Decision Support Systems, Journal of Information Systems, Journal of Information Technology, The European Journal of OR, Computers and OR, Supply Chain Management: An International Journal, and International Journal of Information Management. To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints