collaborative performance

Industrial Management & Data SystemsEmerald Article: Collaborative performance measurement in supply chainDimitris Papakiriakopoulos, Katerina Pramatari

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To cite this document: Dimitris Papakiriakopoulos, Katerina Pramatari, (2010),"Collaborative performance measurement in supply chain", Industrial Management & Data Systems, Vol. 110 Iss: 9 pp. 1297 - 1318

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Collaborative performancemeasurement in supply chain

Dimitris Papakiriakopoulos and Katerina PramatariELTRUN, Department of Management Science and Technology,Athens University of Economics and Business, Athens, Greece

Abstract

Purpose – The objective of this paper is to demonstrate the challenges when developing a commonperformance 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, Partnership

Paper type Research paper

1. 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,the quest for performance is still an open issue (Fawcett et al., 2008).Performance-measurement concepts and tools have been proposed to covermanagement control needs for a single company (Kaplan and Norton, 1995; Andersonand Young, 1999; Otley, 1999). The integrative philosophy of supply chain management

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Collaborativeperformance

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Received 11 February 2010Revised 10 April 2010Accepted 19 June 2010

Industrial Management & DataSystems

Vol. 110 No. 9, 2010pp. 1297-1318

q Emerald Group Publishing Limited0263-5577

DOI 10.1108/02635571011087400

eliminates the boundaries of the single firm and puts emphasis on the effectiveness of thesupply chain as a whole (Bowersox and Closs, 1996; Chan et al., 2003). Relevant researchefforts in measuring performance of supply chains focus either on the identification ofsignificant 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 wassuggested by Holmberg (2000), who identified the fragmented measurement activities ofa Swedish home furnishing business supply chain and proposed the use of systemsthinking when developing PMSs. The importance of the topic has been recentlyrecognized by Busi and Bititci (2006), who have indicated collaborative performancemeasurement as an issue for further research.

The objective of this paper is to demonstrate the challenges when developing acommon PMS in the context of a collaborative supply chain, enabled by informationsharing practices between supplier and retailer. In doing so, we studied the collaborativeprocess of store ordering and shelf replenishment. Based on the analysis of userrequirements and interviews with experts, we concluded a set of performance measuresto be maintained by the collaborative platform. To limit the scope of research, we furtherinvestigated two crucial measures (inventory level and product availability), becausethey:

. 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 inefficienciesto core management control functions of the business processes.

In Section 2 of the paper, we briefly present the background literature of theperformance-measurement field, addressing the pertinent research streams, the types ofperformance measures, and the role of IT and summarize relevant initiatives and casestudies. The Section 3 describes the research methodology and the steps undertaken inorder to design a collaborative PMS. In order to focus on the realistic application and useof the system, a case study of an existing collaborative supply network is described inSection 4, where two important performance measures are examined in detail, followedby the identified challenges. Finally, Section 5 concludes the paper with the study’slimitations and thoughts for further research.

2. Performance measurement in supply chainPerformance measurement is the process of quantifying the effectiveness and efficiencyof action (Neely et al., 1995). The instrument that regularly supports theperformance-measurement process is referred to as PMS. A PMS maintains variousmetrics (performance measures) that are used for different purposes, like supportingdecision 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 managementfunction, evaluating the efficiency and the effectiveness of a process, resource or anoutcome. Most of the studies in the area argue that a PMS should contain financial andnon-financial metrics (Kaplan and Norton, 1995).

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Few performance frameworks have been proposed like activity-based costing(Anderson and Young, 1999), the balanced scorecard (Kaplan and Norton, 1995) andperformance 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 likethe 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 boundariesof 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


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performance measures, like variances against budget, human resource productivity,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 whatto measure and provide valuable information and guidelines for the design of the PMS.Folan and Browne (2005) reviewed the available recommendations and frameworks inthe area of performance measurement and identified more than 30 propositionsregarding how to build a PMS. Within the growing literature of recommendations,guidelines, performance measurement frameworks and suggested measures, littleattention has been paid on case studies that would enable the validation and extractionof 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 andfound substantial implementation barriers. The problems faced during theimplementation 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 theresearch stream of performance measurement is at the stage of identifying difficultiesand pitfalls to be avoided based on practitioner experience.

The need to bridge the gap between theory and practice has motivated the study ofimplementation issues of PMSs and frameworks (Lohman et al., 2004; Wagner andKaufmann, 2004; Fernandes et al., 2006; Searcy et al., 2008). These studies point out theusefulness of adopting a specific performance measurement framework, but they alsohighlight 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 theusers of the single PMS.

The discussion of implementing a PMS shifts the focus from the strategic/managerialperspective of performance, to the operational use and usefulness of an informationsystem. The advocate work of Holmberg (2000) identified that systems thinking has animportant role when developing PMSs in supply chains. In the same direction, Beamon(1999) suggested that a “system” of performance measures is required for accuratemeasurement of the supply chain. The implementation of a PMS addresses questionslike which are the relevant data, how do available data support the selected performancemeasures, 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 chainshould jointly agree on a PMS. Moreover, they suggest a generic process to measureperformance in supply chains with the following steps:

(1) design the PMS;

(2) facilitate measurement though the utilization of a common information sharingand resource-allocation system;

(3) provide incentives to the members of the supply chain; and

(4) intensify performance, which addresses system’s maintenance thoughcomparing 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 andparticipation of the members of the supply chain and in addition address the issue

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of maintaining the system. The approach of Bourne et al. (2000) is also based onsystems thinking, as they identify three different evolving stages for a PMS, namelydesign, implementation and maintenance.

Speakman et al. (1998) argue that collaboration is a dominant approach in supplychain 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;Lee 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 casesetting.

. It focuses on the implementation issues and challenges of the PMS.

. It refers to a collaborative supply chain with daily information sharing activitiesin 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


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of the process. The decision to develop a common PMS has been facilitated by thesuggestions of Simatupang and Shidharan (2003) regarding the jointly agreement onwhat to measure and how to measure. Approaching a common agreement betweenmultiple 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 thestore replenishment process and the business needs associated with it. This furtherguided 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 beenpaid to other collaborative planning, forecasting and replenishment cases within theretail 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 candidateperformance measures that were found relevant to implement by all the tradingpartners.

The presentation of the candidate measures to the experts paved the ground forin-depth interviews and acted as the base to exchange ideas for refinement of themeasures. At a later step, the performance measures were examined under twoperspectives:

(1) whether a common measurement method is acceptable by the trading partners;and

(2) whether the available data of the collaborative network can support theperformance measures.

The final step dealt with the implementation and evaluation of the performancemeasures. Intentionally, all types of performance measures (e.g. financial andnon-financial) were included. In the following sections, we present in more depth thetwo of the selected performance measures, inventory level and product availability,in order to demonstrate the challenges encountered during the development andevaluation of the PMS.

Figure 1.The researchmethod followed

Scope of the collaborative processes

Review by experts

Available data supportsperformance measures

Agree for a commonmeasurement method

Implement the performance measures

Evaluate performance measures

Yes Yes

No

Examine feasibility

Relevant literature

Candidate performance measures

The measure could be supported

No

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3.2 Data availabilityThrough the collaborative platform supporting the store-replenishment process, wehad access to various sources of data. Below an indicative list of the available data ispresented:

. Point-of-sales data are the collection describing the sales of the store on a dailybasis.

. Orders data describe the requests placed from the store to the Central Warehouse(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 whichproducts 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 theretailer and the supplier in collaboration for every store.

. Product assortment is the list of the active products currently available at aspecific store. Additionally, this file has information regarding the deliverymethod of a product (delivered by the CWH or direct store delivery (DSD) by theproduct supplier).

. Physical store audits are based on physical visits of researchers to the store inorder to spontaneously monitor Product Availability of selected products on thestore 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


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to be processes timely and accurately and delivered in a usable manner both tosuppliers and to the store managers.

A startup company, following the software-as-a-service business model, developedand operated the collaboration platform. One of the service extensions was thedevelopment of a PMS, as performance measurement is a necessary tool for successfulmanagement (Phusavat et al., 2009).

Managers from all the trading partners expressed their interest in examining theperformance according to the objective of the collaboration, which has been “to offerhigh service level to consumers by efficiently handling the store ordering processenhanced by information sharing capabilities”. The design of the PMS should be linkedto the collaboration strategy, in order to:

. demonstrate the results achieved through the new store ordering andreplenishment process and

. stimulate learning on the product suppliers’ side wishing to evaluate the effect ofcollaboration and examine the possibility of expanding the collaboration processto other retail chains.

Moreover, the adoption of a specific performance measurement framework (e.g. balancedscorecard) was found to be complex and costly, at least at the initial stage, due to thenarrow and structured scope of the collaboration. Therefore, it was decided to study alimited number of performance measures focusing on the specific collaboration processand on the responsiveness to the consumer. Relevant works in PMS design haveprovided guidelines for the PMS of our case. Table I depicts how existing suggestions inthe literature have affected some design options of the specific PMS.

Based on discussions with the managers, we examined their motives to join thiscollaboration 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 orout-of-stock situations, and the participants expected that they would be able tohandle the problem through collaboration.

Figure 2.The structure of thecollaborative network

Collaboration platform

Retailer distributioncenter (DC)

Backroom Shelf

Direct store delivery

StoreProductsuppliers

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. High forecast inaccuracies mean that demand forecasting could not be used formost of the products.

. Product shelf unavailability (also referred to as out-of-shelf (OOS)) has recentlyemerged as one of the most important problems in the retail sector affectingrevenue streams as well as consumer satisfaction.

. Imperfect orders address a “weak” connection between the retailer’s centraldistribution center (DC) and he stores, implying that the DC is not able to cover thestores’ demand.

Creating performance measures relevant to the problematic areas leverages thecommitment of managers to participate in the design of the PMS. Table II links

Suggested in the literature Impact on the design of the PMS

Reflect strategic alignment (Eccles, 1991;Kaplan and Norton, 1995; Bititci et al., 2000)

Understand the strategy of the collaboration as thetradeoff between effectiveness and responsivenessFocus on the results of collaborationExclude financial performance measures at the initialstage

Monitor critical activities (Azzone et al., 1991;Neely et al., 1995)

Focus on the store-replenishment processLink of problematic areas with specific performancemeasuresFocus at the store level of the supply chain

Measure product delivery from supplier tocustomer (Dixon et al., 1990)

The integrative view of the supply chain makes theconsumer as the only customerFocus at the store level of the supply chain

Provide measures that all members couldunderstand (Dixon et al., 1990)

Use collaboration platform to share performancemeasures in a daily base

Focus on measures that customer can see(Kaplan and Norton, 1995)

Develop performance measure that reflects the servicelevel of the collaboration to the consumer

Table I.Utilizing literature

suggestions in the PMSdesign of the case

Problematic area Interested partner Candidate performance measures

Inventory 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 (Neelyet al., 1995)

Imperfect orders Retailer Delivery reliability performance (Gunasekaran et al.,2004)Response delay (Kleijnen and Smits, 2003)Reliability (Neely et al., 1995)Deliverability (Neely et al., 1995)On-time deliveries (Neely et al., 1995)

Table II.Problems identified and

the associatedperformance measures


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the identified problematic areas with a set of candidate performance measures, as foundin the pertinent literature, and the partner mostly interested in the issue.

From a system architecture perspective, the proposed PMS is located at the top ofthe collaboration platform in order to have access to all the available information andbe visible to the respective trading partners. The PMS was developed as a reportingtool based on an integrative view of the shared data sources. Depending on theemployed measurement method, we distinguish the performance measures into twocategories:

(1) Single performance measures are derived directly by the data sources, aredeterministic in nature and can be expressed though simple formulas.

(2) Composite performance measures extend the available data sources with otherparameters (e.g. probabilities, loss functions, etc.).

Studies in performance system design do not usually include the calculation method foreach suggested performance measure. From a system perspective, this is a majordrawback, because the linkage between the performance measure and the available data ismissing. In our case, defining each performance measure was necessary in order toproceed with the implementation of the PMS. All measures are defined at the storeand product level. Table III provides information for the implementation of the selectedperformance measures. In order to demonstrate the challenges associated with thedevelopment of a common PMS, the measures selected for in-depth investigation areinventory 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 offera view at store level on a daily basis; and

. the former is examined as a single performance measure and the later as acomposite one.

Performancemeasure Type Data used Description Frequency

Inventory level Single Sales Number of items existing in the storefor a certain product

DailyDeliveries

Forecastaccuracy

Composite Forecast plansSales

The difference between the expectedsales and the observed sales.

Weekly

In-store promotionactivities

Seasonal and promotionamplification of the sales are takeninto account

Productavailability

Composite Sales Describes if a product is available onthe shelf of a store or not

DailyInventory levelsProduct assortmentPromotion activities

Imperfectorders

Single Orders Examines if the items and quantitydelivered meet the order of the store

DailyDeliveries

Table III.A view of theperformancemeasures used

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The next sections describe the challenges encountered during the development andevaluation of the two performance measures.

4.2 Measuring inventory at the storeThe 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,then 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 throughthe retail CWH, i.e. retail DC, on a regular basis.

(2) The second category is labeled DSD and includes the products delivered to thestore directly by the supplier.

(3) The last category (CWH/DSD) includes the products which do not meet any ofthe above classes. These products are replenished by both the CWH and theproduct supplier, in a mixture that is not known in advance, and it is subject tofactors 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 products

Store 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%) 2,566Store 7 1,771 (43.14%) 1,770 (43.12%) 564 (13.74%) 4,105Store 8 1,780 (44.82%) 1,721 (43.34%) 470 (11.84%) 3,971Store 9 735 (47.88%) 599 (39.02%) 201 (13.09%) 1,535

Table IV.Classification of products

based on the sales andinventory records


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through the CWH, because the ordering cost is lower compared to the cost of the DSD.The number of products delivered directly to the store varies between 35 and45 percent, depending on the store size. The remaining products (that are neither CWHnor DSD) are classified under the CWH/DSD label. On average, this class represents14 percent of products.

The collaboration platform shares on a daily basis data from the retail chain,including deliveries and sales data. This means that the transactions referring to theCWH products are timely available through the platform and, thus, in the following, welook only at CWH products. We assume that the total delivered quantity (Q) shouldalways exceed the observed sales (S) for a given product and store. Consequently, weadopt the holding inventory formula to calculate the inventory level as a storeperformance measure. By definition, holding inventory is non-negative and expressedby the following formula:

Holding Inventory ¼ Q2 S $ 0 ð1Þ

The percentage of records that equation (1) is violated has been examined using theavailable data (POS and deliveries data). As Table V presents, around 9 percent of therecords has a negative value for the holding inventory. This “unexpected” phenomenonis caused by deliveries occurring in the store and not monitored on time or at all by theinformation system. On the other extreme, the overstocked products are around5 percent, according to the available records, which is significantly high for retailbusiness. The inventory measure, relying on the available information, providesunrealistic results (negative values and high percent of overstocking items), which doesnot reflect the exact situation of the daily store inventory. However, we noticed that theholding inventory value for few product categories with long life cycle, low-pricedproducts and small promotion activity (e.g. snacks and pasta) could be correctlyestimated. Other categories, more expensive, with high promotion activity and frequentproduct introductions (e.g. detergents and shampoo) are on the other extreme, and it isvery 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 holdinginventory is an important barrier for the development of a widely acceptableperformance measure in the specific case. Additionally, the variability of the holding

Negative holdinginventory

(%)

Very low-holdinginventory

(%)

Low-holdinginventory

(%)

Normal levels ofholding inventory

(%)Overstocking

(%)

Store 1 10.78 9.38 16.37 58.57 4.90Store 2 10.56 14.05 28.10 43.98 3.31Store 3 7.70 13.32 25.68 48.07 5.23Store 4 13.42 11.69 18.56 48.91 7.42Store 5 7.7 10.98 26.14 50.07 5.64Store 6 9.11 15.21 34.18 36.76 4.74Store 7 6.32 12.59 30.89 44.77 5.43Store 8 8.15 12.75 27.81 45.63 5.67Store 9 14.55 16.60 32.65 33.76 2.44

Table V.Inaccurate records of theholding inventory

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inventory changes between the stores. To this end, the development of a performancemeasure related to inventory level at the store could not be supported due to informationquality restrictions. Possible options to gather a more realistic inventory view could bebased on the following:

. employment of multiple inventory measurement methods for different stores andproduct categories;

. use of probabilistic models to calculate inventory shrinkages; and

. 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.

RuleID Rule body AccuracyProducts merchandized

(%)

Rule 21 (LastPosDays $ 3) AND (day ¼ ‘Wednesday’)AND (StoreSize ¼ ‘Large’) AND(SD_DailyPosAvg # 2.82) AND(FastMovingIdx . 0.76) 0.82 0.4

Rule 43 (LastPosDays . 6) AND (SD_PosAvg . 7.9) AND(day ¼ ‘Tuesday’) 0.42 0.1

Rule 47 (TypeOfProducts ¼ ‘ADV’) AND (posavg . 1.9)AND (Last_Order . 12) AND(Mean_Order_quantity , 6) 0.91 0.01

Table VI.Indicative rules used for

detecting productsmissing from the shelf


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Although the rules have been found to accurately detect OOS products, they have animportant drawback, because they cannot cover all the different cases of productsmissing from the shelf. For example, Rule 21 characterizes as OOS the products thathave not sold for the last three days (LastPosDays . ¼ 3), the day of detection isWednesday (day ¼ “Wednesday”), the area of interest is only the large stores of theretail chain (Store_Size ¼ “Large”), the standard deviation of sales only for Wednesdayshould be low (SD_DailyPosAvg , ¼ 2.82) and finally the products are fast-movingitems (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 thestore. Thus, on one hand, the collaborative network acquired an accurate mechanism formeasuring product availability; on the other hand, the mechanism only partiallymonitors the products merchandized by the store.

However, a linear correlation has been found between the OOS rate and the number ofproducts the system detects as OOS per day. The higher OOS rate a store has, the moreOOS 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 ofproducts the detection mechanism reports as OOS per day. As expected, stores with agreater OOS problem receive relatively higher counts by the detection mechanism.

The performance measure for estimating product availability was found veryinteresting by the participants, but the measurement method employed was rathercomplicated. Based on the available data of the collaboration platform, an intelligentinformation system was designed and developed but this had limited detectioncapabilities for the products missing from the shelf. Nevertheless, the employedmechanism correctly detected the retail stores encountering the biggest OOS problem,thus offering a reasonable and uniform method to product suppliers and the retailerto agree on the stores having the lower product availability and to start planningcorrective actions.

4.4 Implications and discussionThe available case setting revealed some aspects in the area of performancemeasurement. The need for a universal framework for selecting performance measuresin 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 PMScould be grouped into three broad categories namely:

(1) data management;

(2) business-process management; and

(3) collaboration.

Real world Detection mechanismProducts monitored Average OOS rate (%) Average daily alarmed products

Store 1 4.548 8.91 78Store 2 3.401 11.70 94Store 3 3.120 12.12 115Store 4 4.079 8.53 82Store 5 4.634 9.60 103Store 6 2.870 12.47 117

Table VII.Relationship betweenOOS and alarms by thedetection mechanism

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Data management. Data management includes all the actions performed during the lifecycle of shared data. While the importance of information sharing has been recognized inthe 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 activitiesand 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(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.


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Collaboration. Most of the benefits identified on the product supplier side derivedfrom the practice of information sharing. Based on collaboration and informationsharing, the trading partners reported better performance of decisions in the store-replenishment process (Lee and Whang, 1999). The availability of daily OOS alarms tosuppliers per retail store has been considered as valuable information since it facilitatessupply chain visibility and allows for rapid corrective actions. From the retailer’sperspective, this information is useful only because it provides a uniform approach toestimate 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: whileevery partner activates its individual mechanisms and identifies areas of improvementin 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 aPMS. Bourne (2005) examined 16 different performance measurement cases at differentlevels of design and implementation. His findings suggest that the top managementsupport 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 definitionof 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 beimplemented, as happened to our case. It is also important that non-financial performancemeasures 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 theresources 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 agreedobjectives of collaboration and the results achieved.

Common implementation problems, as already discussed in the literature, have beenfound in the presented case. Lack of a structured development process of the PMS (Hudsonet al., 2001) and increased effort to collect data and support composite performancemeasures (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 thepresented PMS.

At the early 1980s, a PMS was close to the budgetary control and aligned withaccounting procedures (Traditional PMS). Ten years later, the management thinkingapproach broadened the view of performance measurement and initiated the discussionregarding strategic alignment of measuring performance, improvement thoughmeasurement, focus on the quality, etc. The role of supply chain management enabledby information technology allowed the discussion about an integrative approach onmore complex structures and how to manage them though performance measurement(supply chain PMS). We could say that the case presented in this paper discusses whatwe could call a collaborative PMS, making performance measurement a central issue inmanaging collaboration (collaborative PMS). Table VIII summarizes the differenceamong the three classes of PMS.

5. ConclusionsThis paper discusses the development of a PMS in a collaborative context. Althoughcompanies in supply chain networks have the constant need to measure performance,

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the 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.

Traditional PMS Supply chain PMS Collaborative PMS

Measuring Identify performancemeasures

Identify performancedimensions according to thesupply chain structure

Measures derived from theobjective of collaboration

Number ofmeasures

Variable number ofperformance measures

Increased number ofperformance measures tocover all the dimensions

Limited number ofperformance measures

Measuresusage

Bias towards financialmeasures

Focus on financial and non-financial measures

Focus on non-financialmeasures

Motives tomeasure

Measure to improve Measure to understand,identify areas and improve

Measure to understand thesuccess of collaboration

Approach Accounting Management thinkingSystem thinking

Systems thinking

Datamanagement

Significant effort toidentify and gather data

Very significant effort toidentify and gather data

Information sharing enabledby technology

Data issues Gather available data Integrate available data Information qualityScope Firm Firm and trading partners Collaborating partners

Table VIII.Classification of PMSs


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Up to now, the knowledge for implementing and maintaining a common PMS islimited. With this work, we demonstrate some challenges related to the topic. Furtherresearch is required for the identification of additional challenges, opportunities andbarriers though case studies, where collaboration and information sharing are the keycomponents of business-process management. Selecting common performancemeasures is also a challenging area because it deals with important management andorganizational issues like negotiations, strategic fit and alignment. Finally, theacceptance of a common PMS is an important issue, because it is expected to facilitatebenchmarking within a collaborative network, allowing direct comparisons of existingbilateral relationships of the trading partners.

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Further reading

Slack, N. (1991), The Manufacturing Advantage, Mercury Books, London.

About the authorsDimitris Papakiriakopoulos holds a BSc in Informatics and MSc in Information Systems fromAthens University of Economics and Business (AUEB), and a PhD in Information Systems andArtificial Intelligence also from AUEB. He is a Senior Research Officer at the ELTRUN ResearchCentre at AUEB. He has extensive research experience, having been involved in various researchEuropean projects for the last ten years and has more than 15 publications in scientific journalsand 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: [email protected]

Katerina Pramatari is an Assistant Professor at the Department of Management Science andTechnology of the AUEB. She holds a BSc in Informatics and MSc in Information Systems fromAUEB, and a PhD in Information Systems and Supply Chain Management also from AUEB. Shehas won both business and academic distinctions and has been granted eight state and schoolscholarships. Her research and teaching areas are supply and demand chain collaboration,traceability and RFID, e-procurement, e-business integration and electronic services. She haspublished more than 60 papers in edited books, international conferences and scientific journals,including Decision Support Systems, Journal of Information Systems, Journal of InformationTechnology, The European Journal of OR, Computers and OR, Supply Chain Management:An International Journal, and International Journal of Information Management.

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