Exploring supplier performance risk and the buyer's role using chance-constrained data envelopment analysis

Accepted Manuscript

Exploring Supplier Performance Risk and the Buyer’s Role Using Chance-Constrained Data Envelopment Analysis Anthony Ross , Kaan Kuzu , Wanxi Li PII: DOI: Reference:

S0377-2217(15)00903-0 10.1016/j.ejor.2015.09.061 EOR 13280

To appear in:

European Journal of Operational Research

Received date: Revised date: Accepted date:

12 August 2014 6 August 2015 29 September 2015

Please cite this article as: Anthony Ross , Kaan Kuzu , Wanxi Li , Exploring Supplier Performance Risk and the Buyer’s Role Using Chance-Constrained Data Envelopment Analysis, European Journal of Operational Research (2015), doi: 10.1016/j.ejor.2015.09.061

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Highlights We evaluate supplier efficiency with data envelopment analysis and under delivery disruptions. We examine supplier risk by considering buyer's information sharing performance factors. Supplier classification is sensitive to the severity of on-time delivery disruption. Buyer's ability to share timely/accurate information impacts supplier performance. Managers can identify suppliers capable of absorbing information uncertainty and disruptions.

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Exploring Supplier Performance Risk and the Buyer’s Role Using Chance-Constrained Data Envelopment Analysis Anthony Ross* ([email protected] - 414.229.6515) Kaan Kuzu ([email protected] - 414.229.6208) Wanxi Li ([email protected] - 414.229.4235)

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Supply Chain and Operations Management Sheldon B. Lubar School of Business University of Wisconsin-Milwaukee Milwaukee, WI USA 53201-0742 *Corresponding author

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Abstract

Current sourcing paradigms are described by greater complexity in managing suppliers' performance in purchasing cycle execution, product quality and logistics competence. When we combine the stages of the order fulfillment cycle with the buyer's information sharing factors, a

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more complete view of the buyer-supplier exchange becomes useful for performance evaluation of both the suppliers and the buyers. Using operational data obtained from a large

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telecommunications firm, which placed strategic importance on measuring the performance in the purchase order fulfillment cycle stages, this study offers a new context for supplier

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evaluation. This context also conceptualizes several on-time delivery disruption scenarios as supplier delivery performance risk, and investigates the sensitivity of both the buyer’s

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information sharing and the suppliers’ performance capabilities to on-time delivery disruptions. We evaluate supplier performance and segment suppliers through recourse to chance-constrained data envelopment analysis at high and low discriminatory power levels. Our results show that

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there are statistically significant relationships between the dimensions of information sharing by the buying firm and the classification of supplier firms at varying levels of on-time delivery performance risk. We also identify robust suppliers demonstrating the capability to remain efficient across disruption levels despite poor buyer performance on information sharing factors, and provide managerial insights on buyer-supplier exchange. KEYWORDS: Supplier Performance Risk, Information Sharing, Supplier Classification, Data Envelopment Analysis 2

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Exploring Supplier Performance Risk and the Buyer’s Role Using Chance-Constrained Data Envelopment Analysis 1. Introduction Traditional sourcing paradigms were developed to manage conditions described by stable

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demand, reasonable certainty in demand forecasts, high levels of stock, reliable transportation, and high volume production, all supported by fiscal solvency. However, newer sourcing paradigms of multi-sourcing, outsourcing and off-shoring, among others, have increased total non-conformance costs, and further complicated the management of suppliers’ quality and delivery performance. In both sourcing paradigms, understanding a (domestic or foreign)

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supplier’s performance capability is vital to the allocation of purchase decisions. Moreover, the current business climate of increased fiscal insolvency, poor quality in the form of scrap or rework, poor synchronization between the buyer-supplier exchange, and unreliable delivery performance all lead to more pronounced non-conforming performance in the buyer-seller exchange (Hoetker, 2005).

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With rare exception (Eisenhardt and Martin, 2000), studies evoking or testing the resource-based view (RBV) typically assume resource or capability uniqueness resides with the

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organization as a whole, implying homogeneity in terms of the applicability and diffusion of unique resources and capabilities to the suppliers comprising the supply base (Miller et al., 2010;

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Adner and Helfat, 2003). If the firm’s resources and capabilities are indeed internally homogeneous, and evenly distributed throughout the firm, then it is a relatively easy task for top

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managers to specify and codify best practices, or identify appropriate internal role models that each unit can adopt to improve its performance. However, we contend that this assumption may be invalid, and that resources and capabilities are heterogeneously distributed throughout the

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firms comprising the buyer’s supply base. The extreme case of such resource heterogeneity would be that advantages accruing from unique resources and capabilities in one supplier firm do not, or may not even be able to, pass along the same advantages to other suppliers. Such is the case when considering suppliers’ performance risk and the buyer’s information sharing role during the order fulfillment cycle.

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Much importance is accorded to the information flows between the buyer and the supplier exchange to enhance synchronization during the order fulfillment cycle/process (Rezaei and Ortt, 2013; Joshi, 2009; Prahinski and Benton, 2004). For example, sourcing alternatives are vital when suppliers fall short in compliance to order acknowledgement, quality, cost, on-time delivery, or on-time shipment. Conversely, the same is often true when the buying firm falls

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short in sharing revised forecasts, or subjects its suppliers to capacity shirking by changing purchase order requirements (Oehmen et al. 2009). In such case, the buying firm should factor its own performance into the evaluation process. Therefore, newer sourcing paradigms imply that buyer/supplier performance risk is an area in need of investigation in the literature.

Information flows are embedded in the dynamic planning process where the standard

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procedure for firms to react on updated forecasts is by periodically revising the actual production schedule while rolling forward the planning horizon (de Kok and Inderfurth, 1997). Vollman et al. (1988) state that operations with revised plans are connected with discontinuities in maintaining former ordering decisions, which is stated as nervousness syndrome. Nervousness, also known as lack of planning stability, results in considerable adjustment efforts as well as

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possible loss of confidence in planning (de Kok and Inderfurth, 1997). Changes in demand quantities can lead to schedule nervousness and be an obstacle to effective planning

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(Kadipasaoglu and Sridharan, 1995). If the buyer’s information on the required quantities of supplies and the dates the shipments must be at the buyer location is of poor quality, suppliers

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will face severe disruptions in their own production schedules, which in turn will affect their own suppliers’ schedules. Krajewski et al. (2005) identify two reaction strategies, reduce uncertainty

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(for infrequent revisions) and cope with uncertainty (for relatively frequent revisions), suppliers use to respond to schedule changes occurring within a supplier’s manufacturing and delivery lead time. Sahin and Robinson (2005) show that information sharing not only reduces costs, but

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also provides more economic benefits from coordinated decision-making. Liker and Wu (2000) show that significantly fewer short-term changes in schedules contribute to better operational performance of the suppliers. While the generic term ‘supplier risk’ is very broad and replete with many definitions (Khan and Zsidisin, 2012; Tang, 2006; Wu et al., 2010), we adopt the view in this study that supplier risk refers to the adverse effects of deviations in measured supplier performance factors 4

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such as on-time delivery and others. Therefore, we use the term supplier performance risk referring to the effects of deviations in the measured performance factors. We characterize it as either the sudden process-level failures of manufacturing, logistics, or supply management during the buyer-supplier exchange, or, a distortion in planning information that is shared between buyers and their suppliers. There is a continuing need to understand supplier

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performance risk (Craighead et al., 2007; Wagner et al., 2009; Lee and Johnson, 2010). Its practical importance (L’Oreal, 2010; A.T. Kearney, 2011; Bunkley, 2011) evolved from the heavy reliance upon traditional competitive sourcing practices that drove key suppliers to financial ruin (Martha and Subbakrishna, 2002).

This study extends the only other known study of supplier performance risk in a chance

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constrained data envelopment analysis context (Talluri et al., 2006). Our work goes further in several important ways. First, until now, a more robust evaluation of supplier performance risk has been constrained by the absence of a more diverse industrial case application context and the oversight in consideration of the buyer’s role in supplier performance risk. Therefore, we attempt to fill this gap in the literature using operational data obtained from a large telecommunications

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firm that placed strategic importance on its supplier performance management system. Second, the supplier factors considered in this study represent stages of the order fulfillment process or

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cycle. When we combine the stages of the order fulfillment cycle with the buyer’s information sharing factors, a more complete view of the buyer-supplier exchange becomes useful for

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performance evaluation of both the suppliers and the buyer. Finally, in this view, we investigate the relative performance differences and the sensitivity of buyer’s information sharing and

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supplier’s performance capabilities, affected by disruptions in on-time delivery, for efficient and inefficient supplier groups. Taken together, we offer a new perspective for evaluating the supplier performance risk by considering each of these prior limitations under nine levels

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(scenarios) of on-time delivery disruption. The rest of the manuscript is organized as follows. Section 2 melds together the relevant

supplier performance risk and performance evaluation literature. Section 3 presents the data used and the research methodology. A discussion of alternative risk and disruption scenarios is presented along with results in Section 4. Finally, the concluding section offers several interpretations and research implications. 5

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2. Related Literature This section reviews the relevant literature on supplier performance risk and methods of supplier performance evaluation. Several issues require further investigation in the supplier performance literature. These include extending the dimensions beyond simple aggregations of quality, cost and delivery performance to include dimensions reflecting information sharing

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between the buyer and supplier (Prahinski and Benton, 2004; Tuncel and Alpan, 2010), the issues of performance segmentation (Rezaei and Ortt, 2013), and shirking by the buyer or supplier (Swink and Zsidisin, 2006). These all have interpretations for shielding organizations against supplier performance risk (Neiger et al., 2009). From the RBV perspective, resource and capability heterogeneity are central to explaining performance differences among firms (Teece,

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Pisano and Shuen, 1997). If we apply the principle underlying the Austrian School (Jacobson, 1992) to the supply base of a buying organization, then it is conceivable that heterogeneity is likely to exist across the supply base, as suppliers often differ in their ability to innovate and to gather and exploit information regarding the resources and capabilities relevant to performancebased future opportunities with their buyers. However, little is understood regarding supplier

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performance risk in the context of information sharing. The following sections offer several

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premises based upon our interpretation of this relevant literature. 2.1 Related Supplier Performance Risk Literature

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The first premise is that current supplier performance evaluation practices and risk management programs increasingly focus on mitigating the fiscal and the operational impact of

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supplier performance risk. Academic research in this area still draws considerable attention (Ojala and Hallikas, 2006; Tang, 2006; Wu et al., 2010; Khan and Zsidisin, 2012). The supplier

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performance risk literature is fairly new as a research domain. In a broad sense, it includes any uncertainty across the supply chain network that leads to failures in normal business operations for the supplier and/or the buyer, and the deployment of their resources/capabilities (Tomlin, 2006; Rao and Goldsby, 2009). The second premise is that the impact of the supplier performance risk, as one category of supply chain disruption, is an increasingly important topic in the literature, and information sharing (order stability and nervousness for example) in the buyer-supplier exchange may 6

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moderate the severity of risk impact. Kleindorfer and Saad (2005) point to the risks arising from not synchronizing activities and the absence of information sharing between buyers and suppliers. The authors provide empirical evidence of the positive impact of risk management and conclude that strategic and tactical actions should be taken to minimize disruption impacts. Tachizawa and Thomsen (2007) classify supply chain risks according to their source (within the

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company, downstream, and upstream), and point out that improved supplier resiliency and flexible sourcing are two main strategies to mitigate supply risk. Craighead et al. (2007) outline supply chain factors that are positively related to the severity of a supply chain disruption. While literature on this subject is vast, these particular studies are representative as they highlight the ubiquity of risk in the supply chain and the need for additional study from the supplier

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performance risk modeling perspective (Ritchie and Brindley, 2007; Wu et al. 2010). As such, a new step that is needed in this literature is the explication of the relationship between supplier and buyer factors under alternative supplier performance disruption scenarios. Next section details efficiency-based evaluation methods since it is the approach adopted in this examination. 2.2 Data Envelopment Analysis (DEA) vs Chance Constrained Data Envelopment Analysis

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(CCDEA) in Supplier Performance Evaluation under Risk

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Useful existing approaches for supplier performance evaluation include weighted linear models (Timmerman, 1987), Analytical Hierarchy Process (Barbarosoglu and Yazgac, 1997), discrete choice analysis (Verma and Pullman, 1998), total cost of ownership (Ellram, 1995),

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efficiency (Weber et al., 1998), multi-objective programming (Wu et al., 2010) and others. Each has its own unique strengths and limitations. However, efficiency-based approaches consider

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multiple measures simultaneously, are widely accepted (Wu and Blackhurst, 2009; Wu et al.,

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2010), and allow for the examination of supplier performance risk conceptualized in this study. The efficiency-based evaluation method of data envelopment analysis (DEA) is accorded

to Charnes and Cooper (1959), and continues to be widely accepted for supplier evaluation due to its multi-attribute nature (Wu, 2009). It is a non-parametric approach to measure the relative efficiency of organizational units in the presence of multifarious inputs and outputs. Refinements reported in Charnes et al. (1978a, b) developed this mathematical programming model further and are regarded as the “CCR” approach to evaluate efficiency of organizational units (i.e.,

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public schools, manufacturing plants, supplier firms to a buying organization) under constant returns to scale assumptions. DEA computes an efficiency score for each evaluated unit, called Decision Making Unit (DMU), and relates the performance of evaluated units to a piecewiselinear production frontier. It empirically estimates the production function for each evaluated unit based on the ratio of output factors and input factors of the most efficient units. Those units

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with production function values on the production frontier are assigned a value equal to 1, and those under the frontier receive values between zero and one. DEA accommodates differences in firm size, management objectives or other characteristics. Finally, DEA requires no statistical assumptions about the underlying data. Many modeling variants for a variety of problem contexts have appeared over the last four decades (Liu et al., 2000; Chen and Yan, 2011).

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There has been renewed attention to understanding the impact of risk on supplier performance evaluations (Araz and Ozkarahan, 2007; Wu et al., 2010; Lockamy and McCormack, 2010; Jung et al., 2011). The evaluation of supplier performance risk using DEA was not possible until the work by Land et al. (1993). They extended the seminal study by Charnes and Cooper (1959) who introduced chance-constrained programming to DEA (now

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called CCDEA). In CCDEA, input factors or output factors are regarded as stochastic rather than deterministic metrics as in the traditional CCR model (Land et al., 1993); therefore, the CCDEA

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approach to performance evaluation takes the uncertainty or risk in DMUs’ inputs or outputs into account. The evaluation results can then be used to uncover the overall performance outcomes of

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the DMUs (e.g. suppliers). Talluri et al. (2006) and Wu et al. (2010) both point out the continued importance accorded to evaluating and selecting supply chain partners and propose two diverse

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approaches, namely efficiency analysis and multi-objective programming techniques while addressing supplier risk.

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The third premise is that simple segmentation of efficient and inefficient suppliers by

utilizing DEA models is not sufficient so we also i) analyze the behavior of efficient/inefficient suppliers and buyers under different levels of on-time delivery disruption levels, and ii) identify the patterns leading to efficiency and inefficiency of suppliers. Furthermore, we provide the framework for associating the levels of disruption (risk) in the supplier performance to the CCDEA model parameters and offer a new analysis approach. Our study addresses some research gaps using perspectives of efficiency as discussed in Section 3. 8

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3. Evaluating Supplier Performance with Stochastic Factors The fourth premise of this study is that understanding performance variability among suppliers and buyers offers important insights for sourcing decisions and the development needs of these firms (Chang, et al., 2006; Wu and Blackhurst, 2009; Jung et al., 2011). A sudden amplification of operational uncertainty caused by a disruption in operations or information

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sharing often leads to a deviation in expected performance. Similarly, distortions in information (e.g. poor forecast accuracy) or supplier shirking (frequent purchase order changes) in the supply chain will impact the ability to meet expected scorecard targets and thus reduce performance along the supply chain (Lee et al., 1997; Burt et al., 2010). This section begins by describing the dataset that consists only of quantitative data due to our focus on uncertainty in supplier

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performance. Following the dataset description, the methodology discussion covers the traditional CCDEA models and our incorporation of varying levels of supplier performance risk into the CCDEA models of efficiency evaluation. 3.1 The Data

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Table 1 below lists the specific supplier-side (referred to as Output Performance Factors) and buyer-side (referred to as Input Performance Factors) performance factors used in this

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manuscript and their unit of measure. On the supplier-side, operational synchronization, delivery reliability and responsiveness between the supplier and the buyer are generally influenced by the

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order-to-delivery performance capabilities in firm. As observed in several leading purchasing organizations, the general purchase transaction cycle process can be divided into stages

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determined most relevant to the firm. Each stage is then assigned a relevant metric to be monitored during the course of transacting with suppliers.

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Such was the case in the testbed electronics firm in which the stages included the

following specific scorecard metrics: (1) the timely acknowledgement of purchase orders (PORAC), (2) sending shipment notices in advance (PSNOT), (3) releasing shipments on the original promised ship dates (PSHIP), (4) releasing shipments to carriers on the committed date (OTS), and (5) adhering to on-time delivery (OTD) expectations (Liu et al., 2000; Monczka et al., 2011). These metrics reflect many, if not all, of the major stages of the supplier’s replenishment and delivery performance during a typical exchange with buyers. PORAC, 9

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PSNOT and PSHIP reflect the competence of the suppliers in purchasing processes, whereas OTS and OTD reflect the competence of the suppliers in logistics processes. Quality acceptance rate, ACCEPT, is also included on the supplier scorecard of the buyers (Wu et al., 2007; Forker et al., 1999) and reflects the competence of suppliers’ product quality. In the RBV lens, we conclude that under some contexts, disruptions lead to inefficient use of resources or operational

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congestion leading to incomplete or damaged orders both of which lead to rejected shipments. A sample of the full data set used for this study appears in Appendix A. Table 1 Definitions of the output and input performance factors (metrics are measured in %) Area of Competence

Metric PORAC

Purchasing

Output 1

PSNOT

Purchasing

Output 2

PSHIP

Purchasing

Output 3

OTS

Logistics

Output 4

% of orders shipped on or before final ship date

OTD

Logistics

Output 5

% of orders delivered by the due date

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Product Quality

Output 6

% of products/items not rejected upon inspection

BSTAB

Performance Factors

FA COF

Info Share; Order Stability Info Share; Schedule instability/nervousness Info Share; Schedule instability/nervousness

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Factors

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Performance

Input

Definition of Metric

% of order acknowledgements received with a promise ship date within 24 hrs. of PO issue % of supplier’s shipping notices received at buyer within 24 hrs. of ship date % of orders shipped to buyer on or before the ORIGINAL promise ship date

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Output

ID

Input 1

Purchase order stability: frequency that buyer made no change to purchase order

Input 2

Buyer’s forecast accuracy

Input 3

Buyer’s comprehensiveness of forecast

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The work in supplier evaluation has used many different classes of data. We reviewed the long history of literature and found that many authors have used two or more variables in the

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form of proportional data in their evaluation studies. For example, Chen and Chen (2006) actually used six such variables, Reiner and Hoffmann (2006) used three, and Sevkli et al. (2007)

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used several as well. There are also several studies/surveys investigating the kinds of variables that are used to evaluate suppliers in practice (Liu et al., 2000; Jung et al., 2011). The variables we used are very similar to the ones reported in these studies. Given that our data is actual data currently in use by a Fortune 500 Corporation as key metrics of its supplier performance management program, there is practical relevance for its use in this study of supplier risk evaluation with performance disruptions.

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On the buyer-side of performance evaluation, the volatile nature of customers’ preferences and the dynamics of their respective markets are recognized as critical concerns (Sodhi and Lee, 2007). That is, the focal buyer firm uses a scorecard system that incorporates factors reflecting purchase order stability (BSTAB), forecasting accuracy (FA) and comprehensiveness of forecasts (COF). These are buyer factors that represent the competence of

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buyers in information sharing and planning processes, and often affect the supplier’s ability to meet performance expectations (Kahn, 2002; Saloner and Spence, 2002). Purchase order stability (BSTAB) measures the percentage of the orders the buyer made no changes to during the performance review period. Buyer’s Forecast Accuracy (FA) represents the ratio of the actual number of units the buyer purchases to the forecasted number of units to purchase during the

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performance review period. Although the ratio can theoretically exceed 1.0, the company did not observe such a case in actual practice as the managers frequently revised both the planned purchases and/or the actual purchase quantities as part of the regular planning and review meetings. Buyer’s Comprehensiveness of Forecast (COF) calculates the ratio of the number of product lines the buyer actually purchases to the number of product lines the buyer forecasts to

potential lines to purchase.

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purchase. There is no penalty for ordering from product lines that were not initially forecasted as

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Providing accurate forecasts, minimizing changes to released purchase orders, and committing to planned purchase schedules can impact supplier performance (Chopra and Sodhi,

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2004; Hendricks and Singhal, 2009; González-Benito, 2007). Suppliers generally benefit from accurate buyer information on demand schedules, stable orders and sufficient volume

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commitments to cover their production cost structure.

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3.2 CCDEA Model Formulation This section introduces the CCDEA model with deterministic input and stochastic output

factors to evaluate the performance of DMUs (suppliers) under varying levels of supplier performance risk. CCDEA is a popular technique for supplier evaluation and ranking has been used by Land et al. (1993), Sengupta (1990), and Cooper et al. (2002). Land et al. (1993) provides the first established CCDEA model with the following standard notation and formulation:

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Notation Used:

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 ii=1,…I,

supplier sample index (the set of decision-making units), input factors, output factors, sample input matrix, with dimension M * I, sample output matrix, with dimension N * I, row vector of X, row vector of Y, one output factor of the supplier under evaluation, one input factor of the supplier under evaluation, column vector of inputs of the particular DMU investigated, column vector of outputs of the particular DMU investigated, (radial input) contraction factor, DMU’s efficiency score, weights vector, for all DMUs (column vector of DMU loadings, determining the “best practice” for the DMU being evaluated).

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i, j = 1,…,I m = 1,…,M n = 1,…,N X = [xmi] Y = [yni] Xm Yn yn0 xm0 X0 = [xm0] Y0 = [yn0]

Model 0

subject to Prob(𝑌 𝑛 𝜆 ≤ 𝑦𝑛0 ) ≤ 𝑝, 𝑛 = 1, … , 𝑁

(1)

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min 𝜃

(2)

𝜃𝑥𝑚0 ≥ 𝑋 𝑚 𝜆, 𝑚 = 1, … , 𝑀

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𝜃 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑, 𝜆 ≥ 0

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This model assumes that the sample input matrix X (for the input factors) is predetermined and the sample output matrix Y (for the output factors) is random. Specifically, the outputs yni (𝑛 = 1, … , 𝑁) are assumed to be jointly normal conditional on the inputs xmi

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(m= 1, … , 𝑀). The optimal (radial) contraction factor  represents the DMU efficiency scores, whereas the p-level represents the probability that the observed outputs exceed best-practice

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outputs. The “best practice” for the observed DMU is a weighted arithmetic mean of all the observations, and the individual components of the -vector form the weights for the DMUs. In other words,  is the vector of loadings that determine the best practice for the evaluated DMU. Model 0 minimizes  considering two sets of constraints. In the first equation set (1), the model limits the percentage of cases where the observed outputs exceed best practice outputs with a known probability denoted as “p-level”. We refer to this as the discriminatory power level 12

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or power level. In the second equation set (2), we ensure that the best practice suppliers do not use more inputs than 𝜃𝑋0 . It can be shown that Model 0 is easily converted to certainty equivalents depicted in Model 1 below (Charnes and Cooper 1959, 1962). Model 1

subject to 𝐸(𝑌 𝑛 𝜆 − 𝑦𝑛0 ) − 𝐹 −1 (1 − 𝑝)𝜎 ≥ 0, 𝑛 = 1, … , 𝑁 𝜃𝑥𝑚0 − 𝑋 𝑚 𝜆 ≥ 0, 𝑚 = 1, … , 𝑀

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𝜃 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑, 𝜆 ≥ 0

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min 𝜃 (3) (4)

where  is the standard deviation of (Yn- yno) given by equation (5) below, F is the cumulative distribution function of the standard normal distribution and E is the expectation operator.

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𝜎 = √(∑𝐼𝑖=1 ∑𝐼𝑗=1 𝜇𝑖 𝜇𝑗 𝐶𝑜𝑣(𝑦𝑛𝑖 , 𝑦𝑛𝑗 ))

where 𝐶𝑜𝑣 is the covariance operator, and 𝜇𝑖 = {

(5)

𝜆𝑖 , ∀𝑖: 𝑖 ≠ 0, (𝜆0 − 1), 𝑖 = 0.

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As outlined in Land et al. (1993), it is required that 𝜎 ≥ 0, 𝑛 = 1, … , 𝑁. This condition imposes restrictions on the values of 𝜇𝑖 , i.e. 𝜇𝑖 ≥ 0, 𝑖 = 1, … , 𝐼, 𝑖 ≠ 0 and 𝜇0 ≥ −1.

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Model 1 is solved once for each DMU to obtain the DMU-specific contraction factor 𝜃 ∗ . The value of the contraction factor 𝜃 ∗ cannot exceed unity. Depending on the value of 𝜃 ∗, we

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provide three possible interpretations for the performance of the evaluated DMU:

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1) 𝜃 ∗ < 1DMU is CCDEA-inefficient. 2) 𝜃 ∗ = 1 but some constraints contain slack at the optimum: DMU is Type-F inefficient (or sub-efficient). Type-F inefficient suppliers face two possibilities: a) They require more input than the best-practice DMUs do to generate the best level of outputs, or b) they generate less outputs than the best-practice DMUs’ do with the current level of inputs. In either case, Type-F inefficient DMUs’ performance can be further improved since they are characterized by an excess of inputs or insufficient level of outputs. 3) 𝜃 ∗ = 1 and all the constraints are binding at the optimum: DMU is CCDEA-efficient and categorized as Type-E.

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Model 1 is usually adjusted to Model 2 (below) in implementation to avoid possible difficulties that might arise if one or more of the Lagrange multipliers become zero. Model 2 𝑚𝑖𝑛 𝑧 = 𝜃 − 𝜀(𝒆 ∙ 𝑠 + + 𝒆 ∙ 𝑠 − )

− 𝜃𝑥𝑚0 − 𝑋 𝑚 𝜆 − 𝑠𝑚 = 0, 𝑚 = 1, … , 𝑀

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subject to 𝐸(𝑌 𝑛 𝜆 − 𝑦𝑛0 ) − 𝐹 −1 (1 − 𝑝)𝜎𝑛 − 𝑠𝑛+ = 0, 𝑛 = 1, … , 𝑁

(6) (7)

− 𝜃 𝑢𝑛𝑟𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑, 𝜆, 𝑠𝑛+ , 𝑠𝑚 ≥ 0 𝑛 = 1, … , 𝑁, 𝑚 = 1, … , 𝑀

−] In Model 2, 𝑠 + = [𝑠𝑛+ ] and 𝑠 − = [𝑠𝑚 are the column vectors of the output and input

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slack variables, e is the row vector of suitable dimension with unity in all positions, and 𝜀 > 0 is a non-Archimedean infinitesimal. Again, a DMU is CCDEA-efficient if and only if 𝜃 ∗ = 1 and ∗

∗

𝑠 + = 𝑠 − = 0. We implement Model 2 as our solution methodology.

In this study, the deterministic and the chance-constrained (Model 2) DEA models are

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deployed to evaluate, segment and rank 170 suppliers in a novel dataset. In the CCDEA case, a series of computations are presented based upon some guiding assumptions on the individual and

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the joint probability distributions of the output and input factors. First, the output factors for each supplier are regarded as random variables following a normal distribution. Supplier factors

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(PORAC, PSNOT, PSHIP, OTS, OTD and ACCEPT) described in Section 3.1 are collectively considered as CCDEA output factors given that higher values correspond to better supplier performance. Therefore, we assume the variables across all the suppliers, used as output factors,

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follow a normal distribution. Second, the within supplier variability of each output factor is assumed to be identical across the suppliers for all outputs. To achieve this, we compute the

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standard deviation of each output variable across the panel data and use it as the corresponding input parameter for the variable’s normal distribution. As such, the standard deviations for the six output performance factors (PORAC, PSNOT, PSHIP, OTS, OTD, ACCEPT) used in the CCDEA evaluations are 1.6341, 1.6106, 1.7772, 1.8583, 0.2621, and 0.4348, respectively. These values replace s in equation (6) of Model 2. For implementation purposes in this study, the  values represent the supplier performance risk, and are augmented at several levels to investigate the impact of performance uncertainty on supplier evaluation and rankings. 14

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The two probability distribution assumptions outlined above can be relaxed when longitudinal supplier data are available over several time epochs, making it possible to estimate the individual and the joint probability distributions more accurately. Although the resulting evaluation process then becomes more complex, it would remain structurally identical for all instances of Model 2 analyzed in this paper.

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Resilient suppliers tend to have the capability to adjust and adapt to fluctuations in the buyer’s information that is shared. Therefore, in this manuscript, the buyer-related factors (BSTAB, FA and COF) are regarded as inputs whose values fluctuate and the buying firm often views favorably those suppliers capable of sustaining performance despite the fluctuations. Given the perspective summarized above, performance evaluation using deterministic DEA

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models reflects only a snapshot of supplier performance. This static view may mislead the supply manager if she considers the results as an overall performance measure. In addition, the impact of varying levels of risk on supplier evaluation and rankings cannot be ignored. This study implements the chance-constrained DEA approach to supplier performance evaluation under risk and compares the results under varying levels of risk with the deterministic DEA results in

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accordance with the third premise in Section 2.2. The CCDEA efficiency-based approach and the new data available permit the consideration of input factors and output factors originally reported

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by Ross et al. (2009) who examined the linkage between the actions of the buyer and the suppliers using only deterministic models of efficiency evaluation.

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Implementing Model 2, we evaluate the performance of, segment and rank the suppliers using three p-levels (0.1, 0.05 and 0.01). Among the supplier (output) factors, we focus solely on

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investigating the impact of varying levels of risk in the on-time delivery (OTD) variable by setting the delivery performance risk at nine levels. Modifying the magnitude of OTD from its

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baseline value reflects varying intensities of a transportation disruption and is represented by using eight additional values of OTD set at 0.5OTD, 1.25OTD, 1.5OTD, 1.75OTD, 2OTD, 3OTD, 4OTD, and 5OTD. With these settings, the analysis of results is presented next.

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4. Illustrative Case Application for Supplier Performance Risk Evaluation The model results are evaluated in two phases. First, we briefly compare efficiency scores for the deterministic and the three chance-constrained DEA contexts (obtained by altering the p-levels) considering the buyer’s planning information linked to the suppliers’ purchase order cycle performance capability. Second, we evaluate several supplier performance disruption

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scenarios using on-time delivery performance as the output factor of interest. 4.1 Phase I: Comparing DEA and CCDEA Results

Table 2 summarizes the supplier efficiency scores under the deterministic DEA and the

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CCDEA models. Note that, the OTD variable’s risk is set at its original -level for Phase I analysis. In the CCDEA evaluation, the probability that the observed output values exceed best practice values is controlled at three discriminatory power levels or power levels (p-level): 0.1, 0.05, and 0.01. Ordinal rankings of supplier efficiency scores are reported for the deterministic DEA and the CCDEA scenarios with p-level 0.1 in Appendix B Panel A. The supplier rankings

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for p-level 0.01 and the full listing of Appendix B Panel A are available upon request. Table 2 Statistics of supplier efficiency scores under deterministic DEA and CCDEA Type-E Type-F Max Minimum CCDEA

P=0.1

Median

Stdev

0

1

0.8258

0.9315

0.9300

0.0509

40

0

1

0.8323

0.9443

0.9469

0.0479

41

2

1

0.8341

0.9482

0.9528

0.0468

52

1

1

0.8375

0.9555

0.9653

0.0448

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P=0.05 P=0.01

Mean

30

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Deterministic DEA

“Type-E” column reports the total number of efficient suppliers for each model. "Type-F" column reports the total number of inefficient suppliers who have scores 1.

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As reported in Table 2, CCDEA models at the three power levels do result in different portfolios of efficient/inefficient suppliers compared to those provided by the deterministic DEA

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model. While the deterministic DEA provides zero Type-F inefficient suppliers, several Type-F suppliers emerge for decreasing power levels in CCDEA. In other words, by decreasing the power level, the CCDEA model evaluates a larger numbers of suppliers as efficient and as TypeF inefficient. The interpretation here is that if the buying firm classifies efficient suppliers as topperforming and inefficient suppliers as low-performing, then traditional DEA models will misclassify efficient suppliers as inefficient.

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Appendix B Panel A provides further details on the potential for misclassification for power level 0.1 and 0.01. For example, supplier 17 (S17) is inefficient according to the deterministic DEA. However, for all the nine CCDEA scenarios, this supplier is (more accurately) classified as efficient. As a result, segmenting suppliers into subgroups based upon one instance of efficiency scores and then allocating financial incentives, preferred status, or

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additional business decisions to the wrong supplier may actually lead to worse outcomes. We observe a similar pattern of misclassification for suppliers S32, S37, and others highlighted in Appendix B Panel A. Dramatic differences in suppliers’ rankings are observed among the CCDEA models for each one of the power levels. For example, the three lowest-ranked suppliers in the deterministic DEA are suppliers S47, S10 and S54. However, under the CCDEA with

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power levels 0.1 and 0.01, the three worst-performers are actually suppliers S8, S10 and S54. Most firms use some kind of ranking process to segment (or classify) suppliers into subgroups, and then implement various governance models and relationship development strategies based upon sub-groups (Ogden and McCarter, 2004; Joshi, 2009). In this study, CCDEA results are used to segment suppliers and investigate relationships among the supplier

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and the buyer performance factors. Phase II of the analysis considers the performance differences between the efficient and inefficient suppliers using the nine levels of on-time delivery

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performance variability discussed in Section 3.2.

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4.2 Phase II: Segmenting Suppliers Under OTD Performance Risk We explored disruption in on-time delivery performance in the experimental runs by

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modifying the magnitude of standard deviation in suppliers’ OTD. Among the six output factors considered, only OTD is varied from its baseline value reported earlier while the variance of all

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remaining output factors is held constant at their original values. The rationale is that an external or internal disruption event affects each supplier’s delivery performance differently. Modifying the magnitude of OTD from its baseline value yielded a total of nine settings for OTD disruption. The CCDEA efficiency scores for all 170 suppliers are computed for the nine CCDEA scenarios. In this phase, power level 0.05 is eliminated from the analysis without loss of generality, and we instead focus on the high (0.1) and low (0.01) tails of the discriminatory power levels. This 9 x 2 design results in 18 scenarios with the descriptive statistics summarized in Table 3.

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p=0.01 Min

Mean

Median

Stdev

0.8374 0.8375 0.8378 0.8380 0.8383 0.8385 0.8427 0.8472 0.8514

0.9550 0.9555 0.9555 0.9556 0.9559 0.9561 0.9570 0.9583 0.9599

0.9643 0.9653 0.9649 0.9653 0.9653 0.9653 0.9662 0.9677 0.9702

0.0449 0.0448 0.0445 0.0443 0.0442 0.0440 0.0433 0.0424 0.0417

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Table 3 Descriptive statistics of efficiency scores evaluating CCDEA with OTD disruptions p=0.1 Disruption Type Type Type Type Min Mean Median Stdev Level E F E F 39 0 0.8321 0.9440 0.9467 0.0480 52 1 0.50OTD 40 0 0.8323 0.9443 0.9469 0.0479 52 1 OTD 40 0 0.8324 0.9448 0.9471 0.0480 52 2 1.25OTD 40 0 0.8325 0.9449 0.9473 0.0479 52 2 1.50OTD 41 0 0.8327 0.9451 0.9475 0.0477 51 1 1.75OTD 40 0 0.8328 0.9451 0.9478 0.0476 53 0 2OTD 41 1 0.8345 0.9458 0.9496 0.0470 52 1 3OTD 43 0 0.8371 0.9466 0.9503 0.0466 52 1 4OTD 44 0 0.8396 0.9476 0.9516 0.0462 54 0 5OTD "Type E" column reports the total number of efficient suppliers for each scenario. "Type F" column reports the total number of inefficient suppliers who have score 1.

First of all, the number of efficient suppliers is smaller under the high discriminatory power (0.1) than it is under the low discriminatory power (0.01) at all levels of OTD disruption.

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Second, using independent samples T-tests, we confirmed that the group means of the supplier efficiency scores under the high and low discriminatory power levels are statistically different at p=0.05 level. Finally, as Figure 1 displays, suppliers’ mean and median CCDEA scores are lower under the high discriminatory power. This indicates that the low discriminatory power misclassifies some suppliers as efficient and inflates the mean and median efficiency scores

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relative to the high power case. In both power levels, increases in OTD disruption levels lead to higher number of misclassified suppliers. We explore this relationship next by segmenting the

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efficient suppliers from the inefficient ones for all disruption levels. Our interest is to evaluate the differences between these segmented subgroups along the supplier and buyer factors, and to

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determine in what disruption contexts the performance factors influence the OTD performance. Figure 1 Mean and Median CCDEA Scores vs. OTD Performance Disruption Level

CCDEA Score

AC

CE

0.98

Mean Median

0.975 0.97 p=0.01

0.965 0.96 p=0.01

0.955 0.95 p=0.1

0.945 0.94 0.5

p=0.1

1 1.25 1.5 1.75 2

3 Disruption Level ( )

4

5

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4.2.1 Efficient vs Inefficient Suppliers: Buyer and Supplier Dimensions at High Power We varied the suppliers’ OTD performance at eight levels from the baseline (1OTD) level as discussed earlier and indicated in the columns of Table 4 Panels A and B. Table 4 Panel A reports the cross-cluster analysis of variance for the supplier-related performance factors for the efficient/inefficient suppliers at discriminatory power level 0.1. There are several differences

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between the efficient and inefficient suppliers in terms of the performance factors. First, in terms of OTD, the number of efficient suppliers detected increased along with the OTD performance risk level. For example, at 5OTD, there were 44 efficient suppliers, an increase of 10% from the baseline level number of efficient suppliers, suggesting evidence of misclassification of suppliers’ resiliency. Second, at the baseline level (column three in Table 4A) in OTD

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performance, 40 efficient suppliers provided the required acknowledgement of purchase orders 91.5% of the time. The remaining 130 inefficient suppliers responded only 85.6% of the time. Several differences among the two supplier segments also exist for the remaining factors. First, the average values of the factors (Value rows) for efficient suppliers were higher than those

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for inefficient suppliers at each risk level. We found this difference to be statistically significant for PORAC (p<0.05) and ACCEPT (p<0.10). Second, we found statistically significant

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differences in purchase order acknowledgement responsiveness (PORAC) across all levels of OTD performance risk. For both groups, the mean values were clustered tightly between 91.1%

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to 91.9%, and 85.3% to 85.6% for efficient and inefficient suppliers, respectively. Also, for the three additional dimensions of supplier responsiveness (PSNOT, PSHIP and OTS), we found no statistically significant differences among efficient and inefficient suppliers, but the mean scores

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were higher for the efficient suppliers. For the supplier quality (ACCEPT) dimension, we observed that under extreme conditions (>3OTD) there were no significant differences in quality

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performance (ACCEPT). However, the differences were significant for less extreme conditions (<= 3OTD). We infer that efficient suppliers maintain higher resiliency in product acceptance rates despite the increasing uncertainty surrounding rush deliveries or expediting. In the seven remaining scenarios these differences were statistically significant (p < 0.10), and thus may contribute to the resulting higher performance scores.

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Table 4 Panel A ANOVA Results on Supplier Performance Factors (Efficient vs Inefficient Suppliers) High Discriminatory Power (p=0.1) OTD Supplier Disruption Levels (OTD)

Factor PORAC Value**

0.5OTD

Baseline

1.25OTD

1.5OTD

1.75OTD

2OTD

3OTD

4OTD

5OTD

.915/.856

.915/.856

.915/.856

.916/.855

.914/.856

.916/.855

.919/.853

.911/.855

4.009

4.076

4.076

4.076

4.471

3.943

4.471

5.483

3.923

Signif

0.047

0.045

0.045

0.045

0.036

0.049

0.036

0.020

0.049

PSNOT Value**

.908/.884

.909/.884

.909/.884

.909/.884

.910/.883

.908/.884

.910/.883

.903/.886

.902/.886

F-value

0.636

0.732

0.732

0.732

0.840

0.640

0.840

0.355

0.349

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

PSHIP Value**

.855/.831

.850/.832

.850/.832

.850/.832

.850/.832

.848/.833

.850/.832

.853/.831

.854/.830

F-value

0.533

0.292

0.292

0.292

0.311

0.219

0.311

0.486

0.555

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

OTS Value**

.859/.816

.860/.815

.860/.815

.860/.815

.861/.814

.858/.815

.864/.813

.853/.816

.865/.812

F-value

1.608

1.759

1.759

1.759

1.988

1.613

1.988

1.290

1.447

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

.885/.871

.884/.871

.884/.871

.884/.871

.884/.871

.886/.871

.884/.871

.883/.871

.882/.871

Signif

Signif ACCEPT Value** F-value

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Signif

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.915/.856

F-value

2.179 1.913 2.912 2.912 2.912 2.834 3.705 2.834 n.s. n.s. 0.089 0.090 0.090 0.090 0.094 0.056 0.094 39 / 131 40 / 130 40 / 130 40/130 41 / 129 40/ 130 41 / 129 43 / 127 44 / 126 N* N*: # efficient suppliers / #inefficient suppliers; Value**: average value of factor for efficient/inefficient supplier group. “n.s.” means not significant 2.931

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Signif

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Table 4 Panel B below compares the efficient and inefficient supplier groups relative to the buyer-related information sharing inputs across nine levels of disruption. Several significant

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findings also materialized, which illustrate the contributing factor played by the buyer’s role in sharing planning information with each supplier. First, in all instances, the group mean values for

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the efficient suppliers were not only less than the corresponding means for inefficient suppliers, but the differences in group means were statistically significant (p<0.01). Second, for the

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baseline OTD disruption level, the buyer’s purchase orders remain unchanged only 86.2% of the time for efficient suppliers but 91.9% of the time for inefficient suppliers. This pattern was consistent in the results. Moreover, the mean values of the three buyer factors show that efficient suppliers tend to have greater capacity to absorb information uncertainties than the inefficient suppliers do.

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Table 4 Panel B ANOVA Results on Buyer Performance Factors (Efficient vs Inefficient Suppliers) High Discriminatory Power (p=0.1) Factor

OTD Supplier Disruption Levels (OTD) 0.5OTD

Baseline

1.25OTD

1.5OTD

1.75OTD

2OTD

3OTD

4OTD

5OTD

BSTAB Value**

.860/.919

.862/.919

.862/.919

.862/.919

.861/.919

.860/.919

.861/.919

.861/.920

.861/.921

F-value

55.656

52.580

Signif

52.580 1.0e-5

55.806 1.0e-6

56.294 1.0e-5

55.806 1.0e-6

62.359 1.0e-6

66.387 1.0e-5

1.0e-5

FA Value**

.632/.735

.633/.736

.633/.736

.633/.736

.630/.737

.628/.737

.630/.737

.631/.739

.630/.740

F-value Signif

15.715 1.1e-4

15.771 3.6e-4

15.771 1.1e-4

15.771 1.1e-5

17.563 7.0e-5

17.838 4.0e-5

17.563 5.0e-5

18.280 3.0e-5

19.790 9.0e-5

COF Value**

.848/.899

.845/.901

.845/.901

.845/.901

.848/.900

.850/.899

.848/.900

.851/.900

.849/.901

F-value

13.995

16.898

16.898

16.898

15.059

12.757

15.059

13.667

15.491

2.5e-4 39 / 131

5.0e-5 40 / 130

6.0e-5 40 / 130

6.0e-5 40/130

1.6e-4 41 / 129

1.5e-4 40/ 130

4.8e-4 41 / 129

3.0e-4 43 / 127

7.0e-4 44 / 126

N*

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Signif

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1.0e-6

52.580 1.0e-5

N*: # efficient suppliers / #inefficient suppliers; Value**: average value of factor for efficient/inefficient supplier group

4.2.2 Efficient vs Inefficient Suppliers: Buyer and Supplier Dimensions at Low Power The cross-cluster analysis of variance in the supplier-related performance factors for the

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efficient/inefficient suppliers at power level 0.01 is reported in Table 5 Panel A. There was no dominant pattern in the number of efficient suppliers for increasing OTD risk levels. However, efficient suppliers performed better on PORAC than inefficient suppliers did at risk levels

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(0.5OTD, OTD, 2OTD). There was no significant finding for differences in PSNOT and PSHIP. We did find significant between-group differences in OTS at the tail ends of the risk level. The OTS

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performance at the 1.5OTD through 4OTD disruption levels was not significantly different. Differences in supplier quality (ACCEPT) were marginally significant at only some of the higher

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disruption levels. In the RBV sense, we conclude that under extreme conditions, disruptions lead to inefficient use of resources or resource input congestion. The result of this may lead to

AC

operational pressures and actions that negatively affect supplier quality performance. As in the high discriminatory power case, we also compared between-group differences for the buyerrelated inputs and found statistically significant differences between the supplier groups for all buyer-related performance factors (Table 5 Panel B). The mean values for the buyer-related factors are also lower for the efficient suppliers, an indication that efficient suppliers still have a greater capacity to absorb the buyer information inaccuracies than the inefficient suppliers do.

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Table 5 Panel A ANOVA Results on Supplier Performance Factors (Efficient vs Inefficient Suppliers) Low Discriminatory Power (p=0.01) OTD Supplier Disruption Levels (OTD)

Factor PORAC Value** F-value Signif

0.5OTD

Baseline

1.25OTD

1.5OTD

1.75OTD

2OTD

3OTD

4OTD

5OTD

.900/.856

.914/.853

.899/.856

.899/.856

.898/.857

.902/.855

.899/.857

.902/.855

.890/.856

2.858

2.702

2.503

2.500

2.261

3.143

2.487

2.934

2.578

0.093

+

n.s.

n.s.

n.s.

0.078

n.s.

0.089

n.s.

0.102

.916/.878

.930/.875

.922/.876

.909/.881

.907/.882

.909/.881

.908/.882

.902/.882

.917/.877

F-value

1.967

1.953

2.265

1.049

0.841

1.106

0.952

0.957

2.180

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

PSHIP Value**

.856/.828

.862/.826

.862/.825

.859/.826

.870/.822

.859/.826

.857/.827

.857/.827

.857/.827

F-value

0.941

0.905

1.149

1.229

2.556

1.245

0.998

1.057

1.046

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

OTS Value**

.867/.807

.885/.804

.869/.807

.856/.812

.856/.812

.861/.810

.855/.812

.857/.817

.868/.806

F-value

3.675

3.710

4.033

2.014

2.010

2.770

1.945

2.182

4.156

Signif

0.057

0.056

0.046

n.s.

n.s.

n.s.

n.s.

n.s.

0.043

.882/.871

.880/.872

.882/.871

.882/.871

.883/.870

.882/.871

.881/.871

.884/.870

.882/.870

2.619

2.674

2.674

2.516

2.910

2.500

1.605

3.572

2.988

+

+

+

0.107 0.104 0.104 n.s. n.s. n.s. 0.090 0.061 52 / 118 52 / 118 52 / 118 52 / 118 51 / 119 53 / 117 52 / 118 52 / 118 N* N*: # efficient suppliers / # inefficient suppliers; Value**: average value of factor for efficient/inefficient supplier group;+marginal significance. “n.s.” means not significant.

0.086 54 / 116

Signif

ACCEPT Value** F-value

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Signif

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Signif

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PSNOT Value**

Table 5 Panel B ANOVA Results on Buyer Performance Factors (Efficient vs Inefficient Suppliers) Low Discriminatory Power (p=0.01)

F-value Signif FA Value**

0.5OTD .867/.922

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BSTAB Value**

OTD Supplier Disruption Levels (OTD)

Baseline

1.25OTD

1.5OTD

1.75OTD

2OTD

3OTD

4OTD

5OTD

.868/.921

.869/.921

.868/.921

.867/.921

.868/.922

.868/.922

.866/.922

.869/.922

CE

Factor

57.955

53.661

1.0e-6

1.0e-5

52.963 1.0e-5

54.727 1.0e-5

56.602 1.0e-6

57.963 1.0e-5

56.614 1.0e-6

63.822 1.0e-6

55.523 1.0e-5

.643/.742

.640/.743

.644/.741

.635/.745

.645/.742

.647/.740

.643/.742

.648/.741

17.953

17.514

18.896

16.528

21.610

16.573

15.341

17.000

15.617

Signif

1.1e-4

3.6e-4

1.1e-4

1.1e-5

7.0e-5

4.0e-5

5.0e-5

3.0e-5

9.0e-5

COF Value**

.851/.904

.850/.905

.849/.905

.844/.907

.850/.904

.850/.905

.846/.906

.851/.904

.847/.907

F-value

18.392

19.273

20.495

27.266

18.691

19.701

23.802

17.544

24.211

2.5e-4 52 / 118

5.0e-5 52 / 118

6.0e-5 52 / 118

6.0e-5 52 / 118

1.6e-4 51 / 119

1.5e-4 53 / 117

4.8e-4 52 / 118

3.0e-4 52 / 118

7.0e-4 54 / 116

AC

.642/.742

F-value

Signif *

N

N*: # efficient suppliers / #inefficient suppliers; Value**: average value of factor for efficient/inefficient supplier group

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4.3 Interpretations We proposed the idea that current sourcing paradigms heighten the need to better understand supplier performance risk and disruptions because operations synchronization between the buyer and its suppliers is becoming the norm rather than the exception. Being faster, better and cheaper with exceptional quality requires even greater information sharing with a

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capable supply base and monitoring the appropriate set of performance factors. External expenditure savings alone will not be sufficient. Rather, greater process synchronization and performance monitoring/evaluation of buyer and supplier performance are necessary. The results discussed above provide several interpretations that are now folded back into some key premises stated earlier in the paper. Related to our first premise, the work shows that operational

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performance differences in on-time delivery are, in part, influenced by tasks such as purchase order receipt acknowledgement which signals suppliers’ initial resource commitment toward the planning, scheduling and execution costs of completing the buyer’s order. For the second premise about supplier performance risk and the buying firm’s

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information sharing with its suppliers, we show the need for continuous information communication and performance monitoring during the exchange. The results identify several

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best practice templates in this regard. Consider the baseline (1σ) disruption level under high discriminatory levels (Appendix B Panel A) where suppliers S32, S50, S94, S127 and S169 were also classified as efficient across all other levels. We found that the buyer’s average performance

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on BSTAB (0.844), FA (0.526), and COF (0.794) for these particular suppliers was worse than for other efficient and several inefficient suppliers in the dataset (Panel B). Also, the buyer’s

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behavior in terms of sharing reliable information on its forecast accuracy, forecast comprehensiveness and stability of purchase orders also corresponded to statistically significant

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performance differences between efficient and inefficient suppliers. We report the mean, minimum and maximum values across all factors for these robust suppliers in Panel B to provide tolerance ranges for the factors. Disruptions in on-time delivery performance risk can indeed be mitigated by the information sharing capabilities of the buyer. For the third premise, we identify various patterns in the data. For example, in Table 4 Panel A the baseline OTD disruption level shows that efficient suppliers outperform inefficient

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suppliers on OTS and PORAC. But in Table 4 Panel B, all of the buyer’s behaviors are central to efficient performance of suppliers. These factors can be considered most significant to improving performance of the inefficient suppliers yet different constellations of significant performance factors resulted across the nine levels of OTD disruption. A similar pattern was detected for Table 5 Panel A and B. But we suggest that further consideration of alternative discriminatory

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power levels may be of assistance. We also caution that identifying the ideal power level will likely depend on decision-maker’s preferences and the type of data used.

Our fourth premise suggests the combined insights from premises one, two and three can provide a better understanding of the performance drivers for each supplier under consideration. Identifying the weakness areas can provide decision makers better clarity on precisely the

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location (the buyer or supplier) and type of investment activities (information sharing capabilities or logistics capabilities, procurement capabilities, and product quality capabilities) needed to improve buyer and/or supplier performance. 5. Conclusion

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This study offers a new context in which buyer’s information sharing performance factors are considered along with supplier performance factors and several on-time delivery disruption

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scenarios are evaluated. A case illustration using an actual scorecard of performance metrics at a large electronics firm was used and on-time delivery performance disruptions were investigated

PT

in an efficiency context using a large dataset of 170 suppliers that marries traditional supplier metrics (logistics and product quality capabilities) with purchasing capabilities and buyer-

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focused metrics (information sharing capabilities). The buyer’s forecast accuracy, forecast comprehensiveness, and purchase order stability

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are not identical with all suppliers and the differences are statistically significant in our case here. We identified robust suppliers and summarized their performance on the factors used (Appendix B Panel B). The large sample of suppliers also better describes suppliers’ performance possibility set, and provides more accurate estimates of efficiency scores than scores under deterministic approaches. Suppliers’ efficiency was also computed under alternative thresholds of discriminatory power for better stratification of the suppliers. The buyer’s actions along high discriminatory power settings yield different portfolios of efficient suppliers and fewer instances 24

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of Type-F inefficiency compared to their counterparts in the low discriminatory power settings. Specifically, when discriminatory power is high, fewer suppliers are efficient under high disruption levels than they are under low disruption levels, and hence some suppliers are indeed relatively more resilient under extreme disruptions and may be candidates for preferred supplier classification. At the same time, low performers may be candidates for supplier development

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investment by the buying firm in areas such as information sharing capability. The observed changes in supplier rankings (full details are reported in Appendix B Panel A) imply the buying firm should be somewhat conservative and rely upon known and trusted suppliers, but factor into this decision its own performance impact.

The study also reported significant differences in supplier performance factors between

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the segmented supplier groups at both high and low discriminatory power levels. In nearly all cases, efficient suppliers performed significantly better when the buying firm provides stable and reliable information, or communicates changes sooner during exchange cycle. We believe this provides fruitful evidence reported in the empirical survey research that the buyer actions do influence performance. The paper also sheds some early light as to how firms may begin

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exploring its information sharing tactics with suppliers. For example, the buyer variables summarized in Appendix B Panel B can be viewed as levels of effort on BSTAB, FA, and COF

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allocated by the buyer. Achieving higher levels of forecast accuracy with a particular group of suppliers may not provide the needed performance gains that could ensue from more effort

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concentrated on improving BSTAB or COF instead, for some groups of suppliers. Simultaneously, the supplier has insights into the same for its set of performance factors. As the

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first study in the quantitative supplier performance literature to explicitly explore this phenomenon, these issues open new avenues of investigation. In our case, efficient suppliers seemed more capable of performing well under lower levels of purchase order stability, forecast

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accuracy, and buying comprehensiveness (high levels of buyer shirking). But we caution that studies with additional factors and in other industrial settings are warranted. Finally, this work also provides practical guidance and a better understanding of

performance assessment in the buyer-supplier exchange. Incorporation of dimensions of information sharing and coordination between buyers and suppliers with the traditional supplier performance factors, reported elsewhere, leads to more robust supplier performance evaluation 25

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models and paradigms. We also showed that CCDEA approaches with high discrimination power are better at characterizing supplier performance under several scenarios of disruption severity levels. Our study would not have been possible without recourse to CCDEA because we conceptualized performance risk under multiple levels of discriminatory power so that performance would be properly characterized. Shortfalls in buyer performance impede the

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performance of inefficient and efficient suppliers in both low and high disruption-risk scenarios and need to be further addressed in the application of CCDEA. As a result, the study serves as the basis for an examination and theoretical discussion of performance assessment and the role of buyer information sharing.

One possible extension of this work is to design broader scenarios of experiments

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exploring disruption levels which could include other risk factors in the supply chain beyond delivery performance. Such a study may provide a better description of the impact of disruptions on suppliers’ performance. Another extension could focus on disruption levels in additional buyer factors that may be identified from other problem contexts. Although the current line of inquiry was focused on information sharing between the buyer and supplier, and the influence of

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disruptions in supplier OTD performance, other lines of inquiry exist but these all extend beyond the aims of this work. The paper is not without limitations. One limitation is that the data we

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used is not a longitudinal data set, so the mean and the variance of stochastic outputs cannot be estimated for each DMU and some additional assumptions should be imposed. Nevertheless, the

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disruption experiment framework still represents a needed next step in the literature. If supply managers want to acquire robust estimates of supplier ranking under disruption, longitudinal

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performance data is required. We acknowledge that there may be limitations associated with our constant returns to scale assumption given that all of the factors in the dataset are proportions. For example, if the inputs were to double then the outputs should be expected to double.

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However, it is important to recognize that it is not the proportions that double. Rather it is the underlying volume of transactions in the actual data that may double (numerator and denominator) and their conversion to proportions remains bounded on [0,1] for our use here. It is certainly possible to assume variable returns to scale. These limitations represent future research opportunities.

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Appendix A: Sample Data set for supplier and buyer factor scores

92.300 96.400 72.900 93.300 80.188 97.300 96.000 98.884 95.000 92.000 94.178 88.293 89.658 92.001 96.300 70.800 95.027 83.430 97.076 98.674

89.203 97.078 78.998 74.791 94.700 82.100 85.209 74.582 93.576 63.979 75.927 99.547 85.473 91.570 94.201 78.709 81.916 99.100 91.272 74.749

99.000 73.234 87.000 93.000 94.347 80.948 85.000 88.000 94.000 92.311 94.900 83.358 89.281 83.732 68.851 66.029 51.646 76.829 84.904 89.826

ACCEPT 89.203 97.078 78.998 74.791 94.700 82.100 85.209 74.582 93.576 63.979 75.927 99.547 85.473 91.570 94.201 78.709 81.916 99.100 91.272 74.749

99.000 73.234 87.000 93.000 94.347 80.948 85.000 88.000 94.000 92.311 94.900 83.358 89.281 83.732 68.851 66.029 51.646 76.829 84.904 89.826

95.157 92.410 93.005 94.338 89.461 95.075 99.620 94.185 93.473 93.327 94.920 93.519 93.571 91.986 92.357 88.143 93.405 96.596 93.581 91.266

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96.400 98.000 82.883 92.607 86.221 92.612 91.919 82.649 83.335 76.653 84.286 86.887 98.930 95.954 90.386 90.756 90.646 97.182 67.463 94.327

OTD

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89.430 94.754 92.575 94.572 87.983 91.993 83.893 93.474 97.241 97.723 93.833 86.160 98.107 94.210 94.440 89.210 79.049 94.352 95.220 87.659

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PORAC

INPUT FACTORS BSTAB FA COF 83.000 93.000 90.000 89.000 91.000 85.000 94.000 85.000 96.000 87.000 81.109 88.066 90.434 90.961 85.449 92.329 88.272 84.799 80.294 85.892

ED

Supplier S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20

OUTPUT FACTORS PSNOT PSHIP OTS

CCDEA Rank with Supplier Related Disruption Levels (Modifying OTD)

CE

Supplier ID

99 149 51 158 69 154 1 167 145 169 164 60

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S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

DEA Rank

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Appendix B: Panel A (partial listing of suppliers) Supplier Rankings under DEA and CCDEA with High Discriminatory Power (p=0.1)

0.5OTD 86 148 59 161 69 158 1 168 149 169 162 51

Baseline 88 148 59 161 69 158 1 168 149 169 162 51

1.25OTD 88 148 59 161 70 158 1 168 149 169 162 52

1.5OTD 88 148 59 161 70 158 1 168 149 169 162 52

1.75OTD 89 149 59 161 70 158 1 168 148 169 162 52

2OTD 89 149 58 161 69 158 1 168 148 169 162 51

3OTD 90 150 56 160 70 157 1 168 149 169 163 51

4OTD 93 150 55 159 70 156 1 168 149 169 164 51

5OTD 94 152 46 159 70 155 1 168 149 169 164 52

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166 159 163 1 1 84 165 145 73 62 1 100 152 1 78 146 123 114 105 1 1 46 1 47 1 60 143 107 82 128 106 138 134 1 164 80 48 1 129 1 92 170 136 108 94 1 160 67 45 1

166 159 163 1 1 84 165 145 72 61 1 101 152 1 79 146 123 114 104 1 1 46 1 47 1 59 143 107 81 129 106 138 133 1 164 80 48 1 128 1 92 170 136 108 94 1 160 66 45 1

165 159 162 1 1 85 164 145 73 59 1 103 151 1 81 146 123 115 100 1 1 46 1 47 1 60 143 108 79 131 107 141 127 1 166 80 44 1 126 1 89 170 135 109 93 1 161 66 43 1

165 160 162 1 1 86 163 144 73 57 1 104 151 1 82 146 121 115 97 1 1 45 1 47 1 62 143 108 77 131 107 141 127 1 166 80 44 1 126 1 87 170 133 109 91 1 161 66 1 1

CR IP T

166 159 163 1 1 83 165 145 73 63 1 100 152 1 78 146 123 114 106 1 1 46 1 47 1 60 143 107 84 127 105 138 134 1 164 80 49 1 130 1 92 170 135 108 94 1 160 66 48 1

AN US

165 159 164 1 1 83 166 145 73 63 1 100 152 1 78 146 123 114 109 1 1 46 1 47 1 60 141 106 84 126 104 138 134 1 163 80 49 1 131 1 93 170 135 107 94 1 160 66 48 1

M

165 159 164 1 1 83 166 145 72 62 1 100 152 1 78 146 123 114 110 1 1 44 1 45 1 58 141 106 84 126 104 138 134 1 163 79 48 1 131 1 93 170 135 107 94 1 160 64 47 1

ED

PT

165 159 164 1 1 82 166 145 72 64 1 99 151 1 78 146 123 113 110 1 1 44 1 45 1 58 141 105 90 126 103 138 134 1 163 80 47 1 129 1 92 170 135 106 94 1 160 63 48 1

CE

161 156 162 1 32 93 165 139 78 52 1 111 147 1 83 152 127 117 96 39 1 44 1 45 46 66 146 108 72 138 110 142 122 1 168 82 43 1 119 1 87 170 135 109 91 1 163 55 41 1

AC

S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 S45 S46 S47 S48 S49 S50 S51 S52 S53 S54 S55 S56 S57 S58 S59 S60 S61 S62

165 160 161 1 1 90 163 143 74 56 1 104 150 1 83 146 122 115 97 1 1 47 1 48 1 63 144 108 72 133 107 141 124 1 166 80 1 1 123 1 84 170 129 109 91 1 162 67 1 1

32

ACCEPTED MANUSCRIPT

AC

S164 S165 S166 S167 S168 S169 S170

: : : :

No. of Efficient Suppliers

30

: : : :

39

1 56 68 1 1 1 52

40

74 86 1 50 87 1 140 77 144 111 126 155 167 1 1 1 66 118 124 156 99 81 1 1 96 104 1 110 1 55

: : : :

1 56 68 1 1 1 52

: : : : 1 57 69 1 1 1 53

40

: : : : 1 57 69 1 1 1 53

40

74 86 1 49 87 1 142 77 144 111 127 155 167 1 1 1 64 118 125 157 99 82 1 1 96 103 1 110 1 54 : : : : 1 56 68 1 1 1 52

74 88 1 49 83 1 142 76 144 104 129 153 167 1 1 1 64 118 128 158 101 82 1 1 95 102 1 111 1 54 : : : : 1 57 69 1 1 1 52

74 89 1 49 81 1 142 76 145 100 130 154 167 1 1 1 64 119 129 158 103 84 1 1 90 102 1 111 1 54 : : : : 1 58 69 1 1 1 52

CR IP T

74 86 1 50 89 1 140 77 144 111 125 155 167 1 43 1 67 117 124 156 99 81 1 1 97 104 1 110 1 55

AN US

74 86 1 50 91 1 140 76 144 111 125 155 167 1 43 1 67 117 124 156 99 81 1 1 98 105 1 110 1 55

M

73 86 1 49 91 1 140 76 143 113 125 155 167 1 42 1 66 117 124 156 98 80 1 1 99 105 1 109 1 54

ED

73 87 1 49 93 1 139 76 142 114 125 155 167 1 42 1 65 117 124 156 97 79 1 1 101 109 1 108 1 54

CE

: : : :

80 85 1 48 75 1 143 74 144 98 136 155 166 1 36 1 56 120 141 160 102 95 1 1 86 94 1 113 1 59 : : : : 31 65 68 40 1 1 54

PT

S63 S64 S65 S66 S67 S68 S69 S70 S71 S72 S73 S74 S75 S76 S77 S78 S79 S80 S81 S82 S83 S84 S85 S86 S87 S88 S89 S90 S91 S92

1 57 69 1 1 1 53 41

40

41

43

78 86 1 50 79 1 142 75 145 99 132 153 167 1 1 1 64 119 130 158 103 87 1 1 85 101 1 111 1 55 : : : : 1 60 69 1 1 1 53 44

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Panel B: Best Practice Templates for Robust Suppliers (1σ) Supplier ID

BSTAB

FA

COF

PORAC

PSNOT

PSHIP

OTS

OTD

ACCEPT

0.848

0.472

0.775

0.881

0.958

0.847

0.872

0.936

0.858

S50

0.846

0.559

0.780

0.561

0.890

0.890

0.904

0.946

0.854

S94

0.858

0.477

0.836

0.997

0.955

0.977

0.964

0.946

0.831

S127

0.851

0.551

0.778

0.902

0.982

0.891

0.792

0.902

0.858

S169

0.820

0.572

0.799

0.996

0.973

0.987

0.961

0.944

0.923

mean

0.844913

0.526713

0.794198

CR IP T

S32

0.95202

0.91876

0.89888

0.935251

0.865147

0.5616

0.8900

0.8470

0.7921

0.9021

0.8319

max

0.9970

0.9829

0.9870

0.9640

0.9469

0.9231

AC

CE

PT

ED

M

AN US

0.86756

min

34