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5.benchmarking supplier

  1. 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htm Benchmarking Benchmarking supplier risks supplier risks using Bayesian networks Archie Lockamy III Brock School of Business, Samford University, Birmingham, Alabama, USA 409AbstractPurpose – The purpose of this paper is to provide a methodology for benchmarking supplier risksthrough the creation of Bayesian networks. The networks are used to determine a supplier’s external,operational, and network risk probability to assess its potential impact on the buyer organization.Design/methodology/approach – The research methodology includes the use of a risk assessmentmodel, surveys, data collection from internal and external sources, and the creation of Bayesiannetworks used to create risk profiles for the study participants.Findings – It is found that Bayesian networks can be used as an effective benchmarking tool toassist managers in making decisions regarding current and prospective suppliers based upon theirpotential impact on the buyer organization, as illustrated through their associated risk profiles.Research limitations/implications – A potential limitation to the use of the methodologypresented in the study is the ability to acquire the necessary data from current and potential suppliersneeded to construct the Bayesian networks.Practical implications – The methodology presented in this paper can be used by buyerorganizations to benchmark supplier risks in supply chain networks, which may lead to adjustments toexisting risk management strategies, policies, and tactics.Originality/value – This paper provides practitioners with an additional tool for benchmarkingsupplier risks. Additionally, it provides the foundation for future research studies in the use of Bayesiannetworks for the examination of supplier risks.Keywords Benchmarking, Suppliers, Risk management, Bayesian statistical decision theoryPaper type Research paper1. IntroductionIn order to mitigate the effects of increasing levels of global competition, demandingcustomers and employees, shrinking product lifecycles, and decreasing acceptableresponse times on success in the market place, many organizations have becomemembers of formalized extended enterprises known as supply chains. These structurescan be described as organizational networks designed to help firms achieve a competitiveadvantage through improved market responsiveness and cost reductions. Additionally,supply chains can provide organizations with a means for promoting businessinnovation through the adoption of streamlined information flows, restructured businessprocesses, and enhanced collaboration among network members (Sawhney et al., 2006). As organizations increase their dependence on supply chain networks, theybecome more susceptible to their suppliers’ risk profiles. Supplier risk profiles consistof risk events that can have an adverse impact on buyer organizations. Risk eventsare incidents whose occurrences result in the disruption of overall supply chain Benchmarking: An Internationalperformance. Although it is often not possible to precisely predict the occurrence of such Journal Vol. 18 No. 3, 2011events, it is possible to evaluate the probability of their occurrence through the creation pp. 409-427of supplier risk profiles. Therefore, it is essential that buyer organizations have the q Emerald Group Publishing Limited 1463-5771ability to internally benchmark the level of risk associated with suppliers currently DOI 10.1108/14635771111137787
  2. 2. BIJ contained in their networks. In addition, these organizations must possess the means to18,3 assess risk levels associated with potential members of their supply networks. 1.1 Purpose The purpose of this article is to provide a methodology for benchmarking supplier risks through the creation of Bayesian networks. These networks are used to determine410 a supplier’s external, operational, and network risk probability for the creation of supplier risk profiles. These risk profiles can be used to assess a supplier’s potential impact on the buyer organization. Thus, the methodology is proposed as an analytical tool to assist organizations in benchmarking risk levels associated with current and prospective suppliers based upon their associated risk profiles. 1.2 Organization The first section of the article provided its motivation and purpose. A review of the literature pertaining to benchmarking and supply chain risks is provided in Section 2 to provide a theoretical basis for the proposed methodology. Section 3 contains an overview of the research methodology used in this study which includes a discussion on Bayesian networks and data collection procedures. Results and conclusions are then offered in Sections 4 and 5, respectively. Finally, Section 6 provides a discussion on implications regarding study limitations and directions for future research. 2. Literature review Benchmarking can be described as a framework within which indicators and best practices are examined in order to determine potential areas of improvement for an organization (Tavana et al., 2009). In his taxonomy, Zairi (1994) identified the following types of benchmarking: internal, competitive, functional, and generic. O’Dell and Grayson (1998a, b) defined internal benchmarking as “the process of identifying, sharing, and using the knowledge and practices inside one’s own organization.” Christopher (1998) characterized supply chains as organizational networks linked through upstream and downstream processes and activities that produce value in the form of products and services delivered to the hands of the ultimate customer. A prerequisite to effective supply chain management is the alignment of functional and supply chain partner activities with firm strategies which are congruent with organizational structures, processes, cultures, incentives, and people (Abell, 1999). Thus, it is imperative that buyer organizations have the ability to internally benchmark the capabilities and performance of its suppliers within the supply chain network to ensure that supplier activities support the strategic and operational intent of the network. 2.1 Supplier benchmarking Supplier benchmarking has been used in the selection of suppliers (Choy et al., 2003; Lau et al., 2006; Che and Wang, 2008), supply base reduction processes (Ogden and Carter, 2008), and in the assessment of supplier capabilities (Feeny et al., 2005) and performance (Forker and Mendez, 2001; Narasimhan et al., 2001; Bardy, 2010). Supplier benchmarking techniques employed by organizations include artificial intelligence tools (Lau et al., 2006), neural networks (Choy et al., 2003), mathematical models (Che and Wang, 2008), and other analytical techniques (Forker and Mendez, 2001; Farzippor Saen, 2008). Owing to the integrative and collaborative nature of supply chain networks,
  3. 3. Gunasekaran et al. (2001) notes that internal benchmarking among supply chain Benchmarkingmembers is necessary in order to monitor interactive performance drivers and to ensurethat the network is capability of achieving individual and shared performance targets. supplier risks Soni and Kodali (2010) argue that the internal benchmarking of supply chains isnecessary to reduce performance variability among supply chains of the same focalfirm. However, given the dynamic nature of supply chains due to their compositionalchanges over time along with environmental changes, it is equally important to 411internally benchmark collaborative as well as relative individual performance amongall chain members for effective supply chain management (Li and Dai, 2009). Suchactivities facilitate improvements in information sharing, decision synchronization,incentive alignment, and overall supply chain collaboration practices among itsmembership (Simatupang and Sridharan, 2004). Supplier benchmarking can be used as a tool to reveal improvement opportunitieswithin a supply chain for increased supply chain management effectiveness (Esain,2000). The benefits of effective supply chain management include enhanced customersatisfaction and value, along with improved supply chain reactivity (Gaudenzi andBorghesi, 2006). Supply chain reactivity refers to the network’s ability to compresslead times, adapt to unanticipated changes in demand, and to cope with environmentaluncertainty in the market place. However, the interdependencies created amongparticipating organizations via integrated supply chain networks make them morevulnerable to supply chain disruptions, thus increasing risks.2.2 Supplier selection and evaluationFoster and Whiteman (2006) note that there has been a trend towards developing closerworking relationships with fewer suppliers within supply chain networks, resultingin improved supplier performance. Additionally, Choi and Kim (2008) suggest thatbuyer organizations must be not only concerned with a supplier’s performance withinits immediate supply chain network, but also its performance within its own supplynetwork. Therefore, it is increasingly important for buyer organizations to develop thecapacity to systematically select suppliers as members of its network that are capableof meeting or exceeding individual and shared performance objectives. In addition,these organizations must possess the means to routinely evaluate the performance ofthe members of their supply networks. There are a variety of supplier selection and evaluation methodologies offered in theresearch literature, which include the use of the analytic hierarchy process (Routroy,2008), data envelop analysis (Wu et al., 2007a; Wang et al., 2009), fuzzy systems(Jain et al., 2007; Sen et al., 2010; Sevkli, 2010), multiple regression analysis (Lasch,2005; Inemek, 2009), and process capability analysis (Chen and Chen, 2006; Wu et al.,2007b). Recently, sustainability and environmental requirements have become a part ofthe supplier selection and evaluation protocol for a growing number of organizations(Jabbour and Jabbour, 2009). Finally, as organizations continue to increase their level ofrisk via interdependencies created by integrated supply chain networks, researchershave begun to develop risk-based analytical approaches to supplier selection andevaluation (Guido, 2008; Lee, 2009; Ravindran et al., 2010).2.3 Supply chain risksSpekman and Davis (2004) define risk as the probability of variance in an expectedoutcome. Therefore, it is possible to quantify risk since it is possible to assign
  4. 4. BIJ probability estimates to these outcomes (Khan and Burnes, 2007). On the contrary,18,3 uncertainty is not quantifiable and the probabilities of the possible outcomes are not known (Knight, 1921). A joint evaluation of risk and uncertainty conducted by Yates and Stone (1992) suggests that risk implies the existence of uncertainty associated with a given outcome, for if the probability of an outcome is known, there is no risk. Thus, uncertainty can be regarded as a key determinant of risk that may not be entirely412 eradicated, but can be mitigated through the deployment of risk reduction action steps (Slack and Lewis, 2001). In business situations, managers are expected to reduce the organization’s exposure to uncertainty through the deployment of effective risk management strategies. Internal and external uncertainties both provide sources for supply chain risks (Cucchiella and Gastaldi, 2006). Changes in capacity availability, interruptions in information flows, and reductions in operational efficiencies are all possible sources of internal uncertainty. External sources of uncertainty leading to increased supply chain risks include the actions of competitors, price fluctuations, changes in the political environment, and variations in supplier quality. These sources of uncertainty can be considered “risk events” that can lead to supply chain disruptions which inhibit performance. Thus, it is necessary for managers to first understand the various categories of risks along with the events and conditions that drive them before they attempt to devise approaches to reduce supply chain risks (Chopra and Sodhi, 2004). The research literature offers a variety of approaches for categorizing risks in supply chain networks. For example, Treleven and Schweikhart (1988) have classified supply chain risk events based upon their association with the following: supply chain disruptions; price fluctuations; inventory and scheduling changes, technology advancements, and quality issues. Kleindorfer and Wassenhove (2003) designated supply chain co-ordination and supply disruptions as categories of supply chain risks, while Zsidisin et al. (2005) defined supply risk as the probability of an incident associated with inbound supply from individual supplier failures or the supply market occurring, in which its outcomes result in the inability of the purchasing firm to meet customer demand or cause threats to customer life and safety. Paulsson (2004) classified supply chain risks as operational disturbances, tactical disruptions, and strategic uncertainties. Giunipero and Eltantawy (2004) categorized these risks based upon conditions which result in their creation, such as political events, product availability, transportation distances, changes in technology and labor markets, financial instability, and management turnover. Supply chain disruptions, delays, systems, forecasts, intellectual property, procurement, receivables, inventory, and capacity are classifications for supply chain risks offered by Chopra and Sodhi (2004). Several researchers have chosen to categorize supply chain risks in the following manner: demand-side risks resulting from disruptions emerging from downstream supply chain operations (Suttner, 2005); supply-side risks residing in purchasing, supplier activities, and supplier relationships (Wu et al., 2006); and catastrophic risks that, when they materialize, have a severe impact in terms of magnitude in the area of their occurrence (Wagner and Bode, 2006). Nagurney et al. (2005) defined demand-side risk as the uncertainty surrounding the random demands that often occur at the retailer stage of the supply chain. Wu et al. (2006) states that inbound supply risk is defined as the potential occurrence of an incident associated with inbound supply from individual supplier failures or the supply market resulting in the inability of the purchasing firm to meet
  5. 5. customer demand, and as involving the potential occurrence of events associated with Benchmarkinginbound supply that can have significant detrimental effects on the purchasing firm. supplier risks Handfield and McCormack (2007) defined operational, network, and external factorsas categories of supply chain risks. Operational risk is defined as the risk of lossresulting from inadequate or failed internal processes, people or systems. Quality,delivery, and service problems are examples of operational risks. Network risk is definedas risk resulting from the structure of the supplier network, such as ownership, 413individual supplier strategies, and supply network agreements. External risk is definedas an event driven by external forces such as weather, earthquakes, political, regulatory,and market forces. In addition, the authors offer three perspectives for the examinationof risks within supply chain networks. A supplier facing perspective examines thenetwork of suppliers, their markets and their relationship relative to the organization.A customer facing perspective examines the network of customers and intermediaries,their markets and their relationships also relative to the organization. Finally, aninternal facing perspective examines the company, their network of assets, processes,products, systems, and people as well as the company’s markets. This research studyemploys the risk categories offered by Handfield and McCormack along with thesupplier facing perspective in the analysis of supply chain risk.3. Research methodologyThe research methodology for this study includes the use of a risk assessment model,surveys, data collection from internal and external company sources, and the creationof Bayesian networks used to create risk profiles for the study participants. Followingis an overview of Bayesian networks, along with a discussion of the assessment modeland study sample collection procedures.3.1 Bayesian networksA Bayesian network is an annotated directed acyclic graph that encodes probabilisticrelationships among nodes of interest in an uncertain reasoning problem (Pai et al.,2003). The representation describes these probabilistic relationships and includes aqualitative structure that facilitates communication between a user and a systemincorporating a probabilistic model. Bayesian networks are based on the work of themathematician and theologian Rev. Thomas Bayes who worked with conditionalprobability theory in the late 1700s to discover a basic law of probability which came tobe known as Bayes’ theorem. Bayes’ theorem states that: PðHjcÞ £ PðEjH; cÞ PðHjE; cÞ ¼ PðEjcÞThe posterior probability is given by the left-hand term of the equation [P(HjE, c)].It represents the probability of hypothesis H after considering the effect of evidence E onpast experience c. The term P(Hjc) is the a priori probability of H given c alone. Thus, thea priori probability can be viewed as the subjective belief of occurrence of hypothesisH based upon past experience. The likelihood, represented by the term P(EjH,c), gives theprobability of the evidence assuming the hypothesis H and the background informationc is true. The term P(Ejc) is independent of H and is regarded as a normalizing or scalingfactor (Niedermayer, 2003). Thus, Bayesian networks provide a methodology forcombining subjective beliefs with available evidence.
  6. 6. BIJ Bayesian networks represent a special class of graphical models that may be used to18,3 depict causal dependencies between random variables (Cowell et al., 2007). Graphical models use a combination of probability theory and graph theory in the statistical modeling of complex interactions between such variables. Bayesian networks have evolved as a useful tool in analyzing uncertainty. When Bayesian networks were first introduced, assigning the full probability distributions manually was time intensive.414 Solving a Bayesian network with a considerable number of nodes is known to be a nondeterministic polynomial time hard [NP hard] problem (Dagum and Luby, 1993). However, significant advancements in computational capability along with the development of heuristic search techniques to find events with the highest probability have enhanced the development and understanding of Bayesian networks. Correspondingly, the Bayesian computational concept has become an emergent tool for a wide range of risk management applications (Cowell et al., 2007). The methodology has been shown to be especially useful when information about past and/or current situations is vague, incomplete, conflicting, and uncertain. 3.2 Assessment model The study participants are comprised of ten casting suppliers to a major US automotive company. An assessment model developed by Handfield and McCormack (2007) was used to evaluate the risk of each supplier. This model incorporates data from several sources to provide a 360 degree view of a supplier’s risk profile. The risk assessment model is shown in Figure 1. The risk assessment model identifies and quantifies the risk of a supply disruption using a framework that describes the attributes of suppliers, their relationships, and their interactions with the organization performing the assessment. The model consists of: relationship factors (influence, levels of cooperation, power, alignment of interests); past performance (quality, on-time delivery, shortages); human resource factors (unionization, relationship with employees, level of pay compared to the norm); supply chain disruptions history; environment (geographic, political, shipping distance and method, market dynamics); disaster history (hurricane, earthquake, tornado, flood); and financial factors (ownership, funding, payables, receivables). The assessment model uses a set of measures and scales that apply to each risk construct. The model was tested with several companies over a four year period, and validated through actual use in assessing supply risk events. The measures and scales are used to evaluate suppliers, and to provide a numerical score that reflects their individual risk of a disruptive event. A supplier risk profile is then created, expressed as a numerical score given as a result of applying the model and measures. The higher the risk profile score, the higher the supplier’s disruption potential to the supply chain. Appendix 1 contains the actual measures used in this study. In order to apply the risk results to potential events, the survey results were reorganized into operational, network and external risk-related measures, and the results were recalculated for each supplier. The reorganized measures are presented in Appendix 2. 3.3 Study participants The study participants consist of ten automotive casting suppliers to a major automotive company in the US The sample data was collected by first interviewing the supplier’s account representative to discuss the study and the internet-based survey.
  7. 7. Interactions and Benchmarking relationships supplier risks Performance S Relationship 415 The customer’s reputation with S suppliers is also a critical factor S S SC network organizer S Supplier environment Environmental S Supplier attributes Geographic, market, transportation, etc. Human resources S Supply chain disruption Figure 1. Financial Risk assessment model HealthSubsequently, the survey instrument web link was sent in an email to the supplier’saccount representative. The account representative completed the survey, supplierhistorical performance data was evaluated, and an internal analyst conducted anenvironmental analysis of the organization. All risk ratings were assessed using afive-point Likert scale, and a risk index was calculated for each supplier. In addition,each supplier provided a priori probabilities for 12 risk events identified in Appendix 2.The a priori probabilities were determined by a team of company personnel familiarwith the identified risk events as they relate to the ten suppliers. By logicallyexamining the information, the team was able to estimate a priori probability valuespertaining to 12 risk events for each supplier. These probabilities provided the basisfor the construction of Bayesian networks used in the creation of supplier risk profiles.4. ResultsBayesian networks were developed to examine the probability of a failure for tensuppliers in the company’s casting supply chain. Network, operational, and external risklevels were computed using the provided a priori probabilities for the identified riskevents. A depiction of the Bayesian networks used in this study is shown in Figure 2.
  8. 8. BIJ18,3 1 2 3 4 5 6 7 8 9 10 11 12416 Network Operational External risks risks risks Supplier failureFigure 2. Notes: Network key: 1 = misalignment of interest; 2 =supplier financial stress; 3 = supplier leadershipBayesian network change; 4 = tier 2 stoppage; 5 = supplier network misalignment; 6 = quality problems; 7 = deliverystructure for suppliers problems; 8 = service problems; 9 = supplier HRproblems; 10 = supplier locked; 11 = merger/divestiture; 12 = disasters Nodes (circles) represent variables in the Bayesian network. Each node contains states, or a set of probable values for each variable. The values “yes” and “no” represent the two states in which the variables can exist in the network illustrated in Figure 2. Nodes are connected to show causality with arrows known as “edges” which indicate the direction of influence. When two nodes are joined by an edge, the causal node is referred to as the parent of the influenced (child) node. Child nodes are conditionally dependent upon their parent nodes. Thus, in Figure 2, the probability of suppliers experiencing network risks is dependent on the a priori probabilities associated with the following variables: misalignment of interest; supplier financial stress; supplier leadership change; tier 2 stoppage; and supplier network misalignment. The a priori probabilities associated with the variables quality problems, delivery problems, service problems, and supplier human resources (HR) problems directly influence operational risks. External risks are dependent upon the following variables: supplier locked (i.e. company cannot easily switch to another supplier), merger/divestitures, and disasters. The joint probabilities of the computed network, operational, and external risks are then used to determine the probability that a supplier will fail to achieve individual and shared performance expectations.
  9. 9. The a priori probabilities for 12 supply chain risk events that affect network, Benchmarkingoperational, and external risks are presented in Table I for each supplier. These values supplier riskswere used to generate a risk profile using Bayesian networks comprised of network,operational and external risk probabilities along with the supplier’s probability offailure to meet performance expectations. The supplier risk profiles are displayedin Table II. The table reveals that Suppliers A, H, and J have the highest probability offailure to meet performance expectations, while Supplier I has the lowest probability of 417failure. Computations illustrating the development of the risk profile for Supplier A arepresented in Appendix 3. Supplier rankings based upon their risk profiles are presented in Table III. Anexamination of Table III reveals that Suppliers A and H have the highest network riskrankings, while Supplier I has the lowest ranking in this category. In the category ofoperational risk, Supplier A and J exhibit the highest rankings. Suppliers B, D, and Eexhibit the lowest rankings in the area of operational risk. The highest ranking in theexternal risk category is held by Supplier H, while Supplier I holds the lowest externalrisk ranking. Finally, based upon the risk profiles illustrated in Table II, Suppliers A, H,and J have the highest probability of failure ranking among the study participants,while Supplier I has the lowest ranking in this category.5. ConclusionsThe results of the study indicate that not only does Supplier I have the lowest networkand external risk rankings relative to other study participants, but also the lowestranking in the probability of failure category. Given this result, after considering boththe operational and external risks associated with Supplier I, the company may find itprudent to apportion more of its business to this supplier in an effort to decrease risk inthe supply chain network. Supplier B exhibited the second lowest probability of failureranking and may also be a candidate for increased business as a means to reduce risk.Finally, although Supplier D has a relatively high ranking in the external risk category,it exhibited the third lowest ranking in the probability of failure category. Therefore, thecompany may find it worthwhile to engage in cooperative activities with Supplier D tohelp reduce the impact of external risk events. For example, the company mayparticipate with Supplier D in the development of a comprehensive plan for respondingto unforeseen disasters as a means of mitigating their effects on the supply chainnetwork. The results also reveal that Suppliers A, H, and J have unfavorable probability offailure risk profiles relative to the other participants in the study. Supplier A has thehighest rankings in both the network and operational risk categories, while Supplier Halso holds a number one ranking in the categories of network and external risks.Supplier J has the highest ranking in the category of operational risk. A furtherexamination of Table III reveals that these suppliers are ranked either first or second ineach of the four risk categories. This result suggests that the company should considerseveral approaches for reducing its exposure to the risks associated with theaforementioned suppliers. One approach would be for the company to allocate more ofits business to a supplier with a less risky profile, such as Supplier I. After consideringthe suppliers’ network, operational and external risk factors, the company may considerthe joint development of an aggressive supply chain risk management programwhich helps these suppliers achieve significant reductions in each risk category.
  10. 10. BIJ 18,3 418 Table I. risk event variables A priori probabilities for Supplier Supplier Supplier Supplier Misalignment financial leadership Tier 2 network Quality Delivery Service HR Supplier Merger/Supplier of interest stress change stoppage misalignment problems problems problems problems locked divestiture DisastersA 0.20 0.50 0.50 0.31 0.20 0.46 1.00 0.20 0.20 0.18 1.00 0.11B 0.17 0.23 0.23 0.13 0.20 0.23 0.46 0.10 0.12 0.06 1.00 0.08C 0.20 0.50 0.50 0.31 0.12 0.48 0.95 0.20 0.20 0.18 1.00 0.12D 0.16 0.33 0.23 0.16 0.17 0.21 0.52 0.11 0.09 0.09 1.00 0.10E 0.19 0.38 0.23 0.17 0.20 0.22 0.53 0.10 0.07 0.11 1.00 0.13F 0.14 0.46 0.27 0.18 0.14 0.33 0.65 0.09 0.13 0.15 1.00 0.13G 0.16 0.31 0.37 0.15 0.16 0.26 0.57 0.08 0.11 0.11 1.00 0.10H 0.21 0.50 0.50 0.32 0.16 0.47 0.96 0.20 0.20 0.19 1.00 0.16I 0.18 0.23 0.17 0.15 0.16 0.29 0.58 0.11 0.11 0.11 0.80 0.12J 0.20 0.50 0.50 0.31 0.16 0.50 0.96 0.20 0.20 0.18 1.00 0.11
  11. 11. Benchmarking Network risk Operational risk External riskSupplier probability probability probability Probability of failure supplier risksA 0.34 0.47 0.43 0.41B 0.19 0.23 0.38 0.27C 0.33 0.46 0.43 0.40D 0.21 0.23 0.39 0.28 419E 0.23 0.23 0.41 0.29F 0.24 0.30 0.43 0.32G 0.22 0.27 0.41 0.30H 0.34 0.46 0.45 0.41I 0.18 0.27 0.34 0.26 Table II.J 0.33 0.47 0.43 0.41 Supplier risk profilesSupplier Network risk ranking Operational risk ranking External risk ranking Failure rankingA 1 1 2 1B 7 5 5 7C 2 2 2 2D 6 5 4 6E 4 5 3 5F 3 3 2 3G 5 4 3 4H 1 2 1 1 Table III.I 8 4 6 8 Supplier rankings basedJ 2 1 2 1 on risk profilesPossible incentives that the company could offer the suppliers are incremental increasesin business based upon documented improvements in its supplier ranking based on itsrisk profile. Finally, the company may choose to terminate its relationship with thesesuppliers, and allocate its business among its remaining supplier base.6. ImplicationsThe methodology presented in this study can used to internally benchmark supplierrisks on a routine basis in supply chain networks. As part of a supply chaingovernance agreement, suppliers could be required to periodically update of their riskprobability profiles for the risk events outlined in Appendix 2. These updates could beapplied to Bayesian networks to create new risk profiles and rankings for eachsupplier. Adjustments to existing risk management strategies, policies, and tacticscould then be made to reflect the current risk realities associated with the supply chainnetwork. Thus, the methodology can provide a proactive means of managing supplychain risks. The methodology can also be used by organizations to develop supplier risk profilesto determine failure exposure levels. Organizations can then decide if it is in their bestinterest to either assist a supplier in improving its risk profile, or to terminate therelationship. Supplier risk profiles can be used to determine those risk events whichhave the highest probability of occurrence, and the largest potential impact on thesupply chain network. Thus, this methodology can assist organizations along
  12. 12. BIJ with their suppliers in developing comprehensive supplier risk management programs18,3 designed to minimize the occurrence of network, operational, and external risk events. Finally, this methodology can be used as a tool to assist managers in evaluating current and potential suppliers. Suppliers who have been shown to improve their risk profiles over time may be rewarded by a buyer organization via the allotment of more business. Conversely, suppliers who have experienced increases in network, operational,420 or external risk events over an extended period of time may be viewed as “at risk” suppliers whose relationship may require reassessment by the organization. The reassessment could result in removal from the supply network. Potential suppliers willing to provide information for the generation of their risk profiles may then become viable candidates for network inclusion. 6.1 Implementation In order to successfully implement the methodology offered in this study, it will be necessary for organizations to engage in coordinated and collaborative information sharing activities. Fawcett et al. (2009) has developed a conceptual model for the development of enhanced supply chain information sharing over time. The primary components of the model are connectivity, information sharing capability, and willingness. Connectivity refers to an organization’s ability to collect, analyze, and disseminate the required information necessary to support sound decision making within the supply chain network. It is a necessary condition for the enhancement of information sharing capabilities among the members of the network. However, organizations must also be willing to share sensitive decision making information to achieve high levels of coordination and collaboration among network members. Thus, both technological and behavioral dimensions must be considered in implementing this methodology. Not only must organizations have the technological capability to capture, store, update, and disseminate information on the network, operational, and external risk measures outlined in Appendix 2, but also display the willingness to share this information with members of the supply chain network. 6.2 Limitations This study provides an examination of network, operational, and external risk profiles associated with casting suppliers in the automotive industry. Therefore, the results are specific to the study participants. A potential limitation to the use of the methodology presented in this study is the ability to acquire the necessary data from suppliers needed for the construction of the Bayesian networks. There may be circumstances where some participants within a supply chain network are reluctant to share risk profile data with their customers. Moreover, suppliers must be willing to periodically update this data in order to construct risk profiles that are valid and reliable. A limitation to the use of Bayesian networks to model supply chain risks is the proper identification of risk event and risk categories that can impact a supply chain. Since there are a number of approaches available for categorizing supply chain risks, the inability to incorporate all relevant risks into the model could limit its effectiveness in representing a supplier’s true risk profile. Therefore, the data used in the construction of Bayesian networks must represent the supplier’s current risk realities within the supply chain network.
  13. 13. 6.3 Future research BenchmarkingResearch studies which explore the risk profiles for suppliers and supply chain supplier risksnetworks in other industries should be examined using Bayesian networks to determineif industry dynamics significantly influence supply chain risks. These studies couldexplore the magnitude of network, operational, and external risk associated withsuppliers in specific industries. Results from such studies may be used to benchmarksupplier risk levels within a particular industry. 421 Future researchers may also investigate if it may be possible to develop benchmarksrepresenting the maximum risk levels for the variables contained in Appendix 2 in orderfor a supplier or supplier group to maintain its affiliation with the supply chain. Themaximum risk levels may be based on the nature of the industry, or the commodityprovided by the supplier. Buyer organizations may choose to assist key suppliers whoexceed threshold levels in reducing risks, or discontinue their membership in the supplychain network. Finally, future researchers may choose to incorporate financial data in ranking theimpact of a supplier’s network, operational, or external risks on supply chain networks.The focus of such studies could be on the probability that a supplier will have an adverseimpact on the buyer organization’s revenue stream based upon its risk profile. Researchresults from these studies could be used to benchmark the financial impact of supplierfailures on buyer organizations as well as the entire supply chain network.ReferencesAbell, D. (1999), “Competing today while preparing for tomorrow”, MIT Sloan Management Review, Vol. 40 No. 3, pp. 73-81.Bardy, R. (2010), “Comparative supply chain performance: measuring cross-cultural effects. The example of the Bratislava regional automotive manufacturing”, Knowledge & Process Management, Vol. 17 No. 2, pp. 95-110.Che, Z.H. and Wang, H.S. (2008), “Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products”, Computers & Industrial Engineering, Vol. 55 No. 1, pp. 110-33.Chen, K.S. and Chen, K.L. (2006), “Supplier selection by testing the process incapability index”, International Journal of Production Research, Vol. 44 No. 3, pp. 589-600.Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid supply-chain breakdown”, Sloan Management Review, Vol. 46 No. 1, pp. 53-61.Christopher, M. (1998), Logistics & Supply Chain Management: Strategies for Reducing Cost and Improving Services, 2nd ed., Financial Time Prentice-Hall, New York, NY.Cowell, R.G., Verrall, R.J. and Yoon, Y.K. (2007), “Modeling operational risk with Bayesian networks”, Journal of Risk and Insurance, Vol. 74 No. 4, pp. 795-827.Choi, T. and Kim, Y. (2008), “Structural embeddedness and supplier management: a network perspective”, Journal of Supply Chain Management: A Global Review of Purchasing & Supply, Vol. 44 No. 4, pp. 5-13.Choy, K.L., Lee, W.B. and Lo, V. (2003), “An intelligent supplier relationship management system for selecting and benchmarking suppliers”, International Journal of Technology Management, Vol. 26 No. 7, pp. 717-42.Cucchiella, F. and Gastaldi, M. (2006), “Risk management in supply chain: a real option approach”, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 700-20.
  14. 14. BIJ Dagum, P. and Luby, M. (1993), “Approximating probabilistic inference in Bayesian belief networks is NP-Hard”, Artificial Intelligence, Vol. 60 No. 1, pp. 141-53.18,3 Esain, A. (2000), “Networks, benchmarking and development of the strategic supply base: a case study”, International Journal of Logistics: Research and Applications, Vol. 3 No. 2, pp. 157-71. Farzippor Saen, R. (2008), “Using super-efficiency analysis for ranking suppliers in the presence of volume discount offers”, International Journal of Physical Distribution & Logistics422 Management, Vol. 38 No. 8, pp. 637-51. Fawcett, S.E., Wallin, C., Allred, C. and Magnan, G. (2009), “Supply chain information sharing: benchmarking a proven path”, Benchmarking: An International Journal, Vol. 16 No. 2, pp. 222-46. Feeny, D., Lacity, M. and Willcocks, L.P. (2005), “Taking the measure of outsourcing providers”, MIT Sloan Management Review, Vol. 46 No. 3, pp. 41-8. Forker, L.B. and Mendez, B.D. (2001), “An analytical method for benchmarking best peer suppliers”, International Journal of Operations & Production Management, Vol. 21 Nos 1/2, pp. 195-209. Foster, F.D. and Whiteman, C.H. (2006), “Bayesian prediction, entropy, and option pricing”, Australian Journal of Management, Vol. 31 No. 2, pp. 181-206. Gaudenzi, B. and Borghesi, A. (2006), “Managing risks in the supply chain using the AHP method”, The International Journal of Logistics Management, Vol. 17 No. 1, pp. 114-36. Giunipero, L.C. and Eltantawy, R.A. (2004), “Securing the upstream supply chain: a risk management approach”, International Journal of Physical Distribution & Logistics Management, Vol. 34 No. 9, pp. 698-713. Guido, M. (2008), “A decision-maker-centred supplier selection approach for critical supplies”, Management Decision, Vol. 46 No. 6, pp. 918-32. Gunasekaran, A., Patel, C. and Tirtiroglu, E. (2001), “Performance measurement and metrics in a supply chain environment”, International Journal of Operations & Production Management, Vol. 21 No. 1, pp. 71-87. Handfield, R. and McCormack, K. (2007), Supply Chain Risk Management: Minimizing Disruptions in Global Sourcing, Auberbach Publications, Boca Raton, FL. Inemek, A. (2009), “Global supplier selection strategies and implications for supplier performance: Turkish suppliers’ perception”, International Journal of Logistics: Research & Applications, Vol. 12 No. 5, pp. 381-406. Jabbour, A.B.L. and Jabbour, C.J. (2009), “Are supplier selection criteria going green? Case studies of companies in Brazil”, Industrial Management & Data Systems, Vol. 109 No. 4, pp. 477-95. Jain, V., Wadhwa, S. and Deshmukh, S.G. (2007), “Supplier selection using fuzzy association rules mining approach”, International Journal of Production Research, Vol. 45 No. 6, pp. 1323-53. Khan, O. and Burnes, B. (2007), “Risk and supply chain management: creating a research agenda”, International Journal of Logistics Management, Vol. 18 No. 2, pp. 197-216. Kleindorfer, P.R. and Wassenhove, L.K. (2003), “Managing risk in global supply chains”, Wharton Insurance and Risk Management Department Seminar, Wharton University, Philadelphia, PA. Knight, F.H. (1921), Risk, Uncertainty and Profit, Houghton Mifflin, Boston, MA. Lasch, R. (2005), “Supplier selection and controlling using multivariate analysis”, International Journal of Physical Distribution & Logistics Management, Vol. 35 No. 6, pp. 409-25. Lau, H.C.W., Lee, C.K.M., Ho, G.T.S., Pun, K.F. and Choy, K.L. (2006), “A performance benchmarking system to support supplier selection”, International Journal of Business Performance Management, Vol. 8 No. 2, pp. 132-51.
  15. 15. Lee, A.H. (2009), “A fuzzy AHP evaluation model for buyer-supplier relationships with the Benchmarking consideration of benefits, opportunities, costs and risks”, International Journal of Production Research, Vol. 47 No. 15, pp. 4255-80. supplier risksLi, D. and Dai, W. (2009), “Determining the optimal collaborative benchmarks in a supply chain”, International Journal of Production Research, Vol. 47 No. 16, pp. 4457-71.Nagurney, A., Cruz, J., Dong, J. and Zhang, D. (2005), “Supply chain networks, electronic commerce, and supply side and demand side risk”, European Journal of Operational 423 Research, Vol. 164 No. 1, pp. 120-42.Narasimhan, R., Talluri, S. and Mendez, D. (2001), “Supplier evaluation and rationalization via data envelopment analysis: an empirical examination”, Journal of Supply Chain Management: A Global Review of Purchasing & Supply, Vol. 37 No. 3, pp. 28-37.Niedermayer, D. (2003), “An introduction to Bayesian networks and their contemporary applications”, available at: www.niedermayer.ca/papers/bayesian/bayes.html (accessed 25 March 2010).O’Dell, C. and Grayson, C.J. (1998a), “If only we knew what we know: identification and transfer of internal best practices”, California Management Review, Vol. 40 No. 3, pp. 154-74.O’Dell, C. and Grayson, C.J. (1998b), If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practice, The Free Press, New York, NY.Ogden, J.A. and Carter, P.L. (2008), “The supply base reduction process: an empirical investigation”, International Journal of Logistics Management, Vol. 19 No. 1, pp. 5-28.Pai, R., Kallepalli, V., Caudill, R. and Zhou, M. (2003), “Methods toward supply chain risk analysis”, IEEE International Conference on Systems, Man and Cybernetics, Vol. 5 No. 1, pp. 4560-5.Paulsson, U. (2004), “Supply chain risk management”, in Brindley, C. (Ed.), Supply Chain Risk Management, Ashgate, Aldershot.Ravindran, A., Ufuk Bilsel, R. and Wadhwa, V. (2010), “Risk adjusted multicriteria supplier selection models with applications”, International Journal of Production Research, Vol. 48 No. 2, pp. 405-24.Routroy, S. (2008), “Decision framework for supplier evaluation and selection in supply chain”, The ICFAI University Journal of Supply Chain Management, Vol. 5 No. 3, pp. 19-32.Sawhney, M., Wolcott, R.C. and Arroniz, I. (2006), “The 12 different ways for companies to innovate”, Sloan Management Review, Vol. 47 No. 3, pp. 75-81.Sen, C.G., Sen, S. and Baslıgil, H. (2010), “Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology”, International Journal of Production Research, Vol. 48 No. 6, pp. 1603-25.Sevkli, M. (2010), “An application of the fuzzy ELECTRE method for supplier selection”, International Journal of Production Research, Vol. 48 No. 12, pp. 3393-405.Simatupang, T.M. and Sridharan, R. (2004), “Benchmarking supply chain collaboration: an empirical study”, Benchmarking: An International Journal, Vol. 11 No. 5, pp. 484-503.Slack, N. and Lewis, M. (2001), Operations Strategy, 3rd ed., Prentice-Hall, Harlow.Soni, G. and Kodali, R. (2010), “Internal benchmarking for assessment of supply chain performance”, Benchmarking: An International Journal, Vol. 17 No. 1, pp. 44-76.Spekman, R.E. and Davis, E.W. (2004), “Risky business: expanding the discussion on risk and the extended enterprise”, International Journal of Physical Distribution & Logistics Management, Vol. 34 No. 5, pp. 414-33.
  16. 16. BIJ Suttner, U. (2005), “Supply chain risk management: understanding the business requirements from a practitioner perspective”, The International Journal of Logistics Management,18,3 Vol. 16 No. 1, pp. 120-41. Tavana, M., Bourgeois, B.S. and Sodenkamp, M.A. (2009), “Fuzzy multiple criteria base realignment and closure (BRAC) benchmarking system at the Department of Defense”, Benchmarking: An International Journal, Vol. 16 No. 2, pp. 192-221.424 Treleven, S. and Schweikhart, B. (1988), “A risk/benefit analysis of sourcing strategies: single vs. multiple sourcing”, Journal of Operations Management, Vol. 7 No. 4, pp. 93-114. Wagner, S.M. and Bode, C. (2006), “An empirical investigation into supply chain vulnerability”, Journal of Purchasing & Supply Management, Vol. 12 No. 6, pp. 301-12. Wang, Y.M., Chin, K.S. and Leung, J.P. (2009), “A note on the application of the data envelopment analytic hierarchy process for supplier selection”, International Journal of Production Research, Vol. 47 No. 11, pp. 3121-38. Wu, T., Blackhurst, J. and Appalla, R. (2007a), “AIDEA: a methodology for supplier evaluation and selection in a supplier-based manufacturing environment”, International Journal of Manufacturing Technology & Management, Vol. 11 No. 2, pp. 174-92. Wu, T., Blackhurst, J. and Chidambaram, V. (2006), “A model for inbound supply risk analysis”, Computers in Industry, Vol. 57 No. 4, pp. 350-65. Wu, C.W., Shu, M.H., Pearn, W.L. and Liu, K.H. (2007b), “Bootstrap approach for supplier selection based on production yield”, International Journal of Production Research, Vol. 1 No. 20, pp. 1-20. Yates, J.F. and Stone, E. (1992), “The risk construct”, in Yates, J.F. (Ed.), Risk-Taking Behaviour, Wiley, Chichester. Zairi, M. (1994), Measuring Performance for Business Results, Springer, New York, NY, pp. 62-3. Zsidisin, G., Melnyk, S. and Ragatz, G. (2005), “An institutional theory perspective of business continuity planning for purchasing and supply management”, International Journal of Production Research, Vol. 43 No. 16, pp. 3401-20. Appendix 1 Behaviors Relationship Supplier revenue from industry segment Influence of revenue from company Supplier/Company alignment Supplier/Company information sharing Performance Accreditation Engineering support Capacity utilization Capacity change Delivery flexibility Manufacturing employees Service promptness MRR Audit dateTable AI. Audit scoreRisk assessment On-time deliverymeasures (continued)
  17. 17. Human resources Employee turnover Benchmarking Senior staff turnover Union issues supplier risks Pay positionStructureSupply chain disruption Market power Tier II information sharing Tier II performance monitoring 425 Disruption probability Risk management system Material sourcing baseFinancial health Market growth Financial risk indicatorsEnvironmental Market dynamics Merger and acquisition Regulatory Disaster TransportationNetwork Supplier’s customers Supplier customer relationships Alignment Supplier’s supplier Supplier vendor relationships Vendor concentration Code of conduct Table AI.Appendix 2Risk category Risk event Risk measuresNetwork risks Misalignment of interest Influence of revenue from company Supplier revenue from commodity category Supplier/Company Alignment Regulatory Supplier financial stress Customer portfolio Business health indicators Segment portfolio Market growth Financial data sharing Supplier leadership change Company ownership change likelihood Merger and acquisition Senior staff turnover Tier 2 stoppage Process change likelihood Miscommunication between tiers Material change/obsolesce likelihood Risk management system Material sourcing base Market power Regulatory Regulatory change risk likelihood Table AII. Inventory status sharing Network, operational, and (continued) external risk measures
  18. 18. BIJ Risk category Risk event Risk measures18,3 Tier II supplier information sharing Process/Material change notification Supplier network misalignment Supplier customer alignment Vendor concentration426 Operational risks Quality problem Process change likelihood MRR (defects) Audit date Audit score Tier II performance monitoring Quality problems likelihood Manufacturing employees Accreditation Material change/obsolesce likelihood Process/Material change notification Delivery problem Performance data sharing On-time delivery Capacity utilization Tier II information sharing Delivery flexibility Capacity shortage likelihood Manufacturing employees Capacity change Inventory status sharing Order fulfillment information sharing Production schedule sharing Service problem Engineering support Service promptness Employee turnover Human resource issues likelihood New technology opportunity sharing Supplier HR problem Union issues Employee turnover Pay position External risks Supplier locked Accreditation information sharing EPA and FDA report sharing Regulatory Accreditation Merger/divestiture Market dynamics Merger and acquisition Disasters Supplier is providing proof of insurance DisasterTable AII. Transportation Appendix 3. Probability of failure Supplier A Given the risk event relationships exhibited in the Supplier Bayesian Network illustrated in Figure 2 along with the a priori probabilities for risk event variables contained in Table I, the following probability computations regarding network risks, operational risks, external risks, and failure for Supplier A are provided below: P ðProbability of Network Risk EventÞ £ ðProbability of Event OccurrenceÞ PðNetwork RisksÞ ¼ P ðProbability of Event OccurrenceÞ
  19. 19. ½ð0:20Þ £ ð1ފ þ ½ð0:50Þ £ ð1ފ þ ½ð0:50Þ £ ð1ފ þ ½ð0:31Þ £ ð1ފ þ ½ð0:20Þ £ ð1ފ BenchmarkingPðNetwork RisksÞ ¼ 1þ1þ1þ1þ1 supplier risks 1:71 PðNetwork RisksÞ ¼ ¼ 0:34 5 P ðProbability of Operational Risk EventÞ £ ðProbability of Event OccurrenceÞPðOperational RisksÞ ¼ P ðProbability of Event OccurrenceÞ 427 ½ð0:46Þ £ ð1ފ þ ½ð1:00Þ £ ð1ފ þ ½ð0:20Þ £ ð1ފ þ ½ð0:20Þ £ ð1ފ PðOperational RisksÞ ¼ 1þ1þ1þ1 1:86 PðOperational RisksÞ ¼ ¼ 0:47 4 P ðProbability of External Risk EventÞ £ ðProbability of Event OccurrenceÞPðExternal RisksÞ ¼ P ðProbability of Event OccurrenceÞ ½ð0:18 £ ð1ފ þ ½ð1:00Þ £ ð1ފ þ ½ð0:11Þ £ ð1ފ PðExternal RisksÞ ¼ 1þ1þ1 1:29 PðExternal RisksÞ ¼ ¼ 0:43 3 P ½PðNRÞ £ PðOccurrenceފ þ ½PðORÞ £ PðOccurrenceފ þ ½PðERÞ £ PðOccurrenceފPðFailureÞ ¼ P ðProbability of Risk OccurrenceÞ ½ð0:34 £ ð1ފ þ ½ð0:47Þ £ ð1ފ þ ½ð0:43Þ £ ð1ފ PðFailureÞ ¼ 1þ1þ1 1:24 PðFailureÞ ¼ ¼ 0:41 3About the authorArchie Lockamy III, PhD, Certified Fellow in Production and Inventory Management (CFPIM) isthe Margaret Gage Bush Professor of Business and Professor of Operations Management atSamford University. Prior to his academic career, Dr Lockamy held various engineering andmanagerial positions with Du Pont, Procter and Gamble, and TRW. Dr Lockamy has publishedresearch articles in numerous academic journals, and co-authored the book ReengineeringPerformance Measurement: How to Align Systems to Improve Processes, Products and Profits.Dr Lockamy served on the 1997, 1998, 1999, 2000, 2001, and 2002 Board of Examiners for theMalcolm Baldrige National Quality Award via appointment by the United States Department ofCommerce. He also served as Vice President of the Board of Directors of the AmericanProduction and Inventory Control Society (APICS) Educational and Research Foundation.Dr Lockamy is recognized as a CFPIM by APICS, and is certified as an Academic Jonah by theAvraham Y. Goldratt Institute. Archie Lockamy III can be contacted at: aalockam@samford.eduTo purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints