5.benchmarking supplier

Benchmarking supplier risksusing Bayesian networks

Archie Lockamy IIIBrock School of Business, Samford University, Birmingham, Alabama, USA

Abstract

Purpose – 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 theory

Paper type Research paper

1. 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 chainperformance. Although it is often not possible to precisely predict the occurrence of suchevents, it is possible to evaluate the probability of their occurrence through the creationof supplier risk profiles. Therefore, it is essential that buyer organizations have theability to internally benchmark the level of risk associated with suppliers currently

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Benchmarking: An InternationalJournal

Vol. 18 No. 3, 2011pp. 409-427

q Emerald Group Publishing Limited1463-5771

DOI 10.1108/14635771111137787

contained in their networks. In addition, these organizations must possess the means toassess risk levels associated with potential members of their supply networks.

1.1 PurposeThe purpose of this article is to provide a methodology for benchmarking supplierrisks through the creation of Bayesian networks. These networks are used to determinea supplier’s external, operational, and network risk probability for the creation ofsupplier risk profiles. These risk profiles can be used to assess a supplier’s potentialimpact on the buyer organization. Thus, the methodology is proposed as an analyticaltool to assist organizations in benchmarking risk levels associated with current andprospective suppliers based upon their associated risk profiles.

1.2 OrganizationThe first section of the article provided its motivation and purpose. A review of theliterature pertaining to benchmarking and supply chain risks is provided in Section 2to provide a theoretical basis for the proposed methodology. Section 3 contains anoverview of the research methodology used in this study which includes a discussionon Bayesian networks and data collection procedures. Results and conclusions arethen offered in Sections 4 and 5, respectively. Finally, Section 6 provides a discussionon implications regarding study limitations and directions for future research.

2. Literature reviewBenchmarking can be described as a framework within which indicators and bestpractices are examined in order to determine potential areas of improvement for anorganization (Tavana et al., 2009). In his taxonomy, Zairi (1994) identified the followingtypes of benchmarking: internal, competitive, functional, and generic. O’Dell andGrayson (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 linkedthrough upstream and downstream processes and activities that produce value in theform of products and services delivered to the hands of the ultimate customer.A prerequisite to effective supply chain management is the alignment of functional andsupply chain partner activities with firm strategies which are congruent withorganizational structures, processes, cultures, incentives, and people (Abell, 1999). Thus,it is imperative that buyer organizations have the ability to internally benchmark thecapabilities and performance of its suppliers within the supply chain network to ensurethat supplier activities support the strategic and operational intent of the network.

2.1 Supplier benchmarkingSupplier 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 andCarter, 2008), and in the assessment of supplier capabilities (Feeny et al., 2005) andperformance (Forker and Mendez, 2001; Narasimhan et al., 2001; Bardy, 2010). Supplierbenchmarking techniques employed by organizations include artificial intelligence tools(Lau et al., 2006), neural networks (Choy et al., 2003), mathematical models (Che andWang, 2008), and other analytical techniques (Forker and Mendez, 2001; Farzippor Saen,2008). Owing to the integrative and collaborative nature of supply chain networks,

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Gunasekaran et al. (2001) notes that internal benchmarking among supply chainmembers is necessary in order to monitor interactive performance drivers and to ensurethat the network is capability of achieving individual and shared performance targets.

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 tointernally 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


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probability estimates to these outcomes (Khan and Burnes, 2007). On the contrary,uncertainty is not quantifiable and the probabilities of the possible outcomes are notknown (Knight, 1921). A joint evaluation of risk and uncertainty conducted by Yatesand Stone (1992) suggests that risk implies the existence of uncertainty associated witha 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 entirelyeradicated, but can be mitigated through the deployment of risk reduction actionsteps (Slack and Lewis, 2001). In business situations, managers are expected to reducethe organization’s exposure to uncertainty through the deployment of effective riskmanagement strategies.

Internal and external uncertainties both provide sources for supply chain risks(Cucchiella and Gastaldi, 2006). Changes in capacity availability, interruptions ininformation flows, and reductions in operational efficiencies are all possible sources ofinternal uncertainty. External sources of uncertainty leading to increased supply chainrisks include the actions of competitors, price fluctuations, changes in the politicalenvironment, and variations in supplier quality. These sources of uncertainty can beconsidered “risk events” that can lead to supply chain disruptions which inhibitperformance. Thus, it is necessary for managers to first understand the variouscategories of risks along with the events and conditions that drive them before theyattempt to devise approaches to reduce supply chain risks (Chopra and Sodhi, 2004).

The research literature offers a variety of approaches for categorizing risks in supplychain networks. For example, Treleven and Schweikhart (1988) have classifiedsupply chain risk events based upon their association with the following: supplychain disruptions; price fluctuations; inventory and scheduling changes, technologyadvancements, and quality issues. Kleindorfer and Wassenhove (2003) designatedsupply 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 associatedwith 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 customerdemand or cause threats to customer life and safety. Paulsson (2004) classified supplychain risks as operational disturbances, tactical disruptions, and strategic uncertainties.Giunipero and Eltantawy (2004) categorized these risks based upon conditions whichresult in their creation, such as political events, product availability, transportationdistances, changes in technology and labor markets, financial instability, andmanagement turnover. Supply chain disruptions, delays, systems, forecasts, intellectualproperty, procurement, receivables, inventory, and capacity are classifications forsupply chain risks offered by Chopra and Sodhi (2004).

Several researchers have chosen to categorize supply chain risks in the followingmanner: demand-side risks resulting from disruptions emerging from downstream supplychain operations (Suttner, 2005); supply-side risks residing in purchasing, supplieractivities, and supplier relationships (Wu et al., 2006); and catastrophic risks that, whenthey 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 theuncertainty surrounding the random demands that often occur at the retailer stage of thesupply chain. Wu et al. (2006) states that inbound supply risk is defined as the potentialoccurrence of an incident associated with inbound supply from individual supplier failuresor the supply market resulting in the inability of the purchasing firm to meet

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customer demand, and as involving the potential occurrence of events associated withinbound supply that can have significant detrimental effects on the purchasing firm.

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,individual 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:

PðHjE; cÞ ¼PðHjcÞ £ PðEjH; cÞ

PðEjcÞ

The 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.


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Bayesian networks represent a special class of graphical models that may be used todepict causal dependencies between random variables (Cowell et al., 2007). Graphicalmodels use a combination of probability theory and graph theory in the statisticalmodeling of complex interactions between such variables. Bayesian networks haveevolved as a useful tool in analyzing uncertainty. When Bayesian networks were firstintroduced, assigning the full probability distributions manually was time intensive.Solving a Bayesian network with a considerable number of nodes is known to be anondeterministic polynomial time hard [NP hard] problem (Dagum and Luby, 1993).However, significant advancements in computational capability along with thedevelopment of heuristic search techniques to find events with the highest probabilityhave enhanced the development and understanding of Bayesian networks.Correspondingly, the Bayesian computational concept has become an emergent toolfor a wide range of risk management applications (Cowell et al., 2007). Themethodology has been shown to be especially useful when information about pastand/or current situations is vague, incomplete, conflicting, and uncertain.

3.2 Assessment modelThe study participants are comprised of ten casting suppliers to a major USautomotive company. An assessment model developed by Handfield and McCormack(2007) was used to evaluate the risk of each supplier. This model incorporates datafrom several sources to provide a 360 degree view of a supplier’s risk profile. The riskassessment model is shown in Figure 1.

The risk assessment model identifies and quantifies the risk of a supply disruptionusing a framework that describes the attributes of suppliers, their relationships, andtheir interactions with the organization performing the assessment. The model consistsof: 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); supplychain disruptions history; environment (geographic, political, shipping distance andmethod, 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 riskconstruct. The model was tested with several companies over a four year period, andvalidated through actual use in assessing supply risk events. The measures and scalesare used to evaluate suppliers, and to provide a numerical score that reflects theirindividual risk of a disruptive event. A supplier risk profile is then created, expressedas a numerical score given as a result of applying the model and measures. The higherthe 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 riskresults to potential events, the survey results were reorganized into operational,network and external risk-related measures, and the results were recalculated for eachsupplier. The reorganized measures are presented in Appendix 2.

3.3 Study participantsThe study participants consist of ten automotive casting suppliers to a majorautomotive company in the US The sample data was collected by first interviewing thesupplier’s account representative to discuss the study and the internet-based survey.

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Subsequently, 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.

Figure 1.Risk assessment model

SCnetworkorganizer

S

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The customer’s reputation withsuppliers is also a critical factor

Supplier attributes

Supplierenvironment

Geographic, market,transportation, etc.


415

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 twostates in which the variables can exist in the network illustrated in Figure 2. Nodes areconnected to show causality with arrows known as “edges” which indicate the directionof influence. When two nodes are joined by an edge, the causal node is referred to as theparent of the influenced (child) node. Child nodes are conditionally dependent upon theirparent nodes. Thus, in Figure 2, the probability of suppliers experiencing network risksis dependent on the a priori probabilities associated with the following variables:misalignment of interest; supplier financial stress; supplier leadership change; tier2 stoppage; and supplier network misalignment. The a priori probabilities associatedwith the variables quality problems, delivery problems, service problems, and supplierhuman resources (HR) problems directly influence operational risks. External risks aredependent upon the following variables: supplier locked (i.e. company cannot easilyswitch to another supplier), merger/divestitures, and disasters. The joint probabilities ofthe computed network, operational, and external risks are then used to determine theprobability that a supplier will fail to achieve individual and shared performanceexpectations.

Figure 2.Bayesian networkstructure for suppliers

Supplierfailure

Networkrisks

Operationalrisks

Externalrisks

Notes: Network key: 1 = misalignment of interest; 2 =supplier financial stress; 3 = supplier leadershipchange; 4 = tier 2 stoppage; 5 = supplier network misalignment; 6 = quality problems; 7 = deliveryproblems; 8 = service problems; 9 = supplier HRproblems; 10 = supplier locked; 11 = merger/divestiture;12 = disasters

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The a priori probabilities for 12 supply chain risk events that affect network,operational, and external risks are presented in Table I for each supplier. These valueswere 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 offailure. 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.


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0.17

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120.

480.

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0.16

0.33

0.23

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0.17

0.21

0.52

0.11

0.09

0.09

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E0.

190.

380.

230.

170.

200.

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530.

100.

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111.

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0.14

0.46

0.27

0.18

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0.33

0.65

0.09

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0.15

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160.

310.

370.

150.

160.

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570.

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110.

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0.18

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0.11

Table I.A priori probabilities forrisk event variables

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Possible 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

SupplierNetwork riskprobability

Operational riskprobability

External riskprobability Probability of failure

A 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 0.28E 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 0.26J 0.33 0.47 0.43 0.41

Table II.Supplier risk profiles

Supplier Network risk ranking Operational risk ranking External risk ranking Failure ranking

A 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 1I 8 4 6 8J 2 1 2 1

Table III.Supplier rankings based

on risk profiles


419

with their suppliers in developing comprehensive supplier risk management programsdesigned to minimize the occurrence of network, operational, and external risk events.

Finally, this methodology can be used as a tool to assist managers in evaluatingcurrent and potential suppliers. Suppliers who have been shown to improve their riskprofiles over time may be rewarded by a buyer organization via the allotment of morebusiness. Conversely, suppliers who have experienced increases in network, operational,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. Thereassessment could result in removal from the supply network. Potential supplierswilling to provide information for the generation of their risk profiles may then becomeviable candidates for network inclusion.

6.1 ImplementationIn order to successfully implement the methodology offered in this study, it will benecessary for organizations to engage in coordinated and collaborative informationsharing activities. Fawcett et al. (2009) has developed a conceptual model for thedevelopment of enhanced supply chain information sharing over time. The primarycomponents of the model are connectivity, information sharing capability, andwillingness. Connectivity refers to an organization’s ability to collect, analyze, anddisseminate the required information necessary to support sound decision makingwithin the supply chain network. It is a necessary condition for the enhancement ofinformation sharing capabilities among the members of the network. However,organizations must also be willing to share sensitive decision making information toachieve high levels of coordination and collaboration among network members. Thus,both technological and behavioral dimensions must be considered in implementingthis methodology. Not only must organizations have the technological capability tocapture, store, update, and disseminate information on the network, operational, andexternal risk measures outlined in Appendix 2, but also display the willingness toshare this information with members of the supply chain network.

6.2 LimitationsThis study provides an examination of network, operational, and external risk profilesassociated with casting suppliers in the automotive industry. Therefore, the results arespecific to the study participants. A potential limitation to the use of the methodologypresented in this study is the ability to acquire the necessary data from suppliersneeded for the construction of the Bayesian networks. There may be circumstanceswhere some participants within a supply chain network are reluctant to share riskprofile data with their customers. Moreover, suppliers must be willing to periodicallyupdate 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 properidentification of risk event and risk categories that can impact a supply chain. Sincethere are a number of approaches available for categorizing supply chain risks, theinability to incorporate all relevant risks into the model could limit its effectiveness inrepresenting a supplier’s true risk profile. Therefore, the data used in the constructionof Bayesian networks must represent the supplier’s current risk realities within thesupply chain network.

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6.3 Future researchResearch studies which explore the risk profiles for suppliers and supply chainnetworks 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.

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.

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Appendix 1

BehaviorsRelationship Supplier revenue from industry segment

Influence of revenue from companySupplier/Company alignmentSupplier/Company information sharing

Performance AccreditationEngineering supportCapacity utilizationCapacity changeDelivery flexibilityManufacturing employeesService promptnessMRRAudit dateAudit scoreOn-time delivery

(continued )

Table AI.Risk assessmentmeasures

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Appendix 2

Human resources Employee turnoverSenior staff turnoverUnion issuesPay position

StructureSupply chain disruption Market power

Tier II information sharingTier II performance monitoringDisruption probabilityRisk management systemMaterial sourcing base

Financial health Market growthFinancial risk indicators

Environmental Market dynamicsMerger and acquisitionRegulatoryDisasterTransportation

Network Supplier’s customersSupplier customer relationshipsAlignmentSupplier’s supplierSupplier vendor relationshipsVendor concentrationCode of conduct Table AI.

Risk category Risk event Risk measures

Network risks Misalignment of interest Influence of revenue from companySupplier revenue from commodity categorySupplier/Company AlignmentRegulatory

Supplier financial stress Customer portfolioBusiness health indicatorsSegment portfolioMarket growthFinancial data sharing

Supplier leadership change Company ownership change likelihoodMerger and acquisitionSenior staff turnover

Tier 2 stoppage Process change likelihoodMiscommunication between tiersMaterial change/obsolesce likelihoodRisk management systemMaterial sourcing baseMarket powerRegulatoryRegulatory change risk likelihoodInventory status sharing

(continued )

Table AII.Network, operational, and

external risk measures


425

Appendix 3. Probability of failure Supplier AGiven the risk event relationships exhibited in the Supplier Bayesian Network illustrated inFigure 2 along with the a priori probabilities for risk event variables contained in Table I, thefollowing probability computations regarding network risks, operational risks, external risks,and failure for Supplier A are provided below:

PðNetwork RisksÞ ¼

PðProbability of Network Risk EventÞ £ ðProbability of Event OccurrenceÞ

PðProbability of Event OccurrenceÞ

Risk category Risk event Risk measures

Tier II supplier information sharingProcess/Material change notification

Supplier network misalignment Supplier customer alignmentVendor concentration

Operational risks Quality problem Process change likelihoodMRR (defects)Audit dateAudit scoreTier II performance monitoringQuality problems likelihoodManufacturing employeesAccreditationMaterial change/obsolesce likelihoodProcess/Material change notification

Delivery problem Performance data sharingOn-time deliveryCapacity utilizationTier II information sharingDelivery flexibilityCapacity shortage likelihoodManufacturing employeesCapacity changeInventory status sharingOrder fulfillment information sharingProduction schedule sharing

Service problem Engineering supportService promptnessEmployee turnoverHuman resource issues likelihoodNew technology opportunity sharing

Supplier HR problem Union issuesEmployee turnoverPay position

External risks Supplier locked Accreditation information sharingEPA and FDA report sharingRegulatoryAccreditation

Merger/divestiture Market dynamicsMerger and acquisition

Disasters Supplier is providing proof of insuranceDisasterTransportationTable AII.

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PðNetwork RisksÞ ¼½ð0:20Þ £ ð1Þ� þ ½ð0:50Þ £ ð1Þ� þ ½ð0:50Þ £ ð1Þ� þ ½ð0:31Þ £ ð1Þ� þ ½ð0:20Þ £ ð1Þ�

1 þ 1 þ 1 þ 1 þ 1

PðNetwork RisksÞ ¼1:71

5¼ 0:34

PðOperational RisksÞ ¼

PðProbability of Operational Risk EventÞ £ ðProbability of Event OccurrenceÞ


PðOperational RisksÞ ¼½ð0:46Þ £ ð1Þ� þ ½ð1:00Þ £ ð1Þ� þ ½ð0:20Þ £ ð1Þ� þ ½ð0:20Þ £ ð1Þ�

1 þ 1 þ 1 þ 1

PðOperational RisksÞ ¼1:86

4¼ 0:47

PðExternal RisksÞ ¼

PðProbability of External Risk EventÞ £ ðProbability of Event OccurrenceÞ


PðExternal RisksÞ ¼½ð0:18 £ ð1Þ� þ ½ð1:00Þ £ ð1Þ� þ ½ð0:11Þ £ ð1Þ�

1 þ 1 þ 1

PðExternal RisksÞ ¼1:29

3¼ 0:43

PðFailureÞ ¼

P½PðNRÞ £ PðOccurrenceÞ� þ ½PðORÞ £ PðOccurrenceÞ� þ ½PðERÞ £ PðOccurrenceÞ�

PðProbability of Risk OccurrenceÞ

PðFailureÞ ¼½ð0:34 £ ð1Þ� þ ½ð0:47Þ £ ð1Þ� þ ½ð0:43Þ £ ð1Þ�

1 þ 1 þ 1

PðFailureÞ ¼1:24

3¼ 0:41

About 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: [email protected]

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