Industrial Management & Data SystemsAssessing disaster risks in supply chainsArchie Lockamy III

Article information:To cite this document:Archie Lockamy III , (2014),"Assessing disaster risks in supply chains", Industrial Management & DataSystems, Vol. 114 Iss 5 pp. 755 - 777Permanent link to this document:http://dx.doi.org/10.1108/IMDS-11-2013-0477

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Assessing disaster risks insupply chains

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

Abstract

Purpose – As organizations increase their dependence on supply chain networks, they become moresusceptible to their suppliers’ disaster risk profiles, as well as other categories of risk associated withsupply chains. Therefore, it is imperative that supply chain network participants are capable ofassessing the disaster risks associated with their supplier base. The purpose of this paper is to assessthe supplier disaster risks, which are a key element of external risk in supply chains.Design/methodology/approach – The study participants are 15 automotive casting suppliers whodisplay a significant degree of disaster risks to a major US automotive company. Bayesian networksare used as a methodology for examining the supplier disaster risk profiles for these participants.Findings – The results of this study show that Bayesian networks can be effectively used to assistmanagers in making decisions regarding current and prospective suppliers vis-a-vis their potentialrevenue impact as illustrated through their corresponding disaster risk profiles.Research limitations/implications – A limitation to the use of Bayesian networks for modelingdisaster risk profiles is the proper identification of risk events and risk categories that can impact asupply chain.Practical implications – The methodology used in this study can be adopted by managers to assistthem in making decisions regarding current or prospective suppliers vis-a-vis their correspondingdisaster risk profiles.Originality/value – As part of a comprehensive supplier risk management program, organizationsalong with their suppliers can develop specific strategies and tactics to minimize the effects of supplychain disaster risk events.

Keywords Supply chains, Supply networks, Diaster risk events, Diaster risk profiles,Supply chain disaster risks

Paper type Research paper

IntroductionAn increasing number of firms are introducing the supply chain function into theirorganizations as a response to challenges faced in their current business environments.The role of this function is to provide a mechanism for the creation of supply chainnetworks which integrate material, information, and cash flows among independentorganizational units that exists beyond the boundaries of a single enterprise (Bouteet al., 2011). Supply chain management (SCM) seeks to improve the competitiveperformance of the entire network through the application of an integrated approachto the planning and control of material, information, and cash streams among itsmembership ( Jabbour et al., 2011). Additionally, Fantazy et al. (2010) notes that SCMrepresents a significant change to the practice of business, while Ou et al. (2010)suggest that SCM is one of the most effective ways to improve business performance.

An important outcome of the adoption of integrated supply chain networks byorganizations is their increased dependence on inter-organizational relationshipswithin the networks to ensure the efficient and effective flow of materials, informationand cash to all members of the supply chain (Kotzab et al., 2009). Thus, asorganizations increase their reliance on integrated supply chain networks, they becomemore vulnerable to their suppliers’ disaster risk profiles, as well as other risk categories

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0263-5577.htm

Received 19 November 2013Revised 8 January 2014

Accepted 4 February 2014

Industrial Management & DataSystems

Vol. 114 No. 5, 2014pp. 755-777

r Emerald Group Publishing Limited0263-5577

DOI 10.1108/IMDS-11-2013-0477

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linked to supply chains. Supplier disaster risk profiles represent risk events driven byexternal forces such as weather, earthquakes, and man-made calamities. Risk eventsare incidents whose occurrences result in the overall disruption of supply chainperformance. Supplier disaster risks are a key element of external risks in supplychains, which also include unfavorable political events, harmful regulatory policies,and disruptive market forces. Although it is often not possible to precisely predictwhen such events will occur, it is possible to assess the probability of their occurrencevia the development of supplier risk profiles. Thus, it is imperative that organizationshave the capability to analyze the degree of disaster risk associated with suppliers whocomprise their supply chain networks.

The purpose of this paper is to provide a methodology for modeling and assessingdisaster risks in supply chain networks. The methodology uses Bayesian networks forthe development of supply chain risk profiles. The networks are used to determine asupplier’s external, operational, and network risk probabilities and the potential revenueimpact a supplier can have on an organization using a metric called value-at-risk (VAR).The methodology is offered as an evaluative tool to assist managers in the assessment ofdisaster risk levels corresponding to their supply base. An examination of the literaturein the areas of SCM and supply chain risks is presented in the next section of the paper toprovide a theoretical foundation for the proposed methodology. Provided afterwards isan overview of the research methodology used in the study, which includes a discussionon Bayesian network procedures along with data collection procedures. Results andconclusions are offered in the final section of the paper, including implications withrespect to study limitations and directions for future research.

Literature reviewSupply chains are comprised of trading partners which are interconnected by financial,information, and material flows (Fugate et al., 2006). Effective supply chains maximizecustomer value and profits for each trading partner. To enhance supply chaineffectiveness, a growing number of firms are adopting the principles associatedwith SCM (Singh et al., 2005; Li et al., 2006; Gunasekaran et al., 2008). SCM uses a holisticapproach to addressing the fundamental business problem of supplying product tomeet demand in a complex and uncertain world (Kopczak and Johnson, 2003).Hakansson and Persson (2004) note that SCM can be characterized as a strategicmanagement concept that can contribute to the competitiveness and profitability of theindividual firm as well as the entire supply chain. The execution of SCM necessitates themanagement of information, material, and cash flows across multiple functional areasboth within and among organizations (Faisal et al., 2006). A prerequisite to effective SCMis the coordination of functional and supply chain trading partner activities withorganizational strategies that are aligned with organizational structures, core processes,management cultures, incentive systems, and human capital (Abell, 1999).

Organizations involved in the strategic deployment of SCM often find it necessary toalter their business focus to reap its potential benefits (Kopczak and Johnson, 2003).These alterations may include improvements in their ability to acquire and managereliable demand information (Croxton et al., 2002), better management of physical goodsflow through suppliers, manufacturers, distributors, and retailers for enhanced value tofinal customers ( Jammernegg and Reiner, 2007), more focus on cross-functional andcross-enterprise integration (Chen and Kang, 2007), and an increased emphasis onstrategy alignment and continuous improvement (Kushwaha, 2012). Kushwaha notesthat effective SCM provides the means for organizations to mitigate the effects of rapid

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wage inflation in previously low cost labor markets, spikes in commodity prices, andescalating fuel prices via enhanced flexibility and agility. Enhancements in supply chainflexibility and agility result in improved supply chain reactivity, which leads to increasedcustomer satisfaction and value (Gaudenzi and Borghesi, 2006). Supply chain reactivityis defined as the network’s ability to compress lead times, adapt to unanticipated changesin demand, and to adjust to uncertainty in the business environment. However, theintegration of supply chain networks via SCM creates interdependencies amongparticipating trading partners which make them more susceptible to supply chaindisruptions, resulting in increased risks within the network.

Supply chain risksThe subject of supply chain risks has become a growing topic of management research(Peck, 2006). The beginning of the twenty-first century has been marked bywidespread disruptions in supply chains caused by fuel protests, disease outbreaks,terrorist attacks, and the threat of weapons of mass destruction ( Ju’ ttner, 2006).Researchers Spekman and Davis (2004) define risk as the probability of variancein an expected outcome. Therefore, it is possible to quantify risk since it is possibleto assign probability estimates to these outcomes (Khan and Burnes, 2007). Contrarily,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 unknownrisk. Therefore, uncertainty can be regarded as a key determinant of risk that may notbe entirely eradicated, but can be mitigated through the deployment of risk reductionaction steps (Slack and Lewis, 2001). In business situations, managers are expectedto reduce the firm’s exposure to uncertainty through the deployment of effective riskmanagement strategies. Thus, firms need to adopt systematic approaches for themanagement of supply chain risks (Oehmen et al., 2009).

Uncertainties caused by economic business cycles, consumer demands, and naturaland man-made disasters all provide sources for supply chain risks (Tang, 2006).These sources of uncertainty can be categorized as “risk events” that can lead tosupply chain disruptions that inhibit overall performance. Handfield and McCormack(2007) defined operational, network, and external factors as categories of supply chainrisks. Operational risk is defined as the risk of loss resulting from inadequate or failedinternal processes, people or systems. Quality, delivery, and service problems areexamples of operational risks. Network risk is defined as risk resulting from thestructure of the supplier network, such as ownership, individual supplier strategies,and supply network agreements. External risk is defined as an event driven byexternal forces such as weather, earthquakes, political, regulatory, and market forces.Thus, disaster risks are a component of external supply chain risks. The authors offerthree perspectives for the examination of risks within supply chain networks.A supplier facing perspective examines the network of suppliers, their markets andtheir relationship relative to the organization. A customer facing perspective examinesthe network of customers and intermediaries, their markets and their relationshipsalso relative to the organization. Finally, an internal facing perspective examines thecompany, their network of assets, processes, products, systems, and people as well asthe company’s markets. This research study employs the risk categories offered byHandfield and McCormack along with the supplier facing perspective in the analysisof disaster supply chain risks.

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Disaster risksAccording to Silva and Reddy (2011), 73 percent of US firms with more than $1 billionin sales suffered at least on disruption in their supply chain within the past five years,and one of the most frequent causes of these disruptions were natural disasters. Thesedisruptions often paralyze portions of the supply chain in a significant manner, andfor an extend period of time (Altay and Ramirez, 2010). The Taiwan earthquake ofSeptember 1999, which sent shock waves through the global semiconductor market(Papadakis and Ziemba, 2001) and the 2011 Tohoku earthquake and tsunami in Japanwhich affected a wide range of manufacturing industries worldwide are just twoexamples of the potential for significant disruptions to supply chains.

The research literature offers a variety of approaches for the management ofdisaster risks in supply chains. Kleindorfer and Saad (2005) provide a conceptualframework that reflects the joint activities of risk assessment and risk mitigation thatthey believe are fundamental to the management of supply chain disruption risks.Due to the effects disasters can have on the operating cash flow, financial leverage, andtotal asset turnover of supply chain trading partners, Altay and Ramirez (2010)suggest that firms adopt supply chain-wide mitigation strategies. Ju’ ttner (2006) notesthat the development of a supply chain risk management philosophy, along with riskmanagement principles and processes is necessary to mitigate the impact of disasters.A procedure to identify, assess, and manage disaster and other supply chain risks isprovided by Manuj and Mentzer (2008), which includes a risk management mitigationmodel. Other models and approaches for managing disaster and other supply chainrisks are proposed by Martha and Subbakrishna (2002), Hoffman and Greenwald(2005), Berger et al. (2005), Tang (2006), and Oehmen et al. (2009). Although a number ofapproaches are offered in the research literature to assist in the management of supplychain disaster risks, Peck (2006) states that more research is still needed in this area.

Research methodologyThis study employs a research methodology that includes the use of a risk assessmentmodel, surveys, data collection from internal and external company sources, and theformation of Bayesian networks used to create risk profiles for participants of thestudy. Following is an overview of Bayesian networks, along with a discussion ofthe assessment model and study sample collection procedures.

Bayesian networksBayesian networks are annotated directed acyclic graph that encode 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:

PðH jE; cÞ ¼PðH jcÞ�PðEjH ; cÞPðEjcÞ

The posterior probability is given by the left-hand term of the equation [ P(H|E, c)].It represents the probability of hypothesis H after considering the effect of evidence E

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on past experience c. The term P(H|c) is the a-priori probability of H given c alone.Thus, the a priori probability can be viewed as the subjective belief of occurrence ofhypothesis H based upon past experience. The likelihood, represented by the termP(E|H,c), gives the probability of the evidence assuming the hypothesis H and thebackground information c is true. The term P(E|c) is independent of H and is regardedas a normalizing or scaling factor (Niedermayer, 2003). Thus, Bayesian networksprovide a methodology for combining subjective beliefs with available evidence.

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 emergenttool for 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.

Bayesian analysis in supply chain researchPai et al. (2003) were among the first researchers to analyze supply chain risks usingBayesian networks. Their study examined the risk profile associated with a USDepartment of Defense (DoD) supply chain for trinitrotoluene (TNT). The supply chainwas comprised of TNT recovery plants, storage facilities, and ammunition depots.Using Bayesian networks, the researchers were able to establish risk factors andacceptable risk limits for all assets contained in the DoD supply chain. Bayesiannetworks have also been used to conduct diagnostics (Kauffmann et al., 2002; Kaoet al., 2005), cost optimization studies (Narayanan et al., 2005), and flexibility analysis(Wu et al., 2006; Milner and Kouvelis, 2005) in supply chains.

Since the work of Pai et al. (2003), researchers have continued to explore the use ofBayesian networks to analyze and manage supply chain risks. For example, there havebeen a number of studies which examine the use of Bayesian networks as part of adecision support system to manage such risks (Li and Chandra, 2007; Meixell et al.,2008; Shevtshenko and Wang, 2009; Makris et al., 2011; Taskin and Lodree, 2011).Studies by Tomlin (2009) and Chen et al. (2010) demonstrate how Bayesian networkscan be used to manage supply chain uncertainty.

The integration of Bayesian networks into supply chain forecasting methodologiesto mitigate risks has also been examined by several researchers (Yelland, 2010; Yellandet al., 2010; Rahman et al., 2011). Lockamy and McCormack (2009) conducted a studywhich uses Bayesian networks to examine operational risks in supply chains. Theauthors have also used these networks to analyze outsourcing risks in supply chains(Lockamy and McCormack, 2010). Finally, Lockamy (2011) has developed amethodology for benchmarking supplier risks using Bayesian networks.

This research study contributes to the current body of SCM literature by introducinga methodology for evaluating network risks in supply chains. The methodology also

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includes an assessment of the potential revenue impact a supplier can have on anorganization as measured by VAR. The methodology in this study is offered as a toolto assist supply chain managers in the formulation of strategies and tactics designed tomitigate supply chain risks.

Assessment modelThe study participants are composed of 15 casting suppliers who exhibit a significantdegree of disaster risks to a major US automotive company. An assessment modeldeveloped by Handfield and McCormack (2007) was used to evaluate the riskof each supplier. This model incorporates data from several sources to provide a360 degree view of a supplier’s risk profile. The risk assessment model is presentedin Figure 1.

A potential challenge regarding the use of the assessment model is the need for adetailed review of data furnished by suppliers. Such reviews can be costly and timeconsuming, and therefore are usually limited to the firm’s strategic suppliers. However,well-designed, qualitative self-reporting can be very cost effective. As in the case of thisstudy, each supplier was required to answer an online multiple choice survey that takesless than 30 minutes to complete. In addition, given the large number of suppliers inmany supply chains, the buyers or category managers often do not know every detailabout each supplier, thus making self-reporting by suppliers a necessity. With thesequalitative indicators, it is also feasible and affordable to assess several tiers in thesupply network, consequently making the network risk profile broad and deep.

Supplier Environment

Interaction and Relationship

The customer’s reputationwith suppliers is also acritical factor

SCNetwork

Organizer

S

S

S

S

S

S

S

Supplier Attributes

Performance

Relationship

Environmental

Geographic, market,transportation, etc.

Human Resources

Supply ChainDisruption

Financial HealthFigure 1.Risk assessment model

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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 (HR) 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. The financial impactof the risk profiles on company revenues was calculated by identifying the castingsfurnished by individual suppliers, associating those castings with a finished productand product gross revenue, and computing the sum of associated monthly revenues foreach supplier.

Study sample and data collectionThe study sample consists of fifteen automotive casting suppliers who display asignificant degree of disaster risks to a major automotive company in the USA.All of the suppliers are capable of producing the same items for the company. The datawere collected using a four-step process. First, the suppliers’ representatives wereinterviewed to discuss the study and the supplier self-assessment online surveyinstrument to be completed by the representatives. The survey instrument links werethen sent by e-mail to the account representatives. Upon receiving the completedsurveys, the next step was to conduct on-site interviews with key personnel in thesupply chain departments to validate information collected via the survey instrument,and to obtain more specific details on their supply chain risk factors. The third step inthe data collection process was to conduct interviews with commodity managers in thecastings area in an effort to triangulate the data collected from the surveys and supplychain departments. Finally, off-site research was conducted to gather data regardingthe following: market dynamics; mergers, divestitures, and acquisitions; regulatoryissues; disasters; and transportation disruptions. This data was used to measureenvironmental risk factors. A five-point Likert scale was used for the rating of all riskevents, and a risk index was calculated for each supplier. A description of these eventsis provided in Table I.

ResultsBayesian networks were developed to examine the probability of a supplier’s impacton company revenues. A priori network, operational, and external risk levels werecomputed using the collected data for the identified risk events. The a priori

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probabilities resulting from an analysis of the data are provided in Table II. These risklevels were then used to determine a supplier’s probability of revenue impact on thecompany. A depiction of the Bayesian networks used in this study is illustratedin Figure 2.

Nodes (circles) represent variables in the Bayesian network. Each node containsstates, 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 inFigure 2. Nodes are connected to show causality with arrows known as “edges” whichindicate the direction of influence. When two nodes are joined by an edge, the causalnode is referred to as the parent of the influenced (child) node. Child nodes areconditionally dependent upon their parent nodes. Thus, in Figure 2, the probability ofsuppliers experiencing network risks is dependent on the a priori probabilitiesassociated with the following variables: misalignment of interest; supplier financialstress; supplier leadership change; tier 2 stoppage; and supplier network misalignment.The a-priori probabilities associated with the variables quality problems, deliveryproblems, service problems, and supplier HR problems directly influence operationalrisks. 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 arethen used to determine the probability that a supplier will have an adverse impact onthe company’s revenue stream.

In this study, the product of the supplier’s revenue impact probability times itsrevenue impact provides “VAR” dollars. VAR is defined as the minimum loss expectedon a portfolio of assets over a certain holding period at a given probability(Venkataraman, 1997). VAR was developed by financial institutions in the early 1990sto provide senior management with a single number that could easily incorporateinformation on the risk of a portfolio of assets (Engle and Manganelli, 2004). Today,VAR has evolved into a risk measurement tool which can be applied outside of thefinancial management arena, such as in making procurement decisions (Sandersand Manfredo, 2002). VAR can also be used to evaluate and manage supply chain risks.

Risk category Risk event Description

Network risks Misalignment of interest Supplier and company objective are in conflictSupplier financial stress Supplier has significant financial issuesSupplier leadership change Supplier is under new managementTier 2 stoppage Problems associated with supplier’s supplierSupplier networkmisalignment

Problems associated with supplier’s customerbase or its competing suppliers

Operational risks Quality problem Problems associated with poor supplier qualityDelivery problem Problems associated with late supplier deliveriesService problem Problems associated with poor supplier support

servicesSupplier HR problem Supplier has significant issues relating to its

workforceExternal risks Supplier locked Supplier switching is very difficult

Merger/divestiture Significant chance that the supplier will bemerged or divested

Disaster Supplier has a significant chance of beingaffected by a disaster

Table I.Network, operationaland external risk events

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Table II.A-priori probabilities

for risk eventvariables

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The Supply Chain Council defines VAR as the sum of the probability of events timesthe monetary impact of the events for the specific process, supplier, product, orcustomer (Supply Chain Council, 2008). Thus, this metric allows for comparisonsamong suppliers to facilitate supply chain risk management. This study examinesmonthly VAR dollars for the company based upon the risk profiles of each supplier.

The a priori probabilities for the 12 supply chain risk events that affect network,operational, and external risks are presented in Table II 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 ofrevenue impact on the company. The table reveals that the suppliers have a disasterrisk probability range of 8-16 percent.

Disaster risk profiles and VAR resultsComputations for the probability of revenue impact on the company based on the riskprofile of Supplier 1 are presented in Appendix 3. An examination of Appendix 3reveals that Supplier 1 has a 41 percent probability of impacting company revenues.The disaster risk profile and VAR dollars associated with each supplier is illustrated inTable III.

An examination of Table III reveals that Supplier 2 has the highest probabilityof a disaster event (0.8), while Supplier 13 has the lowest probability for such anevent (0.09). However, the disaster risk profile associated with Supplier 6 results in thelargest VAR ($148.8 million). The risk profile of Supplier 15 yields the smallest VAR($2.1 million). The average probability of a disaster event for all suppliers is 0.17, withan average VAR of $37.6 million.

Notes: Network key: 1 = Misalignment of interest; 2 = supplier financial stress; 3 = supplierleadership change; 4 = tier 2 stoppage; 5 = supplier network misalignment; 6 = qualityproblems; 7 = delivery problems; 8 = service problems; 9 = supplier HR problems10 = supplier locked; 11 = merger/divestiture; 12 = disasters

1 2

NetworkRisks

OperationalRisks

SupplierRevenueImpact

ExternalRisks

3 4 5 6 7 8 9 10 11 12

Figure 2.Supplier Bayesiannetwork

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Sensitivity analysisA disaster risk profile sensitivity analysis was conducted for each supplier. Thesensitivity analysis begins with a determination of a supplier’s probability of revenueimpact under three scenarios:

(1) the probability of a disaster event is equal to the disaster risk probabilityderived from the collected data (refer to as the “base case”);

(2) the probability of a disaster event is equal to 0 percent; and

(3) the probability of such an event is equal to 100 percent.

This analysis allows the company to compare VAR revenue impacts at the extremecases relative to the base case to determine the potential benefits of reducing theimpact of the disaster risks represented by specific suppliers, as well as the VARimpact if such an event occurs. The a-priori probabilities associated with network,operational, and other external risk variables were held constant during the sensitivityanalysis. Although it may not be possible to fully eliminate the disaster risksassociated with a supplier’s profile, it may be possible to improve the profile byinstituting proactive supply chain risk management strategies and tactics in areaswhich will yield the maximum benefit. An illustration of the supplier disaster riskprofiles and VAR impacts under the three aforementioned scenarios is providedin Table IV.

An examination of Table IV reveals that the disaster risk profile associated withSupplier 6 results in the largest VAR for the base case ($148.8 million), best case($144.1 million) and worse case ($195.3 million) scenarios. The risk profile of Supplier15 yields the smallest VAR for the base case ($2.1 million), best case ($2.0 million) andworse case ($2.8 million) situations. Supplier 15 also displays the smallest potentialdecrease in VAR ($70,800), while Supplier 6 has the largest potential increase in VAR($46,500,000). The average potential increase and decrease in VAR for all suppliers is$11,633,987 and $1,169,153, respectively. The largest potential percentage decreasein VAR between a supplier’s base case and most favorable disaster risk profile is

SupplierDisasters

probabilityProbability of

revenue impactMonthly revenueimpact (millions)

Value at risk(probability�monthly revenue

impact)

1 0.20 0.41 $18.75 $7,687,5002 0.80 0.27 $31.25 $8,437,5003 0.12 0.40 $217.50 $87,000,0004 0.10 0.28 $180.42 $50,517,6005 0.13 0.29 $75.00 $21,750,0006 0.13 0.32 $465.00 $148,800,0007 0.10 0.29 $89.58 $25,978,2008 0.12 0.30 $17.50 $5,250,0009 0.10 0.30 $290.83 $87,249,000

10 0.16 0.41 $136.25 $55,862,50011 0.12 0.26 $45.83 $11,915,80012 0.11 0.32 $45.83 $14,665,60013 0.09 0.30 $94.58 $28,374,00014 0.11 0.41 $20.83 $8,540,30015 0.12 0.30 $7.08 $2,124,000

Table III.Disaster risk

profiles and valueat risk impacts

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10.

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a$1

8.75

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87,5

00$1

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4$1

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24.4

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75$7

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75$9

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00$3

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30.

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a$2

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2.5

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000

25.0

0.00

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$84,

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40.

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a$1

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35.7

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a$7

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00$2

9,25

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0

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a$4

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0$1

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00$4

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31.3

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0.31

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(con

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Table IV.Sensitivity analysis

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100.

16a

0.41

a$1

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5,86

2,50

0$1

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,500

2.4

$13,

625,

000

24.4

0.00

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$136

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$54,

500,

000

1.00

0.51

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110.

12a

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915,

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$458

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3.9

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Note

:aB

ase

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Table IV.

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6.7 percent for Supplier 8 ($5.2 million vs $4.9 million). The largest potential percentageincrease in VAR between these two risk profiles is 38.5 percent, as exhibited bySupplier 11. Suppliers 1, 10, and 14 displayed both the smallest potential percentagedecrease in VAR between the base case and most favorable disaster risk profile(2.4 percent), along with the smallest potential percentage increase in VAR (24.4percent) between the two risk profiles. Finally, the average potential percentageincrease in VAR is 31.8 percent for all suppliers, while the average potential percentagedecrease in this metric is 3.4 percent.

ConclusionsThis study examined fifteen automotive casting suppliers who display a significantdegree of disaster risks to a major automotive company in the USA. All of the suppliersare capable of producing the same items for the company. As illustrated by thesensitivity analysis, potential increases in VAR due to the suppliers’ disaster riskprofiles are significantly greater than potential decreases based upon an examinationof individual VAR values in the last two columns in Table IV. The data in thesecolumns reveal that the potential VAR percent increase is approximately ten times thepotential percent decrease in VAR for a given supplier. This result implies that thecompany should focus its efforts more on disaster preparedness to mitigate the impactof a supply chain disaster on revenues. These efforts may include seeking alternativesources of supply for critical items, the possession of buffer inventories stored instrategic locations, and the ability to make adjustments to its product mix based on theavailability of materials.

Supplier 15 has the lowest VAR impact on the company under the examinedscenarios, while Supplier 6 has the largest impact. Suppliers 1, 10, and 14 have thelowest potential percent increase (24.4 percent) and decrease (2.4 percent) in VAR.However, Supplier 10 also has the highest risk (16 percent) of suffering a disaster. Thisresult implies that Supplier 10 has an unfavorable disaster risk probability profilewhen compared to those of the other study participants. In addition, this supplier alsohas a relatively high monthly revenue impact ($136 million) on the company. Given thisresult, after considering network, operational, and other external risks associated withSupplier 10, the company may find it prudent to apportion less of its business to thissupplier due to its more risky disaster profile.

The sensitivity analysis also reveals that Supplier 11 has the highest potentialpercentage increase (38.5 percent) in VAR, closely followed by Suppliers 7(37.9 percent) and 2 (37.0 percent). Thus, these suppliers also have an unfavorabledisaster risk probability profile relative to the other participants in the study. Thesesuppliers have a combined monthly revenue impact of $167 million on the company.This result suggests that the company should consider allocating more of its businessto suppliers with less risky disaster profiles. The company should also institutedisaster preparedness strategies and tactics to mitigate the effects of potential supplydisruptions due to these suppliers. Finally, the company may choose to terminate itsrelationship with these suppliers, and allocate its business among its remainingsupplier base. However, supplier termination should not be based upon one risk factor,and should collectively consider all network, operational, and external risk eventsoutlined in Appendix 2.

Supplier 8 exhibited the largest potential percentage decrease in VAR (6.7 percent)during the sensitivity analysis. However, the potential reduction is a relatively smallvalue. Therefore, the company would be better suited to deploy its resources in the

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development of a supply chain risk management program focussed on disasterpreparedness and contingency planning. For example, the development of aconsignment inventory program with trading partners on critical items may be oneapproach for reducing the effects of supplier disasters on the company’s revenuestream. Another approach may be to develop internal capabilities to temporarilyfabricate critical items necessary for the production of finished goods in the event thata supplier experiences a disaster which impedes the flow of those items within thesupply chain.

Managerial implicationsThe methodology presented in this study can be used to monitor disaster risks insupply chain networks. As part of a supply chain governance agreement, supplierscould be required to periodically update their risk probability profiles for the riskevents outlined in Appendix 2. These updates could be applied to Bayesian networksto create new risk profiles for each supplier. Adjustments to existing risk managementstrategies, policies, and tactics could then be made to reflect the current risk realitiesassociated with the supply network. Thus, the methodology can provide a proactivemeans of managing all categories of supply chain risks.

The methodology can also be used by organizations to develop supplier disasterrisk profiles to determine revenue risk exposure levels. Organizations can thendecide if it is in their best interest to either continue or terminate a supplierrelationship based upon its risk profile. Supplier disaster risk profiles can be usedto determine those disaster risk events which have the highest probability ofoccurrence, and the largest potential revenue impact. Thus, this methodology canassist organizations along with their suppliers in developing comprehensive supplierrisk management programs designed to minimize the occurrence of disasters andother risk events.

Finally, this methodology can be used as a tool to assist managers in evaluatingcurrent and potential suppliers. Suppliers who have experienced increases in disasterrisk events over an extended period of time may be viewed as “at risk” suppliers whoserelationship may require reassessment by the organization. The reassessment couldresult in removal from the supply network. Potential suppliers willing to provideinformation for the generation of their risk profiles may then become viable candidatesfor network inclusion.

LimitationsThis study provides an examination of disaster risk profiles associated with castingsuppliers in the automotive industry. Therefore, the results are specific to thestudy participants. A potential limitation to the use of the methodology presentedin this study is the ability to acquire the necessary data from suppliers needed forthe construction of the Bayesian networks. There may be circumstances where someparticipants within a supply chain network are reluctant to share risk profiledata 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 theproper identification of risk event and risk categories that can impact a supplychain. Since there are a number of approaches available for categorizing supplychain risks, the inability to incorporate all relevant risks into the model could limitits effectiveness in representing a supplier’s true risk profile. Therefore, the data

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used in the construction of Bayesian networks must represent the supplier’s currentrisk realities within the supply chain network.

Future researchResearch studies which explore the risk profiles for suppliers and supply networks inother industries should be examined using Bayesian networks to determine if industrydynamics significantly influence supply chain risks. Future researchers may alsoinvestigate how disaster risks can be mitigated within supply chains. For example, itmay be possible to develop inventory management policies, procedures, and programswith a supplier or supplier group to maintain a sufficient flow of materials through thesupply chain during and after a disaster event. Finally, future researchers may chooseto solely focus on the impact of network, operational, or external risks on supplynetworks.

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(The appendix follows over leaf.)

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Appendix 1

BehaviorsRelationship

Supplier revenue from industry segmentInfluence of revenue from companySupplier/Company alignmentSupplier/Company information sharing

PerformanceAccreditationEngineering supportCapacity utilizationCapacity changeDelivery flexibilityManufacturing employeesService promptnessMRRAudit dateAudit scoreOn-time delivery

Human ResourcesEmployee turnoverSenior staff turnoverUnion issuesPay position

StructureSupply chain disruption

Market powerTier II information sharingTier II performance monitoringDisruption probabilityRisk management systemMaterial sourcing base

Financial healthMarket growthFinancial risk indicators

EnvironmentalMarket dynamicsMerger and acquisitionRegulatoryDisasterTransportation

NetworkSupplier’s customersSupplier customer relationshipsAlignmentSupplier’s supplierSupplier vendor relationshipsVendor concentrationCode of conduct

Table AI.Risk assessmentmeasures

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Appendix 2

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 sharingTier 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 turnover

(continued)

Table AII.Network, operational

and externalrisk measures

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Appendix 3. Probability of revenue impact – supplier 1Given 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 revenue impact for Supplier 1 are provided below:

PðNetwork RisksÞ ¼PðProbability of Network Risk EventÞ� ðProbability of Event OccurrenceÞ

PðProbability of Event OccurrenceÞ

PðNetwork RisksÞ ¼ ½ð0:20Þ� ð1Þ� þ ½ð0:50Þ� ð1Þ� þ ½ð0:50Þ� ð1Þ� þ ½ð0:31Þ� ð1Þ� þ ½ð0:20Þ� ð1Þ�1þ 1þ 1þ 1þ 1

PðNetwork RisksÞ ¼ 1:71

5¼ 0:34

PðOperational RisksÞ ¼PðProbability of Operational Risk EventÞ� ðProbability of Event OccurrenceÞ

PðProbability of Event OccurrenceÞ

PðOperational RisksÞ ¼ ½ð0:46Þ� ð1Þ� þ ½ð1:00Þ� ð1Þ� þ ½ð0:20Þ� ð1Þ� þ ½ð0:20Þ� ð1Þ�1þ 1þ 1þ 1

PðOpeartional RisksÞ ¼ 1:86

4¼ 0:47

PðExternal RisksÞ ¼PðProbability of External Risk EventÞ� ðProbability of Event OccurrenceÞ

PðProbability of Event OccurrenceÞ

PðExternal RisksÞ ¼ ½ð0:18Þ� ð1Þ� þ ½ð1:00Þ� ð1Þ� þ ½ð0:11Þ� ð1Þ�1þ 1þ 1

PðExternal RisksÞ ¼ 1:29

3¼ 0:43

Risk category Risk event Risk measures

Human 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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PðRevenueImpactÞ

¼P½PðNRÞ�PðOccurrenceÞ� þ ½PðORÞ� PðOccurrenceÞ� þ ½PðERÞ�PðOccurrenceÞ�

PðProbability of Risk OccurrenceÞ

PðRevenue ImpactÞ ¼ ½ð0:34Þ� ð1Þ� þ ½ð0:47Þ� ð1Þ� þ ½ð0:43Þ� ð1Þ�1þ 1þ 1

PðRevenue ImpactÞ ¼ 1:24

3¼ 0:41

About the author

Dr Archie Lockamy III, PhD, CFPIM is the Margaret Gage Bush Professor of Business andProfessor of Operations Management at the Samford University. Prior to his academic career,Dr Lockamy held various engineering and managerial positions with DuPont, Procter andGamble, and TRW. Dr Lockamy has published research articles in numerous academic journals,and co-authored the book Reengineering Performance Measurement: How To Align Systems

To Improve Processes, Products and Profits. Dr Lockamy served on the 1997-2002 Board ofExaminers for the Malcolm Baldrige National Quality Award via appointment by theUnited States Department of Commerce. He also served as the Vice President of the Board ofDirectors of the American Production and Inventory Control Society (APICS) Educationaland Research Foundation. Dr Lockamy is recognized as a Certified Fellow in Productionand Inventory Management (CFPIM) by APICS, and is certified as an Academic Jonahby the Avraham Y. Goldratt Institute. Dr Archie Lockamy III can be contacted at:aalockam@samford.edu

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints

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