Modeling supplier risks using Bayesian networks
| Published date | 09 March 2012 |
| Pages | 313-333 |
| Date | 09 March 2012 |
| DOI | https://doi.org/10.1108/02635571211204317 |
| Author | Archie Lockamy,Kevin McCormack |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Modeling supplier risks using
Bayesian networks
Archie Lockamy III
Brock School of Business, Samford University, Birmingham,
Alabama, USA, and
Kevin McCormack
College of Management, North Carolina State University, Raleigh,
North Carolina, USA
Abstract
Purpose – To counteract the effects of global competition, many organizations have extended their
enterprises by forming supply chain networks. However, as organizations increase their dependence
on these networks, they become more vulnerable to their suppliers’ risk profiles. The purpose of this
paper is to present a methodology for modeling and evaluating risk profiles in supply chains via
Bayesian networks.
Design/methodology/approach – Empirical data from 15 casting suppliers to a major US
automotive company are analyzed using Bayesian networks. The networks provide a methodological
approach for determining a supplier’s external, operational, and network risk probability, and the
potential revenue impact a supplier can have on the company.
Findings – Bayesian networks can be used to develop supplier risk profiles to determine the risk
exposure of a company’s revenue stream. The supplier risk profiles can be used to determine those risk
events which have the largest potential impact on an organization’s revenues, and the highest
probability of occurrence.
Research limitations/implications – A limitation to the use of Bayesian networks to model
supply chain risks is the proper identification of risk events and risk categories that can impact a
supply chain.
Practical implications – The methodology used in this study can be adopted by managers to
formulatesupply chain risk managementstrategies and tacticswhich mitigate overallsupply chain risks.
Social implications – The methodology used in this study can be used by organizations to reduce
supply chain risks which yield numerous societal benefits.
Originality/value – As part of a comprehensive supplier risk management program, organizations
along with their suppliers can develop targeted approaches to minimize the occurrence of supply chain
risk events.
Keywords United States of America,Automotive industry, Supply chainmanagement, Modelling,
Suppliers, Supplynetworks, Supplier risks, Risk events,Supplier risk profiles, Bayesian networks
Paper type Research paper
1. Introduction
The twenty-first century business environment is characterized by increasing levels of
global competition, demanding customers and employees, shrinking product lifecycles,
and decreasing acceptable response times. In an effort to counteract these marketforces,
many organizations have extended their enterprises outside of their legal boundaries
by formingcompetitive networksof organizations knownas supply chains. Supplychains
represent a coordinated network of organizations interacting to provide a product
or service to the end-user. Supply chain management (SCM) seeks to enhance
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Modeling
supplier risks
313
Received 14 June 2011
Revised 16 August 2011
Accepted 17 August 2011
Industrial Management & Data
Systems
Vol. 112 No. 2, 2012
pp. 313-333
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635571211204317
the competitive performance of the network via the internal integration of an
organization’sfunctional areas, and by effectivelylinking them to the external operations
of suppliers, customers, and other network members (Kim, 2006). Additionally,
Sawhney et al. (2006) identified supply chains as a mechanism for fostering business
innovation withinorganizations through the adoption of streamlined information flows,
restructured business processes, and enhanced collaboration among network members.
As organizations increase their dependence on integrated supply networks, they
become more susceptible to supply chain disruptions. The associated financial and
operational risks of supply chain disruptions represent a major concern to organizations
competing in the global economy (Craighead et al., 2007). For example, Kleindorfer and
Saad (2005) note that due to events such as the Taiwan earthquake of 1999, the 2001
terrorist attack on the World Trade Center, and the 2003 blackout in the Northeastern
sector of the USA, organizations have placed an increased emphasis on supply chain risk
management (SCRM). Moreover, the massive product recall and production shutdown
experienced by the Toyota Motor Corporation in January 2010 had an adverse impact on
its supply chain as well as supplier and customer relations, also illustrating the need for
effective risk management within supply chains (Atkinson, 2010). Finally, increased
risks due to the 2008-2009 global financial crises pose a new challenge faced by supply
chain managers in their quest to mitigate supply chain threats along with possible
disruptions to their supply chains (Murphy, 2009).
The long-run negative effect on an organization’s stock price due to supply chain
disruptions has been documented through a study by Hendricks and Singhal (2005),
illustrating a negative 40 percent return two years after the date of the disruption
announcement. Additionally, Cousins et al. (2004) argue that there are also important
non-financial consequences of supply chain disruptions, such as a reduction in product
quality, damage to property and equipment, lost reputation among customers,
suppliers, and the wider public and delivery delays. Thus, it has become increasingly
important for organizations to assess the risks associated with their supply chains.
1.1 Purpose
The purpose of this article is to introduce a methodology for modeling and evaluating
risks in supply chains, based on a study of 15 casting suppliers to a major US
automotive company. The methodology uses Bayesian networks for the creation of
risk profiles for each supplier. The networks are used to determine a supplier’s
external, operational and network risk probability, and the potential revenue impact
a supplier can have on the organization as measured by value-at-risk (VAR). The
methodology is offered as a tool to assist managers in the formulation of strategies and
tactics to mitigate overall supply chain risks.
1.2 Organization
The paper is organized as follows. Section 1 provided the motivation for and purpose of
the paper. A discussion on SCM and supply chain risks is provided in Sections 2 and 3,
respectively. Section 4 contains an overview of the research methodology and model
used in this study. Section 5 contains the results of the research. Proposed managerial
actions based upon the results of the study are provided in Section 6. Conclusions are
offered in Section 7. Finally, implications regarding study limitations and directions for
future research are presented in Sections 8 and 9, respectively.
IMDS
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