Assessing the Efficiency of Risk Mitigation Strategies in Supply Chains

Published date01 December 2013
Date01 December 2013
Assessing the Efciency of Risk Mitigation Strategies in Supply
Srinivas (Sri) Talluri
, Thomas J. Kull
, Hakan Yildiz
, and Jiho Yoon
Michigan State University
Arizona State University
Mitigating supply chain risk is a critical component of a companys overall risk management strategy. Drawing upon Contingency Theory,
we posit that the appropriateness and effectiveness of risk mitigation strategies are contingent on the internal and external environments
and that there is no one-size-ts-all strategy. While literature on risk management has proposed a variety of tools and techniques for effectively
evaluating and managing supply chain risks, comprehensive assessment of the efciencies of alternative risk mitigation strategies has not been
addressed in the literature. Such an assessment will help managers select the appropriate mitigation strategy for a given decision-making envi-
ronment. To this end, this study is rst of its kind in evaluating and proposing efcient supply chain risk mitigation strategies in the presence
of a variety of risk categories, risk sources, and supply chain congurations. We combine an empirically grounded simulation methodology with
data envelopment analysis and nonparametric statistical methods to analyze and rank alternative mitigation strategies. We nd that the more ef-
cient strategies focus on exibility rather than on redundancy for supply chain failures. Our research presents several interesting and useful man-
agerial insights for deciding what strategies are most capable of mitigating risks in a variety of contexts.
Keywords: supply chain risk; risk mitigation strategies; simulation; data envelopment analysis; nonparametric statistical methods
Recent years have witnessed many disasters that have created
numerous problems for the supply chains of global companies
(Sodhi and Tang 2012), and these disasters and their aftermath
have brought increased attention to the role of risk management
in supply chains (Narasimhan and Talluri 2009). New informa-
tion technologies make it possible to extend supply chains to
global markets (Sahin and Robinson 2002). This increases the
dependence on outside resources and makes rms vulnerable to
failures affecting all partners within the supply chain (Craighead
et al. 2007). Uncertainties in factors such as market conditions,
supply availability, and transportation can interrupt operations,
thereby causing adverse effects for the companies involved (Hen-
dricks and Singhal 2003).
Traditional supply chain designs normally focus on cost ef-
ciency, assuming that the elements in the supply chain will per-
form as expected (see Karabati and Kouvelis 2008). Shef
(2005) provides examples of severe problems that illustrate the
inability of traditionally designed supply chains to deal with
unanticipated events. In denying the assumption that supply
chain elements will perform awlessly, the assessment and selec-
tion of risk mitigation strategies then become a crucial element
in the process of risk management. Unexpected losses arise from
a sequence of failures and/or causal events (Lewis 2003). Orga-
nizations must determine the potential for such a sequence by
understanding the conditions that give rise to such problems,
and, subsequently, they must assess the likelihood of problems
occurring and any negative impact the problems may entail
(Young and Tippins 2001).
Firms use a number of strategies to manage supply chain risks
(Hillman 2006). Mitigation strategies are those in which the rm
takes some action in advance; therefore, the rm incurs the cost
of the mitigating action whether or not an unanticipated event or
outcome occurs (Kleindorfer and Saad 2005). Because a rm is
liable for costs regardless of the situations end results, the effec-
tiveness of a strategy must be judged with respect to its cost and
noncost factors. In this article, we consider the seven risk miti-
gation solutions described in Chopra and Sodhi (2004), which
can be classied into either redundancy or exibility approaches.
These approaches are the two main risk mitigation strategies that
are identied in the literature and utilized in practice (see Rice
and Caniato 2003; Rice and Shef2005; Tomlin 2006; Tang
and Tomlin 2008; Park 2011). Multiple potential sources for
risks produce varying effects on a supply chain and complicate
the selection of a risk mitigation strategy. We base our theoreti-
cal framework on Contingency Theory (CT) because the appro-
priateness and effectiveness of a risk mitigation strategy are
contingent on each organizations internal and external environ-
mental characteristicsthere is no one-size-ts-all strategy.
We seek to provide guidance to academics and managers on
evaluating and selecting risk mitigation strategies by considering
various risk categories, risk sources, and supply chain congura-
tions. Our methodology focuses on an empirically grounded, dis-
crete event simulation, coupled with data envelopment analysis
(DEA) and nonparametric statistical analysis (KruskalWallis
test), to determine the most appropriate mitigation strategies
across a variety of aforementioned conditions and congurations.
To the best of our knowledge, such an analysis has never
been undertaken before and will prove useful for companies
designing supply chains that can better respond to unanticipated
The rest of the article is organized as follows: The next sec-
tion reviews relevant risk management in supply chains literature
and addresses the related gaps. Following the literature review is
a discussion of our theoretical framework. We then present our
Corresponding author:
Professor Srinivas (Sri) Talluri, Michigan State University, Supply
Chain Management, N370 Business Complex, 632 Bouge Street,
East Lansing, MI 48864, USA; E-mail:
Journal of Business Logistics, 2013, 34(4): 253269
© Council of Supply Chain Management Professionals
methodology, analyze the results of our study, and discuss the
managerial implications. The nal section focuses on conclusions
and potential extensions of this research.
In this section, we review studies on supply chain risk manage-
ment that are most relevant to the topic of this article and we
highlight some of the key issues requiring further attention. We
refer the interested reader to Tang (2006), Zsidisin and Ritchie
(2008), Sodhi et al. (2012), and Tang and Musa (2011) for a
thorough review on supply chain risk and disruption manage-
ment literature.
There is a signicant amount of work related to identifying
the types of supply chain risks. Although most of this work does
not explicitly differentiate between the sources and categories of
risk, we nd it very useful to look at risk types through these
two dimensions. With respect to the sources of risk, several stud-
ies exclusively consider either supplier and supply risk (see, e.g.,
Craighead et al. 2007) or customer and demand risk (see, e.g.,
Federgruen 1993; Schwarz and Weng 2000; Qi et al. 2004).
While these two approaches can be useful for addressing issues
in isolation, they do not help form a holistic understanding on
how a strategy performs across multiple sources and types of
risk. Snyder et al. (2006) emphasize a holistic approach by argu-
ing that decision makers should take supply uncertainty into
account during all phases of supply chain planning, just as they
account for demand uncertainty. Our work takes a similarly
encompassing approach and explicitly examines risks emanating
from both supply and demand sides, in addition to considering
internal risks associated with the manufacturer. Our methodology
follows a risk-source classication similar to those in Davis
(1993) and Chopra and Sodhi (2004), but goes beyond these
approaches by empirically testing the efcacy of alternative risk
mitigation strategies under a variety of supply chain congura-
In terms of risk categories, recurrent risks and disruptions are
among the two most studied risk categories in the literature, and
a vast majority of risk events fall into one of these two catego-
ries. Tomlin (2006) recognizes the features of disruptions by cat-
egorizing long-but-rare disruptions and short-but-frequent
disruptions in planning mitigation strategies. Similarly, Chopra
et al. (2007) show the importance of recognizing and decoupling
disruptions and recurrent risks when planning mitigation strate-
gies in a supply chain. We further disaggregate recurrent risks
into delays and distortions following Gaonkar and Viswanadham
(2004), as recurrent risks related to time and quantity of orders
are naturally different. Thus, we focus on three broad risk cate-
gories: delays, disruptions, and distortions. A delay in material
ow can be viewed as a recurrent risk and can occur because of
many reasons, such as variations in transportation or production
lead times. A disruption occurs when the supply chain is radi-
cally and unexpectedly transformed through nonavailability of
certain production, warehousing, distribution, or transportation
options, such as equipment failure. A distortion, also known as
forecast risk,occurs when one or more parameters within the
supply chain system, such as order sizes, stray from their fore-
casted and expected values.
Extensive research has been performed to reveal approaches
rms can use to mitigate certain supply chain risks. For instance,
Shefet al. (2003) describe mechanisms that companies follow
to assess terrorism-related risks, protect the supply chain from
those types of risks, and attain resilience. They report case stud-
ies and interviews with companiesexecutives. Christopher and
Lee (2004) suggest that a key element in any strategy to mitigate
supply chain risks is improved visibility, and they argue that sup-
ply chain condence will increase in proportion to the quality of
supply chain information. Many proposed risk mitigation strate-
gies focus on uncertainty of demand or lead-times through the
use of decision models. For instance, Schmitt et al. (2011) deal
with choosing between risk pooling and risk diversication strat-
egies by considering contingency approaches subject to disrup-
tions. Other risk mitigation strategy literature goes beyond
inventory-based models with demand uncertainty and instead
focuses on production or supply rate changes. For example,
Wang et al. (2010) investigate process improvement and dual-
sourcing strategies to handle supplier reliability, and they present
whether and how characteristics of the supply base inuence
strategy preference. Demirel et al. (2012) also compare single-
and dual-sourcing strategies in the face of production disruptions
using a game theoretical model. These studies show that strate-
gies should address supply variability across multiple tiers in the
supply chain but generally do not take a more comprehensive
view by also considering multiple channels. To overcome this
issue, we focus on a dual-channel supply chain in this study.
In selecting a risk mitigation strategy to counter against a par-
ticular risk type, it is important to test and compare alternative
risk mitigation strategies in a comprehensive manner. Tomlin
(2009) evaluates 12 possible disruption management strategies in
the context of a two-product newsvendor. His results show that
contingent sourcing is preferred to supplier diversication as the
supply risk increases, but diversication is preferred to contin-
gent sourcing as the demand risk increases. With the exception
of Tomlins (2009) study, the majority of the work in this area
tests and compares few strategies in isolation. This article lls
this gap by testing several alternative risk mitigation strategies
under various risk and source combinations.
As detailed above, scholars have utilized a variety of
approaches to analyze supply chain risks under various conditions,
but there are gaps in the extant literature that we address in this
study. Much of the literature does not allow for the simultaneous
incorporation of traditional cost and noncost factors in evaluating
the effectiveness of strategies, which is one of the advantages of
our approach in considering a more holistic evaluation process. In
addition, we utilize industry-specic cost data and perform sensi-
tivity analysis to demonstrate the impact of cost changes in the
evaluation of the mitigation strategies. Moreover, the literature
does not adequately cover the responsive element of supply chain
risk (Sodhi and Tang 2012) and our work addresses this issue to a
certain extent by providing guidelines for what specic strategies
to utilize in response to a particular risk.
Finally, early research in supply chain risk management has
mostly been conceptual, case-based, or survey-based research. In
recent years the focus has shifted toward quantitative models. As
stated by Melnyk et al. (2009), case-based and empirical research
is limited because it is difcult to evaluate how an event taking
place at a supplier affects the performance of the rm and the
254 S. Talluri et al.

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