Disruption/Risk Management in supply chains- a review

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How to Handle Disruptions in Supply Chains – An Integrated
Framework and a Review of Literature
Behzad Behdani1
, Arief Adhitya2
, Zofia Lukszo1
and Rajagopalan Srinivasan3
1
Faculty of Technology, Policy and Management, Delft University of Technology
P.O. Box 5015, 2600 GA Delft, the Netherlands
2
Institute of Chemical and Engineering Sciences, A*STAR (Agency for Science, Technology and
Research)
1 Pesek Road, Jurong Island, Singapore 627833
3
Department of Chemical and Biomolecular Engineering, National University of Singapore
4 Engineering Drive 4, Singapore 117576
Abstract
This paper describes an integrated framework for handling disruptions in supply chains. The
integrated framework incorporates two main perspectives on managing disruptions, namely pre- and
post-disruption perspectives, which are usually treated as separate in the existing frameworks. Next,
the proposed integrated framework is used to review the literature in supply chain risk/disruption
management. The review gives an overview of the key aspects and specific methods that can be used
for each step in the framework. Based on the review, some main observations are also discussed. The
first is that literature has not uniformly discussed different parts of the framework; pre-disruption
steps, such as risk identification and risk treatment, have been explored extensively while post-
disruption steps such as disruption detection and learning have been given far less attention.
Secondly, there is a lack of quantitative (simulation and modeling) studies for handling supply chain
disruptions. These two gaps, therefore, represent avenues for future research on supply chain
risk/disruption management.
Keywords: Supply Chain Management, Risk, Disruption, Integrated Framework, Literature Review
1 Introduction
Supply chain management has been the subject of various business trends in the last two
decades. Globalization of business, outsourcing internal functions and reducing the buffer levels
across the chain by Just-In-Time philosophy are examples of such trends. Reducing the costs and
giving companies the opportunity to better compete against other players in the market have been the
main drivers behind these business strategies. However, as supply chains become more efficient,
they have also become more vulnerable to different disruptions. This is, firstly, because globalization
and the increasing length of the supply chain expose each supply chain to more risk factors. Also, the
impact of disruption propagates faster through the network because of lower buffer stocks and single
sourcing / supplier based reduction (Figure 1).
Before the world of business had become globalized, some types of risk factors like currency
exchange rate fluctuations, social instability and even natural disasters were considered as local or
regional events. However, with increase in global trade, such issues and events are hardly local
anymore; they can easily influence many companies located thousands of miles from the origin of
risk. Some other significant challenges caused by globalization in managing supply chains stem from

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communication difficulties and cultural differences. For example, cultural differences and the
attitude towards food hygiene in China are blamed as one of the main reasons for the increasing rate
of product recalls in recent years (Roth et al., 2008). Another challenge is longer lead-time (and
consequently, higher uncertainty) in the extended supply chains which has resulted in a critical role
for transportation in the global business. As a direct consequence, another important class of risk has
become highlighted in the risk profile of global companies, i.e. transportation risk.
Besides globalization, outsourcing has brought in several new risks. Dependency on the
quality of material and services from the vendor and the possibility of opportunistic behavior for
parties with different (and sometimes, conflicting) goals are examples of these new factors (Kavčič
and Tavčar, 2008). Another factor is "intellectual property risk” (Blackhurst et al., 2008) which
would be a key concern as the business environment shifts towards knowledge-based economies.
In addition to the new types of risks introduced by cost-efficiency trends in managing supply
chains, disruption in one specific part of a global supply chain can ripple through the chain much
faster nowadays. This fast propagation of disruption effects in supply chains is mainly due to the lack
of buffers and extra resources across the network. Traditionally, supply chains were designed with
redundancies in different segments. During normal operations, these are unused and therefore appear
to be inefficiencies. Therefore, seeking to eliminate all forms of wastes, many companies adopted
approaches like lean and JIT aimed at eliminating or significantly reducing these buffers.
Consequently, the operating costs significantly decreased. Companies could also benefit from the
value of money savings by less investment, for example, in the storage facilities. However, during
disruptions, the adverse effects of any initiating event may spread quickly through the supply chain
leaving practically no (or little) buffer time for the companies to look for alternative solutions to
handle the abnormalities (Sheffi, 2005). Furthermore, due to outsourcing and fragmentation of
management in the chain, the decision making process for handling disruptions may be significantly
slower than before.
Figure 1: Business trends and challenges for handling supply chain risk
The increase in the risk factors in the supply chain and the rapid propagation of disruption
have placed many companies in a challenging situation as they have to operate in a more risky
business environment. Paradoxically, the resources needed to manage these risky situations have
become much more limited. Firstly, by implementing the lean philosophy, very few types of
resources including finished goods, work-in-process and raw material inventory are available to
handle the impact of disruptive events. Secondly, by outsourcing, most of companies have lost the
control on the resources and the visibility across their supply chain (Zsidisin et al., 2005). This loss
of control and visibility affects the companies’ ability to detect disruption and limits its degrees of
freedom to cope with abnormalities. Consequently, while the business for supply chains is more
risky, the essential resources to handle disruptive events are also fewer and their control distributed

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among different actors. The explicit outcome of this paradoxical situation is the growing
vulnerability of supply chains and their higher impact on companies’ performance. Ensuring the
smooth operation of supply chains and providing a reliable offering to customers seems to be more
challenging now. To handle this challenge, two options might be considered; a passive option in
which companies avoid the aforementioned supply chain strategies (e.g., JIT, global sourcing or
outsourcing) as they have made supply chains increasingly vulnerable and the active option in which
the risk of these trends is being accepted; however, at the same time, companies try to manage the
risks imposed to supply chains in a systematic way.
Despite many criticisms highlighting the growing vulnerability of supply chains, the value of
global sourcing, JIT and outsourcing in the daily business of companies –especially in a stable
environment and normal conditions- is so significant that for most supply chain managers, the active
option is the final and probably, the only choice. A relevant question, then, would be: “how can
companies systematically manage disruptions in their supply chains?” This paper aims to answer this
question. Specifically, it describes an integrated framework to tackle disruptions in supply chains.
The rest of the paper is structured as follows. Section 2 starts by discussing two common
perspectives on handling supply chain disruptions. Section 3 overviews the frameworks presented in
literature for managing disruptions. Section 4 presents an integrated framework to handle supply
chain disruptions. This integrated framework is used as a classification scheme in Section 5 to
comprehensively review the supply chain risk/disruption management literature. Section 6 concludes
with observations related to the existing literature on supply chain risk/disruption management.
2 Handling disruptions in supply chains - two common perspectives and the need for an
integrated framework
Handling disruptions in each supply chain might take different forms and include different
types of activities. From a "time perspective", all activities can be classified into two major
categories: "Pre-Disruption" and "Post-Disruption" (Figure 2), also called "Prevention" vs.
"Response" (Dinis, 2010; Thun and Hoenig, 2009). Some activities and measures are taken by
companies beforehand to minimize the exposure to potential disruptions. However and despite all
efforts, disruptive events might happen and their influence on supply chain operations must be
managed to restore the system to normality. Another classification used approximately for a similar
purpose is "Proactive" (Predictive) vs. "Reactive" view in managing supply chain risk (Dani and
Deep, 2010). In this classification, proactive risk management refers to taking precautionary
measures to tackle the risk of disruptive events while reactive refers to reacting once an event
materializes.
Figure 2: Two perspectives on handling disruptions in supply chains
Despite the different classifications, managing supply chain disruptions might be supported
by systematic approaches to:
 Identify potential disruptions and recognize/invest in the resources needed to manage them in
advance.

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 Use the available resources to manage disruptions when disruptions happen.
The former is broadly recognized as supply chain risk management (Finch, 2004; Hallikas et
al., 2004; Tang, 2006b; Ritchie and Brindley, 2007) or supply chain risk analysis (Sinha et al., 2004)
and primarily deals with pre-disruption activities (e.g., identification, assessment and mitigation of
potential disruptions). The latter is usually called disruption management (Xiao and Yu, 2006) or
abnormal-situation management (Adhitya et al., 2007a) and focusses on the necessary support in
handling an actual disruption after it has materialized.
For effectively managing disruptions, both perspectives are crucial and must be addressed.
By investing in risk prevention, many problems and disruptive events can be avoided. Moreover,
without defining plans and investing in necessary resources in advance, the disruption management
process would be very slow. However, no company can eliminate or even have plans for every
possible disruption. First of all, some disruptions are not known beforehand. Even for disruptions
that companies expect, they cannot afford to invest in the resources necessary to prevent each one.
Consequently, it is neither possible nor economically sensible to attempt to deal with every possible
disruption. For disruptions that cannot be prevented, more attention might be paid to the response
side of the disruption handling process. For instance, for rare events like an earthquake, companies
would prefer a contingency tactic, as contingent costs are incurred only in the event of a disruption
(Tomlin, 2006).
In addition to the aforementioned issues, having well-designed plans and effectively
executing these plans when a disruption occurs are two separate issues and require different sets of
capabilities (Xiao et al., 2007). In other words, having response plans and putting resources in place
are not necessarily the guarantee for companies’ success in handling disruption; companies must also
know “when” and “how” to use the available resources and plans. These capabilities - sometimes
called predictive intelligence and real-time supply-chain reconfiguration (Blackhurst, et al., 2005) or
smart supply chain (Butner, 2010) - support companies to better utilize the available resources to
manage disruptions when they happen. Furthermore, the reliance on static plans for managing
disruptions that are inherently dynamic events has also been often questioned (Iakovou et al., 2010).
Most response plans are developed based on some assumptions that are seldom reviewed. Indeed,
when a disruption happens, the capability to gather accurate information about the event and the state
of system and also revising the pre-defined plans based on the information are as important as the
response plan itself.
Figure 3: From discrete to integrated perspective in managing supply chain disruptions
To sum up, in managing a disruption, what companies do before and after its occurrence are
both important. These two aspects should not be considered as mutually exclusive (Micheli et al.,
2008). Instead, both of them should be implemented and coordinated to achieve the best performance

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in handling disruptions in supply chains. The two perspectives in managing disruptions must be seen
as integrated and interconnected cycles that provide feedback to and receive feedback from each
other (Pyke and Tang, 2010; Dani and Deep, 2010). This calls for integrated frameworks to handle
pre- and post-disruption activities (Figure 3).
Table 1: An overview of the frameworks to handle disruptions
Perspective Reference Steps in handling disruptions
Pre-disruption
Perspective
Tah and Carr
(2001)
Risk identification, risk assessment, risk analysis, risk handling,
risk monitoring
Harland et al.
(2003)
Map supply network, identify risk and its current location, assess
risk, manage risk, form collaborative supply network risk strategy,
implement supply network risk strategy
Hallikas et al.
(2004)
Risk identification, risk assessment, decision and implementation
of risk management actions, risk monitoring
Norrman and
Jansson (2004)
Risk identification, risk assessment, risk treatment, risk
monitoring, incident handling, contingency planning
Sinha et al. (2004)
Risk identification, risk assessment, risk planning and solution
implementation, conducting failure modes and effect analysis,
continuous improvement
Finch (2004)
Risk identification, risk analysis, risk reduction-transfer and
acceptance, risk monitoring
Kleindorfer and
Saad (2005)
Specifying sources of risk and vulnerabilities, Assessment,
Mitigation
Wu et al. (2006)
Risk classification, risk identification, risk calculation,
implementation/validation
VanderBok et al.
(2007)
Risk planning, risk identification, risk analysis, risk handling, risk
monitoring
Wiendahl et al.
(2008)
Risk identification, risk assessment, risk control
Manuj and
Mentzer (2008)
Risk identification, risk assessment and evaluation, selection of
appropriate risk management, implementation of supply chain risk
management strategy(s), mitigation of supply chain risks
Oehmen et al.
(2009)
Risk identification, risk assessment, risk mitigation
Matook et al.
(2009)
Supplier risk identification, assessment of supplier risks, reporting
and decision of supplier risks, supplier risk management responses,
and supplier risk performance outcomes
Knemeyer et al.
(2009)
Identifying key locations and threats, estimating probabilities and
loss for each key location, evaluating alternative countermeasures
for each key location, selecting countermeasures for each key
location
Adhitya et al.
(2009)
Risk identification, consequence analysis, risk estimation, risk
assessment, risk mitigation, risk monitoring
Cigolini and Rossi
(2010)
Risk analysis, risk assessment, risk control
Post-
disruption
Perspective
Blackhurst et al.
(2005)
Disruption discovery, disruption recovery, supply-chain redesign
Adhitya et al.
(2007a)
Key Performance Indicators (KPI) monitoring, root Cause
Identification, rectification strategy proposal, rectification strategy
selection (optimization), scheduling and coordination
Integrated
Perspective
Pyke and Tang
(2010)
3R framework: Readiness, Responsiveness and Recovery
3 Overview of frameworks to handle supply chain disruptions
Table 1 shows a review of frameworks that offer a process for managing disruptions in
supply chains. As can be seen, the main focus has been on risk management (pre-disruption) process;

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there has been less attention paid to disruption management (post-disruption) activities. There is also
little research that addresses the importance of the two perspectives and offering integrated
frameworks for pre- and post-disruption activities.
For pre-disruption process, the most-frequently-mentioned steps are “risk identification”,
“risk assessment”, “risk handling” and “risk monitoring”, though the terminology is not always used
consistently. For instance, while “risk analysis” is used by Sinha et al. (2004) to describe the whole
process of managing risks, in other papers it has been used for a particular step in the process. With
“risk analysis”, VanderBok et al. (2007) imply assessing the risk factors in terms of the likelihood of
occurrence and the estimated impact (which is usually called “risk assessment”). For Cigolini and
Rossi (2010), however, “risk analysis” is the definition of the system’s boundaries and identifying
the risky events (equivalent with the “risk identification” step). Moreover, for a specific step in risk
management process, different terms are used by different researchers. For instance, a step in which
the risk level for identified disruptions is evaluated, has been termed “risk assessment” (Tah and
Carr, 2001; Hallikas et al., 2004; Norrman and Jansson, 2004), “risk analysis” (Finch, 2004;
VanderBok et al., 2007), “risk calculation” (Wu et al., 2006) and “risk estimation” (Adhitya et al.,
2009).
For the post-disruption process, the work of Blackhurst et al. (2005) has received the most
attention in literature. They described three main steps to handle disruptions: disruption discovery,
disruption recovery, supply-chain redesign. A disruption must be firstly discovered before a firm can
implement solutions to recover. Afterwards, the firm must decide on how the supply chain can be re-
designed to become more resilient in the future.
About the integrated perspective on handling disruptions, one of the few studies is the 3R
framework of Pyke and Tang (2010) which is primarily presented for a specific case of product
safety risk. Pyke and Tang (2010) discuss that the process for managing supply chain operation
“before, during, and after” product recall must include three main stages: “Readiness,
Responsiveness, and Recovery”. Before a potential recall, the company should implement some
policies like TQM practices and statistical sampling inspection to improve product safety and reduce
the likelihood of having a product recall. It should also prepare the necessary channels in case a
recall becomes necessary (Readiness). During the recall, the company should create an action plan
which allows a quick response to the safety problems (Responsiveness). After a recall, the company
should take some steps to restore its supply chain back to normality (Recovery). Moreover, the
company must review the recall procedure and take corrective actions (e.g., product design or
supplier audits) to prevent or reduce the likelihood of future product recalls. Pyke and Tang (2010)
illustrate their framework with three examples of products recalls; one example of poor recall
management in the Ford Pinto case and two successful ones – Black & Decker’s recall of
Spacemaker Plus Coffeemaker in the late 1980s and Mattel’s toy recall in 2007.
A major strength of the 3R framework is that it offers a full picture – including both pre- and
post-disruption perspectives - of what must be done to handle product safety issues. Moreover, it
discusses the feedback between “Recovery” and “Readiness” where the experience of handling one
product recall can be the valuable source to improve the plans and avoid future problems. However,
this framework is very simple and does not discuss the details of each of the three stages and the
necessary sub-steps. Therefore, for example, although they discuss “Readiness”, a detailed
description of how this “Readiness” must be achieved or implemented is lacking in their work. The
other drawback of this framework is that the feedback is solely from Recovery to Readiness.
However, in handling disruptions, it is also possible to have interactions between “Responsiveness”
and “Readiness” stages. For instance, the difficulties in a rapid detection or rapid response to the
product safety problem can give insights to improve the procedures and identify the essential
capabilities to faster detect a safety issue. Finally, the other limitation of this work is its main focus

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on a specific type of disruption which is “product safety”. Of course, three concepts of the
framework are generic; however, the application of model must be shown with cases of other types
of disruptions in supply chains. Despite these issues, the 3R framework of Pyke and Tang (2010) is a
valuable work in the supply chain risk/disruption literature because it is the first and single effort in
the existing supply chain literature which explicitly discussed the benefits of integrated view for a
specific disruptive event and presented a model which accommodates the pre- and post-disruption
steps and their relations.
4 Handling supply chain disruptions – an integrated framework
In this section, an integrated framework for handling disruptions in supply chains is
presented. This framework aims to describe the necessary steps in handling disruptions in the pre-
and post-disruption phases as well as the feedback between the two.
Before describing the framework in more detail, the definition of some main concepts is given.
Definition 1– Supply Chain Disruption:
A Supply Chain Disruption is an event that might happen in any part of the chain and causes
undesired impacts on its (achievement of) objectives and performance.
As a corollary, if an event has no adverse effect on the achievement of the objectives, it is not
regarded as a disruption. This emphasis on the impact on the objectives is essential as it helps better
justify the investment of resources for managing disruptions (Berg et al. 2008).
Definition 2– Disruption Lifecycle:
For each disruption in the supply chain, the lifecycle comprises of two distinct phases (Figure
4). There is a period of time over which a disruption is latent and only a possibility; it exists basically
as a Potential Disruption. When an instance of the disruption occurs in reality, it is an Actual
Disruption.
To best handle a disruption, different decisions must be made and activities performed in
both phases of the lifecycle. The efforts in supply chain risk management (pre-disruption
perspective) are aimed to discover potential disruptions and prevent them from becoming actual
ones. The main goal of supply chain disruption management (post-disruption perspective) is to
handle an actual disruption once it has occurred and to use that experience to learn so as to avoid,
reduce or transfer the risk of happening similar potential disruptions in the future.
Figure 4: Disruption lifecycle
Based on the above, a comprehensive definition for “Supply Chain Disruption Management”
can be stated as follows.

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Definition 3– Supply Chain Disruption Management:
Supply Chain Disruption Management is a structured and continuous process to analyze the
impact of disruptions across the supply chain on predefined objectives and to handle them in their
entire lifecycle.
Such a systematic process would give insight into the steps that must be followed to better
address the capabilities and the resources necessary to handle each Supply Chain Disruption in its
entire lifecycle. For some disruptions, indeed, the main focus might be on pre-disruption
capabilities/actions; for others, the main attention might be on the proper response to disruption
when it happens.
Based on these definitions, an integrated framework to manage disruptions in supply chains is
presented in Figure 5.
Figure 5: Integrated framework to manage disruptions in supply chains
In this framework, managing disruptions is not a project; it is an ongoing process with two
distinct cycles. The first cycle – called the Risk Management Cycle – is about potential disruptions.
Firstly, potential disruptions in the supply chain (or its subset) must be identified (R1: System
Definition & Risk Identification). Next, for the identified potential disruptions, the expected impact
on the objectives and the performance of the system must be evaluated (R2: Risk Quantification).
This evaluation basically seeks to answer two questions for each potential disruption (1) “How likely
is the disruption?” (Likelihood), and (2) “How bad are its consequences?” (Impact) (Sheffi, 2005).
The next step is to determine which potential disruptions need treatment and how they must be
treated (R3: Risk Evaluation & Treatment). After treatment, if the risk level is acceptable, supply
chain operations may proceed. However, the level of risk must be continuously monitored (R4: Risk
Monitoring). Changes in the system (e.g., in customer needs or partner strategies) or in the business
environment (e.g., new regulations or new competitors) can lead to changes in the likelihood or
expected impact of potential disruptions. Moreover, the system boundaries and structure may also
change. A new supplier might be added to the supply base; a new warehouse launched, or a new
product introduced. When the system changes, it is necessary to start the risk management cycle
afresh, starting with a redefinition of the system and its boundary and identifying new potential
disruptions.
The second cycle is Disruption Management Cycle. Despite enough safeguards, sometime, an
actual disruption will happen. To handle the disruption, the first step is detecting the disruption, its
characteristics and also the expected consequences on the system (D1: Disruption Detection). Once
it is detected, the company must react quickly to manage the impact of the disruption and return the

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supply chain to normal and planned operation. The primary response should be on the basis of pre-
defined response plans previously defined in the Risk Evaluation and Treatment step (D2:
Disruption Reaction). If the pre-defined response plan is found to be inadequate to control the
impact of disruption or if no response plan has been defined for the specific disruption, alternative
solutions should be quickly found and implemented (D3: Disruption Recovery). After the actual
disruption has been fully managed, the company must review the process followed, and learn lessons
for handling similar disruptions in the future (D4: Disruption Learning). For example, the response
plans might be revised or the supply chain restructured to reduce the probability or severity of similar
future occurrences. For instance, a new warehouse might be added to the network or a new contract
established with a local supplier. Of course, these modifications imply changes in the system
structure and boundaries; accordingly, it will be a necessary to repeat the risk management cycle
afresh and identify new potential disruptions.
5 Review of literature on supply chain disruption management
Based on the integrated framework presented above, in this section we present a review of the
supply chain Risk/Disruption management literature. The review focuses on the specific methods
used in each step of the framework.
5.1 Risk Identification
Risk Identification is the first step in managing disruptions in supply chains. The purpose of
risk identification is to identify all knowable disruptions. This step is especially important because a
disruption in supply chain cannot be managed unless it is first identified. Risk identification literature
has discussed two important issues – risk identification methods and risk classification schemes.
5.1.1 Risk identification methods
To facilitate risk identification, a wide range of methods are presented in literature. Some of
common methods are shown in Table 2. One of the most frequently available sources to identify
supply chain risks is the expert view. Different methods like survey (Thun and Hoenig, 2009) or
brainstorming (Norrman and Jansson, 2004) can be used for this purpose. Historical data of past
events and review of literature or reports of similar companies can support experts in a better-
informed risk identification process. It is also recommended to involve a cross-functional team of
employees and a diverse group of experts in the process (Hallikas et al., 2004; Norrman and Jansson,
2004). This is beneficial both for the variety of perspectives that a group can provide and to build the
commitment to risk management process in the whole company.
Table 2: Risk identification method
Risk Identification Method Reference
Generic
Approaches
Personnel brainstorming Norrman and Jansson (2004)
Expert interviews
Wu et al. (2006), Canbolat et al. (2008), Tuncel and
Alpan (2010), Yang (2010)
Expert view (survey) Thun and Hoenig (2009), Yang (2010)
Literature review Wu et al. (2006), Canbolat et al. (2008), Yang (2010)
Specific
Approaches
Action Research method and AHP Schoenherr et al. (2008)
Ishikawa Diagrams Wiendahl et al. (2008)
HAZard and Operability (HAZOP) Adhitya et al. (2009)
Among the more systematic methods, Schoenherr et al. (2008) used the Analytic Hierarchy
Process (AHP) to identify the risk factors related to the offshoring decision in a US manufacturing
company. For this purpose, they defined three sourcing characteristics related to the product, the
partner and the environment as main decision objectives. Next, they subdivided the main objectives
into sub-objectives and finally to 17 risk factors.

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Adhitya et al. (2009) discussed the application of HAZard and Operability (HAZOP) method
to supply chain risk identification. The HAZOP method is one of the most widely-used techniques
for hazard identification in process plants. Based on the similarities between the structure of supply
chains and chemical plants, Adhitya et al. (2009) suggested adapting the methods and concepts from
chemical process risk management to supply chains. Similar to a HAZOP study for a process plant
that is performed around process flow diagrams (PFDs), they defined a supply chain flow diagram
(SCFD) and work-flow diagram (WFD) to represent the supply chain structure and operations.
Subsequently, the risk identification can be performed by systematically generating deviations in
different supply chain parameters and identifying their possible causes, consequences, safeguards,
and mitigating actions. For example, the guideword “High” or “Low” can be combined with a flow
“Demand” to indicate the deviation “High Demand” or “Low Demand” respectively and its possible
causes and consequences can be identified by tracing the flows in the diagram.
The other method mentioned in literature is Ishikawa Diagrams which is used by Wiendahl et
al. (2008) to identify the logistic risks for a case study of a forging company. They started with an
objective and the possible negative consequences (like “low output rate”) and made a list of possible
events that may lead to each adverse effect in five main actuating variables – material, machine,
method, human and environment.
Some other methods discussed for risk identification in the supply chain risk literature are
presented in Table 2. Although a wide variety of methods is available to identify risks, the choice of
the risk identification method is case specific. Some factors which may influence the selection of
method are time availability, experience availability, and the complexity of supply chain. In general,
the basic expert-based methods for risk identification (like brainstorming or risk questionnaire) are
fast; however, they need a level of expertise which might not be available inside the company. Of
course, a company may ask external experts and consultants to perform the risk identification;
however this may be expensive and time-consuming and undermine some of the benefits described
above. The other issue with expert-based methods is that for a complex supply chain with several
tiers, they cannot guarantee a complete view of all relevant disruptions. More systematic risk and
disciplined approaches, however, can facilitate a more comprehensive risk identification process.
Moreover, as in most cases the outputs of these methods are repeatable; the results of risk
identification process can be independently evaluated and extended. As a broad recommendation, the
best solution for risk identification is to use a combination of methods (Hallikas et al., 2004). As an
example, instead of performing AHP using one expert, several experts may independently perform
the analysis and share/combine the results to make a final list of potential disruptions.
5.1.2 Risk classification scheme
To facilitate a systematic and comprehensive risk identification process, several classification
schemes are presented in literature (Table 3). Categorizing risks not only improves the effectiveness
and quality of risk identification but also supports better communication among the actors involved
in the process (Stecke and Kumar, 2009).
Location-based classification:
One of the most common risk categorization schemes involves classifying supply chain
disruptions based on the location of the source of disruption. Christopher and Peck (2004) considered
three categories of risk sources – “Internal to the firm, “external to the firm but internal to the supply
chain network”; and “external to the network”. Risk sources “Internal to the firm” are further
subcategorized into “process risks” and “control risks”. The category “external to the firm” includes
“demand” and “supply” risks. Risk sources “external to the network” or “environmental risks” are
exemplified by natural disasters, terrorist attacks and regulatory changes. The term “interaction
risks” is also used for the last group as they arise due to the interaction between a supply chain and

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its environment (Kumar et al., 2010). Similarly, Thun and Hoenig (2009) made a distinction between
“internal company” and “cross-company” risks. The latter are further divided into “purchasing risks”
(upstream) and “demand risks” (downstream). External supply chain risks are also subcategorized
into sociopolitical, economical, technological or geographical disruptions. Separating the risk
sources to “External vs. Internal” is followed by several other researchers (Table 3). This
classification is especially useful as “Internal Risks” are generally within the boundary of the system
– company or supply chain – and the actors have more control on the cause of the disruption
(Trkman and McCormack, 2009). External Risks, however, are more difficult to control and
sometimes even uncontrollable (Wu et al., 2006).
Table 3: Supply chain risk classification
Another location-based classification approach is to categorizethe risk sources into “supply”
and “demand” (Wagner and Bode, 2006; Sodhi and Lee, 2007; Tang and Tomlin, 2008). This
classification can be regarded as offering a “supply chain view” (Sodhi and Lee, 2007). “Supply
risks” are located in the supply base of company (upstream) while “demand risks” are associated
with the demand side and activities downstream of the supply chain. As emphasized by Jüttner
(2005), the “supply and demand risks” describe the direction of potential disruptions and are not
restricted to dyadic relationships between the focal company and directly related vendor and
customer organizations. Thus, for example, second- or third-tier suppliers with which the focal firm
has no direct contact can also be part of the firm’s risk management process (Choi and Krause, 2006;
Kull and Closs, 2008). The two main classes of demand/supply are sometimes complemented with
additional risk classes. For instance, Tang and Tomlin (2008) included a “process risk” category
which consists of risks associated with in-house operations and in-bound and out-bound logistics.
Sodhi and Lee (2007) considered a third class of “contextual risks” that include cultural differences
in multinational operations, environmental, regulations, and exchange rate risks across countries.
Scale-based classification:
Another approach called “scale-based classification” suggested in literature categorizes
disruptions according to their likelihood and impact as:
 Low-Likelihood, High-Impact disruptions: for example, labor strike, terrorist attack or natural
disaster. This class is also termed Value-at-risk (VaR) type disruptions by Ravindran et al.
(2010) and “catastrophes” or “catastrophic events” by Lodree and Taskin (2008), Knemeyer
et al. (2009) and Huang et al. (2009).
Risk Classification Reference
Location-based
Internal/External
Christopher and Peck (2004), Wu et al. (2006), Cucchiella
and Gastaldi (2006), Bogataj and Bogataj (2007), Oehmen
et al. (2009), Thun and Hoenig (2009), Trkman and
McCormack (2009), Kumar et al. (2010), Dani and Deep
(2010), Olson and Wu (2010)
Supply-chain view
Wagner and Bode (2006), Sodhi and Lee (2007), Tang and
Tomlin (2008), Oke and Gopalakrishnan (2009)
Scale-based
Kleindorfer and Saad (2005), Gaonkar and Viswanadham
(2007), Lodree and Taskin (2008), Oke and Gopalakrishnan
(2009), Knemeyer et al. (2009), Huang et al. (2009),
Ravindran et al. (2010)
Other
Cavinato (2004), Chopra and Sodhi (2004), Finch (2004),
Peck (2005), Sheffi (2005), Tang (2006b), Cheng and Kam
(2008), Blos et al. (2009), Matook et al. (2009), Tang and
Musa (2010)

12
 High-Likelihood, Low-Impact disruptions: for example, late delivery or missing quality
requirements. This is frequently called “operational” or “day-to-day” disruptions (Kleindorfer
and Saad, 2005; Huang et al., 2009). “Miss-the-target (MtT) risks” is the other term
suggested by Ravindran et al. (2010).
Table 4: List of most discussed supply chain disruptions
Disruption Reference
Company Level
Production facility
failure
Zsidisin, et al. (2004), Cucchiella and Gastaldi (2006),
Sodhi and Lee (2007), Manuj and Mentzer (2008), Stecke
and Kumar (2009)
Quality problems in End
Product
Manuj and Mentzer (2008), Oke and Gopalakrishnan
(2009), Matook et al. (2009), Pyke and Tang (2010), Dani
and Deep (2010)
IT system failure Sutton (2006), Faisal et al. (2007)
Human resource
problems (e.g., strike)
Stecke and Kumar (2009)
Network
Level
Demand Side
Distribution network
breakdown
Canbolat et al. (2008), Stecke and Kumar (2009),
Demand volatility
Chopra and Sodhi (2004), Sodhi (2005), Tang (2006a-b),
Tang and Tomlin (2008), Canbolat et al. (2008), Huang et al.
(2009), Buscher and Wels (2010)
Supply Side
Quality problems in Raw
Material
Chopra and Sodhi (2004), Zsidisin, et al. (2004), Wu et
al. (2006), Tang (2006a-b), Gaudenzi and Borghesi
(2006), Schoenherr et al. (2008), Kull and Talluri (2008),
Wu and Olson (2008), Roth et al. (2008), Blackhurst et al.
(2008), Chao et al. (2009), Buscher and Wels (2010),
Parmar et al. (2010), Ravindran et al. (2010)
Supplier delay
Gaudenzi and Borghesi (2006), Tang (2006a-b), Wu et al.
(2006), Chopra et al. (2007), Levary (2008), Tang and
Tomlin (2008), Ross et al. (2008), Blackhurst et al.
(2008), Kull and Talluri (2008), Wu and Olson (2008),
Parmar et al. (2010), Buscher and Wels (2010),
Ravindran et al. (2010)
Supplier bankruptcy
Zsidisin, et al. (2004), Sodhi and Lee (2007), Canbolat et
al. (2008)
Transportation
3PL bankruptcy Levary (2008), Stecke and Kumar (2009)
Transportation delay
Wilson (2007), Manuj and Mentzer (2008), Schoenherr et
al. (2008), Canbolat et al. (2008), Blackhurst et al. (2008),
Yang (2010), Sanchez-Rodrigues et al. (2010)
Port strike Stecke and Kumar (2009)
Environment
Natural catastrophes
Hale and Moberg (2005), Papadakis (2006), Ratick et al.
(2008), Schoenherr et al. (2008), Knemeyer et al. (2009),
Oke and Gopalakrishnan (2009), Stecke and Kumar
(2009), Ravindran et al. (2010)
Security Risks (e.g.
terrorist attacks)
Bichou (2004), Sheffi and Rice (2005), Stecke and Kumar
(2009), Yang (2010)
Communication
infrastructure
breakdown
Sutton (2006), Faisal et al. (2007), Blackhurst et al.
(2008), Stecke and Kumar (2009), Deane et al. (2009)
Political/ Economic
instability
Cucchiella and Gastaldi (2006), Manuj and Mentzer
(2008), Matook et al. (2009), Kumar et al. (2010)
Regulatory and legal risk
Zsidisin, et al. (2004), Chopra and Sodhi (2004),
Cucchiella and Gastaldi (2006), Oke and Gopalakrishnan
(2009), Stecke and Kumar (2009), Kumar et al. (2010),
Sanchez-Rodrigues et al. (2010), Yang (2010)

13
A third class of “Medium-Likelihood, Medium-Impact” is discussed by Oke and
Gopalakrishnan (2009) and includes risk factors like changing regulations which does not occur very
frequently, but is normally more frequent than natural disasters. Of course, the impact of new
regulations on the business could be more than the impact of day-to-day disruption but much less
than those of natural disasters.
Other risk classification schemes:
Several other approaches for categorizing risk sources in supply chain can be found in
literature as shown in Table 3. Of these, the multi-level classification of Peck (2005) has received the
most attention. In this conceptual framework, the sources for supply chain risks are presented in four
main levels of “value stream/product or process”, “assets and infrastructure dependencies”,
“organizations and inter-organizational networks” and “environment”. With a similar idea, Cavinato
(2004) discussed that identifying risks and uncertainties in supply chains must focus on five sub-
chains/networks in every supply chain: Physical, Financial, Informational, Relational and
Innovational networks. Blos et al. (2009) also classified supply chain risks into four categories of
vulnerability: financial, strategic, hazard, and operations.
In addition to categorization approaches discussed here, a number of papers discuss only
specific risks or tries to identify risks that occur in certain situations. For example, risk sources
exacerbated by globalization and offshoring are discussed by Tan and Enderwick (2006), Colicchia
et al. (2010) and Deane et al. (2009); Tsai et al. (2008) studied logistics outsourcing risk; Faisal et al.
(2007) identified four types of information risks: information security/breakdown risk, forecast risk,
intellectual property rights risks, and IS/IT outsourcing risks; Sanchez-Rodrigues et al. (2010)
specifically discuss transportation risks and Roth et al. (2008), Chao et al. (2009) and Pyke and Tang
(2010) focus on product quality and recall problems. A list of frequently discussed possible
disruptions is listed in Table 4.
5.2 Risk Quantification
Risk quantification is the process of evaluating the disruptions that have been identified and
developing the basis for making decisions on their relative importance. The risk level of disruptions
is usually quantified in two dimensions; likelihood (or frequency) of the disruption occurring and its
impact on the performance of the supply chain.
5.2.1 Likelihood estimation methods
Appropriate methods to estimate the probability of supply chain disruptions have received
little attention so far. A notable exception is the work of Knemeyer et al. (2009) in which
approaches for probability estimation of catastrophic events are discussed. For catastrophic events,
such as aircraft accidents, historical data is available and can be the basis for estimation. For cases
where limited historical data is the only source, combining the available data with expert estimates
(for example using Delphi Method) can give more reliable insights on disruption likelihood.
Simulation is the other approach that might be used when the factors that might cause a disruption
are well-known. As an example, Knemeyer et al. (2009) discussed a hurricane (or tropical cyclone)
simulator which uses inputs like central pressure, maximum wind radius, etc. from government and
private sources to generate probability distributions for the number, intensity and location of
hurricane activity in a given year. As another effort for disruption likelihood estimation, Mohtadi and
Murshid (2009) developed a dataset of terrorist attacks involving chemical, biological, or radio-
nuclear agents. Based on this, they estimated the likelihood of a catastrophic event using extreme
value theory.
In some other papers, though the likelihood estimation is not the central issue, it has been
discussed as a part of risk management process. In majority of these papers, expert judgment has

14
been suggested as the approach to estimate the likelihood of disruptions. For example, Hallikas et al.
(2004) utilized a five-class scale for qualitative estimation of disruption probability by experts.
Similarly, Yang (2010) used 5-point Likert scales for rating the risk frequency in a questionnaire to
evaluate the risk factors in container security.
5.2.2 Impact estimation methods
Systematic methods for assessing the disruption impact have received more attention in the
supply chain risk management literature. Table 5 presents some of these methods. Analytic
Hierarchy Process (AHP) is among the most commonly used methods. Gaudenzi and Borghesi
(2006) applied AHP in two phases to identify supply chain risk factors and evaluate the intensity of
each factor. Firstly, the supply chain objectives (e.g., on-time delivery, order completeness, order
correctness, and damage-free and defect-free delivery) were prioritized. Subsequently, the relative
importance of the identified factors to each of these objectives was assessed. Similarly, Wu et al.
(2006) have suggested the use of AHP to analyze the risk factors in the supply base. They first
classified supplier-oriented risk factors into several categories and applied AHP technique to
calculate an overall risk index. In another application, Levary (2008) presented a case study in which
AHP has been used to evaluate an existing supplier against two other potential ones. The evaluation
and ranking of the suppliers was based on four criteria of supply reliability – Supplier reliability,
Country risk, Transportation reliability, and Reliability of the supplier’s suppliers. A group of experts
were asked to compare all pairs of criteria and supplier priorities for each criterion. The overall rank
of each supplier was calculated by multiplying a criterion’s priority by supplier’s priority for each
criterion and summing them over all the criteria. Some other recent applications of AHP for
assessing the risk factors in supply chain can be found in Schoenherr et al. (2008) and Enyinda et al.
(2010).
Table 5: Methods for supply chain impact quantification
Risk Quantification Method Reference
Qualitative/semi-
quantitative
AHP
Wu et al. (2006), Gaudenzi and Borghesi (2006),
Levary (2007), Levary (2008), Schoenherr et al.
(2008), Enyinda et al. (2010), Kull and Talluri (2008)
Expert group rating
Norrman and Jansson (2004), Blackhurst et al.
(2008), Matook et al. (2009)
Expert opinion (survey)
Thun and Hoenig (2009), Blos et al. (2009), Yang
(2010), Sanchez-Rodrigues et al. (2010)
Failure mode and effect
analysis (FMEA)
Sinha et al. (2004), Pujawan and Geraldin (2009)
Quantitative
(Modeling and
Simulation)
Petri net Wu et al. (2007), Tuncel and Alpan (2010)
System dynamics Wilson (2007)
Graph theory Faisal et al. (2007), Wang and Ip
Discrete event simulation Munoz and Clements (2008), Kull and Closs (2008)
Inoperability input-output
modeling (IIM)
Bogataj and Bogataj (2007), Wei et al. (2010)
Besides AHP, some other qualitative methods that mainly rely on experts’ judgment and
experience have been proposed. Sinha et al. (2004) used failure mode and effect analysis (FMEA). A
risk priority number (RPN) was calculated by multiplying the values for severity (s) and probability
of occurrence (p). The risk priority numbers helped the team select the highest impact and
recommend corrective actions. The house of risk model (Pujawan and Geraldin, 2009) is a similar
approach with some modifications. Risk agents are factors causing the occurrence of risk events.
Probability rating is assigned to each risk agent and severity rating to each risk event. The aggregate
risk potential of a risk agent is calculated by considering all the related risk events. The risk agents
can then be prioritized based on these aggregate values. Based on a similar approach, preventive

15
actions – each of which impact multiple risk agents and entail a certain cost – can be rated,
quantified, and prioritized.
Besides qualitative approaches for impact estimation, a few quantitative methods have been
presented in literature. Wu et al. (2007) presented a Petri net-based modeling approach, termed
Disruption Analysis Network (DA_NET), to model how disruptions propagate through a supply
chain system and evaluate the impact of the disruption on the system performance. Another
application of Petri Net (PN) based simulation for risk management in supply chains is presented by
Tuncel and Alpan (2010). They used Petri Net (PN) to evaluate the impact of multiple disruption
scenarios (disruptions in demand, transportation and quality) and possible mitigation actions on the
performance of supply chain network.
To study the impact of transportation disruptions on supply chain performance, Wilson
(2007) presented a system dynamics model for a supply chain containing five echelons: retailer,
warehouse, Tier-1 supplier, Tier-2 supplier, and raw material supplier. Different possibilities for
transportation disruption – which might occur between adjacent echelons – are modeled and their
impact on customer orders fulfillment rate and inventory fluctuations are evaluated. As a result, they
concluded that the greatest impact occurs when transportation is disrupted between the Tier-1
supplier and warehouse.
Munoz and Clements (2008) presented a discrete event simulation of the Beer Distribution
Game to quantify the potential lost sales revenue attributed to information and material delays in a
supply chain. Based on the simulation results, they concluded that information delays play a larger
role than material delays as a contributor to lost sales revenue. Accordingly, the use of technologies
such as RFID to increase visibility throughout the supply chain was proposed.
Among more recent quantitative methods for supply chain risk assessment, Wei et al. (2010)
proposed the use of Inoperability Input-output Modeling (IIM) to determine ripple effects caused by
disruption to a particular node. IIM assesses the impacts of disruptions to supply chain networks with
two metrics, namely “inoperability” and “economic losses”, which can provide the decision maker
with the whole picture of the robustness of each node in the network. Accordingly, simulation results
can be used to prioritize nodes for planning potential actions to manage the impact of disruptive
events. The application of model was illustrated with a case of Chinese white alcohol manufacturer.
A potential disruption in one of suppliers was modeled and increasing the number of suppliers as a
possible risk mitigation strategy studied. A similar application of IIM has been discussed by Bogataj
and Bogataj (2007) who evaluate how lead-time perturbations propagate through a supply network.
5.3 Risk Evaluation & Treatment
Once the risk factors have been identified and assessed, the appropriate response must be
selected and implemented. One of the main aspects that is extensively discussed in literature is
different possible treatments to manage the risk of potential disruption. In general, the possible
response to a disruption falls into four categories – risk acceptance, reduction, avoidance and transfer
(Table 6).
5.3.1 Risk acceptance
The consequences and likelihood of a particular disruption might be accepted if the risk level
is less than a specific threshold. Moreover, for cases that the problem owner cannot find a reasonable
response or the cost of available solutions outweighs the anticipated disruption risk, the risk
acceptance might be considered a default action (Tomlin, 2006).

16
Table 6: Summary of risk treatment methods
Risk Treatment Method Reference
Risk Acceptance Tomlin (2006), Khan and Burnes (2007)
Risk
Reduction
Flexibility
Flexible Supply
Base
Choi and Krause (2006), Tomlin (2006), Babich (2006), Tan
and Enderwick (2006), Tang (2006a-b), Chopra et al. (2007),
Burke et al. (2007), Tang and Tomlin (2008), Deane et al.
(2009), Thun and Hoenig (2009), Yang et al. (2009), Tomlin
(2009), Oke and Gopalakrishnan (2009), Wang et al. (2010),
Iakovou et al. (2010)
Flexible Product
Configuration
(Postponement)
Sheffi (2005), Appelqvist and Gubi (2005), Tang (2006a-b),
Babich (2006), Tang and Tomlin (2008), Manuj and Mentzer
(2008), Wagner and Bode (2008), Ji (2009), Yang and Yang
(2010)
Flexible
Manufacturing
Process
Sheffi (2005), Tomlin (2006), Sodhi and Lee (2007), Tang and
Tomlin (2008), Oke and Gopalakrishnan (2009), Hendricks et
al. (2009), Stecke and Kumar (2009), Olson (2010)
Flexible
Transportation
Tang (2006a), Stecke and Kumar (2009), Knemeyer et al.
(2009), Colicchia et al. (2010)
Redundancy
Extra Inventory
(Redundant
Stock)
Chopra and Sodhi (2004), Christopher and Peck (2004), Sheffi
(2005), Hale and Moberg (2005), Tomlin (2006), Tang (2006a),
Khan and Burnes (2007), Mudrageda and Murphy (2007),
Wilson (2007), Ratick et al. (2008), Knemeyer et al. (2009),
Hendricks et al. (2009), Wang and Ip (2009), Stecke and Kumar
(2009)
Backup Supplier
Tomlin (2006), Chopra et al. (2007), Wilson (2007), Sodhi and
Lee (2007), Deane et al. (2009), Knemeyer et al. (2009), Yang
et al. (2009), Xu and Nozick (2009), Wang and Ip (2009),
Tomlin (2009)
Overcapacity Chopra and Sodhi (2004), Goh et al. (2007)
Control/
Incentives
Security
Improvement
Bichou (2004), Sheffi and Rice (2005), Manuj and Mentzer
(2008), Stecke and Kumar (2009), Knemeyer et al. (2009),
Enyinda and Tolliver (2009), Yang (2010)
Demand
Management
Tang (2006a-b), Tang and Tomlin (2008), Stecke and Kumar
(2009), Oke and Gopalakrishnan (2009), Ji (2009), Tomlin
(2009)
Supplier
Qualification
Screening
Sheffi (2005), Sodhi and Lee (2007), Roth et al. (2008),
Ellegaard (2008), Wagner and Bode (2008), Manuj and Mentzer
(2008), Yang et al. (2009), Sanchez-Rodrigues et al. (2010)
Performance-
Based
Contracting
Tang (2006a), Ellegaard (2008), Kim et al. (2010)
Organizational
Aspects
Sheffi (2005), Knemeyer et al. (2009), Blos et al. (2009)
Cooperation
Resource
/Information
Sharing
Christopher and Peck (2004), Hale and Moberg (2005), Kull
and Closs (2008), Bakshi and Kleindorfer (2009), Tuncel and
Alpan (2010)
Collective
Response
Planning
Hallikas et al (2004), Tan and Enderwick (2006), VanderBok et
al. (2007), Knemeyer et al. (2009), Stecke and Kumar (2009),
Butner (2010)
Risk Avoidance
Tang (2006b), Tomlin (2006), Wu and Olson (2008), Levary
(2008), Manuj and Mentzer (2008), Stecke and Kumar (2009),
Xu and Nozick (2009), Knemeyer et al. (2009)
Risk Transfer
Giannoccaro and Pontrandolfo (2004), Manuj and Mentzer
(2008), Wagner and Bode (2008), Stecke and Kumar (2009),
Knemeyer et al. (2009)

17
5.3.2 Risk reduction
For disruptions with unacceptable level of risk, appropriate actions must be taken to reduce
the risk to the acceptable threshold. The risk of disruption can be reduced by reducing the likelihood
of its occurrence or reducing the severity of disruption (Zsidisin et al., 2005; Tang and Tomlin,
2008). For this purpose, many different approaches including flexibility, redundancy, control, and
cooperation are discussed in literature.
- Flexibility: One way for managing disruptions is to create flexibility in the supply chain.
Flexibility is the ability to take different positions to better respond to an abnormal situation and
rapidly adapt to significant changes in the supply chain (Lee, 2004). The necessary condition for
flexibility in the supply chain is having multiple interchangeable resources (Ji, 2009). For example,
by having different manufacturing plants in different locations with the capability to produce similar
set of products a company can move the business among them should one fail because of disruption
in its supply chain.
Different types of flexibility strategies are suggested in literature to handle the risk of supply
chain disruptions.
Flexible supply base:
One method for mitigation of risk in a supply chain is establishing a flexible supply base. To
have a flexible supply base, two main tactics are commonly discussed in literature. The first one is to
diversify the material supply through multiple-sourcing (Tang, 2006a; Tomlin, 2006; Thun and
Hoenig, 2009; Iakovou et al., 2010; Chopra et al., 2007; Tomlin, 2009; Burke et al., 2007).
Subsequently, when the supply from one of suppliers is being disrupted (as a result of, e.g., supplier
failure or transportation disruptions), the company can temporarily shift the orders (or at least some
part of its demand) to the other supplier.
Another element of flexible supply base is flexible contracting with suppliers (Tang and
Tomiln, 2008; Tang, 2006b). In a flexible supply contracts, the ordering firm can adjust the agreed
quantity or time of delivery. Thus, with disruption in one of suppliers, a manufacturer has the
immediate option to increase the order size from other suppliers (Wang et al., 2009; Babich, 2006).
Flexible supply contracting is also beneficial to cope with demand risks (Tang and Tomlin, 2008). In
fact, the buyer can change the supply orders (increase or decrease volumes and change delivery
timing) with fluctuations in the demand in the downstream of supply chain.
Flexible product configuration (Postponement):
Postponement – also known as "delayed differentiation” (Anand and Girotra, 2007) or “late
product differentiation” (Wagner and Bode, 2008) – is one of frequently discussed methods to handle
the demand risks in the supply chain (Sheffi, 2005; Tang and Tomlin, 2008). The main assumption
in this strategy is that the production process can be divided into two sub-processes of “general
production” and “customization”. Based on postponement strategy, the product customization must
be made at a point in supply chain which is closer to the customer and the uncertainty on the demand
of specific products is less (Ji, 2009). Consequently, postponement reduces the demand risk, because
the product will stay in an undifferentiated state as long as possible allowing to better react to
unexpected market shifts (Manuj and Mentzer, 2008; Wagner and Bode, 2008; Yang and Yang,
2010). This also means less inventory is needed to achieve the same service level. In a case study of
a consumer electronics retailer, Appelqvist and Gubi (2005) showed that postponement might
decrease inventories by 40-80 percent.
Flexible manufacturing process:
In a flexible manufacturing process, different types of products can be manufactured in the
same plant at different volumes (Tang and Tomlin, 2008) and the production can be moved easily

18
among different plants (Sheffi, 2005). With a flexible process, companies can shift to other products
when the demand for some specific products is disrupted (e.g., because of new safety regulations) or
if a disruption in the supply base impacts the normal rate of production for some products. It is also
possible to shift the demand to other plants when the production in a specific plant is impacted by a
disruptive event.
Flexible transportation:
Flexibility in transportation is a critical issue for smooth operation of supply chains,
especially for companies that source globally and fulfill different markets in different places around
the world. The flexibility in the transportation can be primarily achieved by multi-modal logistics
strategy (Tang, 2006a; Knemeyer et al., 2009; Colicchia et al., 2010); to prevent the supply chain
breakdown due to disruptions in the one mode of transportation, some companies may prefer to
utilize multiple modes of transportation including air, ground, and sea shipping. Other options such
as working with multiple logistics companies and defining alternative routes for the case of
disruption are also important to better handle transportation disruptions (Tang, 2006a; Stecke and
Kumar, 2009).
- Redundancy: One way to manage the risk of potential disruptions is creating redundancies
across supply chain. In general, redundancies are considered as expensive options for handling
disruptions because they are put to use only when certain unanticipated events occur (Sheffi, 2005).
For example, contracting with a local backup supplier to supply the needed material (or a part of it),
when the main global supplier is disrupted, can be a costly decision. More administrative cost might
be imposed to find and monitor the secondary supplier. Moreover, higher unit prices might be paid
for low-volume material delivery from a secondary supplier. However, as a disruption occurs (e.g.,
an emergency in the main supplier facilities), the secondary supplier can be used to ensure a steady
flow of materials across the chain. The disruption reaction process is also faster as lots of hours
needed to find (and set) the alternative resources can be saved.
Redundancy in supply chain can take different forms as discussed in the following.
Extra inventory (redundant stock):
One of possible approaches to handle disruption is keeping buffer stocks in different parts of
the supply chain. A company might carry extra inventory for finished goods to handle the fluctuation
in market demand (demand risk) or have a buffer in the raw material storage to cope with potential
disruptions in the supply base (e.g., late raw material order delivery). Despite its advantage to
prevent production shutdown and avoiding stock-outs, carrying additional inventory can result in
increased costs and reduced quality (Sheffi, 2005). This strategy is mostly advised for items that
have a low holding cost and will not be outdated (Wilson, 2007).
Backup supplier:
Contracting with a backup supplier helps companies to insure the raw material stream against
possible disruptions in the main supplier (Tomlin, 2006; Sodhi and Lee, 2007; Chopra et al., 2007;
Tomlin, 2009). This is usually materialized with a "capacity reservation contract" in which a
secondary supplier guarantees any amount of delivery up to the reserved capacity (Xu and Nozick,
2009). Therefore, comparing with buffer stock, a company can mitigate the risk in the supply base
without incurring the cost of keeping excess inventory.
One aspect in selecting extra suppliers is avoiding "share of similar disruption risk" among
different suppliers (Wilson, 2007). For example, sourcing from two suppliers in the same region
would impact the material supply when a disaster (e.g., an earthquake) happens in that region.
Likewise, when suppliers deliver their materials in similar transportation routes, (e.g., they use

19
similar ports), a disruption in that route (e.g., closure of port because of strike) might leave company
with no option to supply its needed material.
Overcapacity:
Another method that can be beneficial in mitigating the supply chain risk is
designing/installing certain level of excess capacity in some key nodes of the network (Chopra and
Sodhi, 2004; Goh et al., 2007). Consequently, the disruption impact can be partially absorbed in the
chain. For example, an emergency shutdown in one of production plants in a multi-plant enterprise
can be handled for a period of time by over-production in other plants if they have excess
manufacturing capacity. The excess capacity can also be used to manage the daily variations in
customer demand, i.e., demand risk (Chopra and Sodhi, 2004).
- Controls / Incentives: The potential disruptions in the supply chain might be reduced with
higher level of control and supervision. Moreover, some incentives might be created to involve
different actors in managing disruptive event in the supply chain.
Security improvement:
One method for risk mitigation, especially the intentional threats such as terrorism attacks,
thefts or piracy risk, is enhancing the security (Manuj and Mentzer, 2008; Stecke and Kumar, 2009).
Stecke and Kumar (2009) has enumerated there types of security initiatives in the supply chain:
physical security (e.g., increasing security personnel or implementing camera systems), information
security (e.g., firewalls, antiviruses) and freight security (e.g., cargo inspections before shipping and
tracking the track cargo movements by RFID and GPS systems).
Demand management:
One contingent response to a disruption in supply chain is demand management strategy.
This might happen in two main ways (Ji, 2009); firstly, shifting the demand across product portfolio
which is also termed “responsive pricing” or “flexible pricing” by Tang and Tomlin (2008), or
“demand switching” (Tomlin, 2009). Thus, when a disruption (e.g., delayed supply of certain
components) interrupts manufacturing of some specific products, a firm can use the price mechanism
and promotions to temporarily change the demand pattern and shift customer choices to available
products. The classic example of this form of demand management is the Dell response to Taiwan
earthquake in 1999 (Tang, 2006a).
The second form of demand management is shifting demand across time. Facing a disruption,
a company may start negotiating with the customers or offer discounts to change their orders’ due
date or accept a delayed shipment.
Supplier qualification screening:
A well-established quality control process would decrease the exposure to supply chain risks
in several ways. Firstly, it allows better/faster identifying the possible cause of disruptions, reducing
their frequency and also avoiding the propagation of problem to the downstream of supply chain
(Sanchez-Rodrigues et al., 2008). This is especially important for customer-related disruptions such
as product recalls due to safety and product quality issues (Roth et al., 2008; Pyke and Tang, 2010).
In addition, regularly auditing suppliers might reduce supply chain risks by giving suppliers an
incentive to improve the weaknesses that may cause disruptions (Yang et al., 2009).
Performance-based contracting:
Proper incentive mechanism can be used in a performance-based contract so that the
suppliers provide prompt recovery services to the customer during disruption (Kim et al., 2010).
Organizational aspects:

20
One other issue which might impact the success of disruption management is increasing the
knowledge of employees with training programs (Stecke and Kumar, 2009). A training program
must inform workers about how to avoid the potential risky actions (which consequently reduces the
probability of disruption in the plant) and also better handle the abnormalities when they occur
(which, in fact, reducing the expected impact of disruption). Moreover, the response plans that are
developed for specific disruptions must be regularly rehearsed and, if necessary, modified
(Knemeyer et al., 2009). Other organizational aspects include better communication and the creation
of a chief risk officer (CRO) (Blos et al., 2009). Sheffi (2005) called all these aspects creating the
“corporate security culture".
- Cooperation: In contrast with the unilateral control actions, cooperative responses to supply
chain disruptions involve joint agreement/action by several actors in the chain (Jüttner et al., 2003).
Two possible cooperative strategies are discussed here.
Collective response planning:
Planning for supply chain disruption management might involve all actors in the chain
(Hallikas et al., 2004; Vanderbok et al., 2007). As an example, Stecke and Kumar (2009) mentioned
cases of auto manufacturers such as GM where these companies help their suppliers develop certain
strategies to mitigate disruptions. Collective response planning is especially important as modern
supply chains are complex systems and no one actor has all the necessary information for identifying
and mitigating the possible risks in the system (Butner, 2010). Additionally, in a joint risk
management process, the options that might be too expensive to be implemented by a single partner
can be discussed and agreed upon (Hallikas et al., 2004). One of these methods is investing in shared
resources as discussed further below.
Resource/information sharing:
In some cases, the necessary capital might be a significant barrier to implement a specific risk
mitigation option. Particularly, for those disruptions that the likelihood is not so high but the
expected impact on supply chain operation can be significant (Hale and Moberg, 2005). In these
cases, collaboration would help companies to pool resources and share the expenses of disruption
response. Tang (2006a) discussed the cases of Toyota and Sears in which these companies keep
certain inventories at some locations in their supply chains so that all retailers in the nearby region
share these inventories for the case of disruption or demand fluctuation. With similar idea, Hale and
Moberg (2005) presented an optimization-based decision process to design a network for shared
Just-In-Case resources. Firstly, an estimation of the necessary resources must be made. Next, the
decision about the maximum time it should take for each facility in the supply chain to gain access to
shared resources must be made. Finally, a mathematical programming model is used to determine the
number and locations of storage areas for shared resources. Bakshi and Kleindorfer (2009) discussed
how to divide cost between supply chain partners in a joint investment to increase supply chain
resilience, showing that co-opetition (cooperation while competition continues) is superior to
competition. Information sharing between distributors and retailers could also mitigate both demand
and supply risks by allowing them to adjust their policies accordingly (Kull and Closs, 2008).
5.3.3 Risk avoidance
For some disruptions, the risk level is so high that even with partial reduction in the
likelihood or impact of disruption, the risk level is still unacceptable. For these disruptive events, the
decision of avoiding the risk and eliminating the possible causes, might be the only reasonable
decision. Moving the production facilities to safe locations (e.g., places with less probability of
earthquake) or working with suppliers located in safe areas (Stecke and Kumar (2009), focus on
secure markets or products with constant demand (Thun and Hoenig, 2009) and dropping

21
troublesome suppliers from supply base (Manuj and Mentzer, 2008) are examples of risk avoidance
in the supply chain.
5.3.4 Risk transfer
Another method that can be used to control risk is shifting the negative consequences of a
risky factor to another entity inside or outside the supply chain. The classic example of risk transfer
is insurance (Lodree and Taskin, 2008). Different part of the supply chain such as facilities,
transport, and labor can be insured against natural disasters, accidents, and theft (Stecke and Kumar,
2009).
Contracting is also frequently discussed as a form of risk transfer or risk sharing in the supply
chain. A contract defines the way in which the risk arising from different sources of uncertainty (like
demand and price) is shared among different actors in the supply chain (Giannoccaro and
Pontrandolfo, 2004). Accordingly, with a fare contracting, preferably, certain risks will be allocated
to the parties who are in the best position to manage those risks.
5.3.5 Issues in selecting appropriate treatment:
In selecting a safeguard or an appropriate risk treatment method, several aspects should be
taken into account. First of all, the related costs and gains expected from implementing each
treatment action must be carefully evaluated and compared with other options and also with the case
of no action (Sheffi, 2005; Berg et al., 2008). In addition, investing in risk mitigation measures may
provide additional benefits in better managing the day-to-day supply chain mismatches (e.g.,
protection against demand forecast errors). This impact on the daily operation of supply chain must
be considered in the evaluation of options as well (Sheffi, 2005).
The second key issue is the impact of a disruption on other potential disruptions in the
network (Chopra and Sodhi, 2004; Faisal et al., 2006). Reducing the level of risk for one specific
disruption may increase the risk level for another one or even introduce new sources of risk. For
example, although deep relationship with suppliers might provide extra resources in the case of an
emergency in the chain, it can also increase the exposure of focal company to some new types of
risk, e.g., intellectual property risks (Choi and Krause, 2006). Another example is carrying extra
inventory to reduce the risk of delays, which, on the other hand, incurs much greater risk of
inventory-related type such as material outdating or spoilage (Olson and Wu, 2010). Therefore, the
interdependencies among different disruptions can be a crucial factor to make a final decision in
selecting the treatment.
As another issue in selecting a possible response, it is recommended to consider whole risk
profile – and not single disruption – in selecting the safeguards (Knemeyer et al., 2009). For instance,
carrying extra raw material inventory may reduce the risk of supplier emergency shutdown and also
transportation disruption due to a port strike.
Finally, appropriate risk management strategies might depend on the network configuration
(Cheng and Kam, 2008) and company size (Ellegaard, 2008). The supply risk management practices
of small manufacturing companies were found to be different from large manufacturers. The former
employed local sourcing, source loyalty, knowledge protection, and focused on fair, dependable,
similar, and responsive suppliers, while the latter typically included extensive global sourcing,
information gathering, and knowledge sharing (Ellegaard, 2008).
5.4 Risk Monitoring
Supply chain risks change constantly. The likelihood and severity of disruption may change;
some risks can be reduced or even eliminated, while new risk factors may appear. A necessary part
of supply chain risk management process is monitoring the changes in the network, customer needs,

22
technology, partner strategies and competitors and updating the risk assessment correspondingly
(Hallikas et al., 2004; Pyke and Tang, 2010). However, despite its profound importance, there is very
little rigorous research presenting the methods, tools and procedures for supply chain risk
monitoring. Two fairly recent papers that discuss risk monitoring methods are Blackhurst et al.
(2008) and Schoenherr et al. (2008). For a case of an automotive manufacturer, Blackhurst et al.
(2008) presented a multi-criteria scoring procedure for measuring, tracking and analyzing supplier
and part specific risk indices over time to identify trends towards higher risk levels. With this
method, the focal company can better recognize the potential risky parts of the supply base. In a
similar way, Schoenherr et al. (2008) presented an AHP-based decision making tool which was used
to assess risk factors and alternatives in an international sourcing context. They discussed that the
evaluation must be a constant process; the selected alternatives need to be reevaluated regularly, and
risk factors must be reassessed with potential changes that are occurring in the market environment.
5.5 Disruption Detection
An effective response to a disruption requires detecting quickly the location and nature of
disruption. With faster detection of disruption in the chain, corrective actions can begin quickly, the
escalation of the disruption impact can be avoided, and consequently, the impact of the disruption
can be reduced.
To quickly detect a disruption, several aspects are regarded as important issues and some
capabilities are listed in literature.
5.5.1 Visibility and information access
One of the issues frequently discussed in literature is improving the “end-to-end” visibility in
the supply chain (Christopher and Lee, 2004; Glickman and White, 2006). Supply chain visibility is
the ability to track the status of supply chain (e.g., the health of different parts of chain, the material
in transit in the network, etc.) from suppliers to end customers (Christopher and Lee, 2004). It is
primarily achieved by collaborative relationships and real-time sharing of correct information among
actors in the chain (Blackhurst et al., 2005). The information sharing – which may include the actual
or forecast demand, inventory levels (excess, shortage), and processing capacities (Stecke and
Kumar, 2009; Tang, 2006a) – helps companies to faster discover an abnormal situation in the
network and also have a better understanding of the available resources to handle disruptions (Li et
al., 2006).
The other issue which improves the visibility in the supply chain – and accordingly, faster
detection of disruption – is investing in performance monitoring and early warning systems (Stecke
and Kumar, 2009). For instance, a firm may have various IT systems for monitoring the material
flows (inventory level, quality, product delivery and sales) or information flows (demand forecasts,
production schedule, etc.) along the supply chain. These monitoring systems would reduce the
detection time by tracking the deviations in the performance of supply chain (Huang et al., 2009).
Moreover, the implementation of technologies like RFID that will increase the speed of information
flow throughout a supply chain can assist to minimize the time of detection of disruption (Tang,
2006a; Munoz and Clements, 2008).
5.5.2 Information analysis tools
In addition to information availability, developing tools for analyzing the information is an
important issue in disruption detection. These tools support the decision makers to better understand
the disruption profile, how far disruptions propagate through a supply chain and which parts of the
system may be affected more by the event (Blackhurst et al., 2005). Some of the tools to analyze the
real-time information on disruptions are presented in recent literature on supply chain disruption
management. Li et al. (2006) introduced a Directed Acyclic Supply Network (DASN) model and an

23
Impact Network (INet) model to quantify the impact of a disruption on the supply chain
performance, and to understand the potential value of timely information sharing. Li et al. (2006)
discussed two types of questions can be answered with these models: the time question (how soon
the disruption impacts on the supply of materials to a firm at a downstream stage) and the cost
question (how much the disruption impacts the cost of production).
Another effort to develop information analysis tools is presented by Wu et al. (2007). They
discussed a PN-based modeling methodology to determine how disruptions propagate in supply
chains and how those disruptions affect the supply chain operation. Accordingly, the user can assess
which areas of the supply chain and which performance measures (such as cost or lead-time) might
be affected by the disruption.
Among more recent works, Huang et al. (2009) described a dynamic system model and used
it to check if a disruption (like demand shock) can be absorbed by supply chain without affecting the
expected order delivery performance. Tuncel and Alpan (2010) also proposed a Petri net based
decision support tool for tracking of material and information flow in the SC network. The PN model
is also used to analyze the effects of disruptions on the overall system’s performance.
5.5.3 Disruption causal analysis
The other key aspect – which is somewhat related to previous issue of information analysis –
is searching for the real cause or causes for an abnormal event (Adhitya et al., 2007a; Gaonkar and
Viswanadham, 2007). This is especially important when the triggering event cannot be directly
observed and only the deviation in the supply chain performance can be detected.
The causal analysis might also investigate the causal relations among disruptions as one
disruptive event might cause another set of events in the supply chain (Gaudenzi and Borghesi,
2006). For example, a problem for a 3PL service provider, which is responsible for managing the
material delivery for a focal company and its supplier, would directly and indirectly impact the
company’s performance. The causal analysis helps the decision makers to define the corrective
actions in disruption reaction and recovery steps.
5.6 Disruption Reaction & Recovery
After a disruption is detected, a company must quickly react to cope with the impact and
restore the normal operation of supply chain. Some key issues are discussed in literature for better
reaction to disruption in supply chains.
5.6.1 Resource finding and (re-)allocation
Disruptions in the supply chain may lead to the shortage of resources and efficient use of
available resources is a crucial issue in handling the event (Dani and Deep, 2010).
For example, an earthquake in a specific region might impact the supply of some specific
materials to the manufacturing plants. Consequently, the firm must look for alternative supplies.
Moreover, because of new resource constraints, some of pre-defined plans for supply chain operation
may not remain optimal or even feasible and should be revised according to the new situation (Xiao
et al., 2005; Huang et al., 2006; Adhitya et al., 2007b).
The resource finding to handle a disruption must start with an evaluation of needs and
resource requirements (Charles et al., 2010). Subsequently, a company must determine the remaining
resources after disruption and also look for alternative resources to cover the needs. Some of
necessary resources to react to a disruptive event must be previously planned/invested in the risk
treatment step. For example, carrying extra inventory, investing in flexible supply contracts or
flexible transportation will provide some degrees of freedom in handling disruptions in the real-time
(Stecke and Kumar, 2009; Tomlin, 2006). Moreover, some actions like product redesign or demand

24
shifting can provide extra resources to manage the impact of event (Sheffi, 2005). Multiple partners
in a supply chain may also decide to temporarily share some of resources to better handle an event
(Mudrageda and Murphy, 2007).
Based on the available/found resources, possible reactions to a disruption can be defined and
the appropriate options must be implemented (Adhitya et al., 2007a). In some situations, finding the
appropriate option can be a challenge because several (and sometimes, conflicting) objectives must
be considered in managing disruptions. To better handle this difficulty, simulation and modeling can
provide a sound basis to analyze the possible responses to a disruption (Adhitya et al., 2007a; Tuncel
and Alpan, 2010).
5.6.2 Communication and information sharing
The other factor that would influence an effective disruption reaction is the continuous
sharing of information among different actors in the supply chain (Dani and Deep, 2010). Generally,
an abnormal situation in the supply chain is characterized by high degree of uncertainty and lack of
accurate information (Chen et al., 2010); for example, the supplier’s estimation of its trouble might
be biased. Moreover, for most actors in the chain, it might not be feasible to make reasonable
estimates of disruption profile, especially in the early stages. The uncertainty about the extent of the
disruption and lack of knowledge about the recovery effort between stakeholders will definitely
impact the optimum resource allocation across supply chain and might delay the supply chain
recovery to its normal operation.
5.6.3 Coordination of activities and actors
Proper coordination is usually a key requirement for managing disruptions in a supply chain;
because, in many cases, managing disruption is a cross-functional and even cross-company process
and requires close involvement of several actors in the chain (Blackhurst et al., 2005; Braunscheidel
and Suresh, 2009; Hendricks et al. 2009).
Moreover, collaboration among different actors by sharing the existing resources calls for an
effective coordination scheme. For example, managing a disruption in one manufacturing plant in a
multi-plant enterprise might necessitate sharing the resources among the plants. In these cases, lack
of coordination and inter-functional conflicts may slow down the efforts to manage disruption and
worsen the disruption effects (Stecke and Kumar, 2009).
5.7 Learning & SC Redesign
Once a supply chain recovers from a disruptive event, it is necessary to capture the lessons
learned from the disruption management process (Dani and Deep, 2010). This step is generally
ignored in literature. One exemption is the description of Norrman and Jansson (2004) of Ericsson’s
proactive supply chain risk management approach after a fire at its supplier plant (Philips microchip
plant in Albuquerque) in March 2000. After this disruptive event, Ericsson has developed and
implemented a new organization, new processes and tools to “minimize risk exposure in the supply
chain”. Pyke and Tang (2010) also described cases of product safety recalls in which the impacted
companies have designed new plans to prevent the safety problems in future and reported the plans
to stakeholders to rebuild the confidence. Another study on the companies learning after disruptive
events is presented by Papadakis (2006). He conducted an empirical analysis on the stock
performance of four major personal computer (PC) producers after the 1999 earthquake in Taiwan
and the computer memory price increases afterwards. It is shown that push- vs. pull-type supply
chains reacted differently to the disruption. The process presented by Papadakis (2006) to analyze
the impact of disruptions and the effectiveness of different policies can be applied to other cases to
document the lessons learned from a supply chain disruption.

25
6 Analysis of literature and identified gaps
A detailed analysis of papers in this review is presented in Appendix A. For each paper we
have shown the steps of framework that are discussed. The methods that are used in each paper are
also presented. Based on this analysis, some observations and gaps in the research can be identified.
1) Uneven coverage of framework’s steps in the supply chain risk/disruption literature:
It is clearly evident from the survey that literature has not uniformly covered all the parts of the
integrated framework (Figure 6). Overall, the disruption management cycle (i.e., disruption
detection, disruption reaction and recovery and disruption learning) have received less attention
than the risk management cycle (i.e., scope definition and risk identification, risk quantification,
risk evaluation& treatment and risk monitoring). Different steps of the risk management cycle
have also received different levels of attention. For example, although the literature on risk
identification and risk treatment steps is very informative, the risk monitoring step, with a few
exceptions, is generally overlooked. Moreover, comparing with risk identification and treatment,
a smaller amount of literature pertains to rigorous approaches for risk quantification.
Figure 6: The focus of literature on different steps of disruption management
2) Lack of quantitative approaches for handling disruptions:
Another finding is that there is a relative lack of quantitative approaches (simulation and
modeling) to support decision makers in handling supply chain disruptions. As can be seen in
Figure 7, qualitative methods like survey, interview and case study are the dominant research
methods used in the supply chain risk and disruption literature. Very few papers have used
quantitative methods for supply chain risk analysis. Moreover, the majority of existing
quantitative works focus on analytical modeling – to support generic conclusions about supply
chains. Flexible simulation frameworks that support decision making in specific cases are lacking
in the existing literature. This implies that issues are mainly addressed from a general, high-level
view. A good example is the vast literature which lists general recommendations for possible risk

26
treatments; however, specialized tools for applying these generic methods to specific cases have
not received adequate attention. This reduces the practical value of the recommendations since in
any real applications – to implement a specific risk treatment option – the detailed supply chain
structure, procedures and circumstances for a specific company must be taken into account.
Developing models which capture the important characteristics of supply chains and can be used
for decision support is therefore an important direction for future research.
Figure 7: Research methods used in supply chain risk/disruption literature
Figure 8: Two perspectives on handling disruptions in supply chains

27
7 Concluding remarks
In this paper, an integrated framework for handling disruption in supply chain has been
presented. This integrated framework was next used to present a structured review of the literature on
supply chain disruption/risk management. The analysis of literature identified the important
approaches as well as the key issues which have been discussed by researchers for each step. A
summary of these issues is presented in Figure 8. Two observations and research gaps were also
discussed in this paper. The first was that literature on supply chain risk/disruption has been very
much concentrated around pre-disruption steps. Different steps of risk management cycle have also
received different levels of attention. Some steps like risk identification and risk treatment have been
explored extensively while others like risk monitoring and risk quantification have not been
adequately investigated. This implies that, in making the integrated framework operational, some
steps could be detailed on the basis of literature available, whereas other steps need to be filled in,
building on practices and tools developed in related fields. The second observation is the relative
lack of simulation and modeling studies to support decision makers to better handle supply chain
disruptions in different steps of framework. The lack is more evident for post-disruption steps and
hence attractive for future research.
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58. Kumar, S.K., Tiwari, M.K. and Babiceanu, R. F. (2010), Minimisation of supply chain cost
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62. Li, G., Lin, Y., Wang, S. and Yan, H. (2006). Enhancing agility by timely sharing of supply
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63. Manuj, I. and Mentzer, J.T. (2008). Global supply chain risk management strategies,
64. Matook, S., Lasch, R. and Tamaschke, R. (2009). Supplier development with benchmarking
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65. Micheli, G.J.L., Cagno, E. and Zorzini, M. (2008). Supply risk management vs. supplier
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66. Mohtadi, H. and Murshid, A. P. (2009). Risk analysis of chemical, biological, or radionuclear
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67. Mudrageda, M. and Murphy, F.H. (2007). Designing Safety Space in a Supply Chain to
Handle System-Wide Disruptions, Naval Research Logistics 54(3): 258-264.
68. Munoz, A. and Clements, M.D. (2008). Disruptions in information flow. A revenue costing
supply chain dilemma, Journal of Theoretical and Applied Electronic Commerce Research
3(1): 30-40.
69. Norrman, A. and Jansson, U. (2004). Ericsson’s proactive supply chain risk management
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70. Oehmen, J. Ziegenbein, Alard, A. and R. Schonsleben, P. (2009). System-oriented supply
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71. Oke, A. and Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study
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72. Olson, D.L. and Wu, D.D. (2010). A review of enterprise risk management in supply chain,
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73. Olson, E. G. (2010). Supply chain opportunity in an uncertain economic recovery, Supply
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74. Parmar, D., Wu, T., Callarman, T., Fowler, J. and Wolfe, P. (2010). A clustering algorithm for
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31
75. Papadakis, I. S. (2006). Financial performance of supply chains after disruptions: an event
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76. Peck, H. (2005). Drivers of supply chain vulnerability: An integrated framework,
77. Pujawan, I. N. and Geraldin, L. H. (2009). House of risk: a model for proactive supply chain
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78. Pyke, D. and Tang, C.S., (2010). How to mitigate product safety risks proactively- Process,
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79. Ratick, S., Meacham, B. and Aoyama, Y. (2008). Locating backup facilities to enhance supply
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80. Ravindran, A.R., Bilsel, R.U., Wadhwa, V. and Yang, T. (2010). Risk adjusted multicriteria
supplier selection models with applications, International Journal of Production Research
48(2): 405-424
81. Ritchie, B. and Brindley, C. (2007). Supply chain risk management and performance: A
guiding framework for future development, International Journal of Operations and
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82. Ross, A.M., Rong, Y. and Snyder, L.V. (2008). Supply disruptions with time-dependent
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83. Roth, A.V., Tsay, A.A., Pullman, M.E. and Gray, J.V. (2008). Unraveling the food supply
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84. Sanchez-Rodrigues, V., Potter, A. and Naim, M.M., (2010). The impact of logistics
uncertainty on sustainable transport operations, International Journal of Physical Distribution
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85. Schoenherr, T., Rao Tummala, V.M. and Harrison, T.P. (2008). Assessing supply chain risks
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a US manufacturing company, Journal of Purchasing and Supply Management 14(2): 100-
111.
86. Sheffi, Y. (2005). Preparing for the big one, Manufacturing Engineer 84(5): 12-15.
87. Sheffi, Y., and Rice, J. (2005). A supply chain view of the resilient enterprise, MIT Sloan
Management Review 47(1): 41–48.
88. Sinha, P. R., Whitman, L. E. and Malzahn, D. (2004). Methodology to mitigate supplier risk
in an aerospace supply chain, Supply Chain Management: An International Journal 9(2): 154-
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89. Sodhi, M. (2005). Managing demand risk in tactical supply chain planning for a global
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90. Sodhi, M.S. and Lee, S. (2007). An analysis of sources of risk in the consumer electronics
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91. Stecke, K.E. and Kumar, S. (2009). Sources of supply chain disruptions, factors that breed
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92. Sutton, S. G. (2006). Extended-enterprise systems’ impact on enterprise risk management,
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93. Tah, J.H.M. and Carr, V. (2001). Towards a framework for project risk knowledge
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94. Tan, W.-J. and Enderwick, P. (2006), Managing threats in the Global Era: The impact and
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32
96. Tang, C. (2006b).Perspectives in supply chain risk management, International Journal of
Production Economics 132(2): 451–488.
97. Tang, C. and Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks,
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98. Tang, O. and Musa, S.N. (2011). Identifying risk issues and research advancements in supply
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99. Thun, J. and Hoenig, D. (2009). An Empirical Analysis of Supply Chain Risk Management in
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100.Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing
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102.Trkman, P. and McCormack, K. (2009). Supply chain risk in turbulent environments – a
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103.Tsai, M.-C., Liao, C.-H. and Han, C.-s. (2008). Risk perception on logistics outsourcing of
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33
118.Xu, N. and Nozick, L. (2009). Modeling supplier selection and the use of option contracts for
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Management 41(4): 44–57.

34
Appendix A: Analysis of articles in literature review
Reference
Elements of Framework Covered by Literature1
Research Method used in the Research Literature
DisruptionDetection
DisruptionReaction&
Recovery
DisruptionLearning
RiskIdentification
RiskQuantification
RiskEvaluation&
Treatment
Riskmonitoring
Theoretical/conceptual
works
Qualitative Quantitative
review
Surveysand
Interviews
Casestudy
Empirical
(Statisticalanalysis
ofempiricaldata)
Analytical
modeling(and
optimization)
Simulation
modeling
Adhitya et al. (2007a) +++ +++ * *
Adhitya et al. (2007b) +++ * *
Adhitya et al. (2009) +++ + * *
Appelqvist and Gubi (2005) + *
Babich (2006) ++ *
Bakshi and Kleindorfer (2009) ++ *
Berg et al. (2008) + + + + + * * *
Bichou (2004) ++ *
Blackhurst et al. (2005) +++ +++ +++ ++ ++ * *
Blackhurst et al. (2008) +++ + +++ *
Blos et al. (2009) +++ ++ *
Bogataj and Bogataj (2007) ++ ++ *
Braunscheidel and Suresh (2009) ++ ++ *
Burke et al. (2007) ++ *
Canbolat et al. (2008) +++ ++ +++ * *
Cavinato (2004) +++ *
Charles et al. (2010) + + + * *
Chao et al. (2009) ++ *
Chen et al. (2010) ++ *
Cheng and Kam (2008) +++ + *
Choi and Krause (2006) ++ *
Chopra and Sodhi (2004) +++ +++ *
Chopra et al. (2007) ++ *
Christopher and Lee (2004) ++ ++ *
Christopher and Peck (2004) ++ ++ +++ +++
Cigolini and Rossi (2010) + + *
Colicchia et al. (2010) ++ + ++ *
Cucchiella and Gastaldi (2006) ++ ++ *
Dani and Deep (2010) +++ ++ *

35
Reference
DisruptionDetection
DisruptionReaction&
Recovery
DisruptionLearning
RiskIdentification
RiskQuantification
RiskEvaluation&
Treatment
Riskmonitoring
works
review
Surveysand
Interviews
Casestudy
Empirical
ofempiricaldata)
Analytical
modeling(and
optimization)
Simulation
modeling
Deane et al. (2009) + ++
Ellegaard (2008) ++ *
Enyinda and Tolliver (2009) + ++ *
Enyinda et al. (2010) ++ *
Faisal et al. (2006) +++ + * *
Faisal et al. (2007) +++ ++ ++ * *
Finch (2004) +++ *
Gaonkar and Viswanadham (2007) +++ ++ * *
Gaudenzi and Borghesi (2006) +++ * *
Glickman and White (2006) + + ++ *
Goh et al. (2007) + *
Hale and Moberg (2005) ++ +++ *
Hallikas et al. (2004) ++ + * *
Harland et al. (2003) +++ * *
Hendricks et al. (2009) +++ *
Huang et al. (2006) + + *
Huang et al. (2009) + ++ *
Iakovou et al. (2010) ++ *
Ji (2009) +++ * * *
Jüttner (2005) +++ ++ *
Kavčič and Tavčar (2008) ++ *
Khan and Burnes (2007) +++ +++ *
Kim et al. (2010) ++ *
Kleindorfer and Saad (2005) +++ ++ ++ * *
Knemeyer et al. (2009) ++ ++ *
Kull and Closs (2008) +++ *
Kull and Talluri (2008) + ++ +++ * * *
Kumar et al. (2010) ++ ++ * *
Levary (2007) ++ ++ + * *
Levary (2008) + + + * *
Li et al. (2006) + + * *

36
Reference
DisruptionDetection
DisruptionReaction&
Recovery
DisruptionLearning
RiskIdentification
RiskQuantification
RiskEvaluation&
Treatment
Riskmonitoring
works
review
Surveysand
Interviews
Casestudy
Empirical
ofempiricaldata)
Analytical
modeling(and
optimization)
Simulation
modeling
Manuj and Mentzer (2008) +++ +++ *
Matook et al. (2009) + +++ * *
Micheli et al. (2008) ++ ++ * *
Mohtadi and Murshid (2009) ++ *
Mudrageda and Murphy (2007) ++ *
Munoz and Clements (2008) ++ + *
Norrman and Jansson (2004) +++ *
Oehmen et al. (2009) ++ + * *
Oke and Gopalakrishnan (2009) +++ +++ * *
Olson and Wu (2010) +++ *
Olson (2010) + *
Parmar et al. (2010) ++ *
Papadakis (2006) + *
Peck (2005) +++ ++ * *
Pujawan and Geraldin (2009) ++ + ++ * *
Pyke and Tang (2010) ++ ++ ++ ++ * *
Ratick et al. (2008) ++ *
Ravindran et al. (2010) + ++ * *
Ritchie and Brindley (2007) ++ + * *
Ross et al. (2008) ++ *
Roth et al. (2008) ++ * *
Sanchez-Rodrigues et al. (2010) ++ ++ + *
Schoenherr et al. (2008) +++ ++ ++ + *
Sheffi (2005) ++ ++ *
Sheffi and Rice (2005) ++ +++ *
Sinha et al. (2004) ++ ++ *
Sodhi (2005) + ++ *
Sodhi and Lee (2007) +++ + *
Stecke and Kumar (2009) +++ + +++ * *
Sutton (2006) + *
Tan and Enderwick (2006) ++ ++ *

37
Reference
DisruptionDetection
DisruptionReaction&
Recovery
DisruptionLearning
RiskIdentification
RiskQuantification
RiskEvaluation&
Treatment
Riskmonitoring
works
review
Surveysand
Interviews
Casestudy
Empirical
ofempiricaldata)
Analytical
modeling(and
optimization)
Simulation
modeling
Tang (2006b) +++ *
Tang (2006a) +++ +++ *
Tang and Tomlin (2008) ++ +++ * *
Tang and Musa (2011) +++ +++ *
Thun and Hoenig (2009) ++ ++ ++ *
Tomlin (2006) +++ *
Tomlin (2009) +++ *
Trkman and McCormack (2009) +++ + * * *
Tsai et al. (2008) ++ * *
Tuncel and Alpan (2010) +++ +++ ++ * *
VanderBok et al. (2007) ++ *
Wang and Ip (2009) ++ ++ * *
Wang, et al. (2010) ++ *
Wagner and Bode (2006) +++ ++ *
Wagner and Bode (2008) +++ *
Wei et al.(2010) ++ + *
Wiendahl et al. (2008) ++ + + *
Wilson (2007) ++ ++ *
Wu et al. (2006) +++ +++ * *
Wu et al. (2007) ++ *
Wu and Olson (2008) +++ *
Xiao et al. (2005) + *
Xiao and Yu (2006) + *
Xu and Nozick (2009) ++ * *
Yang et al. (2009) ++ ++ *
Yang (2010) ++ *
Yang and Yang (2010) ++ *
Zsidisin et al. (2004) +++ *
1- The relevance of each paper for each step of framework is shown with “+” sign:
+++) this step is a main goal for this paper, some issues related to this step is discussed in this paper or a specific method is presented;
++) this step is relatively discussed in this paper;

38
+) this step is not the main objective of this paper but it is solely mentioned or issues are presented without a detailed discussion.

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