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International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage: http://www.tandfonline.com/loi/tprs20
Supply chain risk management: a literature review
William Ho, Tian Zheng, Hakan Yildiz & Srinivas Talluri
To cite this article: William Ho, Tian Zheng, Hakan Yildiz & Srinivas Talluri (2015) Supply chain risk management: a literature review, International Journal of Production Research, 53:16, 5031-5069, DOI: 10.1080/00207543.2015.1030467
To link to this article: http://dx.doi.org/10.1080/00207543.2015.1030467
Published online: 15 Apr 2015.
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Supply chain risk management: a literature review
William Hoa, Tian Zhengb, Hakan Yildizc and Srinivas Talluric*
aDepartment of Management and Marketing, The University of Melbourne, Carlton, Australia; bSolution Department, China Merchants Loscam (Shenzhen) Investment Holding Co., Ltd, Shenzhen, China; cDepartment of Supply Chain Management, Eli Broad
Graduate School of Management, Michigan State University, East Lansing, MI, USA
(Received 18 March 2014; accepted 28 February 2015)
Risk management plays a vital role in effectively operating supply chains in the presence of a variety of uncertainties. Over the years, many researchers have focused on supply chain risk management (SCRM) by contributing in the areas of defining, operationalising and mitigating risks. In this paper, we review and synthesise the extant literature in SCRM in the past decade in a comprehensive manner. The purpose of this paper is threefold. First, we present and categorise SCRM research appearing between 2003 and 2013. Second, we undertake a detailed review associated with research developments in supply chain risk definitions, risk types, risk factors and risk management/mitigation strategies. Third, we analyse the SCRM literature in exploring potential gaps.
Keywords: supply chain risk management; risk types; risk factors; risk management methods; literature review
1. Introduction
In recent years, supply chain disruptions have impacted the performance of companies. The case of Ericsson is well known in this domain. Due to a fire at a Phillips semiconductor plant in 2000, the production was disrupted, which eventually led to Ericsson’s $400 million loss (Chopra and Sodhi 2004). The earthquake, tsunami and the subsequent nuclear crisis that occurred in Japan in 2011 caused Toyota’s production to drop by 40,000 vehicles, costing $72 million in profits per day (Pettit, Croxton, and Fiksel 2013). The catastrophic Thailand flooding of October 2011 affected the supply chains of computer manufacturers dependent on hard discs, and also disrupted the supply chains of Japanese automotive companies with plants in Thailand (Chopra and Sodhi 2014). In order to control and mitigate the negative effects caused by such risks, a significant amount of work in the area of supply chain risk management (SCRM) is undertaken in both academia and practitioner circles.
In the last decade, five journal articles reviewing the literature in SCRM have been published. Tang (2006a) reviewed more than 200 journal articles that applied quantitative models that are published between 1964 and 2005. He classified the articles into four categories, i.e. supply management, demand management, product management and information management for managing supply chain risks. Rao and Goldsby (2009) reviewed 55 journal articles pub- lished between 1998 and 2008, and synthesised the diverse literature into a typology of risk factors, including environ- mental, industrial, organisational, problem-specific and decision-maker related factors. Tang and Musa (2011) adopted the literature citation analysis on 138 journal articles published between 1995 and first half of 2008, and identified and classified potential risks associated with material flow, financial flow and information flow. Colicchia and Strozzi (2012) also applied the citation network analysis on 55 journal articles published between 1994 and 2010, and identified the evolutionary patterns and emerging trends in SCRM. Sodhi, Son, and Tang (2012) reviewed 31 journal articles pub- lished between 1998 and 2010 to formulate their own perception of diversity in SCRM. They also conducted open- ended surveys with two focus groups of supply chain researchers, and subsequently a close-ended survey with more than 200 supply chain researchers to present three gaps in SCRM: definition gap (lack of clear consensus on the defini- tion of SCRM), process gap (inadequate coverage of responses to risk incidents) and methodology gap (insufficient use of empirical methods).
Although the aforementioned review articles make significant contributions to SCRM, there are three significant knowledge gaps that motivate us to carry out this study. First, each of these review articles focuses on a particular topic of SCRM as summarised in Table 1, such as risk classification (Tang and Musa 2011), risk factor analysis (Rao and
*Corresponding author. Email: [email protected]
© 2015 Taylor & Francis
International Journal of Production Research, 2015 Vol. 53, No. 16, 5031–5069, http://dx.doi.org/10.1080/00207543.2015.1030467
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Goldsby 2009), risk management methods (Tang 2006a) or research gap identification (Colicchia and Strozzi 2012). None of these review articles cover all the SCRM topics. As illustrated in Table 1, the articles published between 2003 and 2013 applying qualitative risk management methods were never reviewed. Besides, certain topics in particular years were also not covered as represented by the shaded cells in Table 1. In addition, none of these review articles are recent enough to cover many new studies published after 2010. More specifically, 170 out of 224 journal articles reviewed in this paper were not studied in extant review articles, including 93 journal articles published after 2010 plus 77 journal articles published between 2003 and 2010. Finally, all but two of these papers reviewed only a relatively small number of articles. More specifically, Sodhi, Son, and Tang (2012) reviewed 31 articles, and Rao and Goldsby (2009) and Colicchia and Strozzi (2012) reviewed 55 articles given the focal area of interest. In order to fill these gaps, this paper presents a comprehensive review of all relevant journal articles in the area of SCRM appearing between 2003 and 2013, and undertakes an effective classification scheme. Our work also proposes a new definition for SCRM by classifying supply chain risk types, risk factors and risk management methods. Finally, we analyse the literature in exploring poten- tial gaps contributing towards risk management in supply chains.
The remainder of this paper is organised as follows. Section 2 provides an introduction to the research methodology and develops a conceptual framework for classifying the supply chain risks. Section 3 summarises the existing defini- tions of supply chain risks and SCRM, and proposes new definitions. Sections 4, 5 and 6 present supply chain risk types, risk factors and risk management methods, respectively. Section 7 discusses the research analyses and observa- tions. Section 8 identifies gaps in the area of SCRM and recommends future research directions, and finally, Section 9 concludes the paper.
2. Research methodology
There is a continuous growth in the number of articles focusing on SCRM in the past few years as seen in Figure 1. In view of this, we reviewed the journal articles published between 2003 and 2013. The research methodology, as illus- trated in Figure 2, is as follows. First, the search terms were defined. The keywords used in the search process were ‘supply chain’ and ‘risk’. Second, various academic databases were utilised to identify the journal articles including EBSCOhost, Emerald, IEEExplore, Ingenta, Metapress, ProQuest, ScienceDirect, Springer, Taylor and Francis, and Wiley. To achieve the highest level of relevance, only peer-reviewed articles written in English and published in Interna- tional Journals were selected, whereas conference papers, master and doctoral dissertations, textbooks, book chapters and notes were excluded in this review. As opposed to Tang and Musa (2011), we have not imposed a restriction on the list of journals to ensure that we capture every relevant study regardless of the journal it was published in. Third, several criteria were determined and used to filter the articles. With respect to the criteria, abstracts of articles were examined to check if they cover one or more of the SCRM topics, including supply chain risk types, risk factors, risk management methods and research gaps identification. The articles were excluded if they do not meet one of these filtra- tion criteria. Fourth, the reference lists of the shortlisted articles were also carefully evaluated to ensure that there were no other articles of relevance which were omitted in the search. Finally, the content of each article was thoroughly reviewed to ensure that the article fits into the context of SCRM and studies at least one of the SCRM topics. This analysis resulted in 224 journal articles.
Figure 1. Distribution of number of journal articles over the last 11 years.
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In order to classify and analyse these articles, we develop a conceptual framework of supply chain risks as shown in Figure 3. In synthesising various points of views from the literature, we discover that supply chain risks can be divided into two categories – macro-risks and micro-risks (referred as catastrophic and operational by Sodhi, Son, and Tang (2012);
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Figure 2. Flowchart of the research methodology.
Figure 3. Conceptual framework of supply chain risks.
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disruption and operational by Tang (2006a)). Macro-risks refer to adverse and relatively rare external events or situations which might have negative impact on companies. Macro-risks consist of natural risks (e.g. earthquakes and weather-related disasters) and man-made risks (e.g. war and terrorism and political instability). On the other hand, micro-risks refer to relatively recurrent events originated directly from internal activities of companies and/or relationships within partners in the entire supply chain. Generally, macro-risks have much greater negative impact on companies in relation to micro-risks. Furthermore, micro-risks can be divided into four subcategories: demand risk, manufacturing risk, supply risk and infras- tructural risk. Manufacturing risk refers to adverse events or situations within the firms that affect their internal ability to produce goods and services, quality and timeliness of production, and profitability (Wu, Blackhurst, and Chidambaram 2006). Demand and supply risks refer to adverse events at the downstream and upstream partners of a firm, respectively (Zsidisin 2003; Wagner and Bode 2008). In order to ensure the healthy functioning of a supply chain, information technol- ogy (Chopra and Sodhi 2004), transportation (Wu, Blackhurst, and Chidambaram 2006) and financial systems (Chopra and Sodhi 2004; Wu, Blackhurst, and Chidambaram 2006), are also of critical importance. Any disruptions in these systems can also lead to serious problems in a supply chain. Therefore, we classify the risks relating to these three systems as infrastructural risk.
3. Definitions
There is no consensus on the definition of ‘supply chain risk’ and ‘SCRM’ (Sodhi, Son, and Tang 2012; Diehl and Spinler 2013). Without a common understanding and clear definition, researchers would find it difficult to communicate with practitioners and gain access to industry to carry empirical studies. Moreover, a consistent definition helps research- ers identify and measure the likelihood and impact of the entire set of supply chain risks, and evaluate the effectiveness of SCRM methodologies. Therefore, it is imperative to obtain a clear definition of these terms (Sodhi, Son, and Tang 2012; Diehl and Spinler 2013). Sections 3.1 and 3.2 summarise the existing definitions of supply chain risk and SCRM, and also propose new definitions.
3.1 Supply chain risk
Several researchers provided different definitions for supply risk (Zsidisin 2003; Ellis, Henry, and Shockley 2010) and supply chain risk (Jüttner, Peck, and Christopher 2003; Wagner and Bode 2006; Bogataj and Bogataj 2007) as sum- marised in Table 2. Although these definitions have applicability in specific domains, such as supply risk (Zsidisin 2003; Ellis, Henry, and Shockley 2010), information flow risk, material flow risk and product flow risk (Jüttner, Peck, and Christopher 2003), they focus on a specific function or a part of a supply chain, and do not span across the entire chain. Given this, and according to the conceptual framework in Figure 3, we define supply chain risk as: ‘the likeli- hood and impact of unexpected macro and/or micro level events or conditions that adversely influence any part of a supply chain leading to operational, tactical, or strategic level failures or irregularities’.
Table 2. Definitions of supply chain risk given by researchers.
Authors Definitions of supply chain risk Scopes
Zsidisin (2003, 222) The probability of an incident associated with inbound supply from individual supplier failures or the supply market occurring, in which its outcomes result in the inability of the purchasing firm to meet customer demand or cause threats to customer life and safety
Supply risk only
Jüttner, Peck, and Christopher (2003, 200)
Any risks for the information, material and product flows from original suppliers to the delivery of the final product for the end user
Information, material and product flow risks
Wagner and Bode (2006, 303)
The negative deviation from the expected value of a certain performance measure, resulting in negative consequences for the focal firm
General risks
Bogataj and Bogataj (2007, 291)
The potential variation of outcomes that influence the decrease of value added at any activity cell in a chain
General risks
Ellis, Henry, and Shockley (2010, 36)
An individual’s perception of the total potential loss associated with the disruption of supply of a particular purchased item from a particular supplier
Supply risk only
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3.2 Supply chain risk management
Several researchers provided definitions for SCRM, which are summarised in Table 3. While all these definitions have emphasised collaboration with supply chain partners, some of the limitations are related to their focus on specific ele- ments of SCRM and their lack of spanning the SCRM processes in their entirety, type of SCRM methods and types of events. Given this, and based on the conceptual framework in Figure 3, we define SCRM as: ‘an inter-organisational collaborative endeavour utilising quantitative and qualitative risk management methodologies to identify, evaluate, mitigate and monitor unexpected macro and micro level events or conditions, which might adversely impact any part of a supply chain’.
4. Supply chain risk types
Among the 224 reviewed journal articles, 20 articles discussed supply chain risk types as presented in Table 4. Eleven of these articles simply identified the risk types without classification (Harland, Brenchley, and Walker 2003; Cavinato 2004; Chopra and Sodhi 2004; Bogataj and Bogataj 2007; Blackhurst, Scheibe, and Johnson 2008; Manuj and Mentzer 2008; Tang and Tomlin 2008; Wagner and Bode 2008; Tang and Musa 2011; Tummala and Schoenherr 2011; Samvedi, Jain, and Chan 2013). Six of these articles classified the risk types into two categories, such as internal and external (Wu, Blackhurst, and Chidambaram 2006; Trkman and McCormack 2009; Kumar, Tiwari, and Babiceanu 2010; Olson and Wu 2010), or operational and disruption (Tang 2006a; Ravindran et al. 2010). In addition, three of these articles divided sup- ply chain risk types into three categories with a similar idea but used different terms (Jüttner, Peck, and Christopher 2003; Christopher and Peck 2004; Lin and Zhou 2011). The three categories are organisational risk or internal risk (e.g. process and control risks), network-related risk or risk within the supply chain (e.g. demand and supply risks), and envi- ronmental risk or risk in the external environment (e.g. natural disasters, war and terrorism and political instability).
Among the 20 articles discussed above, only two articles classified the supply chain risk types according to the degree of the negative impact on companies (Tang 2006a; Ravindran et al. 2010). Note that macro-risks, discussed in Section 2, are akin to disruption risks (Tang 2006a) and value-at-risk (VaR) (Ravindran et al. 2010), whereas micro-risks are similar to operational risks (Tang 2006a) and miss-the-target (Ravindran et al. 2010). Besides, some micro-risks (demand, manufacturing and supply risks) have been extensively proposed and studied. Comparatively, other risks (information, transportation and financial risks) have been paid much less attention. Most importantly, our conceptual framework for the supply chain risk classification, illustrated in Figure 3, is believed to be unique and more comprehensive given that it considers a holistic set of risk types with various degrees of impact (macro- and micro-risks), in both external and internal supply chain (demand, manufacturing and supply risks) and different types of flow (information, transportation and financial risks). This holistic risk classification has not been proposed by the previous studies.
5. Supply chain risk factors
Among the 224 reviewed journal articles, 14 articles discussed supply chain risk factors. Risk factors are various events and situations that drive a specific risk type. The first group of scholars (8 out of 14 articles) identified risk factors of
Table 3. Definitions of SCRM given by researchers.
Authors Definitions of SCRM Scopes
Jüttner, Peck, and Christopher (2003) and Jüttner (2005, 124)
The identification and management of risks for the supply chain, through a coordinated approach amongst supply chain members, to reduce supply chain vulnerability as a whole
Identification and management processes
Norrman and Jansson (2004, 436) To collaborate with partners in a supply chain apply risk management process tools to deal with risks and uncertainties caused by, or impacting on, logistics related activities or resources
Generic processes
Tang (2006a, 453) The management of supply chain risks through coordination or collaboration among the supply chain partners so as to ensure profitability and continuity
Generic processes
Goh, Lim, and Meng (2007, 164–165)
The identification and management of risks within the supply network and externally through a coordinated approach amongst supply chain members to reduce supply chain vulnerability as a whole
Identification and management processes
Thun and Hoenig (2011, 243) Characterised by a cross-company orientation aiming at the identification and reduction of risks not only at the company level, but rather focusing on the entire supply chain
Identification and mitigation processes
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multiple risk types (Chopra and Sodhi 2004; Cucchiella and Gastaldi 2006; Wu, Blackhurst, and Chidambaram 2006; Manuj and Mentzer 2008; Tuncel and Alpan 2010; Wagner and Neshat 2010; Tummala and Schoenherr 2011; Samvedi, Jain, and Chan 2013). For example, Chopra and Sodhi (2004) explored several risk factors, as shown in Table 5, for various risk types as shown in Table 4. The second group of scholars (3 out of 14 articles) explored factors of specific risk types. For example, Zsidisin and Ellram (2003) considered five supply risk factors. Kull and Talluri (2008) also focused on supply risk, and considered somewhat similar factors. Tsai (2008) focused on time-related factors imposing significant influences on the cash flow risk. The last group of scholars (3 out of 14 articles) merely showed a list of potential risk factors without classification (Gaudenzi and Borghesi 2006; Schoenherr, Tummala, and Harrison 2008; Hahn and Kuhn 2012a).
Majority of the risk factors discussed in these 14 articles can be classified into five categories according to our con- ceptual framework, as shown in Table 5, including macro, demand, manufacturing, supply and infrastructural (informa- tion, transportation and financial) factors. First, we found that some of the identified risk factors are vague, and it is
Table 4. Supply chain risk types identified by researchers.
Authors Risk types
Harland, Brenchley, and Walker (2003)
Strategic, operations, supply, customer, asset impairment, competitive, reputation, financial, fiscal, regulatory and legal risks
Jüttner, Peck, and Christopher (2003)
• Environmental risk • Network-related risk • Organisational risk
Cavinato (2004) Physical, financial, informational, relational and innovational risks Chopra and Sodhi (2004) Disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory and
capacity risks Christopher and Peck (2004) • External to the network: environmental risk
• External to the firm but internal to the supply chain network: demand and supply risks • Internal to the firm: process and control risks
Tang (2006a) • Operational risks: uncertain customer demand, uncertain supply and uncertain cost • Disruption risks: earthquakes, floods, hurricanes, terrorist attacks, economics crises
Wu, Blackhurst, and Chidambaram (2006)
• Internal risks: internal controllable, internal partially controllable, internal uncontrollable • External risks: external controllable, external partially controllable, external uncontrollable
Bogataj and Bogataj (2007) Supply, process (production or distribution), demand, control and environmental risks Blackhurst, Scheibe, and
Johnson (2008) Disruptions/disasters, logistics, supplier dependence, quality, information systems, forecast, legal, intellectual property, procurement, receivables (accounting), inventory, capacity, management and security risks
Manuj and Mentzer (2008) Supply, demand, operational and other risks Tang and Tomlin (2008) Supply, process, demand, intellectual property, behavioural and political/social risks Wagner and Bode (2008) Demand side, supply side, regulatory and legal, infrastructure risk and catastrophic risks Trkman and McCormack (2009) • Endogenous risks: market and technology turbulence
• Exogenous risks: discrete events (e.g. terrorist attacks, contagious diseases, workers’ strikes) and continuous risks (e.g. inflation rate, consumer price index changes)
Kumar, Tiwari, and Babiceanu (2010)
• Internal operational risks: demand, production and distribution, supply risks • External operational risks: terrorist attacks, natural disasters, exchange rate fluctuations
Olson and Wu (2010) • Internal risks: available capacity, internal operation, information system risks • External risks: nature, political system, competitor and market risks
Ravindran et al. (2010) • Value-at-risk (VaR): labour strike, terrorist attack, natural disaster • Miss-the-target (MtT): late delivery, missing quality requirements
Lin and Zhou (2011) • Risk in the external environment • Risk within the supply chain • Internal risk
Tang and Musa (2011) Material flow, financial flow and information flow risks Tummala and Schoenherr
(2011) Demand, delay, disruption, inventory, manufacturing (process) breakdown, physical plant (capacity), supply (procurement), system, sovereign and transportation risks
Samvedi, Jain, and Chan (2013) Supply, demand, process and environmental risks
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more appropriate to consider them as risk types rather than risk factors, e.g. risks affecting suppliers and risks affecting customers (Manuj and Mentzer 2008); demand risk, logistics risk, supplier risk and transportation risk (Schoenherr, Tummala, and Harrison 2008). We excluded such risk factors in Table 5, and only included the relevant factors. Second, consistent with the findings in Section 4, demand, manufacturing and supply risks have attracted the most attention. There exists an abundant set of factors, which would give rise to demand, manufacturing and supply risks. Compara- tively, there are less factors suggested for information, transportation, financial, and micro-risks. Third, according to our definition of supply chain risk in Section 3.1, different supply chain risk types would have different levels of negative impact and would lead to operational-, tactical-, or strategic-level failures. Similarly, different risk factors within the same risk type would also have different levels of negative impact. Nevertheless, these articles simply identified and/or classified the potential risk factors without quantifying and assessing the degrees of negative impact.
Table 5 shows the risk factors proposed by particular authors (i.e. which articles proposed which risk factors). As there are many duplicated factors in Table 5, it is synthesised into Table 6 so as to help readers identify factors of par- ticular risk types efficiently, and differentiate between macro- and micro-risk factors more easily. Note that some of these risk factors are associated with generic risk types, such as inbound supply risk (Wu, Blackhurst, and Chidambaram 2006), while some others are factors of specific risk types, such as cash flow risk (Tsai 2008). Before incorporating such risk factors listed in Table 6 into a particular supply chain, industrial characteristics and features of supply chain should be taken into account.
6. SCRM methods
In the past decade, a number of qualitative and quantitative methods and tools have been developed and applied to man- age supply chain risks. Section 6.1 presents the research studying specific or individual SCRM process, such as risk identification, risk assessment, risk mitigation and risk monitoring. Section 6.2 discusses other research focusing on more than one process or integrated management. Note that some sections are relatively lengthy, because those areas have attracted more attention, whereas some other sections are relatively concise, which means that they have been under-researched. The following subsections help in understanding whether individual or integrated management process has attracted more attention, and which SCRM process has been the most prevalently studied.
6.1 Individual SCRM process
6.1.1 Risk identification
Risk identification is the first step in the SCRM process. It involves the identification of risk types, factors or both. The first group of researchers developed qualitative or quantitative methods for identifying potential supply chain risks, such as the analytic hierarchy process (AHP) method (Tsai, Liao, and Han 2008), a supply chain vulnerability map (Blos et al. 2009) and a conceptual model (Trkman and McCormack 2009). Another group of researchers focused on risk factor identification using the AHP (Gaudenzi and Borghesi 2006), and the hazard and operability analysis method (Adhitya, Srinivasan, and Karimi 2009). Some other scholars proposed qualitative tools to identify both risk types and risk factors, such as a qualitative value-focused process engineering methodology (Neiger, Rotaru, and Churilov 2009) and a supply chain risk identification system, based on knowledge-based system approach (Kayis and Karningsih 2012).
Most of the above articles applied qualitative methods for risk identification (Adhitya, Srinivasan, and Karimi 2009; Blos et al. 2009; Neiger, Rotaru, and Churilov 2009; Trkman and McCormack 2009; Kayis and Karningsih 2012). They did not prioritise nor quantify the negative impact of neither risk types nor risk factors.
6.1.2 Risk assessment
Risk assessment is associated with the probability of an event occurring and the significance of the consequences (Harland, Brenchley, and Walker 2003). In the past decade, a number of risk assessment methods have emerged, espe- cially for supply risk assessment. Owing to the abundant published articles in this area, we classify them according to the risk types studied in the conceptual framework, including macro- and micro-risk assessments.
6.1.2.1 Macro risk assessment. Ji and Zhu (2012) evaluated the salvable degrees of the affected areas in a destructive earthquake by the extension technique. They developed a bi-objective optimisation model with the urgent relief demand time-varying fill rate maximisation and distribution time-varying window minimisation to distribute supplies to the identified affected area sets. The methodology was illustrated with a hypothetical numerical example.
5038 W. Ho et al.
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T ab le
5 .
C la ss ifi ca ti o n o f su p p ly
ch ai n ri sk
fa ct o rs
id en ti fi ed
b y re se ar ch er s in to
th e co n ce p tu al
fr am
ew o rk .
A u th o rs
F ac to rs
M ac ro
fa ct o rs
M ic ro
fa ct o rs
D em
an d fa ct o rs
M an u fa ct u ri n g fa ct o rs
S u p p ly
fa ct o rs
In fo rm
at io n fa ct o rs
T ra n sp o rt at io n fa ct o rs
F in an ci al
fa ct o rs
Z si d is in
an d
E ll ra m
(2 0 0 3 )
In ab il it y to
h an d le
v o lu m e d em
an d
ch an g es ; fa il u re s to
m ak e d el iv er y
re q u ir em
en ts ; ca n n o t
p ro v id e co m p et it iv e
p ri ci n g ; te ch n o lo g ic al ly
b eh in d co m p et it o rs ;
in ab il it y to
m ee t q u al it y
re q u ir em
en ts
C h o p ra
an d S o d h i
(2 0 0 4 )
N at u ra l
d is as te r; w ar
an d te rr o ri sm
In ac cu ra te
fo re ca st s;
b u ll w h ip
ef fe ct
o r in fo rm
at io n
d is to rt io n ;
d em
an d
u n ce rt ai n ty
L ab o u r d is p u te ; ra te
o f
p ro d u ct
o b so le sc en ce ;
in v en to ry
h o ld in g co st ;
co st o f ca p ac it y ;
ca p ac it y fl ex ib il it y
S u p p li er
b an k ru p tc y ;
D ep en d en cy
o n a si n g le
so u rc e o f su p p ly ; th e
ca p ac it y an d
re sp o n si v en es s o f
al te rn at iv e su p p li er s;
h ig h ca p ac it y u ti li sa ti o n
at su p p ly
so u rc e;
in fl ex ib il it y o f su p p ly
so u rc e;
p o o r q u al it y o r
y ie ld
at su p p ly
so u rc e;
g lo b al
o u ts o u rc in g ;
p er ce n ta g e o f a k ey
co m p o n en t o r ra w
m at er ia l p ro cu re d fr o m
a si n g le
so u rc e;
in d u st ry w id e ca p ac it y
u ti li sa ti o n ; lo n g - v s.
sh o rt -t er m
co n tr ac ts ;
su p p ly
u n ce rt ai n ty
In fo rm
at io n
in fr as tr u ct u re
b re ak d o w n ; sy st em
in te g ra ti o n o r ex te n si v e
sy st em
s n et w o rk in g ; E -
co m m er ce
E x ce ss iv e h an d li n g d u e
to b o rd er
cr o ss in g s o r to
ch an g e in
tr an sp o rt at io n
m o d es
E x ch an g e ra te ; fi n an ci al
st re n g th
o f cu st o m er s
C u cc h ie ll a an d
G as ta ld i (2 0 0 6 )
P o li ti ca l
en v ir o n m en t
M an u fa ct u ri n g
y ie ld
A v ai la b le
ca p ac it y ;
in te rn al
o rg an is at io n
S u p p li er
q u al it y
In fo rm
at io n d el ay s
P ri ce
fl u ct u at io n s;
st o ch as ti c co st
G au d en zi
an d
B o rg h es i (2 0 0 6 )
C u st o m er
fr ag m en ta ti o n ;
h ig h le v el
o f
S h o rt li fe
ti m e p ro d u ct s;
li n k ed
p h as es
in m an u fa ct u ri n g ; st o ck
N ar ro w
n u m b er
o f
in te rm
ed ia te
su p p li er s;
lo w
in te rm
ed ia te
L ac k o f in fo rm
at io n
tr an sp ar en cy
b et w ee n
lo g is ti cs
an d m ar k et in g
L ac k o f o u tb o u n d
ef fe ct iv en es s; tr an sp o rt
p ro v id er s’
(C o n ti n u ed )
International Journal of Production Research 5039
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T ab le
5 .
(C o n ti n u ed ).
A u th o rs
F ac to rs
M ac ro
fa ct o rs
M ic ro
fa ct o rs
D em
an d fa ct o rs
M an u fa ct u ri n g fa ct o rs
S u p p ly
fa ct o rs
In fo rm
at io n fa ct o rs
T ra n sp o rt at io n fa ct o rs
F in an ci al
fa ct o rs
se rv ic e re q u ir ed
b y cu st o m er s;
se ri o u s
fo re ca st in g
er ro rs ; sh o rt
le ad
ti m es
d ri v en
su p p ly
ch ai n ;
w ar eh o u se
an d
p ro d u ct io n d is ru p ti o n
su p p li er s’
in te g ra ti o n ;
la ck
o f in te g ra ti o n w it h
fi n al -p ro d u ct
su p p li er ;
la ck
o f in te rm
ed ia te
su p p li er s’
v is ib il it y ; la ck
o f fi n al -p ro d u ct
su p p li er s’
v is ib il it y
fr ag m en ta ti o n ; la ck
o f
tr an sp o rt p ro v id er s’
in te g ra ti o n ; d am
ag es
in tr an sp o rt ; n o tr an sp o rt
so lu ti o n al te rn at iv es
W u , B la ck h u rs t,
an d
C h id am
b ar am
(2 0 0 6 )
F ir e
ac ci d en ts ;
E x te rn al
le g al
is su es ;
p o li ti ca l/
ec o n o m ic
st ab il it y
S u d d en
sh o o t-
u p d em
an d ;
Q u al it y ; p ro d u ct io n
ca p ab il it ie s/ ca p ac it y ;
p ro d u ct io n fl ex ib il it y ;
te ch n ic al /k n o w le d g e
re so u rc es ; em
p lo y ee
ac ci d en ts ; la b o u r st ri k es
S u p p li er
m an ag em
en t;
su p p li er
m ar k et
st re n g th ;
co n ti n u it y o f su p p ly ;
se co n d -t ie r su p p li er
In te rn et
se cu ri ty
O n -t im
e d el iv er y ;
ac ci d en ts in
tr an sp o rt at io n ; m ar it im
e p ir at e at ta ck ; re m o te
h ig h -w
ay th ef t
C o st ; fi n an ci al
an d
in su ra n ce
is su es ; lo ss
o f
co n tr ac t; lo w
p ro fi t
m ar g in ; m ar k et
g ro w th ;
m ar k et
si ze
K u ll an d T al lu ri
(2 0 0 8 )
D el iv er y fa il u re ; co st
fa il u re ; q u al it y fa il u re ;
fl ex ib il it y fa il u re ;
g en er al
co n fi d en ce
fa il u re
M an u j an d
M en tz er
(2 0 0 8 )
D em
an d
v ar ia b il it y ;
fo re ca st er ro rs ;
co m p et it o r
m o v es
In v en to ry
o w n er sh ip ;
as se t an d to o ls
o w n er sh ip ; p ro d u ct
q u al it y an d sa fe ty
S u p p li er
o p p o rt u n is m ;
in b o u n d p ro d u ct
q u al it y ;
tr an si t ti m e v ar ia b il it y
C u rr en cy
fl u ct u at io n s;
w ag e ra te
sh if ts
S ch o en h er r,
T u m m al a,
an d
H ar ri so n (2 0 0 8 )
S o v er ei g n
ri sk ; n at u ra l
d is as te rs /
te rr o ri st s
A N S I co m p li an ce ;
p ro d u ct
q u al it y ;
en g in ee ri n g an d
in n o v at io n
W ro n g p ar tn er ;
su p p li er ’s su p p li er
m an ag em
en t
O n -t im
e/ o n -b u d g et
d el iv er y
P ro d u ct
co st
T sa i (2 0 0 8 )
L ea d ti m e fo r in te rn al
p ro ce ss in g an d th e
ti m in g o f it s re la te d ca sh
o u tfl o w s; cr ed it p er io d s
fo r ac co u n ts re ce iv ab le
to it s cu st o m er s an d th e
p at te rn
o f ea rl y
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co ll ec ti o n o f ac co u n ts
re ce iv ab le ; cr ed it p er io d s
fo r ac co u n ts p ay ab le
fr o m
it s su p p li er s an d
th e p at te rn
o f ea rl y
p ay m en t o f ac co u n ts
p ay ab le
T u n ce l an d A lp an
(2 0 1 0 )
D efi ci en t o r
m is si n g
cu st o m er
re la ti o n
m an ag em
en t
fu n ct io n ; h ig h
co m p et it io n in
th e m ar k et p la ce
O p er at o r ab se n ce ;
st ri k es ; d is sa ti sf ac ti o n
w it h w o rk ; in su ffi ci en t
m ai n te n an ce ; in st ab le
m an u fa ct u ri n g p ro ce ss ;
lo ss
o f m o ti v at io n ; la ck
o f ex p er ie n ce
o r
tr ai n in g ; in su ffi ci en t
b re ak s; w o rk in g
co n d it io n s
M o n o p o ly ; co n tr ac tu al
ag re em
en ts ;
te ch n o lo g ic al
ch an g es ;
lo w
te ch n ic al
re li ab il it y
S tr es s o n cr ew
; la ck
o f
tr ai n in g ; lo n g w o rk in g
ti m es ; n eg li g en tl y
m ai n te n an ce ; o ld
te ch n o lo g y ; se le ct ed
d el iv er y m o d es
an d
p er io d
W ag n er
an d
N es h at
(2 0 1 0 )
S h o rt p ro d u ct s’
li fe
cy cl es ;
cu st o m er s’
d ep en d en cy ;
lo w
in -h o u se
p ro d u ct io n
L ea n in v en to ry ;
ce n tr al is ed
st o ra g e o f
fi n is h ed
p ro d u ct s
S m al l su p p ly
b as e;
su p p li er s’
d ep en d en cy ;
si n g le
so u rc in g
G lo b al
so u rc in g
n et w o rk ; su p p ly
ch ai n
co m p le x it y
T u m m al a an d
S ch o en h er r
(2 0 11 )
N at u ra l
d is as te rs ;
te rr o ri sm
an d
w ar s; re g io n al
in st ab il it y ;
g o v er n m en t
re g u la ti o n s
O rd er
fu lfi lm
en t
er ro rs ;
in ac cu ra te
fo re ca st s;
in fo rm
at io n
d is to rt io n ;
d em
an d
u n ce rt ai n ty
L ab o u r d is p u te s; co st s
o f h o ld in g in v en to ri es ;
ra te
o f p ro d u ct
o b so le sc en ce ; p o o r
q u al it y ; lo w er
p ro ce ss
y ie ld s; h ig h er
p ro d u ct
co st ; d es ig n ch an g es ;
la ck
o f ca p ac it y
fl ex ib il it y ; co st o f
ca p ac it y
S in g le
so u rc e o f su p p ly ;
ca p ac it y an d
re sp o n si v en es s o f
al te rn at iv e su p p li er s;
su p p ly
u n ce rt ai n ty ;
su p p li er
fu lfi lm
en t;
q u al it y o f se rv ic e,
in cl u d in g re sp o n si v en es s
an d d el iv er y
p er fo rm
an ce ; su p p li er
fu lfi lm
en t er ro rs ;
se le ct io n o f w ro n g
p ar tn er s; h ig h ca p ac it y
u ti li sa ti o n at
su p p ly
so u rc e;
in fl ex ib il it y o f
su p p ly
so u rc e;
p o o r
q u al it y o r p ro ce ss
y ie ld
at su p p ly
so u rc e;
In fo rm
at io n
in fr as tr u ct u re
b re ak d o w n s; la ck
o f
ef fe ct iv e sy st em
in te g ra ti o n o r ex te n si v e
sy st em
n et w o rk in g ; la ck
o f co m p at ib il it y in
IT p la tf o rm
s am
o n g su p p ly
ch ai n p ar tn er s
E x ce ss iv e h an d li n g d u e
to b o rd er
cr o ss in g s o r
ch an g e in
tr an sp o rt at io n
m o d e;
p o rt ca p ac it y an d
co n g es ti o n ; cu st o m
cl ea ra n ce s at
p o rt s;
tr an sp o rt at io n
b re ak d o w n s; p ap er w o rk
an d sc h ed u li n g ; p o rt
st ri k es ; la te
d el iv er ie s;
h ig h er
co st s o f
tr an sp o rt at io n ;
d ep en d en cy
o n
tr an sp o rt at io n m o d e
ch o se n
R at e o f ex ch an g e
(C o n ti n u ed )
International Journal of Production Research 5041
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T ab le
5 .
(C o n ti n u ed ).
A u th o rs
F ac to rs
M ac ro
fa ct o rs
M ic ro
fa ct o rs
D em
an d fa ct o rs
M an u fa ct u ri n g fa ct o rs
S u p p ly
fa ct o rs
In fo rm
at io n fa ct o rs
T ra n sp o rt at io n fa ct o rs
F in an ci al
fa ct o rs
su p p li er
b an k ru p tc y ;
p er ce n ta g e o f a k ey
co m p o n en t o r ra w
m at er ia l p ro cu re d fr o m
a si n g le
so u rc e
H ah n an d K u h n
(2 0 1 2 a)
D em
an d
u n ce rt ai n ty
R es o u rc e b re ak d o w n ;
q u al it y is su es
S u p p ly
u n ce rt ai n ty ;
su p p li er
so lv en cy
In te re st ra te
le v el ;
ex ch an g e ra te s
S am
v ed i, Ja in , an d
C h an
(2 0 1 3 )
T er ro ri sm
; p o li ti ca l
in st ab il it y ;
n at u ra l
d is as te rs ;
ec o n o m ic
d o w n tu rn s;
so ci al
an d
cu lt u ra l
g ri ev an ce s
S u d d en
fl u ct u at io n s;
m ar k et
ch an g es ;
co m p et it io n
ch an g es ;
fo re ca st er ro rs
M ac h in e fa il u re ; la b o u r
st ri k e;
q u al it y p ro b le m s;
te ch n o lo g ic al
ch an g e
O u ts o u rc in g ; su p p li er
in so lv en cy ; q u al it y ;
su d d en
h ik e in
co st s
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6 .
S u m m ar y o f su p p ly
ch ai n ri sk
fa ct o rs .
M ac ro
ri sk
fa ct o rs
M ic ro
ri sk
fa ct o rs
D em
an d ri sk
fa ct o rs
M an u fa ct u ri n g ri sk
fa ct o rs
S u p p ly
ri sk
fa ct o rs
In fr as tr u ct u ra l ri sk
fa ct o rs
In fo rm
at io n ri sk
fa ct o rs
T ra n sp o rt at io n ri sk
fa ct o rs
F in an ci al
ri sk
fa ct o rs
• N at u ra l
d is as te r
• W ar
an d
te rr o ri sm
• F ir e
ac ci d en ts
• P o li ti ca l
in st ab il it y
• E co n o m ic
d o w n tu rn s
• E x te rn al
le g al
is su es
• S o v er ei g n
ri sk
• R eg io n al
in st ab il it y
• G o v er n m en t
re g u la ti o n s
• S o ci al
an d
cu lt u ra l
g ri ev an ce s
• In ac cu ra te
d em
an d fo re ca st s
• S er io u s
fo re ca st in g er ro rs
• B u ll w h ip
ef fe ct
o r
in fo rm
at io n
d is to rt io n
• D em
an d
u n ce rt ai n ty
• S u d d en
sh o o t- u p
d em
an d
• D em
an d
v ar ia b il it y
• C u st o m er
fr ag m en ta ti o n
• H ig h le v el
o f
se rv ic e re q u ir ed
b y cu st o m er s
• C u st o m er
d ep en d en cy
• D efi ci en t o r
m is si n g cu st o m er
re la ti o n
m an ag em
en t
fu n ct io n
• S h o rt le ad
ti m es
• S h o rt p ro d u ct s’
li fe
cy cl e
• C o m p et it o r m o v es
• C o m p et it io n
ch an g es
• M ar k et
ch an g es
• H ig h co m p et it io n
in th e m ar k et
• L o w
in -h o u se
p ro d u ct io n
• O rd er
fu lfi lm
en t
er ro rs
• L ab o u r d is p u te s/
st ri k es
• E m p lo y ee
ac ci d en ts
• O p er at o r ab se n ce
• D is sa ti sf ac ti o n w it h
w o rk
• L ac k o f ex p er ie n ce
o r tr ai n in g
• In su ffi ci en t b re ak s
• W o rk in g co n d it io n s
• P ro d u ct
o b so le sc en ce
• In v en to ry
h o ld in g
co st
• S to ck
d ri v en
su p p ly
ch ai n
• In v en to ry
o w n er sh ip
• L ea n in v en to ry
• P ro d u ct io n fl ex ib il it y
• P ro d u ct io n
ca p ab il it ie s/ ca p ac it y
• P ro d u ct s q u al it y an d
sa fe ty
• T ec h n ic al /k n o w le d g e
re so u rc es
• E n g in ee ri n g an d
in n o v at io n
• S h o rt er
li fe
ti m e
p ro d u ct s
• L in k ed
p h as es
in m an u fa ct u ri n g
• W ar eh o u se
an d
p ro d u ct io n d is ru p ti o n
• In su ffi ci en t
m ai n te n an ce
• In st ab le
m an u fa ct u ri n g
p ro ce ss
• C en tr al is ed
st o ra g e
o f fi n is h ed
p ro d u ct s
• In ab il it y to
h an d le
v o lu m e
d em
an d ch an g es
• F ai lu re s to
m ak e d el iv er y
re q u ir em
en ts
• C an n o t p ro v id e
co m p et it iv e
p ri ci n g
• T ec h n o lo g ic al ly
b eh in d
co m p et it o rs
• In ab il it y to
m ee t
q u al it y
re q u ir em
en ts
• S u p p li er
b an k ru p tc y
• S in g le
su p p ly
so u rc in g
• S m al l su p p ly
b as e
• S u p p li er s’
d ep en d en cy
• S u p p ly
re sp o n si v en es s
• H ig h ca p ac it y
u ti li sa ti o n ay
su p p ly
so u rc e
• G lo b al
o u ts o u rc in g
• N ar ro w
n u m b er
o f in te rm
ed ia te
su p p li er s
• L ac k o f
in te g ra ti o n w it h
su p p li er s
• L ac k o f
su p p li er s’
v is ib il it y
• In fo rm
at io n
in fr as tr u ct u re
b re ak d o w n
• S y st em
in te g ra ti o n
o r ex te n si v e
sy st em
s n et w o rk in g
• E -c o m m er ce
• In fo rm
at io n d el ay s
• L ac k o f in fo rm
at io n
tr an sp ar en cy
b et w ee n lo g is ti cs
an d m ar k et in g
• In te rn et
se cu ri ty
• L ac k o f
co m p at ib il it y in
IT p la tf o rm
s am
o n g
su p p ly
ch ai n
p ar tn er s
• E x ce ss iv e h an d li n g d u e
to b o rd er
cr o ss in g s o r
ch an g e in
tr an sp o rt at io n m o d es
• L ac k o f o u tb o u n d
ef fe ct iv en es s
• T ra n sp o rt p ro v id er s’
fr ag m en ta ti o n
• N o tr an sp o rt so lu ti o n
al te rn at iv es
• O n -t im
e/ o n -b u d g et
d el iv er y
• D am
ag es
in tr an sp o rt
• A cc id en ts in
tr an sp o rt at io n
• M ar it im
e p ir at e at ta ck
• R em
o te
h ig h -w
ay th ef t
• S tr es s o n cr ew
• L ac k o f tr ai n in g
• L o n g w o rk in g ti m es
• N eg li g en tl y
m ai n te n an ce
• O ld
te ch n o lo g y
• T ra n sp o rt at io n
b re ak d o w n s
• P o rt st ri k es
• G lo b al
so u rc in g
n et w o rk
• S u p p ly
ch ai n
co m p le x it y
• P o rt ca p ac it y an d
co n g es ti o n
• C u st o m
cl ea ra n ce s at
p o rt s
• E x ch an g e ra te
• C u rr en cy
fl u ct u at io n s
• In te re st ra te
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6.1.2.2 Demand risk assessment. A number of researchers analysed the impact of demand volatility on inventory man- agement (Ballou and Burnetas 2003; Cachon 2004; Talluri, Cetin, and Gardner 2004; Betts and Johnston 2005; Sodhi 2005; Xiao and Yang 2008; Radke and Tseng 2012). Some of them provided useful insights on safety stock reduction. Ballou and Burnetas (2003) compared a traditional inventory planning approach with one that is based on filling cus- tomer demand from any one of several stocking locations, referred to as cross filling, while considering the dispersion of demand among stocking locations. It was revealed that cross filling can help reducing safety stocks. Talluri, Cetin, and Gardner (2004) developed a safety stock model and benchmarked it with existing models for managing make-to-s- tock inventories under demand and supply variations. Based on a case study at an over-the-counter pharmaceutical com- pany, the proposed safety stock model performed well in terms of cost savings. Betts and Johnston (2005) presented the multi-item constrained inventory model to compare just-in-time (JIT) replenishment with component substitution under stochastic demand. The analysis showed that JIT replenishment is more effective than component substitution because of less investment in safety stock.
Some other scholars analysed the impact of demand visibility and bullwhip effect on supply chain performance. Smaros et al. (2003) used a discrete-event simulation model to show that a partial improvement of demand visibility can improve production and inventory control efficiency. Reiner and Fichtinger (2009) developed a dynamic model to evaluate supply chain process improvements under consideration of different forecast methods. They pointed out that dampening of the order variability decreases the bullwhip effect and the average on-hand inventory but with the prob- lem of a decreasing service level. Sucky (2009) suggested that the variability of orders increases as they move up the supply chain from retailers to wholesalers to manufacturers to suppliers. He concluded that the bullwhip effect is over- estimated if a simple supply chain is assumed and risk pooling effects are present.
A common limitation of the above articles is that most of the proposed methods were not implemented in real indus- trial cases (Ballou and Burnetas 2003; Smaros et al. 2003; Cachon 2004; Betts and Johnston 2005; Sodhi 2005; Xiao and Yang 2008; Reiner and Fichtinger 2009; Sucky 2009; Radke and Tseng 2012). Lack of actual implementation and verification would make the potential users doubtful about the effectiveness and efficiency of the proposed methods. Besides, several of the above articles simplified the studied problems with stylised supply chains (Ballou and Burnetas 2003; Smaros et al. 2003; Cachon 2004).
6.1.2.3 Manufacturing risk assessment. There exist three research studies on manufacturing risk assessment. They applied different methods to assess different manufacturing risks in different supply chains. Cigolini and Rossi (2010) proposed the fault tree approach to analyse and assess the operational risk at the drilling, primary transport and refining stages of an oil supply chain. They concluded that different stages are affected by various operational risks according to the differences in plants. Therefore, each plant should be provided with a specifically conceived risk management pro- cess. Dietrich and Cudney (2011) applied a Pugh method adaption to assess risk coupled with manufacturing readiness level for emerging technologies in a global aerospace supply chain. They revealed that executive management can evaluate the entire emerging technology portfolio more effectively with the proposed methodology. Tse and Tan (2011) constructed a product quality risk and visibility assessment framework using the margin incremental analysis for a toy manufacturing company. They argued that better visibility of risk in supply tiers could minimise the quality risk.
There exist limitations in the above articles. Cigolini and Rossi (2010) only focused on three stages of an oil supply chain, while ignoring operational risk assessment at some other crucial stages (e.g. design, construction and outsourcing). The risk assessment matrix proposed by Dietrich and Cudney (2011) is fairly simplistic as it is based on only three levels (i.e. ‘green’, ‘yellow’ and ‘red’). Tse and Tan (2011) neither quantified risks and their factors, nor proposed any mitigating actions for the identified manufacturing risk.
6.1.2.4 Supply risk assessment. Supply risk assessment has attracted much attention. Most of the articles studied the supplier evaluation and selection problem while considering a variety of supply risks, such as poor quality (Talluri and Narasimhan 2003; Talluri, Narasimhan, and Nair 2006), late delivery (Talluri and Narasimhan 2003; Talluri, Narasimhan, and Nair 2006), uncertain capacity (Kumar, Vrat, and Shankar 2006; Viswanadham and Samvedi 2013), dispersed geographical location (Chan and Kumar 2007), supplier failure (Kull and Talluri 2008; Ravindran et al. 2010; Ruiz-Torres, Mahmoodi, and Zeng 2013), supplier’s financial stress (Lockamy and McCormack 2010), supply disruption (Wu and Olson 2010; Meena, Sarmah, and Sarkar 2011), poor supplier service (Wu et al. 2010; Chen and Wu 2013), suppliers’ risk management ability and experience (Ho, Dey, and Lockström 2011) and lack of supplier involvement (Chaudhuri, Mohanty, and Singh 2013). A wide range of quantitative methods have been proposed to deal with this problem, including mathematical programming and data envelopment analysis (DEA) approaches (Talluri and Narasimhan 2003; Kumar, Vrat, and Shankar 2006; Talluri, Narasimhan, and Nair 2006; Ravindran et al. 2010; Wu and Olson 2010; Wu et al. 2010; Meena, Sarmah, and Sarkar 2011), multicriteria decision-making and AHP approaches
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(Chan and Kumar 2007; Blackhurst, Scheibe, and Johnson 2008; Kull and Talluri 2008; Ho, Dey, and Lockström 2011; Chen and Wu 2013; Viswanadham and Samvedi 2013), Bayesian networks (Lockamy and McCormack 2010), decision tree approach (Ruiz-Torres, Mahmoodi, and Zeng 2013) and fuzzy-based failure mode and effect analysis (FMEA) with ordered weighted averaging approach (Chaudhuri, Mohanty, and Singh 2013).
In addition to the above supply risks, some other supply risks have also been analysed and assessed, such as sec- ond-tier supply failure (Kull and Closs 2008), offshore sourcing risk (Schoenherr, Tummala, and Harrison 2008), unreli- able dual sourcing network (Iakovou, Vlachos, and Xanthopoulos 2010), supplier non-conformance risk (Wiengarten, Pagell, and Fynes 2013), supplier incapability (Johnson, Elliott, and Drake 2013) and supplier unreliability (Cheong and Song 2013).
Different from the above approaches focusing on the assessment of supply risks, the following articles studied sup- ply risk assessment methods and models. Zsidisin et al. (2004) examined tools and techniques that purchasing organisa- tions implement for assessing supply risk within an agency theory context. They indicated that purchasing organisations can assess supply risk with techniques that focus on addressing supplier quality issues, improving supplier processes and reducing the likelihood of supply disruptions. Ellegaard (2008) applied a case-based methodology to analyse the supply risk management practices of 11 small company owners (SCOs). They confirmed that the 11 studied SCOs applied almost the same supply risk management practices, which can be characterised as defensive. Wu and Olson (2008) used simulated data to compare three types of risk evaluation models: chance-constrained programming, DEA and multi-objective programming models. Results from three models are consistent with each other in selecting pre- ferred suppliers. Azadeh and Alem (2010) benchmarked three types of supplier selection models under certainty, uncer- tainty and probabilistic conditions, including DEA, Fuzzy DEA and chance-constrained DEA. Results from three models are also consistent with each other with respect to the worst suppliers.
Supplier evaluation and selection has attracted the most attention is this category. Many of these articles focused on conceptual model development and demonstration using simulated data (Chan and Kumar 2007; Ravindran et al. 2010; Wu and Olson 2010; Wu et al. 2010; Meena, Sarmah, and Sarkar 2011; Viswanadham and Samvedi 2013; Ruiz-Torres, Mahmoodi, and Zeng 2013). Thus, the use of real data to test the efficacy of these methods is still missing. Moreover, some of these articles have other technical limitations. For example, Talluri and Narasimhan (2003) and Talluri, Narasimhan, and Nair (2006) only utilised a single input measure in the DEA analyses. Kull and Talluri (2008) assumed current supplier capabilities will remain unchanged into the future. Lockamy and McCormack (2010) assumed that all suppliers are willing to share their accurate and reliable risk profile data with their customers. Ruiz-Torres, Mahmoodi, and Zeng (2013) assumed all the input parameters and supplier characteristics to be deterministic.
6.1.2.5 Financial risk assessment. There are four research studies on financial risk assessment. Two of them focused on specific financial risks. Tsai (2008) modelled the supply-chain-related cash flow risks by the standard deviations of cash inflows, outflows and net flows of each period in a planning horizon. They recommended the best policy of using asset- backed securities to finance accounts receivable as a means to shorten the cash conversion cycle and lower the cash inflow risk. Liu and Nagurney (2011) developed a variational inequality model to study the impact of foreign exchange risk and competition intensity on supply chain companies that are involved in offshore-outsourcing activities. Their simulation results indicated that in general the risk-averse firm has lower profitability and lower risk than the risk-neutral firm.
On the other hand, two of the studies focused on generic financial risk. Franca et al. (2010) formulated a multi- objective programming model with the Six Sigma concepts to evaluate financial risk. They showed that the financial risk decreases as the sigma level increases. Liu and Cruz (2012) studied the impact of corporate financial risk and eco- nomic uncertainty on the values, profits and decisions of supply chains. They found that suppliers are willing to sacrifice some profit margins to gain more businesses from manufacturers with lower financial risk and with lower sensitivity to economic uncertainty. A common drawback with these approaches is that they focused on simulated data instead of using real case data.
6.1.2.6 Information risk assessment. Durowoju, Chan, and Wang (2012) used discrete-event simulation to investigate the impact of disruption in the flow of critical information needed in manufacturing operations on collaborating mem- bers. They revealed that the retailer experiences the most uncertainty in the supply chain, while the holding cost consti- tutes the most unpredictable cost measure when a system failure breach occurs. In their study, a generic information technology risk was studied and no risk factors were identified nor quantified.
6.1.2.7 General risk assessment. Articles that do not assess specific risk types are described in this section. The topics of these articles are diversified and there are four major categories. First, a number of researchers attempted to evaluate, assess and quantify generic supply chain risks. Brun et al. (2006) developed a so-called supply network opportunity
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assessment package methodology to evaluate advanced planning and scheduling and supply chain management imple- mentation projects with risk analysis. Bogataj and Bogataj (2007) used parametric linear programming model to measure the costs of risk based on the net present value of activities. Wu, Blackhurst, and O’grady (2007) proposed a disruption analysis network approach to determine how changes or disruptions propagate in supply chains and calculated their impact on the supply chain system. Kumar, Tiwari, and Babiceanu (2010) applied the artificial bee colony technique, genetic algorithms and particle swarm optimisation to identify operational risk factors, their expected value and probabil- ity of occurrence, and associated additional cost. Khilwani, Tiwari, and Sabuncuoglu (2011) proposed the hybrid Petri-net approach for modelling, performance evaluation and risk assessment of a supply chain. Olson and Wu (2011) used DEA and the Monte Carlo simulation to identify various risk performance measures for outsourcing, and compared expected performance of vendors under risk and uncertainty in a supply chain. Wang et al. (2012) applied fuzzy AHP to assess risk of implementing various green initiatives in the fashion industry. Samvedi, Jain, and Chan (2013) applied fuzzy AHP and fuzzy TOPSIS approaches to quantify the risks in a supply chain, and aggregated the values into a com- prehensive risk index.
The second category is concerned with the assessment of relationship between supply chain risks and strategies. Craighead et al. (2007) suggested that the best practices in purchasing, including supply base reduction, global sourcing and sourcing from supply clusters might have negative impact on the severity of supply chain disruptions. Laeequddin et al. (2009) suggested that the supply chain members should strive to reduce the membership risk levels to build trust rather than striving to build trust to reduce the risk. Tomlin (2009) found that contingent sourcing is preferred to sup- plier diversification as the supply risk increases, while diversification is preferred to contingent sourcing as the demand risk increases. Hult, Craighead, and Ketchen (2010) studied supply chain investment decisions when facing high levels of risk uncertainty. They extended real options theory to the supply chain context by examining how different types of options are approached relative to supply chain project investments. Wang, Gilland, and Tomlin (2011) applied the unconstrained and constrained mathematical programming models to assess the relationship between various supply chain strategies and the regulatory trade risk. They established that the direct and split strategy profits increase in the non-tariff barriers price variance but decrease in the mean price.
Third, Jüttner and Maklan (2011) and Pettit, Croxton, and Fiksel (2013) both evaluated the supply chain resilience. Jüttner and Maklan (2011) revealed that knowledge management seems to enhance the supply chain resilience by improving flexibility, visibility, velocity and collaboration capabilities of the supply chain. Pettit, Croxton, and Fiksel (2013) suggested a correlation between increased resilience and improved supply chain performance.
Fourth, Wagner and Neshat (2010) and Berle, Norstad, and Asbjørnslett (2013) both assessed supply chain vulnera- bility. Wagner and Neshat (2010) concluded that if supply chain managers were more capable of measuring and manag- ing supply chain vulnerability, they could reduce the number of disruptions and their impact. Berle, Norstad, and Asbjørnslett (2013) argued identifying the ‘vulnerability inducing bottlenecks’ of transportation systems allows for realising more robust versions of these systems in a cost-effective manner.
While the above-mentioned methods addressed a variety of issues, they are not devoid of limitations. Brun et al. (2006) considered the deterministic characteristics of projects in their risk analysis. Kumar, Tiwari, and Babiceanu (2010) focused on a single-product supply chain network. Wagner and Neshat (2010) claimed that the applicability of their proposed approach heavily depends on the availability of data that quantifies the factors of supply chain vulnerabil- ity. Khilwani, Tiwari, and Sabuncuoglu (2011) indicated that the proposed method is incapable of modelling the changes performed in the network during the risk management process. Wang et al. (2012) pointed out that the functionality of their model is heavily dependent on the knowledge, expertise and communication skills of assessors. Berle, Norstad, and Asbjørnslett (2013) studied a simplified version of a real transportation system. Samvedi, Jain, and Chan (2013) emphasised that their risk index is simply generic rather than industry-specific.
6.1.3 Risk mitigation
In this section, we classify risk mitigation methods in a similar manner as the risk assessment methods are discussed in Section 6.1.2.
6.1.3.1 Macro-risk mitigation. Hale and Moberg (2005) used a five-stage disaster management framework for secure site location selection. The framework consists of planning, mitigation, detection, response and recovery. However, the proposed set covering location model minimises the number of secure site locations rather than the level of risk expo- sure. In order to help firms succeed before, during and after a major disruption, Tang (2006b) presented nine strategies to manage the inherent fluctuations efficiently and make the supply chains more resilient. The strategies are postpone- ment, strategic stock, flexible supply base, make-and-buy, economic supply incentives, flexible transportation, revenue
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management, dynamic assortment planning and silent product rollover. However, the proposed mitigation strategies were not assessed and benchmarked to see which are more effective and efficient.
6.1.3.2 Demand risk mitigation. Significant number of researches focused on demand risk mitigation and supply chain decision-making under stochastic demand. The first group of researchers determined the optimal-order placement and replenishment plan in order to minimise the impact of demand uncertainty. Various methodologies have been developed and applied, including automatic pipeline inventory and order-based production control system algorithm (Towill 2005), two-period financial model (Aggarwal and Ganeshan 2007), buyer’s risk adjustment model (Shin and Benton 2007), multiple regression model (Hung and Ryu 2008), simulation model (Schmitt and Singh 2012), newsvendor model (Arcelus, Kumar, and Srinivasan 2012; Tang, Musa, and Li 2012) and mathematical programming, such as stochastic integer linear programming model (Snyder, Daskin, and Teo 2007), mixed-integer stochastic programming model (Lejeune 2008), stochastic linear programming model (Sodhi and Tang 2009) and mixed integer nonlinear programming model (Kang and Kim 2012).
The second group of researchers analysed the forecasting techniques to minimise demand risk. Guo, Fang, and Whinston (2006) constructed a macro-prediction market model, which can aggregate information about demand risk to achieve accurate demand forecast sharing in the supply chain. Datta et al. (2007) modified the forecasting tech- nique called Generalised Autoregressive Conditional Heteroskedasticity to model demand volatility and better man- age risk. Crnkovic, Tayi, and Ballou (2008) presented a simulation-based decision support framework to evaluate and select alternative forecasting methods in uncertain demand environments. Sayed, Gabbar, and Miyazaki (2009) presented an improved genetic algorithm to choose the best weights among the statistical methods and to optimise the forecasted activities combinations that maximise profit, which in turn, balance risk of overstocking and stockouts.
The third group of researchers proposed the risk-sharing contracts to minimise the loss due to uncertain demand. Chen, Chen, and Chen (2006), Xiao and Yang (2009) and Chen and Yano (2010) focused on two-tier supply chains, and proposed risk-sharing contracts to minimise the loss of manufacturer (e.g. overproduction) and the loss of retailers (e.g. overstocking) under demand uncertainty (Chen, Chen, and Chen 2006; Xiao and Yang 2009) or weather-sensitive demand (Chen and Yano 2010). Different from the above, Kim (2013) studied a four-tier supply chain under dynamic market demands, and proposed the bilateral contracts with order quantity flexibility. It was revealed that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service.
The following articles also focus on demand risk mitigation but do not fall into the aforementioned subcategories. Rao, Swaminathan, and Zhang (2005) showed that a firm can optimise expected profits by quoting a uniform guaranteed maximum lead time to all customers under demand uncertainty. Huang, Chou, and Chang (2009) presented a dynamic system model of manufacturing supply chains, which can proactively manage disruptive events and absorb the demand shock. Ben-Tal et al. (2011) applied a multiperiod deterministic linear programming to generate a robust logistics plan that can mitigate demand uncertainty in humanitarian relief supply chains.
There are limitations associated with some of the above articles. For example, Rao, Swaminathan, and Zhang (2005) assumed the lead time to all customers for all products are the same. Chen, Chen, and Chen (2006) and Guo, Fang, and Whinston (2006) assumed that retail prices are exogenously set and are the same for all retailers. Snyder, Daskin, and Teo (2007) assumed demand parameters are known with certainty. Shin and Benton (2007) did not consider all inven- tory variables, such as safety stock, service level and reorder point. Hung and Ryu (2008) used students as surrogates for the actual purchasing and supply chain managers in a supply chain experiment. Lei, Li, and Liu (2012) assumed the relationship between demand and price is linear.
6.1.3.3 Manufacturing risk mitigation. The following articles focused on mitigation of various manufacturing risk factors, including quality risk (Kaya and Özer 2009; Hung 2011; Sun, Matsui, and Yin 2012), lead time uncertainty (Li 2007), random yield risk (He and Zhang 2008), non-conforming product design (Khan, Christopher, and Burnes 2008), capacity inflexibility (Hung 2011) and machine failures (Kenné, Dejax, and Gharbi 2012). The methods used are longitudinal case study (Khan, Christopher, and Burnes 2008), newsvendor model (Li 2007), linear programming model (Kaya and Özer 2009), stochastic dynamic model (Kenné, Dejax, and Gharbi 2012), P-chart solution model (Sun, Matsui, and Yin 2012), unconstrained and constrained mathematical programming models (He and Zhang 2008) and integrated methodology, combining analytic network process (ANP), fuzzy GP, five forces analysis and VaR (Hung 2011).
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There are limitations associated with some of the above articles. Li (2007) and Kenné, Dejax, and Gharbi (2012) considered only one type of products in their models. He and Zhang (2008) and Sun, Matsui, and Yin (2012) considered one supplier and one retailer in their analyses. Kaya and Özer (2009) assumed the demand function to be linear.
6.1.3.4 Supply risk mitigation. A significant amount of work is related to supply risk mitigation. Earlier studies in the review period carried out empirical studies, which showed that supply risk can be mitigated by implementing behaviour-based management techniques (Zsidisin and Ellram 2003), by building strategic supplier relationships (Giunipero and Eltantawy 2004; Hallikas et al. 2005), through early supplier involvement (Zsidisin and Smith 2005), by adopting business continuity planning as a formal risk management technique (Zsidisin, Melnyk, and Ragatz 2005) and by reducing supply base complexity (Choi and Krause 2006).
Most of the attention has been confined to the sourcing decisions. First, some scholars determined the optimal num- ber of suppliers in the presence of catastrophic risks (Berger, Gerstenfeld, and Zeng 2004) or supplier failure risks (Ruiz-Torres and Mahmoodi 2007). It was found that additional suppliers are needed when the disaster loss increases significantly (Berger, Gerstenfeld, and Zeng 2004) or the suppliers become less reliable (Ruiz-Torres and Mahmoodi 2007). Second, some scholars evaluated single-, dual- or multiple-sourcing strategies. There is a consensus that a dual- sourcing strategy outperforms a single-sourcing one in the presence of a supply disruption (Yu, Zeng, and Zhao 2009; Li, Wang, and Cheng 2010; Xanthopoulos, Vlachos, and Iakovou 2012). However, the benefits of multiple-sourcing strategies are not significant. Costantino and Pellegrino (2010) identified the probabilistic benefits of adopting the multi- ple sourcing strategies in risky environments for a specific case. Fang et al. (2013) demonstrated that the addition of a third or more suppliers brings much less marginal benefits. Third, a number of scholars determined the supplier selection and order allocation to minimise supply risk using quantitative methods, such as fuzzy multicriteria decision-making model (Haleh and Hamidi 2011), newsvendor model (Giri 2011), unconstrained and constrained mathematical program- ming models (Chopra, Reinhardt, and Mohan 2007; Gümüş, Ray, and Gurnani 2012), stochastic linear programming model (Keren 2009), multistage stochastic programming model (Shi et al. 2011), mixed integer nonlinear programming model (Meena and Sarmah 2013), stochastic mixed integer programming approach (Sawik 2013a), mixed integer pro- gramming model (Sawik 2013b) and fuzzy stochastic multi-objective programming model (Wu et al. 2013). It was found that the suppliers with high disruption probability or with high prices are allocated the lowest fractions of the total demand or are not selected at all (Sawik 2013a). Besides, the cost of supplier has more influence on order allocation than supplier’s failure probability (Meena and Sarmah 2013).
The following articles also focus on supply risk mitigation but do not fall into the aforementioned subcategories, such as evaluation and selection of the optimal disruption management strategy (Tomlin 2006; Yang et al. 2009; Colicchia, Dallari, and Melacini 2010; Schmitt 2011), determination of the optimal inventory level or policies (Schmitt, Snyder, and Shen 2010; Glock and Ries 2013; Son and Orchard 2013), investigation of how managers mitigate global sourcing risks (Christopher et al. 2011; Vedel. and Ellegaard 2013), risk and quality control of a supplier (Tapiero 2007), allocation of supplier development investments among multiple suppliers (Talluri, Narasimhan, and Chung 2010), analysis of the impact of strategic information acquisition and sharing on supply risk mitigation (Wakolbinger and Cruz 2011), examination of the effectiveness of hybrid push–pull strategy for supply risk mitigation (Kim et al. 2012) and exploration of actions to proactively mitigate supplier insolvency risk (Grötsch, Blome, and Schleper 2013).
There are limitations associated with some of the above articles. Berger, Gerstenfeld, and Zeng (2004) assumed that the probability of the unique event that brings down a particular supplier is the same for all suppliers. Zsidisin and Smith (2005) only conducted a single case study. Ruiz- Keren (2009) studied a simple supply chain with two tiers in a single period environment. Yang et al. (2009), Schmitt (2011), Meena and Sarmah (2013) and Son and Orchard (2013) assumed the demand to be deterministic. Yu, Zeng, and Zhao (2009) assumed the supplier’s capacity to be infinite. Talluri, Narasimhan, and Chung (2010) suggested that their model is inappropriate for selecting new candidate suppliers for supplier development. Christopher et al. (2011) only considered the perspectives of the buying firm. Giri (2011) and Xanthopoulos, Vlachos, and Iakovou (2012) considered a single period and a single product in their studies. Glock and Ries (2013) focused on homogeneous suppliers, which restricts its applicability to industries with homogeneous mass products. Grötsch, Blome, and Schleper (2013) conducted a survey with comparatively small sample size. Sawik (2013a) did not consider the quality of supplied parts. Vedel. and Ellegaard (2013) analysed a limited set of in-depth interviews in one industry.
6.1.3.5 Transportation risk mitigation. There is only one study that we identified that relates to transportation risk mit- igation. Hishamuddin, Sarker, and Essam (2013) formulated an integer nonlinear programming model to determine the optimal production and ordering quantities for the supplier and retailer, as well as the duration for recovery subject to transportation disruption, which yields the minimum relevant costs of the system. Their results showed that the optimal
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recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters. They studied a simple two-tier supply chain with one supplier and one retailer, and assumed the demand to be deterministic.
6.1.3.6 Financial risk mitigation. Hofmann (2011) discussed the concept of natural hedging in supply chains. They found that natural hedging of currency and commodity price fluctuations can reduce supply chain vulnerability. Raghavan and Mishra (2011) constructed a nonlinear programming model to show that if one of the firms in the supply chain has sufficiently low cash, a joint decision on the loan amount is beneficial for the lender and the borrowing firms than an independent decision. Lundin (2012) applied the network flow modelling to mitigate the financial risks in the cash supply chains. Their results showed that centralisation from two to one central bank storage facilities led to unin- tended increases in transportation costs and financial risk.
There are limitations associated with the above articles. Hofmann (2011) used a brief literature review and a concep- tual research design in their study. Raghavan and Mishra (2011) considered a simple two-tier supply chain with one manufacturer and one retailer. Lundin (2012) only considered transportation and cash opportunity costs, while neglecting production and warehousing costs.
6.1.3.7 Information risk mitigation. Du, Lee, and Chen (2003) suggested companies to construct attribute correspon- dence matrices for databases so that they can share data with both upstream and downstream supply chain partners with- out leaking information to competitors. They only considered the vertical relationships of companies, while neglecting the horizontal relationships of new partners. Le et al. (2013) examined how data sharing has the potential to create risk for enterprises in retail supply chain collaboration, and proposed an association rule hiding algorithm to remove sensi- tive knowledge from the released database, and minimise the data distortion.
6.1.3.8 General risk mitigation. There exists a broad range of researches focusing on general risk mitigation, and there are two major categories. First, a number of scholars conducted empirical research or developed quantitative methods to investigate the effective ways of minimising supply chain risks. Their results showed that supply chain risks can be mitigated by increasing flexibility (Tang and Tomlin 2008; Manuj and Mentzer 2008; Skipper and Hanna 2009; Yang and Yang 2010; Chiu, Choi, and Li 2011; Talluri et al. 2013), building collaborative relationships among supply chain members (Faisal, Banwet, and Shankar 2006; Lavastre, Gunasekaran, and Spalanzani 2012; Leat and Revoredo-Giha 2013; He 2013; Chen, Sohal, and Prajogo 2013), sharing information in the supply chain (Christopher and Lee 2004; Faisal, Banwet, and Shankar 2006), managing suppliers (Xia, Ramachandran, and Gurnani 2011; Wagner and Silveira-Camargos 2012), adopting co-opetition (Bakshi and Kleindorfer 2009), increasing agility (Braunscheidel and Suresh 2009), implementing corporate social responsibility activities (Cruz 2009, 2013), understanding diverse organisa- tion cultures (Dowty and Wallace 2010) and applying a new pull system called the multi Kanban system for disassem- bly (Nakashima and Gupta 2012).
Second, several scholars developed quantitative models or framework to mitigate supply chain risks, such as a so-called super network model that integrates global supply chain networks with social networks (Cruz, Nagurney, and Wakolbinger 2006), the Supply Chain Risk Structure Model and the Supply Chain Risk Dynamics Model (Oehmen et al. 2009), the house of risk that combines the QFD and FMEA (Pujawan and Geraldin 2009), and a two-stage stochastic integer programming model (Hahn and Kuhn 2012b).
There are limitations associated with some of the above articles as well. Cruz, Nagurney, and Wakolbinger (2006) assumed that the manufacturers are involved in the production of a homogeneous product. Manuj and Mentzer (2008) focused on internal stakeholders only. Tang and Tomlin (2006) did not examine the benefits of a combination of different flexibility strategies. Bakshi and Kleindorfer (2009) and Chiu, Choi, and Li (2011) studied simple supply chains with only one supplier and one retailer. Braunscheidel and Suresh (2009) and Skipper and Hanna (2009) sur- veyed a limited range of respondents. Xia, Ramachandran, and Gurnani (2011) assumed exogenous wholesale prices. Lavastre, Gunasekaran, and Spalanzani (2012) used simple statistical tools (average and standard deviation). He (2013) used the additive demand function instead of the multiplicative demand model.
6.1.4 Risk monitoring
Comparatively, risk monitoring has attracted less attention in the literature. Zhang et al. (2011) developed an integrated abnormality diagnosis model, combining the fuzzy set theory and the radial base function neural network, to provide pre-warning signals of production quality in the food production supply chain. Their simulation results showed that the proposed pre-warning system can effectively identify abnormal data types, and accurately determine whether a warning
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should be issued. The limitations are that the model was not verified using real data and only quality risk was considered.
6.2 Integrated SCRM processes
In addition to the research discussed in Section 6.1, several researchers focused and studied integrated SCRM processes.
6.2.1 SCRM conceptual frameworks
A wide variety of qualitative and quantitative-based conceptual frameworks have been proposed to deal with more than one process of SCRM. Majority of these studies focused on two SCRM processes, such as risk identification and assess- ment (Peck 2005; Smith et al. 2007; Cheng and Kam 2008; Wagner and Bode 2008), risk identification and mitigation (Christopher and Peck 2004; Oke and Gopalakrishnan 2009) and risk assessment and mitigation (Kleindorfer and Saad 2005; Blome and Schoenherr 2011; Giannakis and Louis 2011; Speier et al. 2011; Hahn and Kuhn 2012a; Kumar and Havey 2013).
Kern et al. (2012) found that superior risk identification supports the subsequent risk assessment and this in turn leads to better risk mitigation. As there is a significant relationship between these three SCRM processes, more focus should be confined to three instead of two processes. Some researchers developed conceptual framework for the risk identification, assessment and mitigation processes (Ritchie and Brindley 2007; Foerstl et al. 2010; Bandaly et al. 2012; Kern et al. 2012; Ghadge et al. 2013). The five major components in their framework are risk identification, risk assess- ment, risk consequences, risk management response and risk performance outcomes.
A common drawback of the above articles is due to the conceptualisation of their frameworks. The frameworks were not verified using real case data or their implementation was not explicitly described. Besides, there are specific limita- tions related to some of the above articles. Peck (2005), Ritchie and Brindley (2007), Oke and Gopalakrishnan (2009) and Ghadge et al. (2013) all conducted a single case study. Smith et al. (2007) did not measure the consequences of information technology risks. Speier et al. (2011) did not examine the supply chain design initiatives from a cost perspective. Kern et al. (2012) used perceptual data from single informants.
6.2.2 SCRM procedures or approaches
Unlike the articles presented in Section 6.2.1 which are conceptual in nature, the following articles proposed detailed procedures or approaches for SCRM. Most of these articles applied qualitative approaches. There are five major steps for SCRM, such as analyse the supply chains (Harland, Brenchley, and Walker 2003; Cucchiella and Gastaldi 2006), identify the risk types and factors (Harland, Brenchley, and Walker 2003; Chopra and Sodhi 2004; Hallikas et al. 2004; Norrman and Jansson 2004; Cucchiella and Gastaldi 2006; Knemeyer, Zinn, and Eroglu 2009; Tummala and Schoenherr 2011), assess the likelihood of occurrence and overall impact (Harland, Brenchley, and Walker 2003; Hallikas et al. 2004; Norrman and Jansson 2004; Cucchiella and Gastaldi 2006; Knemeyer, Zinn, and Eroglu 2009; Tummala and Schoenherr 2011), select and implement risk mitigation strategies (Harland, Brenchley, and Walker 2003; Chopra and Sodhi 2004; Hallikas et al. 2004; Norrman and Jansson 2004; Cucchiella and Gastaldi 2006; Knemeyer, Zinn, and Eroglu 2009; Tummala and Schoenherr 2011) and continuously improve (Hallikas et al. 2004; Norrman and Jansson 2004; Tummala and Schoenherr 2011). Comparatively, risk identification, assessment and mitigation have attracted the most attention as found in section 6.2.1. More focus should be confined to pre-SCRM (analyse the supply chains) and post-SCRM (continuously improve).
There is relatively less work proposing quantitative approaches for the integrated SCRM. Also, the quantitative approaches only covered two SCRM processes, such as risk identification and assessment (Wu, Blackhurst, and Chidambaram 2006), risk assessment and mitigation (Tuncel and Alpan 2010) and risk identification and mitigation (Xia and Chen 2011; Diabat, Govindan, and Panicker 2012). Nevertheless, these quantitative approaches have their advantages in terms of quantifying the likelihood of occurrence and overall impact of risk factors with AHP (Wu, Blackhurst, and Chidambaram 2006) or the failure mode, effects and criticality analysis technique (Tuncel and Alpan 2010) and measuring the effectiveness and efficiency of risk mitigation strategies using the Petri-net-based simulation (Tuncel and Alpan 2010), risk identification and mitigation via ANP approach (Xia and Chen 2011) and interpretive structural modelling (Diabat, Govindan, and Panicker 2012).
A limitation associated with the qualitative articles is that most of them mainly explain the steps or phases of the SCRM approaches but not demonstrate how the approach can be applied (Chopra and Sodhi 2004; Hallikas et al. 2004; Cucchiella and Gastaldi 2006; Knemeyer, Zinn, and Eroglu 2009; Tummala and Schoenherr 2011). Only two of them
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clearly showed their approaches with the aid of real-life cases. For example, Norrman and Jansson (2004) demonstrated their four-step SCRM approach using the case of Ericsson. Sinha, Whitman, and Malzahn (2004) applied their supply chain operations reference model in the aerospace supply chains. Along the same lines, there are also drawbacks with some of the quantitative articles. Wu, Blackhurst, and Chidambaram (2006) limited the scope of their model to a single- tier environment. Tuncel and Alpan (2010) focused only on the point of view of the manufacturer. Diabat, Govindan, and Panicker (2012) stated that their model is highly dependent on the judgements of the expert team.
6.2.3 Supply chain network design
A number of articles formulated mathematical programming models for the optimal supply chain network design prob- lem, which consists of location, production, transportation and inventory decisions. The models identified and mitigated various risk types, such as demand risk (Goh, Lim, and Meng 2007; Poojari, Lucas, and Mitra 2008; Park, Lee, and Sung 2010; Georgiadis et al. 2011; Qiang and Nagurney 2012; Baghalian, Rezapour, and Farahani 2013), manufacturing risk (Qiang and Nagurney 2012; Kumar and Tiwari 2013), supply risk (Mak and Shen 2012; Baghalian, Rezapour, and Farahani 2013) and financial risk (Goh, Lim, and Meng 2007; Azaron et al. 2008; Azad and Davoudpour 2013).
A wide range of mathematical programming models has been developed, including multistage stochastic program- ming model (Goh, Lim, and Meng 2007), multi-objective stochastic programming model (Azaron et al. 2008), two-stage stochastic integer programming model (Poojari, Lucas, and Mitra 2008), integer nonlinear programming model (Park, Lee, and Sung 2010), mixed integer linear programming model (Georgiadis et al. 2011), stochastic linear programming model (Mak and Shen 2012), linear programming model (Qiang and Nagurney 2012), convex mixed integer program- ming model (Azad and Davoudpour 2013), stochastic mixed integer nonlinear programming model (Baghalian, Rezapour, and Farahani 2013) and mixed integer nonlinear programming model (Kumar and Tiwari 2013).
A common drawback is that most of the above articles did not apply their proposed models in real cases but simply used simulated data to prove their effectiveness and efficiency, except Baghalian, Rezapour, and Farahani (2013) who studied a real-life case in the rice industry of a country in the Middle East.
7. Observations
Among 224 journal articles reviewed in this paper, 208 articles applied quantitative or qualitative research methods to deal with the SCRM processes, including risk identification, risk assessment, risk mitigation and risk monitoring as discussed in Section 6. Some observations based on these 208 methodology articles are made in the following subsections.
7.1 Quantitative vs. qualitative methods
Figure 4 illustrates the distribution of number of journal articles applying quantitative and qualitative methods between 2003 and 2013. Quantitative methods consist of analytical (e.g. mathematical programming, simulation, etc.) and empiri- cal (e.g. exploratory factor analysis, structural equation modelling, etc.). There are 159 (76.44%) articles using quantita- tive methods and 49 (23.56%) applying qualitative methods. It is evident from Figure 4 that the number of articles using quantitative methods has been increasing since 2004, whereas the application of qualitative methods is steady. In 2013, the number of articles using quantitative methods is three times more than those applying qualitative methods. The only year in which the qualitative methods were more than quantitative methods is 2004. This underscores the fact that during initial years in the development of any new area, qualitative work plays an important role in terms of defin- ing concepts, identifying factors and developing frameworks followed by quantitative work focusing on assessment and evaluation tools. Most of the qualitative methods are applied for the risk identification (Cavinato 2004; Chopra and Sodhi 2004; Christopher and Peck 2004) and risk management philosophy (Christopher and Lee 2004; Giunipero and Eltantawy 2004; Zsidisin et al. 2004). Thus, it is obvious that the qualitative methods are mainly used to categorise or identify risk and construct SCRM ideas.
7.2 Applied quantitative methods
Quantitative methods have been developed and applied extensively for SCRM. While some researchers used a single method, other scholars have focused on integrated approaches, combining two or more methods. Tables 7 and 8 show the individual and integrated research methods, respectively. Among 159 quantitative-based articles, there are 119 arti- cles using individual methods, and the number of articles proposing integrated methods is 40, 74.84% vs. 25.16%.
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According to Table 7, the most popular individual analytical approach is mathematical programming (47 out of 119 arti- cles or 39.50%), followed by newsvendor model (10 out of 119 articles or 8.40%) and simulation (10 out of 119 articles or 8.40%). Besides, the most popular individual empirical approach is multiple regression model (3 out of 119 articles or 2.52%). Obviously, the empirical methods have attracted much less attention than the analytical methods, 7 vs. 112. One of the key reasons is that it is difficult for researchers to communicate with practitioners and gain access to industry to carry out empirical studies as mentioned in Section 3.
From Table 8, it is evident that the most prevalent integrated analytical approach is fuzzy-based multi-objective mathematical programming (3 out of 40 articles or 7.50%), followed by Fuzzy AHP (2 out of 40 articles or 5.00%) and Fuzzy TOPSIS (2 out of 40 articles or 5.00%). Among the integrated analytical methods, we see that fuzzy methods, AHP and DEA are the most common methods used along with others. This is not surprising as these methods are useful to tackle the difficulty of quantifying risk as it is inherently intangible in many cases. Similarly, the application of empirical methods is not as prevalent as that of analytical methods, 4 vs. 36.
Although the integrated methods have attracted less attention in the literature, certain techniques can be integrated to overcome the limitations or enhance the performance of the original methods. For instance, fuzzy set theory can be used to overcome the limitation of deterministic nature and exact value characteristic of multi-objective mathematical pro- gramming (Kumar, Vrat, and Shankar 2006; Wu et al. 2010; Ji and Zhu 2012). In addition, AHP can be incorporated into a QFD approach in order to ensure consistency of judgments (Ho, Dey, and Lockström 2011). Therefore, integrated methods will play a vital role in the area of SCRM in the future.
7.3 SCRM processes
All of the 208 quantitative- and qualitative-based articles are classified according to the four major SCRM processes in Table 9. This classification clearly identifies the most widely studied process, and more importantly, depicts the relation- ships between particular research methods and SCRM processes. First, Table 9 clearly shows that the majority of researchers studied the individual process (143 quantitative-based plus 28 qualitative-based articles, 171 articles in total or 82.21%) rather than the integrated processes (16 quantitative-based plus 21 qualitative-based articles, 37 articles in total or 17.79%). Even with the integrated processes, researchers focused on two SCRM processes generally as revealed in Section 6.2. As there is a significant relationship between all SCRM processes, more attention should be given to legitimately integrated processes instead of individual or fragmented processes. Second, among 171 articles focusing on the individual process, it is evident that the risk mitigation process (84 quantitative-based plus 17 qualitative-based arti- cles, 101 articles in total or 59.06%) has attracted the most attention. With respect to the quantitative methods, the application of analytical methods is much more than that of empirical methods (78 vs. 6 articles). Third, risk assessment is the second most widely studied process (56 quantitative-based plus 6 qualitative-based articles, 62 articles in total or 36.26%). Similarly, 54 out of 56 quantitative-based articles applied analytical methods in the risk assessment process. It is not a surprise that the risk assessment and mitigation processes are widely covered by quantitative methods since risk assessment includes quantifying the likelihood and impact of risky events. Similarly, the effectiveness of risk mitigation strategies requires explicit quantification of effectiveness and efficiency of such strategies. Moreover, risk mitigation
Figure 4. Distribution of number of quantitative and qualitative methods over the last 11 years.
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Table 7. Summary of individual quantitative methods.
Methods No. of articles Authors
1. Analytical methods 112 Mathematical programming 47
• Unconstrained and constrained mathematical programming
• Linear programming • Nonlinear programming • Integer nonlinear programming • Stochastic linear programming • Stochastic integer linear programming • Max-mix linear programming • Mixed integer linear programming • Multi-objective mixed integer linear
programming • Multi-stage stochastic programming • Two-stage stochastic integer programming • Convex mixed integer programming • Integer linear programming • Multi-objective stochastic programming • Multi-period deterministic linear programming • Parametric linear programming • Quadratic programming • Stochastic dynamic programming
9 Tomlin (2006), Chopra, Reinhardt, and Mohan (2007), He and Zhang (2008), Tang and Tomlin (2008), Chen and Yano (2010), Iakovou, Vlachos, and Xanthopoulos (2010), Wang, Gilland, and Tomlin (2011), Gümüş, Ray, and Gurnani (2012), Xanthopoulos, Vlachos, and Iakovou (2012)
5 Kaya and Özer (2009), Meena, Sarmah, and Sarkar (2011), Schmitt (2011), Qiang and Nagurney (2012), Radke and Tseng (2012)
5 Cruz, Nagurney, and Wakolbinger (2006), Cruz (2009), Raghavan and Mishra (2011), Kang and Kim (2012), Kim et al. (2012)
4 Baghalian, Rezapour, and Farahani (2013), Hishamuddin, Sarker, and Essam (2013), Kumar and Tiwari (2013), Meena and Sarmah (2013)
4 Sodhi (2005), Keren (2009), Sodhi and Tang (2009), Mak and Shen (2012)
3 Snyder, Daskin, and Teo (2007), Lejeune (2008), Sawik (2013a) 2 Talluri and Narasimhan (2003), Yang et al. (2009) 2 Georgiadis et al. (2011), Sawik (2013b) 2 Ravindran et al. (2010), Wakolbinger and Cruz (2011) 2 Goh, Lim, and Meng (2007), Shi et al. (2011) 2 Poojari, Lucas, and Mitra (2008), Hahn and Kuhn (2012b) 1 Azad and Davoudpour (2013) 1 Hale and Moberg (2005) 1 Azaron et al. (2008) 1 Ben-Tal et al. (2011) 1 Bogataj and Bogataj (2007) 1 Talluri, Narasimhan, and Chung (2010) 1 Kenné, Dejax, and Gharbi (2012)
Newsvendor model 10 Cachon (2004), Rao, Swaminathan, and Zhang (2005), Chen, Chen, and Chen (2006), Li (2007), Tomlin (2009), Giri (2011), Xia, Ramachandran, and Gurnani (2011), Arcelus, Kumar, and Srinivasan (2012), Tang, Musa, and Li (2012), Cheong and Song (2013)
Simulation 10 Smaros et al. (2003), Crnkovic, Tayi, and Ballou (2008), Kull and Closs (2008), Colicchia, Dallari, and Melacini (2010), Durowoju, Chan, and Wang (2012), Schmitt and Singh (2012), Berle, Norstad, and Asbjørnslett (2013), Glock and Ries (2013), Kim (2013), Son and Orchard (2013)
Analytic hierarchy process 3 Wu, Blackhurst, and Chidambaram (2006), Gaudenzi and Borghesi (2006), Schoenherr, Tummala, and Harrison (2008)
Game theory 3 Xiao and Yang (2008, 2009), Li, Wang, and Cheng (2010) Decision tree approach 2 Berger, Gerstenfeld, and Zeng (2004), Ruiz-Torres and Mahmoodi
(2007) Interpretive structural modelling 2 Faisal, Banwet, and Shankar (2006), Diabat, Govindan, and Panicker
(2012) Variational inequality model 2 Liu and Nagurney (2011), Cruz (2013) Analytic network process 1 Xia and Chen (2011) Automatic pipeline inventory and order-based
production control system algorithm 1 Towill (2005)
Association rule hiding algorithm 1 Le et al. (2013) Approximate dynamic programming algorithm 1 Fang et al. (2013) Bayesian networks 1 Lockamy and McCormack (2010) Buyer’s risk adjustment quantity discount model 1 Shin and Benton (2007) Cash conversion cycle 1 Tsai (2008) Comparisons of chance-constrained programming,
data envelopment analysis, and multi-objective programming models
1 Wu and Olson (2008)
Constrained multi-item (Q, r) inventory model 1 Betts and Johnston (2005) Disruption analysis network approach 1 Wu, Blackhurst, and O’grady (2007)
(Continued)
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naturally lends itself to prediction and prescription, which quantitative methods focus on. Surprisingly, in the last eleven years, there is only one article studying the risk monitoring process in a comprehensive manner (Zhang et al. 2011). Fourth, among 37 articles focusing on the integrated processes, the application of qualitative methods is slightly more than that of quantitative methods (21 vs. 16 articles). As discussed in Sections 6.2.1 and 6.2.2, the qualitative approaches used in the integrated processes are conceptual in nature or they simply explain the steps or phases but do not demonstrate how the approaches can be applied.
7.4 Risk types focused
Among the 208 methodology-based articles, 140 of them focused on specific risk types, whereas 68 of them simply proposed methods to deal with generic risks. Table 10 demonstrates the distribution of 140 articles in terms of risk types. The most widely studied risk type is the supply risk (70 articles). As mentioned in Sections 6.1.2.4 and 6.1.3.4, most of the researchers studied the supplier assessment and mitigation problems with risk considerations. Demand risk (39 articles) and manufacturing risk (13 articles) are the second and third commonly focused risk types, respectively. This is consistent with the findings in Section 4 that demand, manufacturing and supply risks have attracted the most attention. Besides, the majority of the articles focused on a particular risk type. Only three out of 140 articles studied two risk types simultaneously (Goh, Lim, and Meng 2007; Qiang and Nagurney 2012; Baghalian, Rezapour, and Farahani 2013). This is a gap that can be addressed by future work in this domain.
Table 7. (Continued).
Methods No. of articles Authors
Dynamic system model 1 Huang, Chou, and Chang (2009) Expected profit functions 1 Yu, Zeng, and Zhao (2009) Fault tree approach 1 Cigolini and Rossi (2010) Federated databases 1 Du, Lee, and Chen (2003) Harsanyi–Selten–Nash bargaining framework 1 Bakshi and Kleindorfer (2009) Hybrid Petri-net 1 Khilwani, Tiwari, and Sabuncuoglu (2011) Margin incremental analysis 1 Tse and Tan (2011) Macro-prediction market model 1 Guo, Fang, and Whinston (2006) Mean–variance analysis 1 Chiu, Choi, and Li (2011) Multicriteria scoring models 1 Blackhurst, Scheibe, and Johnson (2008), Multi-Kanban system for disassembly 1 Nakashima and Gupta (2012) Network flow modelling 1 Lundin (2012) Pugh method adaption 1 Dietrich and Cudney (2011) Principal-agent model 1 Lei, Li, and Liu (2012) P-chart solution model 1 Sun, Matsui, and Yin (2012) Random yield model 1 He (2013) Safety stock evaluation method 1 Talluri, Cetin, and Gardner (2004) Single stochastic period approximation 1 Schmitt, Snyder, and Shen (2010) Specifying sources of risk vulnerabilities, assessment
and mitigation framework 1 Kleindorfer and Saad (2005)
Stochastic economic-order quantity model 1 Ballou and Burnetas (2003) Supply network opportunity assessment package
methodology 1 Brun et al. (2006)
Supply chain resilience assessment and management 1 Pettit, Croxton, and Fiksel (2013) Two-period financial modelling 1 Aggarwal and Ganeshan (2007) 2. Empirical methods 7 Multiple regression model 3 Hung and Ryu (2008), Laeequddin et al. (2009), Skipper and Hanna
(2009) Partial least squares analysis 1 Kern et al. (2012) Quantitative survey analysis 1 Speier et al. (2011) Real options theory 1 Hult, Craighead, and Ketchen (2010) Statistical analysis 1 Lavastre, Gunasekaran, and Spalanzani (2012) Total 119
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7.5 Application areas
From Table 11, we observe that 18 areas or industries have been studied. The majority of the articles focused on a par- ticular industry (59 out of 67 articles, or 88.06%). Eight out of 67 articles focused on two or more industries (Zsidisin et al. 2004; Zsidisin, Melnyk, and Ragatz 2005; Wagner and Bode 2008; Blos et al. 2009; Wagner and Neshat 2010; Blome and Schoenherr 2011; Christopher et al. 2011; Kern et al. 2012). The most popular application area is the automotive industry (15 articles), followed by electronics industry (12 articles) and aerospace industry (9 articles). It shows that the SCRM methods have been widely applied to manufacturing supply chains (84 articles) whereas service
Table 8. Summary of integrated quantitative methods.
Methods No. of articles Authors
1. Analytical methods 36 Fuzzy set theory; Multi-objective mathematical programming 3 Kumar, Vrat, and Shankar (2006), Wu
et al. (2010), Ji and Zhu (2012) Analytic hierarchy process; Fuzzy set theory 2 Chan and Kumar (2007), Wang et al.
(2012) Fuzzy analytic hierarchy process; Fuzzy technique for order preference by
similarity to the ideal solution 2 Samvedi, Jain, and Chan (2013),
Viswanadham and Samvedi (2013) Analytic hierarchy process; Goal programming 1 Kull and Talluri (2008) Analytic hierarchy process; Quality function deployment 1 Ho, Dey, and Lockström (2011) Analytic hierarchy process; A modified failure mode and effect analysis 1 Chen and Wu (2013) Analytic network process; Fuzzy goal programming; Five forces analysis;
Value-at-risk 1 Hung (2011)
Artificial bee colony technique; Genetic algorithms; Particle swarm optimisation 1 Kumar, Tiwari, and Babiceanu (2010) Chance-constrained data envelopment analysis; Nonlinear programming 1 Talluri, Narasimhan, and Nair (2006) Data envelopment analysis; Fuzzy data envelopment analysis; Chance-constrained
data envelopment analysis; Monte Carlo simulation 1 Azadeh and Alem (2010)
Data envelopment analysis; Monte Carlo simulation 1 Olson and Wu (2011) Data envelopment analysis; Value-at-risk 1 Wu and Olson (2010) Data envelopment analysis; Simulation; Nonparametric statistical methods 1 Talluri et al. (2013) Decision tree approach; Mathematical programming 1 Ruiz-Torres, Mahmoodi, and Zeng
(2013) Extended dynamic demand forecast and inventory model 1 Reiner and Fichtinger (2009) Economic Value Added; Stochastic programming 1 Hahn and Kuhn (2012a) Failure mode, effects and criticality analysis technique; Petri-nets 1 Tuncel and Alpan (2010) Failure mode and effect analysis; Quality function deployment 1 Pujawan and Geraldin (2009) Forecasting and statistical techniques 1 Sucky (2009) Fuzzy set theory; Radial base function neural network 1 Zhang et al. (2011) Fuzzy set theory; Multicriteria decision-making 1 Haleh and Hamidi (2011) Fuzzy set theory; Stochastic multi-objective programming 1 Wu et al. (2013) Fuzzy set theory; Failure mode and effect analysis; Ordered weighted averaging 1 Chaudhuri, Mohanty, and Singh (2013) Generalised Autoregressive Conditional Heteroskedasticity; Vector Auto
Regression 1 Datta et al. (2007)
Genetic algorithm; Statistical methods 1 Sayed, Gabbar, and Miyazaki (2009) Graph theory; Supply chain vulnerability index 1 Wagner and Neshat (2010) Lagrangian relaxation; Integer nonlinear programming model 1 Park, Lee, and Sung (2010) Monte Carlo simulation; Real options approach; Sensitivity analysis 1 Costantino and Pellegrino (2010) Multi-objective optimisation; Six Sigma 1 Franca et al. (2010) Neyman–Pearson theory; Statistical quality control 1 Tapiero (2007) Supply chain risk structure model; Supply chain risk dynamics model 1 Oehmen et al. (2009) Variational inequality model; Capital asset pricing model; Net present value 1 Liu and Cruz (2012) 2. Empirical methods 4 Analytic hierarchy process; Survey; Wards’ and K-mean clustering; Nonparametric
Spearman rank correlation test 1 Tsai, Liao, and Han (2008)
Cluster analysis; Factor analysis 1 Hallikas et al. (2005) Exploratory factor analysis; Regression models; Reliability tests 1 Zsidisin and Ellram (2003) Structural equation modelling technique; Partial least squares analysis 1 Braunscheidel and Suresh (2009) Total 40
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al . (2 0 0 7 ),
E ll eg aa rd
(2 0 0 8 ), Jü tt n er
an d M ak la n (2 0 11 ),
Jo h n so n , E ll io tt , an d D ra k e (2 0 1 3 ), W ie n g ar te n ,
P ag el l, an d F y n es
(2 0 1 3 )
A n a ly ti ca l: B al lo u an d B u rn et as
(2 0 0 3 ), S m ar o s et
al . (2 0 0 3 ), T al lu ri an d N ar as im
h an
(2 0 0 3 ), C ac h o n
(2 0 0 4 ), T al lu ri , C et in , an d G ar d n er
(2 0 0 4 ), B et ts an d Jo h n st o n (2 0 0 5 ), S o d h i (2 0 0 5 ), B ru n et
al .
(2 0 0 6 ), K u m ar , V ra t, an d S h an k ar
(2 0 0 6 ), T al lu ri , N ar as im
h an , an d N ai r (2 0 0 6 ), B o g at aj
an d B o g at aj
(2 0 0 7 ), C h an
an d K u m ar
(2 0 0 7 ), W u , B la ck h u rs t, an d O ’g ra d y (2 0 0 7 ), B la ck h u rs t, S ch ei b e,
an d
Jo h n so n (2 0 0 8 ), K u ll an d C lo ss
(2 0 0 8 ), K u ll an d T al lu ri (2 0 0 8 ), S ch o en h er r, T u m m al a,
an d H ar ri so n
(2 0 0 8 ), T sa i (2 0 0 8 ), W u an d O ls o n (2 0 0 8 ), X ia o an d Y an g (2 0 0 8 ), R ei n er
an d F ic h ti n g er
(2 0 0 9 ),
S u ck y (2 0 0 9 ), T o m li n (2 0 0 9 ), A za d eh
an d A le m
( 2 0 1 0 ), C ig o li n i an d R o ss i (2 0 1 0 ), F ra n ca
et al .
(2 0 1 0 ), Ia k o v o u , V la ch o s, an d X an th o p o u lo s (2 0 1 0 ), K u m ar , T iw ar i, an d B ab ic ea n u (2 0 1 0 ), L o ck am
y an d M cC
o rm
ac k (2 0 1 0 ), R av in d ra n et
al . (2 0 1 0 ), W ag n er
an d N es h at
(2 0 1 0 ), W u an d O ls o n (2 0 1 0 ),
W u et
al . (2 0 1 0 ), D ie tr ic h an d C u d n ey
(2 0 11 ), H o , D ey , an d L o ck st rö m
(2 0 11 ), K h il w an i, T iw ar i, an d
S ab u n cu o g lu
(2 0 11 ), L iu
an d N ag u rn ey
(2 0 11 ), M ee n a,
S ar m ah , an d S ar k ar
(2 0 11 ), O ls o n an d W u
(2 0 11 ), T se
an d T an
(2 0 11 ), W an g , G il la n d , an d T o m li n (2 0 11 ), D u ro w o ju , C h an , an d W an g (2 0 1 2 ),
Ji an d Z h u (2 0 1 2 ), L iu
an d C ru z (2 0 1 2 ), R ad k e an d T se n g (2 0 1 2 ), W an g et
al . (2 0 1 2 ), B er le , N o rs ta d ,
an d A sb jø rn sl et t (2 0 1 3 ), C h au d h u ri , M o h an ty , an d S in g h (2 0 1 3 ), C h en
an d W u (2 0 1 3 ), C h eo n g an d
S o n g (2 0 1 3 ), P et ti t, C ro x to n , an d F ik se l (2 0 1 3 ), R u iz -T o rr es , M ah m o o d i, an d Z en g (2 0 1 3 ), S am
v ed i,
Ja in , an d C h an
(2 0 1 3 ), V is w an ad h am
an d S am
v ed i (2 0 1 3 )
E m p ir ic a l: L ae eq u d d in
et al . (2 0 0 9 ), H u lt , C ra ig h ea d , an d K et ch en
(2 0 1 0 )
N u m b er : 6
P er ce n ta g e:
2 .8 8 %
N u m b er : 5 6
P er ce n ta g e:
2 6 .9 2 %
M it ig at io n
C h ri st o p h er
an d L ee
(2 0 0 4 ), G iu n ip er o an d
E lt an ta w y (2 0 0 4 ), Z si d is in
an d S m it h (2 0 0 5 ),
Z si d is in , M el n y k , an d R ag at z (2 0 0 5 ), C h o i an d
K ra u se
(2 0 0 6 ), T an g (2 0 0 6 b ), K h an , C h ri st o p h er ,
an d B u rn es
(2 0 0 8 ), M an u j an d M en tz er
(2 0 0 8 ),
D o w ty
an d W al la ce
(2 0 1 0 ), Y an g an d Y an g
(2 0 1 0 ), C h ri st o p h er
et al . (2 0 11 ), H o fm
an n (2 0 11 ),
W ag n er
an d S il v ei ra -C am
ar g o s (2 0 1 2 ), C h en ,
S o h al , an d P ra jo g o (2 0 1 3 ), G rö ts ch , B lo m e,
an d
S ch le p er
(2 0 1 3 ), L ea t an d R ev o re d o -G
ih a (2 0 1 3 ),
V ed el . an d E ll eg aa rd
(2 0 1 3 )
A n a ly ti ca l: D u , L ee , an d C h en
(2 0 0 3 ), B er g er , G er st en fe ld , an d Z en g (2 0 0 4 ), H al e an d M o b er g (2 0 0 5 ),
R ao , S w am
in at h an , an d Z h an g (2 0 0 5 ), T o w il l (2 0 0 5 ), C h en , C h en , an d C h en
(2 0 0 6 ), C ru z,
N ag u rn ey ,
an d W ak o lb in g er
(2 0 0 6 ), F ai sa l, B an w et , an d S h an k ar
(2 0 0 6 ), G u o , F an g , an d W h in st o n (2 0 0 6 ),
T o m li n (2 0 0 6 ), A g g ar w al
an d G an es h an
(2 0 0 7 ), C h o p ra , R ei n h ar d t, an d M o h an
(2 0 0 7 ), D at ta
et al .
(2 0 0 7 ), L i (2 0 0 7 ), R u iz -T o rr es
an d M ah m o o d i (2 0 0 7 ), S h in
an d B en to n (2 0 0 7 ), S n y d er , D as k in , an d
T eo
(2 0 0 7 ), T ap ie ro
(2 0 0 7 ), C rn k o v ic , T ay i, an d B al lo u (2 0 0 8 ), H e an d Z h an g (2 0 0 8 ), L ej eu n e (2 0 0 8 ),
T an g an d T o m li n (2 0 0 8 ), B ak sh i an d K le in d o rf er
(2 0 0 9 ), C ru z (2 0 0 9 ), H u an g , C h o u , an d C h an g
(2 0 0 9 ), K ay a an d Ö ze r (2 0 0 9 ), K er en
(2 0 0 9 ), O eh m en
et al . (2 0 0 9 ), P u ja w an
an d G er al d in
(2 0 0 9 ),
S ay ed , G ab b ar , an d M iy az ak i (2 0 0 9 ), S o d h i an d T an g (2 0 0 9 ), X ia o an d Y an g (2 0 0 9 ), Y an g et
al .
(2 0 0 9 ), Y u , Z en g , an d Z h ao
(2 0 0 9 ), C h en
an d Y an o ( 2 0 1 0 ), C o li cc h ia , D al la ri , an d M el ac in i (2 0 1 0 ),
C o st an ti n o an d P el le g ri n o (2 0 1 0 ), L i, W an g , an d C h en g (2 0 1 0 ), P ar k , L ee , an d S u n g (2 0 1 0 ), S ch m it t,
S n y d er , an d S h en
(2 0 1 0 ), T al lu ri , N ar as im
h an , an d C h u n g (2 0 1 0 ), B en -T al
et al . (2 0 11 ), C h iu , C h o i,
an d L i (2 0 11 ), G ir i (2 0 11 ), H al eh
an d H am
id i (2 0 11 ), H u n g (2 0 11 ), R ag h av an
an d M is h ra
(2 0 11 ),
S ch m it t (2 0 11 ), S h i et
al . (2 0 11 ), W ak o lb in g er
an d C ru z (2 0 11 ), X ia , R am
ac h an d ra n , an d G u rn an i
(2 0 11 ), A rc el u s, K u m ar , an d S ri n iv as an
(2 0 1 2 ), G ü m ü ş, R ay , an d G u rn an i (2 0 1 2 ), H ah n an d K u h n
(2 0 1 2 b ), K an g an d K im
(2 0 1 2 ), K en n é,
D ej ax , an d G h ar b i (2 0 1 2 ), K im
et al . (2 0 1 2 ), L ei , L i, an d L iu
(2 0 1 2 ), L u n d in
(2 0 1 2 ), M ak
an d S h en
(2 0 1 2 ), N ak as h im
a an d G u p ta
(2 0 1 2 ), S ch m it t an d S in g h
(2 0 1 2 ), S u n , M at su i, an d Y in
(2 0 1 2 ), T an g , M u sa , an d L i (2 0 1 2 ), X an th o p o u lo s, V la ch o s, an d Ia k o v o u
(2 0 1 2 ), C ru z (2 0 1 3 ), F an g et
al . (2 0 1 3 ), G lo ck
an d R ie s (2 0 1 3 ), H e (2 0 1 3 ), H is h am
u d d in , S ar k er , an d
E ss am
(2 0 1 3 ), K im
(2 0 1 3 ), L e et
al . (2 0 1 3 ), M ee n a an d S ar m ah
(2 0 1 3 ), S aw
ik (2 0 1 3 a, b ), S o n an d
O rc h ar d (2 0 1 3 ), T al lu ri et
al . (2 0 1 3 ), W u et
al . (2 0 1 3 )
E m p ir ic a l: Z si d is in
an d E ll ra m
(2 0 0 3 ), H al li k as
et al . (2 0 0 5 ), H u n g an d R y u (2 0 0 8 ), B ra u n sc h ei d el
an d S u re sh
(2 0 0 9 ), S k ip p er
an d H an n a (2 0 0 9 ), L av as tr e,
G u n as ek ar an , an d S p al an za n i (2 0 1 2 )
(C o n ti n u ed )
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T ab le
9 .
(C o n ti n u ed ).
S C R M
p ro ce ss es
Q u al it at iv e m et h o d s
Q u an ti ta ti v e m et h o d s
N u m b er : 1 7
P er ce n ta g e:
8 .1 7 %
N u m b er : 8 4
P er ce n ta g e:
4 0 .3 8 %
M o n it o ri n g
A n a ly ti ca l: Z h an g et
al . (2 0 11 )
N u m b er : 0
P er ce n ta g e:
0 N u m b er : 1
P er ce n ta g e:
0 .4 8 %
In te g ra te d p ro ce ss es
H ar la n d , B re n ch le y,
an d W al k er
(2 0 0 3 ), C h o p ra
an d S o d h i (2 0 0 4 ), C h ri st o p h er
an d P ec k (2 0 0 4 ),
H al li k as
et al . (2 0 0 4 ), N o rr m an
an d Ja n ss o n
(2 0 0 4 ), S in h a,
W h it m an , an d M al za h n (2 0 0 4 ),
P ec k (2 0 0 5 ), C u cc h ie ll a an d G as ta ld i (2 0 0 6 ),
R it ch ie
an d B ri n d le y (2 0 0 7 ), S m it h et
al . (2 0 0 7 ),
C h en g an d K am
(2 0 0 8 ), W ag n er
an d B o d e (2 0 0 8 ),
K n em
ey er , Z in n , an d E ro g lu
(2 0 0 9 ), O k e an d
G o p al ak ri sh n an
(2 0 0 9 ), F o er st l et
al . (2 0 1 0 ),
B lo m e an d S ch o en h er r (2 0 11 ), G ia n n ak is an d
L o u is (2 0 11 ), T u m m al a an d S ch o en h er r (2 0 11 ),
B an d al y et
al . (2 0 1 2 ), G h ad g e et
al . (2 0 1 3 ),
K u m ar
an d H av ey
(2 0 1 3 )
A n a ly ti ca l: K le in d o rf er
an d S aa d (2 0 0 5 ), W u , B la ck h u rs t, an d C h id am
b ar am
(2 0 0 6 ), G o h , L im
, an d
M en g (2 0 0 7 ), A za ro n et
al . (2 0 0 8 ), P o o ja ri , L u ca s, an d M it ra
(2 0 0 8 ), T u n ce l an d A lp an
(2 0 1 0 ),
G eo rg ia d is et
al . (2 0 11 ), X ia
an d C h en
(2 0 11 ), D ia b at , G o v in d an , an d P an ic k er
(2 0 1 2 ), H ah n an d
K u h n (2 0 1 2 a) , Q ia n g an d N ag u rn ey
(2 0 1 2 ), A za d an d D av o u d p o u r (2 0 1 3 ), B ag h al ia n , R ez ap o u r, an d
F ar ah an i (2 0 1 3 ), K u m ar
an d T iw ar i (2 0 1 3 )
E m p ir ic a l: S p ei er
et al . (2 0 11 ), K er n et
al . (2 0 1 2 )
N u m b er : 2 1
P er ce n ta g e:
1 0 .1 0%
N u m b er : 1 6
P er ce n ta g e:
7 .6 9 %
T o ta l
N u m b er : 4 9
P er ce n ta g e:
2 3 .5 6%
N u m b er : 1 5 9
P er ce n ta g e:
7 6 .4 4 %
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supply chains (6 articles) are fairly unexplored. Given the importance of service supply chains, it is critical for research- ers to place more focus on managing risks in this area. Besides, all the applications are limited to the private sector and there is no article focusing on the public sector.
8. Research gaps and recommendations
Opportunities for further research in the area of SCRM are abundant. We find that the supply risk holds a very large proportion of all risk types, while other risk types received limited consideration, especially the infrastructural risk. There is clearly a research gap in the domain of infrastructural risks such as transportation, information and financial risk as well as macro risks. Since infrastructure plays a critical role in managing supply chain effectively, the emphasis
Table 10. Summary of risk types studied by the quantitative and qualitative SCRM methods.
Risk types No. of articles References
Supply risk 70 Talluri and Narasimhan (2003), Zsidisin and Ellram (2003), Berger, Gerstenfeld, and Zeng (2004), Giunipero and Eltantawy (2004), Zsidisin et al. (2004), Hallikas et al. (2005), Zsidisin and Smith (2005), Zsidisin, Melnyk, and Ragatz (2005), Choi and Krause (2006), Kumar, Vrat, and Shankar (2006), Talluri, Narasimhan, and Nair (2006), Tomlin (2006), Wu, Blackhurst, and Chidambaram (2006), Chan and Kumar (2007), Chopra, Reinhardt, and Mohan (2007), Ruiz-Torres and Mahmoodi (2007), Tapiero (2007), Blackhurst, Scheibe, and Johnson (2008), Ellegaard (2008), Kull and Closs (2008), Kull and Talluri (2008), Schoenherr, Tummala, and Harrison (2008), Wu and Olson (2008), Keren (2009), Trkman and McCormack (2009), Yang et al. (2009), Yu, Zeng, and Zhao (2009), Azadeh and Alem (2010), Colicchia, Dallari, and Melacini (2010), Costantino and Pellegrino (2010), Foerstl et al. (2010), Iakovou, Vlachos, and Xanthopoulos (2010), Li, Wang, and Cheng (2010), Lockamy and McCormack (2010), Ravindran et al. (2010), Schmitt, Snyder, and Shen (2010), Talluri, Narasimhan, and Chung (2010), Wu et al. (2010), Wu and Olson (2010), Blome and Schoenherr (2011), Christopher et al. (2011), Giri (2011), Haleh and Hamidi (2011), Ho, Dey, and Lockström (2011), Meena, Sarmah, and Sarkar (2011), Schmitt (2011), Shi et al. (2011), Wakolbinger and Cruz (2011), Gümüş, Ray, and Gurnani (2012), Kern et al. (2012), Kim et al. (2012), Mak and Shen (2012), Xanthopoulos, Vlachos, and Iakovou (2012), Baghalian, Rezapour, and Farahani (2013), Chaudhuri, Mohanty, and Singh (2013), Chen and Wu (2013), Cheong and Song (2013), Fang et al. (2013), Grötsch, Blome, and Schleper (2013), Glock and Ries (2013), Johnson, Elliott, and Drake (2013), Meena and Sarmah (2013), Ruiz-Torres, Mahmoodi, and Zeng (2013), Sawik (2013a,b), Son and Orchard (2013), Vedel. and Ellegaard (2013), Viswanadham and Samvedi (2013), Wiengarten, Pagell, and Fynes (2013), Wu et al. (2013)
Demand risk 39 Ballou and Burnetas (2003), Smaros et al. (2003), Cachon (2004), Talluri, Cetin, and Gardner (2004), Betts and Johnston (2005), Rao, Swaminathan, and Zhang (2005), Sodhi (2005), Towill (2005), Chen, Chen, and Chen (2006), Guo, Fang, and Whinston (2006), Aggarwal and Ganeshan (2007), Datta et al. (2007), Goh, Lim, and Meng (2007), Shin and Benton (2007), Snyder, Daskin, and Teo (2007), Crnkovic, Tayi, and Ballou (2008), Hung and Ryu (2008), Lejeune (2008), Poojari, Lucas, and Mitra (2008), Xiao and Yang (2008), Huang, Chou, and Chang (2009), Reiner and Fichtinger (2009), Sayed, Gabbar, and Miyazaki (2009), Sodhi and Tang (2009), Sucky (2009), Xiao and Yang (2009), Chen and Yano (2010), Park, Lee, and Sung (2010), Ben-Tal et al. (2011), Georgiadis et al. (2011), Arcelus, Kumar, and Srinivasan (2012), Kang and Kim (2012), Lei, Li, and Liu (2012), Qiang and Nagurney (2012), Radke and Tseng (2012), Schmitt and Singh (2012), Tang, Musa, and Li (2012), Baghalian, Rezapour, and Farahani (2013), Kim (2013)
Manufacturing risk
13 Li (2007), He and Zhang (2008), Khan, Christopher, and Burnes (2008), Kaya and Özer (2009), Cigolini and Rossi (2010), Dietrich and Cudney (2011), Hung (2011), Tse and Tan (2011), Zhang et al. (2011), Kenné, Dejax, and Gharbi (2012), Qiang and Nagurney (2012), Sun, Matsui, and Yin (2012), Kumar and Tiwari (2013)
Financial risk 10 Goh, Lim, and Meng (2007), Azaron et al. (2008), Tsai (2008), Franca et al. (2010), Hofmann (2011), Liu and Nagurney (2011), Raghavan and Mishra (2011), Liu and Cruz (2012), Lundin (2012), Azad and Davoudpour (2013)
Macro risk 6 Hale and Moberg (2005), Kleindorfer and Saad (2005), Tang (2006b), Knemeyer, Zinn, and Eroglu (2009), Ji and Zhu (2012), Kumar and Havey (2013)
Information risk
4 Du, Lee, and Chen (2003), Smith et al. (2007), Durowoju, Chan, and Wang (2012), Le et al. (2013)
Transportation risk
1 Hishamuddin, Sarker, and Essam (2013)
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on managing and mitigating these types of risks is important as we move forward. Also, in comparison to demand and supply risks, the area of manufacturing or process risk has not received much attention, which is another key avenue for future research.
Every organisation may face all the five suggested risk types. While focusing on a particular risk type has its advan- tages, interdependencies and interrelationships among various risk types is certainly an issue that needs to be further explored. Investigating the joint impact of such risks can lead to better management of supply chains than treating each risk type in isolation. This is an area that we recommend scholars in this domain to consider as we move forward.
There exists an abundant set of factors, which would give rise to supply chain disruptions. However, there is lack of research measuring the correlations between risk factors and corresponding risk types, or the probability of occurrence of particular risk types associated with their factors. Field and case studies are necessary to investigate and estimate such correlations and focus on developing methods to evaluate the probabilities of occurrence of particular risk types so that methods can be developed to appease such risks through mitigation strategies.
Although there is an increasing amount of research in the area of SCRM, most of them are theoretical in nature. For instance, a wide variety of SCRM management methods and conceptual frameworks have emerged, however, they have not been validated empirically. To fill this gap, scholars could use primary data to investigate the applicability and effectiveness of those SCRM models in practical situations. Besides, scholars could also assess the adaptability and flexibility of the SCRM models by applying them to different companies in the same or different sectors, and in the same or different countries.
Some sectors have been underrepresented over the past decade. For instance, the public sector has not been fully investigated. As many governments in the globe are exposed to various internal and external risks, further knowledge
Table 11. Summary of industries studied by the quantitative and qualitative SCRM methods.
Application areas No. of articles References
Automotive 15 Kumar, Vrat, and Shankar (2006), Blackhurst, Scheibe, and Johnson (2008), Kull and Talluri (2008), Wagner and Bode (2008), Blos et al. (2009), Trkman and McCormack (2009), Lockamy and McCormack (2010), Wagner and Neshat (2010), Blome and Schoenherr (2011), Ho, Dey, and Lockström (2011), Hofmann (2011), Kern et al. (2012), Sun, Matsui, and Yin (2012), Wagner and Silveira-Camargos (2012), Grötsch, Blome, and Schleper (2013)
Electronics 12 Harland, Brenchley, and Walker (2003), Zsidisin et al. (2004), Sodhi (2005), Zsidisin, Melnyk, and Ragatz (2005), Wagner and Bode (2008), Blos et al. (2009), Huang, Chou, and Chang (2009), Blome and Schoenherr (2011), Christopher et al. (2011), Kern et al. (2012), Kim et al. (2012), Chen and Wu (2013)
Aerospace 9 Sinha, Whitman, and Malzahn (2004), Zsidisin et al. (2004), Zsidisin and Smith (2005), Zsidisin, Melnyk, and Ragatz (2005), Wagner and Bode (2008), Christopher et al. (2011), Dietrich and Cudney (2011), Kern et al. (2012), Chaudhuri, Mohanty, and Singh (2013)
Fashion 6 Brun et al. (2006), Khan, Christopher, and Burnes (2008), Blome and Schoenherr (2011), Christopher et al. (2011), Wang et al. (2012), Vedel. and Ellegaard (2013)
Food 6 Wagner and Bode (2008), Laeequddin et al. (2009), Dowty and Wallace (2010), Christopher et al. (2011), Zhang et al. (2011), Diabat, Govindan, and Panicker (2012)
Pharmaceutical 6 Talluri and Narasimhan (2003), Talluri, Cetin, and Gardner (2004), Gaudenzi and Borghesi (2006), Talluri, Narasimhan, and Nair (2006), Wagner and Bode (2008), Kern et al. (2012)
IT 5 Zsidisin et al. (2004), Wu, Blackhurst, and Chidambaram (2006), Smith et al. (2007), Wagner and Bode (2008), Ravindran et al. (2010)
Agricultural 4 Ritchie and Brindley (2007), Pujawan and Geraldin (2009), Baghalian, Rezapour, and Farahani (2013), Leat and Revoredo-Giha (2013)
Chemical 4 Kleindorfer and Saad (2005), Wagner and Bode (2008), Foerstl et al. (2010), Kern et al. (2012) Energy 4 Adhitya, Srinivasan, and Karimi (2009), Cigolini and Rossi (2010), Blome and Schoenherr (2011),
Kern et al. (2012) Telecommunications 4 Norrman and Jansson (2004), Zsidisin et al. (2004), Wagner and Neshat (2010), Hung (2011) Logistics 3 Wagner and Bode (2008), Blome and Schoenherr (2011), Berle, Norstad, and Asbjørnslett (2013) Metal 3 Hallikas et al. (2005), Wagner and Bode (2008), Kern et al. (2012) Retail 3 Tsai, Liao, and Han (2008), Oke and Gopalakrishnan (2009), Le et al. (2013) Banking 2 Blome and Schoenherr (2011), Lundin (2012) Machinery 2 Wagner and Bode (2008), Kern et al. (2012) Insurance 1 Blome and Schoenherr (2011) Toy manufacturing 1 Tse and Tan (2011)
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can be contributed. Similarly, the renewable energy sector has not been a part of any specific research. Specifically, bioenergy projects are especially vulnerable to risks associated with the biomass supply chain. For example, the type and reliable supply of biomass is important as not all biomass is compatible with all boiler systems. The incorrect choice of biomass and supplier can lead to project failure (Scott, Ho, and Dey 2013).
The majority of scholars focused on manufacturing supply chains (e.g. automotive, electronics, aerospace and so on), whereas service supply chains have attracted much less attention in the past decade. In view of the important role of service industry (e.g. banking, insurance, health care and so on) in present economy, the literature relating to service- based SCRM must be enriched.
It is evident that the risk monitoring process has received the least attention by researchers compared with the other three processes, including risk identification, risk assessment and risk mitigation. Among all 224 articles reviewed, there is only one article studying early warning monitoring of risks in the food manufacturing supply chains (Zhang et al. 2011). As a robust risk prevention system is more cost-effective than risk mitigation in practice, scholars should extend the literature by developing an early warning monitoring system with adaptive risk indicators for various types of supply chains and validating the system empirically.
As discussed, risk mitigation has been extensively studied with a wide range of mitigation strategies proposed. How- ever, there is lack of research in benchmarking these strategies. Researchers and practitioners have not comprehensively addressed the selection of the most appropriate strategies in particular scenarios. In most cases, the efficacy of a specific strategy is investigated in extant research. Although Talluri et al. (2013) attempted to evaluate seven individual risk mit- igation strategies under different scenarios, they did not consider the joint impact of these strategies. To fill this research gap, scholars could evaluate and select the best mitigation strategies among various individual and integrated strategies with respect to both efficiency and effectiveness.
There exist a number of conceptual frameworks and approaches covering all four SCRM processes. Besides risk identification, risk assessment, risk mitigation and risk monitoring, risk recovery should also be studied and incorporated into the SCRM approaches so as to enable the supply chain to quickly return to its original state during the occurrence of a disruption. Although Hishamuddin, Sarker, and Essam (2013) studied the recovery aspect, their focus was on recov- ery schedule instead of recovery strategies/methods for a simple two-tier supply chain with one supplier and one retailer. In view of its importance, but scarce studies on risk recovery, scholars could expand the existing SCRM approaches by incorporating a risk recovery phase.
Finally, it would be worthwhile to quantify the benefits and costs of SCRM. For example, scholars could measure the value added to the organisations after implementing SCRM methods/strategies. Besides, scholars could apply a multiple case study approach to analyse and benchmark the payoffs or losses between those companies incorporating SCRM and non-SCRM adopters in the same sector while exposing to similar risk types. These studies would attract more organisations focusing on SCRM, and also shed light on effective practices for implementing SCRM to receive the maximum payoff.
9. Conclusions
In this paper, we reviewed 224 international journal articles appearing between 2003 and 2013 targeting the area of SCRM. We categorised all these articles according to definitions, types, factors and SCRM methods. This paper made several contributions to the field of SCRM. First, we provided a new definition to supply chain risk and SCRM. The new definitions are clearer and more specific than the existing ones, and enable a common understanding between researchers and practitioners. This will not only help researchers communicate with practitioners and gain access to industry to conduct empirical studies, but also help researchers identify and measure the likelihood and impact of the entire supply chain risks, and evaluate the effectiveness of SCRM methodologies. Second, we proposed five common risks arising across various types of supply chains, including macro risk, demand risk, manufacturing risk, supply risk and infrastructural risk (information risk, transportation risk and financial risk). This comprehensive classification could help researchers and practitioners identify various risk types with differing degrees of impact that are both external and internal to supply chains. Third, combining various points of views of scholars, we created a holistic list of potential factors affecting the five common risk types. This will not only help researchers and practitioners identify and classify the potential risk factors, but also provide a starting point for creating a supply chain risk index model. Fourth, we classified both quantitative and qualitative SCRM methods according to four major SCRM processes, including risk identification, risk assessment, risk mitigation and risk monitoring. This will provide useful insights to researchers and practitioners for SCRM, such as which methods (qualitative vs. quantitative; individual and integrated) are applicable in particular SCRM processes. Fifth, we revealed ten research gaps and proposed corresponding potential research direc- tions in the area of SCRM. We hope our recommendations for further research directions would aid academics conduct
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more impactful studies in SCRM, which in turn assists practitioners in managing supply chain risks more effectively and efficiently via knowledge transfer.
While we have considered a comprehensive evaluation of SCRM work, our research is not devoid of limitations. There are three main limitations of this paper. First, we only reviewed international journal articles, while excluding the conference papers, master and doctoral dissertations, textbooks, book chapters, unpublished articles and notes. Second, this paper is solely based on the analyses from the point of view of academics while failing to incorporate the views of practitioners. Third, the goal of this study was to present and categorise recent SCRM research and explore potential research gaps. With that in mind, an overarching question is not posed as usually done in more specific literature reviews. Using the categorisation and summary results of this paper, further studies can delve into specific areas that have been under-researched and extend studies that have focused on mature areas of SCRM.
Acknowledgements The authors would like to acknowledge the three anonymous reviewers for their insightful and constructive comments, and the finan- cial support received by one of the authors from the Faculty of Business and Economics, The University of Melbourne.
Disclosure statement
No potential conflict of interest was reported by the authors.
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- Abstract
- 1. Introduction
- 2. Research methodology
- 3. Definitions
- 3.1 Supply chain risk
- 3.2 Supply chain risk management
- 4. Supply chain risk types
- 5. Supply chain risk factors
- 6. SCRM methods
- 6.1 Individual SCRM process
- 6.1.1 Risk identification
- 6.1.2 Risk assessment
- 6.1.3 Risk mitigation
- 6.1.4 Risk monitoring
- 6.2 Integrated SCRM processes
- 6.2.1 SCRM conceptual frameworks
- 6.2.2 SCRM procedures or approaches
- 6.2.3 Supply chain network design
- 7. Observations
- 7.1 Quantitative vs. qualitative methods
- 7.2 Applied quantitative methods
- 7.3 SCRM processes
- 7.4 Risk types focused
- 7.5 Application areas
- 8. Research gaps and recommendations
- 9. Conclusions
- Acknowledgements
- Disclosure statement
- References
