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IJPDLM
48,8
Supply chain resilience: a
systematic literature review and
typological framework
842
Received 16 February 2017
Revised 18 October 2017
20 February 2018
23 May 2018
28 May 2018
Accepted 29 May 2018
Cigdem Gonul Kochan
The James F. Dicke College of Business, Ohio Northern University,
Ada, Ohio, USA, and
David R. Nowicki
Department of Marketing and Logistics, University of North Texas,
Denton, Texas, USA
Abstract
Purpose – The study of supply chain resilience (SCRES) continues to gain interest in the academic and
practitioner communities. The purpose of this paper is to present a focused review of the SCRES literature by
investigating supply chain (SC) capabilities, their relationship to SCRES outcomes and the underpinning
theoretical mechanisms of this relationship.
Design/methodology/approach – The paper uses the systematic literature review approach to examine
383 articles published between 2000 and 2017, ultimately down selecting to the most relevant 228
peer-reviewed studies. Context-interventions-mechanisms-outcomes (CIMO) logic is applied to organize and
synthesize these peer-reviewed studies. A typological framework is developed from the CIMO-based
classification of the SCRES literature.
Findings – The findings of this study outline the gaps in the SCRES literature and present an agenda for
future research.
Research limitations/implications – This paper presents an exploratory research; therefore, the
typological model presented is just one of the possible perspectives.
Practical implications – The typology of SCRES literature can help practitioners to understand SCRES
and to measure and assess the resilience of SCs.
Originality/value – The paper provides clear definitions of SCRES constructs, develops a typological
framework to further understand SCRES and identifies SCRES measures and assessment techniques.
Keywords Supply chain resilience, Supply chain risk, Systematic literature review, Supply chain vulnerability
Paper type Literature review
International Journal of Physical
Distribution & Logistics
Management
Vol. 48 No. 8, 2018
pp. 842-865
© Emerald Publishing Limited
0960-0035
DOI 10.1108/IJPDLM-02-2017-0099
Introduction
Today’s supply chains (SCs) are global, complex networks that aim to deliver products in the
right quantity, right place and right time in unpredictable markets. Instability in global markets
exposes SCs to disruptions (Pettit et al., 2010). A SC’s ability to cope with disruptions is limited
to practitioners’ understanding of SC vulnerabilities and risks. However, applying traditional
risk management strategies to each link in the global SC for every possible disruption is
difficult (Pettit et al., 2010). To address the insufficiency of traditional risk management
practices, practitioners now focus on building supply chain resilience (SCRES) strategies that
not only identify, monitor and reduce SC risks and disruptions, but also react quickly and
cost-effectively (Melnyk et al., 2010). This study reviews the extant literature to understand the
concept of SCRES, its factors and outcomes that improve a firm’s competitive position.
The first widespread study on SCRES occurred in the UK following the transportation
disruptions from the fuel protests in 2000 (Pettit et al., 2010). Since then, a substantial amount of
conceptual work is published that defines SCRES (Ponomarov and Holcomb, 2009; Ponis and
Koronis, 2012), presents antecedents and consequences of SCRES (Christopher and Peck, 2004;
Briano et al., 2010) and offers practical guidelines (Sheffi, 2005b; Sheffi and Rice, 2005).
However, there is a lack of consensus over a well-grounded definition of SCRES (Mensah and
Merkuryev, 2014; Tukamuhabwa et al., 2015; Kamalahmadi and Parast, 2016) and lack of
clarity in relationships between SCRES and its constructs (Ali et al., 2017). Hohenstein et al.
(2015) explained that this lack of clarity is due to the “divergent concepts from theory building.”
To enhance the clarity, researchers use the systematic literature review (SLR) approach and
conduct comprehensive reviews of the SCRES (Hohenstein et al., 2015; Tukamuhabwa et al.,
2015; Kamalahmadi and Parast, 2016; Ali et al., 2017). Although these studies advance the
SCRES literature, there is no overarching SCRES typology. This paper develops a typological
SCRES framework to classify, synthesize and report on the SCRES literature, and to address
the research question:
RQ1. How can SC capabilities and vulnerabilities create specific outcomes in the contexts
of SCRES?
This study makes three key contributions to the SCRES literature. First, the typological
framework offers a foundation for developing and testing hypotheses that examine the
effects of SCRES constructs on SCRES outcomes. Next, we adopt the context-interventionsmechanisms-outcomes (CIMO) framework as part of the SLR approach. Only a few studies
exist in the SC literature that adopt all attributes of CIMO framework (Pilbeam et al., 2012),
and none of them in the area of SCRES. Finally, we provide an extensive review of
the analytical methods in the SCRES literature to gain an understanding of SCRES
measures and to provide a basis for future research.
The organization of this paper is as follows. The first section describes the research
methodology. The second section presents the findings of the study organized by a
typological framework developed based on the CIMO logic of SCRES. The third section
presents the implications and future research directions. Finally, the fourth section discusses
limitations and conclusion.
Research methodology
SLR approach
Originated from the medical sciences, SLR has been widely used in the management and
organization sciences (Tranfield et al., 2003; Denyer and Tranfield, 2009). Our methodology
follows five review stages to improve the validity and quality of the SLR findings: question
formulation, locating studies, article selection and evaluation, analysis and synthesis, and
reporting and using the results.
Stage 1: question formulation. We adapt CIMO logic (Denyer and Tranfield, 2009) to
synthesize and report the SLR-based findings to form our research question:
RQ1. How can SC capabilities and vulnerabilities create specific outcomes in the contexts
of SCRES?
According to CIMO logic, we suggest interventions (I), SC capabilities and vulnerabilities,
produce different SCRES outcomes (O) based on various mechanisms (M), SCRES theories
that depend on specific SCRES contexts (C), SCRES disruptions and risk.
Stage 2: locating studies. SCRES-related keywords including supply resilience, supply
chain resiliency and resilient supply chain were searched using supply chain* and resilien*
codes in the title, keywords or in the abstract of peer-reviewed journal articles. Three
academics with SC expertise were consulted to minimize bias. Databases searched included:
ABI/Inform Complete, EBSCOhost, Science Direct, Wiley, Emerald, Taylor & Francis, Web of
Science and Google Scholar.
Stage 3: study selection and evaluation. We studied peer-reviewed journal articles over
17 years (2000–2017) and used the following Colicchia and Strozzi’s (2012) selection criteria
to determine what articles to include and exclude from the present study:
(1) search for peer-reviewed articles published in the last 17 years in variety of
databases: 383 articles;
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resilience
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(2) ensure substantive relevance by requiring that selected articles contain at least
keywords “resilien*” and “supply chain” in their title or abstract: 280 articles;
(3) eliminate substantively irrelevant articles by excluding papers related to very
narrow aspects or contexts: 241 articles;
844
(4) ensure substantive and empirical relevance by reading all remaining abstracts for
substantive context and empirical content: 235 articles; and
(5) further, ensure substantive and empirical relevance by reading all remaining articles
in their entirety: 228 articles.
Out of 228 articles, 16 of the articles were published in the Supply Chain Management: An
International Journal, representing 7 percent of all publications. Top peer-reviewed journals
that publish SCRES articles and the number of articles published are as follows:
•
Supply Chain Management: An International Journal: 16;
•
International Journal of Production Research: 12;
•
International Journal of Production Economics: 10;
•
Transportation Research Part E: Logistics and Transportation Review: 7;
•
Journal of Cleaner Production: 7;
•
International Journal of Logistics Research and Applications: A Leading Journal of
Supply Chain Management: 6;
•
International Journal of Physical Distribution & Logistics Management: 6;
•
Journal of Business Logistics: 6;
•
Sustainability: 6; and
•
Omega: 5.
Figure 1 shows SCRES publications increasing from 2003 to 2017. Yet, the number of
publications remains low compared to other SCM areas (Pereira et al., 2014). Therefore,
scholars consider SCRES an underexplored research area (Ponomarov and Holcomb, 2009;
Blackhurst et al., 2011; Hohenstein et al., 2015).
Stage 4: analyses and syntheses. According to Tranfield et al. (2003), a SLR should
synthesize the findings of individual studies into a new or different arrangement. In this
stage, we adapt Pilbeam et al.’s (2012) categories to extract and analyze data in the SLR:
60
55
50
40
35
30
27
19
20
25
18
13
8
10
4
3
2
3
4
6
6
07
20
08
20
09
20
10
20
11
20
12
20
13
20
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20
15
20
17
-S 20
ep 16
te
m
be
r
20
05
03
06
20
20
20
20
04
0
Figure 1.
Number of
publications by year
descriptive (year, journal and title), methodology (article type and theoretical lens) and
thematic (context, intervention, mechanisms and outcome).
Stage 5: reporting and using the results. The SLR approach summarizes the literature to
make the findings more understandable for practitioners and researchers. Consistent with
Tranfield et al. (2003), we offer descriptions, examples and an audit trail justifying
conclusions in our SLR findings.
Findings
Defining SCRES
Definitions of “Resilience” are seen in physical, ecological and socio-ecological systems,
psychology, economy, disaster management, engineering and organizational research
(Ponomarov and Holcomb, 2009). Christopher and Peck (2004) are the first authors to
apply the ecosystem definition of resilience to the context of SCs. Table I shows the
various SCRES definitions.
The wide variety of SCRES definitions suggests a lack of consensus (Mensah and
Merkuryev, 2014; Tukamuhabwa et al., 2015). We use Wordle, an online visualization tool, to
identify the frequency of keywords/concepts used in SCRES definitions. The keyword
analysis indicates that the authors mainly define SCRES as an ability. We further find that
ability and capability are used interchangeably.
Supply chain
resilience
845
A typology of SCRES
SCRES is achieved by enabling the shift toward desirable states in which failure modes
would not occur (Carvalho, Azevedo and Cruz-Machado, 2012; Carvalho, Barroso, Machado,
Azevedo and Cruz-Machado, 2012; Carvalho, Cruz-Machado, and Tavares, 2012). To enable
such a shift toward desirable state, resilience needs to be designed into the SC (Christopher
and Peck, 2004). To provide a further understanding of how firms can design SCRES into
their SCs, this paper develops a SCRES typological framework (Figure 2). The framework is
now discussed in the context of the CIMO logic.
SCRES contexts (C). We analyzed SCRES articles across industry sectors such as
agri-food, automotive, chemical and petrochemical, coal, counterfeit, humanitarian and
disaster relief, electronic, energy, healthcare, military, and retail[1]. Across these diverse
industries, there are two common contexts: disruptions and risk.
Author
Selected SCRES definitions
Christopher and Peck The ability of the system to return to its original state or move to a new more desirable
(2004, p. 2)
state after being disturbed
Ponomarov and
The adaptive capability of the supply chain to prepare for unexpected events, respond
Holcomb (2009,
to disruptions, and recover from them by maintaining continuity of operations at the
p. 131)
desired level of connectedness and control over structure and function
Ponis and Koronis
The ability to proactively plan and design the supply chain network for anticipating
(2012, p. 925)
unexpected disruptive events, respond adaptively to disruptions while maintaining
control over structure and function an transcending to a post-event robust state of
operations, if possible, more favorable than the one prior to the event, thus gaining
competitive advantage
Tukamuhabwa et al. The adaptive capability of a supply chain to prepare for and/or respond to
(2015, p. 8)
disruptions, to make a timely and cost-effective recovery, and therefore progress to a
post-disruption state of operations-ideally, a better state than prior to disruption
Kamalahmadi and
The adaptive capability of a supply chain to reduce the probability of facing sudden
Parast (2016, p. 121) disturbances, resist the spread of disturbances by maintaining control over structures
and functions, and recover and respond by immediate and effective reactive plans to
transcend the disturbance and restore the supply chain to a robust state of operations
Table I.
Definitions of SCRES
Internal Supply
Chain Risks
• Demand risk
• Supply risk
• Process risk
• Control risk
Supply Chain Risks
• Environmental
risk
External Supply
Chain Risks
• Efficiency
• Dispersion
• Market position
• Security
• Collaboration
• Financial Strength
• Revenue
management
• Market strength
• Organizational
culture
• Anticipation
Readiness
• Natural disasters
• Man-made disasters
• Uncertain demand
• Uncertain supply yields
• Uncertain lead times
• Uncertain supply
capacity
• Uncertain supply cost
• Resource limits
• Supplier
• Customer
• Infrastructure
• Deliberate threats
• Agility
• Velocity
• Visibility
• Flexibility
• Redundancy
Responsiveness
• Adaptability
• Crises
management
• Resource
mobilization
• Communication
strategies
• Consequence
mitigation
Recovery
• Supply chain
structure
• Supplier chain
design
characteristics
• Supply chain
complexity
Supply Chain Capabilities
• Turbulence
• Regulatory, legal,
and bureaucratic
• Financial
External Disruptions
Internal Disruptions
Structural
Vulnerabilities
• Dynamic capabilities (DCT)
• Resourced-based view
(RBV)
• Systems and control
theories
• Relational view (RV)
• Grey theory
• Contingency theory
• Information processing
theory
• Normal accident theory
• Game theory
• Complex adaptive systems
(CAS)
• Graph theory
• Competing values theory
• Signaling theory
• Transaction cost analysis
• Complexity theory
• Social capital
• Rational choice
• Strategic choice
Theories
Internal
Vulnerabilities
Supply Chain Vulnerabilities
Supply Chain Disruptions
External
Vulnerabilities
Mechanisms
Internal Firm
Risks
Figure 2.
Typology of
SCRES based on
the CIMO logic
Interventions
• Sustained Competitive
Advantage
(Unbalanced SCRES: Low
vulnerabilities and high
capabilities)
• Eroded Profitability
(Unbalanced SCRES: high
vulnerability and low
capabilities)
• Excessive Risk
(Balanced SCRES)
• Improved Performance
Outcomes
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Context
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SC disruption. Natural and man-made disasters, SC complexity and global competition have
made SCs more prone to disruptions (Tang and Tomlin, 2008; Wagner and Neshat, 2010).
Consistent with the literature, we categorize disruptions based on sources they arise from:
external sources such as natural disasters (e.g. earthquakes, hurricanes, tsunami’s) and
man-made disasters (e.g. accidents, wars, terrorist attacks, strikes, financial crises or
sabotage) and internal sources such as uncertain demand, supply yields, lead times, supply
capacity and supply cost (Christopher and Peck, 2004; Tang, 2006; Ponomarov and
Holcomb, 2009; Wagner and Neshat, 2010) (Figure 2).
SC risk. SC risk is closely related to SC vulnerability (Colicchia and Strozzi, 2012). As SC
risks increase, companies become more vulnerable to unforeseen disruptions. In line with
the literature, we categorize risks in SCs as: internal to the firm – including process and
control risks, external to the firm and internal to the SC network – including demand and
supply risks and external to the SC – including environmental risks (Bogataj and Bogataj,
2007; Christopher and Peck, 2004; Fiksel, 2003) (Figure 2).
Risk management is a critical capability that enhances SCRES (Colicchia and Strozzi,
2012). Efficient supply chain risk management (SCRM) reduces vulnerability by reducing
the likelihood of a further disruption (Sheffi and Rice, 2005); therefore, increasing SCRES
(Bogataj and Bogataj, 2007).
In practice, SCRM is only possible when the probabilities and the effects of the SC
disruptions on SC performance are known (Wagner and Bode, 2008). Traditional risk
assessment strategies cannot adequately deal with the unexpected events (Pettit et al., 2010)
and are not adequately justified in the literature (Ponomarov, 2012). Thus, scholars suggest
firms must develop logistics processes and capabilities and understand SC vulnerabilities to
improve SCRES (Fiksel et al., 2015).
SCRES interventions (I). SC vulnerability. Svensson (2000) is the first author to develop
a conceptual framework for vulnerability that focuses on SCs. Vulnerabilities
(Sheffi 2005b), vulnerability drivers/factors (Wagner and Bode, 2006; Pettit et al., 2010),
risks ( Jüttner and Maklan, 2011), resiliency reducers (Blackhurst et al., 2011), risk sources/
factors and risk drivers terms are often used interchangeably. For this study, these terms
are synthesized as SC vulnerabilities. Table II shows the SC taxonomy of vulnerabilities,
adapted from Pettit et al.’s (2010).
SC capabilities. Resilience is designed into SCs by integrating SC capabilities (Tang and
Tomlin, 2008; Sheffi 2005b). Authors use SC capabilities (Rice and Caniato, 2003; Blackhurst
et al., 2011), capability factors (Zhang et al., 2011; Pettit et al., 2010), logistics capabilities
(Ponomarov and Holcomb, 2009), resilience capabilities ( Jüttner and Maklan, 2011), SC
characteristics (Carvalho, Azevedo and Cruz-Machado, 2012; Carvalho, Barroso, Machado,
Azevedo and Cruz-Machado, 2012; Carvalho, Cruz-Machado and Tavares, 2012) and resilience
strategies (Sheffi, 2005a) interchangeably. In the present study, we use the term SC capabilities.
Researchers explore various SC capabilities that can reduce vulnerabilities, detect,
prevent or reduce the occurrences of SC disruptions (Craighead et al., 2007). Effective
implementation of a SC’s capabilities leads to improved performance when matched with its
vulnerabilities (Carvalho, Azevedo and Cruz-Machado, 2012; Carvalho, Barroso, Machado,
Azevedo and Cruz-Machado, 2012; Carvalho, Cruz-Machado and Tavares, 2012). Ponomarov
and Holcomb (2009) suggested that integrating SC capabilities will lead to a firm’s
sustainable competitive advantage.
Our review of the literature reveals that there is a problem of inconsistent terminology
for the concepts of SC capabilities as well. The terms resilience, robustness, reliability,
agility and flexibility are used interchangeably (Christopher and Rutherford, 2004; Schmitt
and Singh, 2012). In an attempt to provide clarity, we identify and compare these
interrelated concepts from the SCRES literature.
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Main factors
External
Turbulence
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Regulator, legal and
bureaucratic
Financial
Internal
Resource limits
Sensitivity
Supplier
Customer
Infrastructure
Deliberate threats:
Author
Svensson (2002), Christopher and Rutherford (2004), Peck (2005), Sheffi (2005a),
Wagner and Bode (2006), Pettit et al. (2010), Wagner and Neshat (2010)
Wagner and Bode (2006), Wagner and Neshat (2010), Blackhurst et al. (2011)
Sheffi (2005a), Pettit et al. (2010)
Pettit et al. (2010), Blackhurst et al. (2011)
Svensson (2002), Peck (2005), Sheffi (2005a), Pettit et al. (2010)
Sheffi (2005a, b), Wagner and Bode (2006), Craighead et al. (2007), Tang and Tomlin
(2008), Wagner and Neshat (2010), Blackhurst et al. (2011)
Wagner and Bode (2006), Wagner and Neshat (2010)
Wagner and Bode (2006), Craighead et al. (2007), Pettit et al. (2010),
Wagner and Neshat (2010)
Svensson (2002), Christopher and Rutherford (2004),
Peck (2005), Sheffi (2005a)
Structural
Supply chain structure Sheffi (2005a), Pettit et al. (2010), Wagner and Neshat (2010)
Design characteristics Craighead et al. (2007), Blackhurst et al. (2011)
Complexity
Svennson (2002), Peck (2005), Sheffi (2005a, b), Wagner and Bode (2006),
Table II.
Craighead et al. (2007), Tang and Tomlin (2008), Pettit et al. (2010),
A taxonomy of supply
chain vulnerabilities
Wagner and Neshat (2010)
The robustness is defined as the ability of a SC to carry out its functions for a variety of
plausible functions (Klibi et al., 2010; Colicchia and Strozzi, 2012). Robust SCs can resist
disruptions, remain effective in the event of a disruption, whereas resilient SCs can adopt a new
desirable state after being disturbed (Vlajic et al., 2012). Therefore, robust SCs are not necessarily
resilient, but rather a resilient SC is a robust SC that can respond to unexpected shifts in the level
and variability of output (Christopher and Rutherford, 2004). Therefore, robustness can be
identified as one of the dimensions of SCRES (Wieland and Wallenburg, 2013).
Some authors see SC agility and flexibility concepts as capabilities of SCRES (Christopher
and Peck, 2004; Sheffi, 2005b; Ponomarov and Holcomb, 2009; Pettit et al., 2010), while others
view these concepts and SCRES as different approaches (Charles et al., 2010; Carvalho,
Azevedo and Cruz-Machado, 2012; Carvalho, Barroso, Machado, Azevedo and Cruz-Machado,
2012; Carvalho, Cruz-Machado and Tavares, 2012). Some authors consider flexibility as a
dimension of agility (Tang and Tomlin, 2008; Carvalho, Azevedo and Cruz-Machado, 2012;
Carvalho, Barroso, Machado, Azevedo and Cruz-Machado, 2012; Carvalho, Cruz-Machado and
Tavares, 2012) and others view flexibility and agility as different concepts (Christopher and
Rutherford, 2004). In alignment with Tang and Tomlin (2008), we use Christopher and Peck’s
(2004) definition of agility: “the ability to respond rapidly to unpredictable changes.” Thus, we
suggest flexibility, visibility and velocity are dimensions of agility.
SCRES is related to the development of responsiveness capabilities through redundancy
and agility (Rice and Caniato, 2003; Carvalho, Azevedo and Cruz-Machado, 2012; Carvalho,
Barroso, Machado, Azevedo and Cruz-Machado, 2012; Carvalho, Cruz-Machado and Tavares,
2012). Some studies define redundancy as a dimension of flexibility ( Jüttner and Maklan, 2011;
Carvalho, Azevedo and Cruz-Machado, 2012; Carvalho, Barroso, Machado, Azevedo and
Cruz-Machado, 2012; Carvalho, Cruz-Machado, and Tavares, 2012), others define redundancy
and flexibility as different resilience capabilities (Rice and Caniato, 2003; Sheffi, 2005a).
Carvalho et al. (2011) and Tang and Tomlin (2008) suggested that flexibility is related to
investments in infrastructure and resources before they actually are needed, whereas
redundancy is concerned with maintaining the capacity to respond to disruptions in the
supply network, largely through investments in capital and capacity prior to the point of need.
Although redundant capacity may or may not be used, flexibility entails restructuring to the
previously existing capacity (Rice and Caniato, 2003). Consistent with the above arguments,
we, therefore, consider flexibility and redundancy different resilience capabilities.
According to our review of the literature, the role of relational capabilities/relational
competencies in achieving SCRES is underexplored. Scholars investigate the impact of
relational capabilities on SCRES such as communication, cooperation and integration
(Wieland and Wallenburg, 2013); trust, norms and obligations ( Johnson et al., 2013);
adaptation and interdependence (Mandal, 2013); and collaboration (Scholten and Schilder,
2015). Table III shows our SC capabilities taxonomy adapted from Pettit et al. (2010). Based
We categorize SC capabilities based on Ponomarov and Holcomb’s (2009) SCRES phases.
SCRES mechanisms (M). According to Denyer et al. (2008), achieving a certain outcome
requires a specific mechanism. In this section, we identify 20 theories applied as
mechanisms to investigate SCRES and its relationships with various concepts.
Our findings show resource-based view (RBV ) (Wernerfelt, 1984; Barney, 1991) is a
common theoretical lense used to explain SCRES. In SCRES research, RBV provides a basis
to explore relationships among specific resources, capabilities, and performance. To address
the criticism that RBV is static in nature, SCRES authors use theories such as dynamic
capabilities theory (DCT) (Teece et al., 1997), contingency theory (Lawrence and Lorsch,
1967), systems theory (ST) (Von Bertalanffy, 1950), and relational view (RV ) (Dyer and
Singh, 1998) that extend or complement RBV.
Mandal (2013) utilized RV coupled with RBV and DCT to explore the relationships
among relational resources/competencies and developed a theory-driven conceptual model
that explains SCRES as a dynamic capability. Similarly, Wieland and Wallenburg (2013)
applied RV as the theoretical basis for explaining the relationships between relational
competencies and dimensions of resilience. Ponomarov and Holcomb (2009) used DCT as an
extension of RBV to explain the relationships linking logistics capabilities, SCRES and
sustainable competitive advantage. Brandon-Jones et al. (2014) suggested RBV and DCT are
inadequate to pinpoint contingencies that capture the capabilities and resources and
extended RBV to contingency theory. Blackhurst et al. (2011) extended RBV to ST and
suggested that the effect of disruptions on a SC varies depending on the level of SCRES.
Tukamuhabwa et al. (2015) proposed complex adaptive systems theoretical framework to
examine SCRES and enhance the understanding of SCRES.
Despite increasing number of studies using theory to explain SCRES, the need remains
for well-grounded theoretical lenses (Ali et al., 2017). Lack of theoretical ground limits the
explanation for SCRES from the theoretical perspective. Table IV shows the use of theory in
SCRES research.
SCRES outcomes. In alignment with Pettit et al. (2010), we define SCRES outcomes as
improved performance when a SC balances capabilities and vulnerabilities, excessive risk
when a SC has high vulnerabilities and low capabilities and eroded profitability when a SC
has low vulnerabilities and high capabilities (Figure 2).
Nature of the selected studies
We classify the selected articles as empirical (65), literature reviews (43) and analytical (85)
articles based on Hohenstein et al.’s (2015) research methodology categorization.
Empirical research. Table V shows the type of empirical research method, the number of
articles and authors. Our findings suggest that there is a lack of focus on survey,
longitudinal and field studies.
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Main factors
Author
Responsiveness
Agility–flexibility–sourcing
850
Fiksel (2003), Rice and Caniato (2003), Sheffi (2005a), Peck (2005), Sheffi
(2006), Tang (2006), Tang and Tomlin (2008), Ponomarov and Holcomb (2009),
Pettit et al. 2010
Agility–flexibility–fulfillment Rice and Caniato (2003); Rice and Caniato (2003); Fiksel (2003), Peck (2005),
Sheffi (2005a), Tang (2006), Tang and Tomlin (2008), Ponomarov and Holcomb
(2009), Pettit et al. (2010), Zhang et al. (2011)
Agility–velocity
Ponomarov and Holcomb (2009), Pettit et al. (2010),
Charles et al. (2010), Jüttner and Maklan (2011), Zhang et al. (2011),
Blackhurst et al. (2011)
Agility–visibility
Rice and Caniato (2003), Peck (2005), Sheffi (2005a), Ponomarov and Holcomb
(2009), Pettit et al. (2010), Zhang et al. (2011), Blackhurst et al. (2011),
Carvalho, Azevedo and Cruz-Machado (2012), Carvalho, Barroso,
Machado, Azevedo and Cruz-Machado (2012), Carvalho, Cruz-Machado,
and Tavares (2012)
Redundancy
Rice and Caniato (2003), Sheffi (2005a), Sheffi (2006), Tang and Tomlin (2008),
Pettit et al. (2010), Jüttner and Maklan (2011), Zhang et al. (2011), Blackhurst
et al. (2011), Carvalho, Azevedo and Cruz-Machado (2012), Carvalho, Barroso,
Machado, Azevedo and Cruz-Machado (2012), Carvalho, Cruz-Machado and
Tavares (2012)
Anticipation
Efficiency
Dispersion
Market position
Security
Collaboration
Financial strength
Revenue management
Organization culture
Anticipation
Recovery
Adaptability
Table III.
A taxonomy of supply Recovery
chain capabilities
Fiksel (2003), Sheffi (2005a), Sheffi (2006), Pettit et al. (2010), Zhang et al. (2011)
Rice and Caniato (2003), Fiksel (2003), Sheffi (2005a), Tang (2006), Pettit et al.
(2010), Zhang et al. (2011)
Pettit et al. (2010)
Rice and Caniato (2003), Peck (2005), Sheffi (2005a), Tang (2006), Pettit et al.
(2010)
Rice and Caniato (2003), Fiksel (2003), Peck (2005), Sheffi (2005a),
Tang (2006), Ponomarov and Halcomb (2009), Pettit et al. (2010),
Carvalho, Azevedo and Cruz-Machado (2012), Carvalho, Barroso,
Machado, Azevedo and Cruz-Machado (2012), Carvalho, Cruz-Machado
and Tavares (2012)
Rice and Caniato (2003), Fiksel (2003), Tang (2006), Pettit et al. (2010)
Tang and Tomlin (2008), Zhang et al. (2011)
Rice and Caniato (2003), Sheffi (2005a), Pettit et al. (2010), Zhang et al. (2011),
Blackhurst et al. (2011)
Rice and Caniato (2003), Fiksel (2003), Hamel and Valikangas (2003), Peck
(2005), Sheffi (2005a), Tang (2006), Craighead et al. (2007), Pettit et al. (2010),
Blackhurst et al. (2011)
Rice and Caniato (2003), Fiksel (2003), Peck (2005), Sheffi (2005a), Tang (2006),
Ponomarov and Halcomb (2009), Pettit et al. (2010), Zhang et al. (2011),
Blackhurst et al. (2011)
Rice and Caniato (2003), Sheffi (2005a), Tang (2006), Craighead et al. (2007),
Tang and Tomlin (2008), Pettit et al. (2010)
Literature reviews. This study further categorizes the literature reviews as conceptual
articles and SLRs (Table VI).
Analytical research. In this section, we focus on the SCRES analytical research articles
that use mathematical modeling and simulation methods (Table VII).
Our findings show the majority of the analytical SCRES studies focus on mathematical
programming. Lead time, inventory level, and cost are the most extensively used measures
to examine SCRES.
Theory
Dynamic capabilities (DCT)
Number of
articles Authors
16
Resource-based view (RBV )
14
Control theory
5
Systems theory (ST)
4
Relational view (RV )
4
Grey
4
Contingency
Information processing
Normal accident
Game
Complex adaptive systems
Graph
3
2
2
2
2
2
Complexity
Competing values
Signaling
Transaction cost analysis
Complexity
Social capital
Rational choice
Strategic choice
2
2
1
1
1
1
1
1
Ponomarov and Holcomb (2009), Golgeci and Ponomarov (2013),
Mandal (2013), Sang and Rha (2016), Vijaya et al. (2016),
Dabhilkar et al. (2016), Eltantawy (2016a, b), Mandal et al. (2016),
Roy et al. (2016), Chowdhury and Quaddus (2017), Gabler et al.
(2017), Krishnan and Pertheban (2017), Li, Pedrielli, Lee and
Chew (2017), Li, Dong, Jin and Kang (2017), Li, Wu, Holsapple
and Goldsby (2017), Mandal (2017a, b), Mandal et al. (2017)
Ponomarov and Holcomb (2009), Blackhurst et al. (2011), Mandal
(2013), Brandon‐Jones et al. (2014), Eltantawy (2016a, b), Mandal
et al. (2016), Roy et al. (2016), Cheng and Lu (2017), Dubey et al.
2017, Gabler et al. (2017), Mandal (2017a), Mandal et al. 2017,
Yang and Hsu (2017)
Ivanov et al. (2012), Ivanov and Sokolov (2013), Ivanov et al.
(2014), Ehlen et al. (2014), Levalle and Nof (2017)
Blackhurst et al. (2011), Zhang et al. (2011), Raj et al. (2015),
Munoz and Dunbar (2015)
Mandal (2013), Wieland and Wallenburg (2013), Dubey et al.
(2017), Yang and Hsu (2017), Chowdhury and Quaddus (2017)
Rajesh and Ravi (2015), Rajesh and Ravi (2017), Parkouhi and
Ghadikolaei, Wang et al. (2017)
Boone et al. (2013), Brandon‐Jones et al. (2014), Arani et al. (2016)
DuHadway et al. (2017), Mandal (2017a)
Marley et al. (2014), Chowdhury and Quaddus (2017)
Bakshi and Kleindorfer (2009), Zahiri et al. (2017)
Day (2014), Tukamuhabwa et al. (2015)
Kim et al. (2015), Li, Pedrielli, Lee and Chew (2017), Li, Dong,
Jin and Kang (2017), Li, Wu, Holsapple and Goldsby (2017)
Gunasekaran et al. (2015), Papadopoulos et al. (2017)
Gabler et al. (2017), Mandal (2017c)
Stevenson and Busby (2015)
Eltantawy (2016a, b)
Gunasekaran et al. (2015)
Johnson et al. (2013)
Urciuoli et al. (2014)
Arani et al. (2016)
Implications and future directions
Managerial implications
When a firm fails to understand its SC’s potential vulnerabilities, risks and to develop a
mitigation strategy, then its survival is in jeopardy. Firms must enhance their SCRES to
cope with disruptions and remain competitive. This paper develops a typological framework
that outlines the antecedents and consequences of SCRES. The typological framework and
taxonomies will assist managers in identifying vulnerabilities and capabilities within their
SCs. Furthermore, the clear definitions and distinctions of the interrelated terms will help
practitioners better understand the factors influencing SCRES.
Research Implications
This study’s findings highlight several gaps in the existing SCRES literature and provide an
agenda for future research. First, there is a lack of a unanimous SCRES definition and its
concepts that confirm the infancy of the SCRES concept. Second, findings suggest that in SCRES
research, there is a shift from resilience definitions and principles to resilience measurement.
Supply chain
resilience
851
Table IV.
Summary of theories
applied in SCRES
research
IJPDLM
48,8
Research
methodology
Number of
articles
Authors
Case study
33
Survey
28
Field study
Longitudinal case
and field study
2
2
852
Table V.
Empirical studies
Table VI.
Literature review
research
Research
methodology
Number of
articles
Conceptual
articles
30
SLRs
13
Pettit et al. (2010), Blackhurst et al. (2011), Jüttner and Maklan (2011),
Cabral et al. (2012), Khan et al. (2012), Azevedo et al. (2013), Carvalho et al.
(2011) (2013), Johnson et al. (2013), Leat and Revoredo-Giha (2013), Pettit
et al. (2013), Govindan et al. (2014) (2015), Nikookar et al. (2014), Scholten
et al. (2014), Urciuoli et al. (2014), Haraguchi and Lall (2015), Sprecher
et al. (2015), Stevenson and Busby (2015), Scholten and Schilder (2015),
Pereira and da Silva (2015), Smith et al. (2016), Lam and Bai (2016), Agigi
et al. (2016), Hosseini and Al Khaled (2016), Sharma and Srivastava
(2016), Vargas and González (2016), Golicic et al. (2017), Hooks et al.
(2017), Rajesh (2017), Rezapour et al. (2017), Tubis et al. (2017),
Zahiri et al. (2017)
Zsidisin and Wagner (2010), Mandal (2012), Golgeci and Ponomarov
(2013), Wieland and Wallenburg (2013), Pettit et al (2013), Brandon‐
Jones et al. (2014), Dubey et al. (2014), Ambulkar et al. (2015), Sang and
Rha (2016), Roy et al. (2016), Aigbogun et al. (2016), Arani et al. (2016),
Chowdhury and Quaddus (2016), Mandal et al. (2016), Roy et al.
(2016), Papadopoulos et al. (2017), Brusset and Teller (2017),
Cheng and Lu (2017), Chowdhury and Quaddus (2017), Dubey et al.
(2017), Krishnan and Pertheban (2017), Li et al. (2017b), Liu et al. 2017,
Mandal (2017a, b, c), Mandal et al. (2017), Subramanian and
Abdulrahman (2017), Yang and Hsu (2017)
Marley et al. (2014), Emmanuel-Yusuf et al. (2017)
Jüttner and Maklan (2011), Boone et al. (2013)
Authors
Christopher and Peck (2004), Christopher and Rutherford (2004), Christopher
and Lee (2004), Bradley (2005), Sheffi and Rice (2005), Sheffi (2005a), Sheffi
(2006), Mascaritolo and Holcomb (2009), Briano et al. (2010), Melnyk et al. (2010),
Pettit et al. (2010), VanVactor (2011), Carvalho, Azevedo and Cruz-Machado
(2012), Carvalho, Cruz-Machado and Tavares (2012), Guo (2013), Palin (2013),
Aigbogun et al. (2014), Carvalho et al. (2014), Day (2014), Mensah and Merkuryev
(2014), Sáenz and Revilla (2014), Fiksel et al. (2015), Edgeman et al. (2016),
Eltantawy (2016a, b), Manning and Soon (2016), Zherlitsyn and Kravchenko
(2016), Gabler et al. (2017), Bevilacqua et al. (2017), Lakhal (2017)
Pereira (2009), Ponomarov and Holcomb (2009), Klibi et al. (2010), Ponis and
Koronis (2012), Pereira et al. (2014), Hohenstein et al. (2015), Tukamuhabwa et al.
(2015), Kamalahmadi and Parast (2016), Wang et al. (2016), Ali et al. (2017),
Behzadi, O’Sullivan, Olsen and Zhang (2017), Behzadi, O’Sullivan, Olsen,
Scrimgeour, and Zhang (2017), Levalle and Nof (2017), Linnenluecke (2017)
Next, findings reveal the number of empirical studies has increased in recent years. However,
there is a lack of field studies, longitudinal studies, and studies that use secondary data. We
believe that this is an important direction for future research and call on researchers to conduct
longitudinal, field and secondary data studies to develop and validate theoretical and conceptual
SCRES research. Finally, there is limited use of theoretical frameworks to explain SCRES
phenomena. RBV, DCT and ST are the common theories used to investigate SCRES. There is a
need to apply theories that address the dynamic nature of SCRES (Ali et al., 2017). In addition,
Research methodology
Supply chain
resilience
Authors (2010–2017)
Mathematical models
Integer linear programming (ILP) and Aviral et al. (2011), Xiao and Wang (2014), Cardoso et al. (2015),
mixed-integer linear programming
Sadghiani et al. (2015), Liu et al. (2016), Kamalahmadi and
(MILP)
Mellat-Parast (2016)
Mixed-integer non-linear programming Hasani and Khosrojerdi (2016), Rezapour et al. (2017)
(MINLP)
Multi-objective linear programming
Xiao et al. (2012), Fanga et al. (2012), Das and Lashkari (2017)
(MOLP)
Decision envelopment analysis (DEA) Azadeh et al. (2013), Amalnick and Saffar (2017), Pourhejazy et al. (2017)
Fuzzy decision envelopment analysis Azadeh et al. (2013), Pournader et al. (2016)
(F-DEA)
Fuzzy multi-objective programming
Rabbani et al. (2015), Fahimnia and Jabbarzadeh (2016), Lee (2017)
(FMOP)
Multi-objective stochastic mixed-integer Vijaya et al. (2016)
programming (MOS-MIP)
Goal programming (GP)
Chen et al. (2016)
Fuzzy mathematical programming (FP) Kristianto et al. (2014), Adtiya et al. (2014), Sahu et al. 2016, Arsovski
et al. (2017), Sahu et al. (2017)
Stochastic programming including
Klibi and Martel (2012), Kristianto et al. (2014), Sadghiani et al.
(2015), Behzadi, O’Sullivan, Olsen and Zhang (2017), Behzadi,
single-stage or two-stage stochastic
O’Sullivan, Olsen, Scrimgeour, and Zhang (2017), Khalili et al. (2017),
programming (SP)
Namdar et al. (2017)
Economic optimization models (EM)
Wieland (2013), Yang and Xu (2015), Maheshwari et al. (2017),
Lücker and Seifert (2017)
853
Multi-criteria decision analysis
Multi-criteria decision Analysis
Connelly et al. (2016), Collier et al. (2017)
(MCDA)
Fuzzy multi-criteria decision-making
Fakoor et al. (2013), Foroozesh et al. (2017)
model (F-MCDM)
Analytic hierarchy process (AHP)
Hosseini and Al Khaled (2016), Wang et al. (2017)
Analytical network process (ANP)
Cabral et al. (2012)
Fuzzy analytical network process
Parkouhi and Ghadikolaei (2017)
(F-ANP)
Fuzzy technique for order of preference Adtiya et al. (2014)
by similarity to ideal solution (F-TOPSIS)
Decision-making trial and evaluation Rajesh and Ravi (2017)
laboratory (DEMATEL)
Network models
Bayesian network (BN)
Graph modeling (GM)
Independent layered network (ILN)
Degree and locality based attachment
(DLA)
Cluster supply chain network model
(CSCN)
Simulation
Agent-based simulation (ABS)
Discrete event simulation (DES)
Chen et al. (2017)
Soni et al. (2014), Kim et al. (2015)
Gong et al. (2013)
Zhao et al. (2011), Klibi and Martel (2012)
Geng et al. (2013)
Wu et al. (2013), Xu et al. (2014)
Carvalho, Azevedo and Cruz-Machado (2012), Carvalho, Barroso,
Machado, Azevedo and Cruz-Machado (2012), Carvalho,
Cruz-Machado, and Tavares (2012), Berle et al. (2013), Wang et al.
(2013), Schmitt and Singh (2012), Zhao et al. (2011), Ivanov (2017a, b)
(continued )
Table VII.
Analytical research
(modeling and
simulation
approaches)a
IJPDLM
48,8
Research methodology
Authors (2010–2017)
System dynamics simulation (SD)
Spiegler et al. (2016), Li, Pedrielli, Lee and Chew (2017), Li, Dong,
Jin and Kang (2017), Li, Wu, Holsapple and Goldsby (2017)
Li, Pedrielli, Lee, and Chew (2017), Li, Dong, Jin, and Kang (2017), Li,
Wu, Holsapple and Goldsby (2017)
Xiao et al. (2012), Fanga et al. (2012), Azadeh et al. (2013), Geng et al.
(2013), Huang et al. (2014), Kim et al. (2015), Raj et al. (2015), Munoz
and Dunbar (2015)
Monte Carlo simulation
854
Table VII.
Other simulation methods
Other
Resilient shock absorber model (RSA) Bhattacharya et al. (2013)
Survival model- cox proportional
Raj et al. (2015)
hazard model (Cox-PH Model)
Grey relational analysis (GRA)
Rajesh and Ravi (2015), Wang et al. (2017)
Resiliency enhancement analysis via
Harrison et al. (2013)
deletion and insertion (READI)
Note: A full table of citations, including 2010 can be obtained from the authors
the role of relational capabilities/relational competencies in SCRES is underexplored. Future
research should exploit different theories, especially relational theories to gain a better
understanding of relational capabilities of SCRES.
We recommend further conceptualization of SCRES using different research perspectives
and incorporating behavioral and relational aspects of the SCRES. Furthermore, in line with
Hohenstein et al. (2015), we suggest authors investigate SCRES in different cultures.
Limitations and conclusion
This paper has two limitations. First, this paper presents exploratory research; therefore,
the typological model is just one of the possible perspectives. Second, the review is limited
to 228 peer-reviewed articles and excludes book chapters, conference proceedings, and
PhD dissertations.
In this study, we develop a new typological framework drawing upon the SLR findings
using the CIMO logic framework to address our fundamental research question:
RQ1. How can SC capabilities and vulnerabilities create specific outcomes in the
contexts of SCRES?
The research objectives of this paper are to identify and define SCRES factors; examine the
antecedents and consequences of SCRES applying the CIMO logic; develop a typological
framework; and identify gaps and future research directions. This study contributes to the
SCRES literature by offering clear definitions of the supporting constructs of SCRES, by
developing a typological framework based on CIMO logic to establish a consistent
understanding of SCRES, by identifying measures and assessment techniques of SCRES
through an extensive review of the existing analytical approaches. Also, we develop
taxonomies that include the vulnerabilities and capabilities that are examined in the
literature. Therefore, this study has implications for both research and practice as it
contributes to the managerial understanding of the relationships between the constructs
and outcomes of the SCRES based on the context.
Note
1. Citations for each industry can be obtained from the authors.
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Corresponding author
Cigdem Gonul Kochan can be contacted at: cigdem.kochan@gmail.com
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