An Empirically Derived Framework for Global SC Resilience

An Empirically Derived Framework of Global Supply ResiliencyJennifer Blackhurst1, Kaitlin S. Dunn2, and Christopher W. Craighead2

1Iowa State University2The Pennsylvania State University

In today’s global business environment, supply chains have increased in both length and complexity. This increase in length and com-plexity coupled with a focus on improving efficiency, such as lean manufacturing practices, may lead to higher levels of supply chain

risk where the likelihood of a disruption severely impacting supply chain performance increases. Resilient supply chains have been tou-ted as a means to reduce the likelihood and severity of supply chain disruptions. However, there is little empirical evidence relative tothe factors that contribute to or detract from supply resiliency. Using systems theory and the resource-based view of the firm as thetheoretical underpinnings, this study provides an in-depth systematic investigation of supply resiliency. Adopting a theory-buildingapproach based on a multi-industry empirical investigation, this study derives empirical generalizations linking 19 supply chain charac-teristics to supply resiliency. The study culminates in a framework that could be used to assess the level of resiliency in a supply base.Building on this framework, the study also provides a supply resiliency matrix that can be utilized to classify supply chains, or supplychains segments according to the level of resiliency realized. This article concludes by proposing several future research directions andissues that may be investigated in more detail.

Keywords: supply chain management; supply resiliency; supply risk; supply disruptions; systems theory; resource-based view of thefirm

INTRODUCTION

In today’s global business environment, supply chains haveincreased in both length and complexity (Blackhurst et al.2005). This increase in length and complexity coupled with afocus on improving efficiency, such as lean manufacturingpractices, may lead to higher levels of supply chain riskwhere the likelihood of a disruption (defined as events thatinterrupt the regular flow of goods or services within asystem—cf. Svensson 2000; Kleindorfer and Saad 2005;Craighead et al. 2007) severely impacting supply chain per-formance increases (Chopra and Sodhi 2004; Zsidisin et al.2005b; Wagner and Bode 2008).

Recently, interest in supply chain disruptions has increasedas disruptions have been shown to culminate in negativeconsequences to a firm’s operations and performance. Thesenegative consequences can have immediate detrimentalimpact as well as longer term effects on the firm. Ericsson,for example, reported a $400 million loss when it did notreceive chip deliveries from a Philips plant due to a 10-minfire (Latour 2001). A survey by Rice and Caniato (2003) esti-mate the daily cost of a supply chain disruption to be any-where from $50 to $100 million. The negative impact ofdisruptions often extends beyond short-term losses. Publiclytraded firms experience significant decreases in shareholdervalue after announcing a disruption in their supply chain(Knight and Pretty 1998, 2003; Hendricks and Singhal 2003,2005a,b). Although firms cannot completely eliminate theprobability of disruptions within their supply network(Blackhurst et al. 2005; Zsidisin et al. 2005a), scholars have

explored various actions that can be taken to reduce vulnera-bility to risks and the likelihood of disruptions (e.g., Norr-man and Jansson 2004; Craighead et al. 2007; Deane et al.2009). Companies, for example, can build resilience in theirsupply networks, which enhances a firm’s ability to absorbdisruptions or enables the supply network to return to stableconditions faster and thus has a positive impact on firmperformance (Sheffi and Rice 2005). Sheffi and Rice (2005)contend that building a resilient supply chain should be astrategic initiative because the flow of goods through a sup-ply network is vital to a firm’s existence. Likewise, Zsidisinet al. (2005a) note that a firm’s ability to survive after a dis-ruption is directly related to the level of resiliency withintheir supply chain. In fact, a number of supply chain riskresearchers note the importance of increasing resiliencywithin a supply chain (e.g., Zsidisin and Ellram 2003; Zsi-disin et al. 2004). In this article, we specifically define supplychain resilience as a firm’s ability to recover from disruptiveevents (Rice and Caniato 2003; Sheffi and Rice 2005).

Extant research in the area of supply chain disruptionsand the selection of strategies that help firms build a resilientsupply chain is informative. For example, Manuj and Ment-zer (2008a) provide an important and foundational step inshedding insight into how firms can begin to develop effec-tive supply chain risk management strategies. Manuj andMentzer (2008a) propose the temporal focus on the firm,level of flexibility in the supply chain, and supply chain envi-ronment (demand and supply side risks) affect the selectionof a mitigation strategy and are moderated by the composi-tion of the risk management team. Risk management strate-gies are broken down into six broad categories and theauthors note that managers need to understand the advanta-ges and disadvantages of each strategy and when they areappropriate. While research progress has been made, moreattention is needed to more fully develop our understandingof disruptions and resilience. Research gaps have been noted

Corresponding author:Jennifer Blackhurst, Supply Chain and Information SystemsDepartment, College of Business, Iowa State University,3131 Gerdin Business Building, Ames, IA 50011, USA;E-mail: [email protected]

Journal of Business Logistics, 2011, 32(4): 374–391� Council of Supply Chain Management Professionals

by several researchers. Hendricks and Singhal (2005b), forexample, state that it is critical for firms to enhance the resil-iency in their supply chains and call for research that investi-gates specific tactics that help firms develop such capabilities.Hendricks et al. (2008) note the lack of rigorous empiricalevidence that examines which strategies reduce the probabil-ity and severity of disruptions. Hendricks et al. (2008) sug-gest working closely with companies to produce case studiesthat capture specific characteristics of supply chain disrup-tions to shed light on the effectiveness of various mitigationstrategies. Other scholars recognize the large amount of workthat remains to be done in regards to supply chain disrup-tions and specific strategies to help firms increase the resil-iency within their supply networks (Hallikas et al. 2002;Zsidisin and Ellram 2003; Braunscheidel and Suresh 2009).This gap in the literature, namely the lack of understandingin how to help firms develop strategies to improve their resil-ience to supply chain disruptions, provides the motivationfor this research.

While disruptions may occur within upstream and down-stream portions of the supply chain, our research focuseson the supply side of the chain. We adopt Sheffi and Rice’s(2005) view of a resilient firm where vulnerability to a dis-ruption is decreased through specific mechanisms thatimpact resiliency. A firm’s resiliency enhancers are definedas: attributes that increase a firm’s ability to quickly andefficiently recover from a disruptive event. Conversely, afirm’s resiliency reducers have the opposite effect and aredefined as: attributes that decrease a firm’s ability toquickly and efficiently recover from a disruptive event. Ourstudy intends to provide an in-depth systematic investiga-tion of supply resiliency by employing a multi-industryempirical investigation of supply disruptions and corre-sponding supply design characteristics that enhance (resil-iency enhancers) or take away from (resiliency reducers)supply resiliency. As the body of knowledge relative to sup-ply resiliency is still in its infancy and findings are frag-mented across the literature, this study is designed to beexploratory in nature. While this study builds upon thefoundational work of Manuj and Mentzer (2008a) and oth-ers (e.g., Zsidisin and Ellram 2003; Elkins et al. 2005;Blackhurst et al. 2008) we did not rely solely on the currentbody of knowledge as this could result in missing importantelements of supply resiliency that have not yet been identi-fied. In essence, the objective of this research was to movetoward a holistic view of supply resiliency that is groundedin practice and thus qualitative methods were deemed asappropriate (Eisenhardt 1989; Yin 1994; Locke 1996;Strauss and Corbin 1998). From our findings, we deriveseveral empirical generalizations (Handfield and Melnyk1998) that culminate in a supply resiliency framework,which offers both research and managerial implications.This work answers the calls for empirical research thatinvestigates specific strategies to enhance supply resilience.While our empirical generalizations and framework cer-tainly need to be thoroughly examined in future research,we believe our research contributes to a solid foundation tofurther the maturation of the supply resilience body ofknowledge.

THEORETICAL FOUNDATION

Since our study is exploratory in nature with the intent ofdeveloping, as opposed to testing, theory (Handfield andMelnyk 1998), we used two theoretical bases to guide andframe our research (Eisenhardt 1989). Specifically, we usesystems theory (Bertalanffy 1951) as a broad framework toorganize supply chain characteristics that detract from resil-iency (reducers) and the resource-based view (RBV) of thefirm (Barney 1991) as a means to frame our findings relativeto organizational mitigation capabilities that increase resil-iency (enhancers). We briefly discuss these two theoreticalbases in light of supply disruptions and resiliency.

Systems theory

Organizations are systems (Bertalanffy 1951) that are openand therefore are influenced by and interact with the exter-nal environment (Katz and Kahn 1978). As supply chainsare composed of nodes that are interconnected by the phys-ical flow of materials (Towill et al. 1992), systems theory isan intuitive and widely used theoretical base in supplychain literature (cf. Frankel et al. 2008; Manuj and Mentzer2008b; Skipper et al. 2008). As open systems, organizationsrely on a steady flow of inputs that originate and areextracted from sources in the environment to sustain theiroperations (Deeter-Schmelz 1997; Zsidisin and Ellram2003), which illustrates the systems theory concept ofnegentropy—organizational self maintenance due to thepresence of environmental inputs (Bertalanffy 1956). Werecognize there are various types of organizational environ-ments. In this article, we refer to the task environment,which views the supply side of the chain as a source ofenvironmental inputs (cf. Dill 1958). As a reminder,although organizations interchange with their environment(i.e., receive materials from upstream suppliers and deliverproducts to downstream customers), we limit our investiga-tion to the supply side.

As open systems, the necessary inputs from the environ-ment will vary depending on the industry and a firm’s posi-tion in the supply network. In a manufacturing supply chain,for example, raw materials may be considered inputsupstream, whereas semifinished products may be consideredinputs farther downstream. Ideally, inputs flow from theenvironment (i.e., supplier) to the focal firm as scheduledand in a desired quantity and quality thus contributing tonegentropy. This ideal state of the system is altered whenunexpected events (i.e., disruptions) interrupt the normalflow of goods (Svensson 2000; Hendricks and Singhal 2003;Kleindorfer and Saad 2005). These disruptions, which wedefine as ‘‘unplanned and unanticipated events that disruptthe normal flow of goods and materials within a supplychain’’ (Craighead et al. 2007, 132), manifest themselves invarious forms. Disruptions, for example, can be anythingfrom a truck breaking down or a supplier’s workforce goingon strike, to extreme weather conditions that result in poweroutages or transportation issues. The impact of disruptionson a system varies depending on the level of resiliency withinthe supply chain.

Framework of Global Supply Resiliency 375

The resiliency of a supply chain and the recovery timefrom a disruption should be inversely related. In otherwords, as the resiliency of a supply chain increases the totalrecovery time decreases. A supply chain’s resiliency lies on acontinuum and thus a supply network can be classified asbeing more or less resilient. A vulnerable (i.e., less resilient)supply chain’s operation is volatile because it does not pos-sess the capabilities to continue operating when disruptionsoccur (Sheffi and Rice 2005). Therefore, the supply chain isvulnerable to disruptive events. Conversely, resilient supplychains have the ability to absorb or avoid disruptionsentirely (Sheffi and Rice 2005). Certain supply design charac-teristics may impact supply resiliency. We incorporate theconcepts of systems theory coupled with the structure of asupply chain (entities such as suppliers, manufacturers, andwarehouses connected by flows of materials) to develop aframework for our findings relative to supply resiliencyreducers:

1. Flows, which are associated with activities (e.g., transpor-tation) related to the extraction of inputs from the envi-ronment (Buckley 1967).

2. Flow units, which are the units (e.g., raw materials, sub-assemblies, finished products, service parts) that areextracted from the environment and are inputs to thefocal firm (Bertalanffy 1950).

3. Source, which is the supply node in the environmentwhere the inputs (i.e., flow units) originated (Scott andDavis 2003).

Resource-based view

Although supply chain disruptions are inevitable, organiza-tions may be able to lessen the disruption severity via mitiga-tion capabilities (e.g., Craighead et al. 2007) and thusenhance the resiliency of their supply chain. We use theRBV of the firm (Wernerfelt 1984; Barney 1991) to framethe mitigation capabilities. RBV, like systems theory, hasbeen a widely utilized theoretical base in supply chainresearch (cf. Mentzer et al. 2004; Esper et al. 2007; Byrdet al. 2008).

The RBV of the firm regards the firm as a collection ofresources and capabilities that may culminate in enhancedperformance (Wernerfelt 1984). Resources, which may betangible (e.g., technology) or intangible (e.g., knowledge),and may be combined to create capabilities that determinehow firms react to various internal and external threats andopportunities (Wernerfelt 1984; Barney 1991). For ourresearch purposes, we view these capabilities, which may bedefined as complex interactions and coordination of peopleand other resources (Grant 1991), as the mechanism to miti-gate the potential impact of a disruption and thus enhancesupply resiliency. This view is consistent with other research-ers. For example, research has purported that the impact ofa disruption is moderated when firms implement mitigationtactics before, or right after, a disruption occurs. Manuj andMentzer (2008a), divide the speed of risk into three uniquecategories: (1) the rate of the event that leads in loss, (2) the

rate losses occurs, and (3) the rate at which the risk event isdiscovered. Implementing mitigation strategies on a timelybasis after a disruption is discovered can significantly reducethe overall impact (Craighead et al. 2007; Manuj and Ment-zer 2008a). The longer it takes for a firm to execute mitiga-tion strategies, the more time a disruption has to propagate,grow, and negatively impact firm performance.

Barney (1991) describes three categories of resources thatmay be used to create capabilities. We use these categoriesto frame our results relative to supply resiliency enhancers:

1. Physical Capital Resources, which are tangible assets ofthe firm such as physical technology, equipment, andinventory (Williamson 1975).

2. Human Capital Resources, which are intangible assets ofthe firm such as management training, education, andexperience (Becker 1964).

3. Organizational and Interorganizational Capital Resour-ces, which are intangible assets of the firm such as theplanning, controlling, and coordinating of systems orthe relationships between the focal firm and firms withinthe environment (e.g., suppliers) (Tomer 1987).

Barney (1991) contends that each resource category aloneis not sufficient to create unique capabilities. Therefore,interaction and coordination of all three resource categoriesare necessary to create effective mitigation tactics. A firm,for example, that routinely shares information among supplychain members may discover that a disruption has occurred,but then rely on a highly trained manager to select and exe-cute the mitigation strategy. In this scenario, human capitalresources are used in conjunction with organizational and in-terorganizational capital resources.

Although we use the three above categories to frame ourfindings relative to resiliency enhancers, we recognize thatfirms are heterogeneous relative to their resource endow-ments (Teece et al. 1997). More importantly, when firms dis-cover resource gaps that need to be filled (Grant 1991) theymay be somewhat limited relative to the speed at which theoverall resource endowment may be altered. Teece et al.(1997, 514) state that resources ⁄ capabilities are ‘‘sticky’’ inthat ‘‘firms are, to some degree, stuck with what they haveand may have to live with what they lack.’’ Notwithstandingthis issue, firms can close resource gaps (Grant 1991) particu-larly in the longer run (Teece et al. 1997) and can quicklychange the rate at which they commit investments (moneyand time) to obtain additional resources (Dierickx and Cool1989). However, it is important to note that some resourcesmay be more difficult and slower to be realized than othersas firms strive to build resiliency.

METHODOLOGY

This study employed case research methodology (Eisenhardt1989), which is a rigorous and well-established researchmethod (Eisenhardt 1989; Yin 1994; Ellram 1996; Gibbertet al. 2008). The case research methodology was chosenbecause it is particularly well suited for conducting research

376 J. Blackhurst et al.

in areas with little preexisting theory (Meredith 1998; Vosset al. 2002; Gibbert et al. 2008) and facilitates conceptualframework development by using data gathered throughinteractions with key informants (Mahapatra et al. 2010).

Data for this study were gathered in two distinct phasesthat used multiple data sources and data collection methods.In the first phase, we performed an in-depth investigation ofa U.S.-based automobile manufacturer (Table 1). As notedin Voss et al. (2002) limiting the number of cases allowsscholars to achieve depth, rather than breadth, of knowl-edge, which was our primary objective of this phase. Specifi-cally, we obtained insights from a major automobile originalequipment manufacturer, two of its overseas first-tier suppli-ers, and a key warehouse ⁄distribution center involved in glo-bal product flow. Consistent with other case-based research,such as Ellram and Siferd (1998), Esper et al. (2007), Maha-patra et al. (2010), and Wu and Choi (2005), we adhered toguidelines and protocols described in Yin (1994) and Eisen-hardt (1989) and followed qualitative data analysis proce-dures outlined in Miles and Huberman (1994).

As discussed in Voss et al. (2002), it is extremely impor-tant to seek key informants who are knowledgeable aboutthe phenomenon being investigated. Thus, consistent withtraditional case-based research guidelines, key informantsfrom the automobile manufacturer (hereafter referred to asAutoMfg) were carefully selected from various positionswithin the firm to provide a holistic perspective of the firm’soperations in light of disruptions and resiliency. We collectedthe majority of data on a multiday visit to AutoMfg’s facil-ity. Similar to the interviews conducted in Closs et al. (2008),Esper et al. (2007), and Wu and Choi (2005), each interviewlasted between 45 and 90 min. The interviews were scheduledin intervals that enabled us to discuss the findings and refinethe interview protocol between sessions, which is commonpractice in case-based research while crafting instrumentsand protocols (Eisenhardt 1989; Stuart et al. 2002). The finalset of questions used to guide the interviews is shown inAppendix A.

Triangulation, which is the use of multiple data sourcesto obtain a more comprehensive understanding of the

Table 1: Automobile manufacturer (AutoMfg) case study—data source, method, and content

Position in supply chain Data source ⁄method Data content

AutoMfg focal entity Key informants:Semistructured interviews:Lasted 45–90 minConducted by face-to-face or bytelephone with seven executives

Supply chain design characteristics that amplify orincrease the impact of a disruption; mitigation strategiesemployed, and disruption recovery tactics used

Risk identification ⁄ assessment and classificationSupply chain risk and disruption management strategiesSupply chain risk management capabilities and activitiesInternational procurement and logistics process ⁄flows

Archival records:Presentations by:Operations ManagerRisk AnalystTwo Purchasing Engineers

First-tier supplierChina

Key informants:Semistructured interviews withAccount Manager for AutoMfgConducted by face-to-face or bytelephone


Archival records:Presentations by:Account Manager from AutoMfg

Product flow from supplier’s facility to AutoMfgSupply chain disruption management strategies

First-tier supplierSouth Korea

Key informants:Semistructured interviews withAccount Manager for AutoMfgConducted by face-to-face or bytelephone


Archival records:Presentations by:Account Manager from AutoMfg

Product flow from supplier’s facility to AutoMfgSupply chain disruption management strategies

Distribution centerEast Cost of UnitedStates

Key informant:Written response to interviewquestions from the InternationalReceiving Manager

(Note: The manager requested tosubmit written responses instead ofan interview)



phenomenon under investigation (Ellram 1996; Stuart et al.2002), is a key component of case research (McCutcheon andMeredith 1993; Yin 1994; Meredith 1998; Voss et al. 2002).Triangulation is commonly used to help establish constructvalidity in case study research (e.g., Bourgeois and Eisenhardt1988; Ellram and Siferd 1998; Choi and Hong 2002; Manrodtand Vitasek 2004; Wu and Choi 2005; Smaros 2007) and isoften considered to be a major strength of this research strat-egy (Yin 1994; Ellram 1996; Gibbert et al. 2008).

More specifically, triangulation helps overcome potentialdiscrepancies or limitations in the data by combining multi-ple forms of data that measure the same phenomenon (Yin1994; Gibbert et al. 2008). The use of data triangulation sig-nificantly increases the reliability and rigor of the researchfindings (McCutcheon and Meredith 1993; Ellram 1996).During this phase of the research, we extended great effortsto facilitate the triangulation process. First, we conductedsemistructured interview sessions with personnel in a varietyof functional areas such as logistics, procurement, opera-tions, and risk management. Again, the interviews werescheduled in a manner that allowed us to debrief and refinethe interview protocol between sessions (Eisenhardt 1989;Stuart et al. 2002). Second, we participated in presentationsmade by key personnel. The presentations addressed impor-tant aspects of AutoMfg’s global procurement process andcomprehensive risk management program. We were able tointeract with the informants during their presentations,which enabled us to probe beyond the surface to obtain richand more in-depth information. The presentations rangedfrom overviews of the global procurement and logistics pro-cesses ⁄flows to risk management capabilities and activities.Finally, we were able to examine archival records anddirectly observe risk mechanisms that AutoMfg utilized.

After reaching the point of theoretical saturation with Au-toMfg’s key informants, which is when collecting additionaldata yields no new insights (Eisenhardt 1989), we then gath-ered data from two of their first-tier suppliers. The supplierswere responsible for providing items in different procurementcategories and were based in separate countries. One supplierwas based in Korea and the other supplier was based inChina. Key informants from each of the first-tier suppliersparticipated in semistructured interviews, which were guidedby the same set of questions that were employed during theinterviews with AutoMfg’s key informants. Both first-tiersuppliers also made presentations relative to the flow ofproducts and disruptions ⁄ risk management. Once again,these presentations were interactive in nature and thusallowed us to probe for critical insights into the researchquestions initially identified. All interactions with AutoMfg’stwo first-tier suppliers took place at AutoMfg’s facility andwere conducted in English. Finally, we obtained insightsfrom a key warehouse ⁄distribution center located on theEast Coast of the United States. We were unable to conductinterviews with key informants at the distribution center;however, they responded to the interview questions in writ-ten format. We also had several conference calls with keypersonnel at AutoMfg both before and after the on-site visitto clarify and confirm information. Consistent with othercase study research (e.g., Bourgeois and Eisenhardt 1988),

we adhered to guidelines outlined by Eisenhardt (1989) andVoss et al. (2002) by having two of the researchers involvedin each interview and presentation. As noted by Eisenhardt(1989) and Miles and Huberman (1994), having tworesearchers involved in the data collection process is advan-tageous for several reasons. First, complementary insightscan be captured, which increases the ‘‘creative potential ofthe study’’ (Eisenhardt 1989, 538). Second, having multipleinvestigators enables convergence of observations and thus‘‘enhances confidence in the findings’’ (Eisenhardt 1989,538). Every session was recorded with a digital voice recor-der (Bourgeois and Eisenhardt 1988; Yin 1994; Voss et al.2002). The recordings were used to clarify and validate thedata when necessary. We followed the ‘‘24-hr rule’’ recom-mended by Yin (1994) and utilized by Bourgeois and Eisen-hardt (1988) that requires all notes relating to interviews andpresentations be transcribed within one day of the data col-lection. After the on-site data collection process, we reviewedthe data and refined our notes, which enabled us to integratethe findings from each source in a comprehensive manner.

The second phase of this study consisted of semistructuredtelephone interviews with executives at six firms in variousindustries (Table 2). The primary objective of this phase wasto obtain breadth, rather than depth, of knowledge. As sug-gested by Eisenhardt (1989) and McCutcheon and Meredith(1993), theoretical sampling was employed to strategicallyselect firms with an assorted collection of product flow andsupply chain design characteristics. Theoretical sampling iscommonly used in case study research and is consistent withseveral case-based research studies, including Choi and Hong(2002), Closs et al. (2008), and Wu and Choi (2005). Byselecting firms from different industry sectors that occupied avariety of positions along a supply chain, we were able toobtain various perspectives on the interview questions. Allexecutives interviewed were responsible for managing theirfirm’s product flow from various overseas suppliers and heldtitles, such as Senior Manager of Import Operations, Direc-tor of Global Distribution, and Chief Operating Officer.

The interview questions and data collection proceduresemployed in phase 1 were also used in this phase. Tworesearchers, for example, participated in each interview, sub-sequently transcribed their notes, and discussed the findingsvia phone after every session (Bourgeois and Eisenhardt1988; Eisenhardt 1989; Voss et al. 2002). These debriefingswere conducted within a 12-hr period following the interviewand thus adhered to the ‘‘24-hr rule’’ (Bourgeois and Eisen-hardt 1988; Yin 1994). Once all phone interviews were com-plete, we met face-to-face to organize and analyze the datato identify key findings within and across each interview(Jick 1979; Eisenhardt 1989). When we reached the point oftheoretical saturation (Eisenhardt 1989), we stopped collect-ing data. Although there is no exact number of cases thatshould be included in a study, generally 4–10 cases are ideal(Eisenhardt 1989). In case study research, however, it is notthe number of cases that is important, but rather that theresearcher continues collecting and analyzing data until theyreach theoretical saturation (Eisenhardt 1989). In fact, rigor-ous case-based research has been conducted with a widevariety of cases, including a single case (Boyer et al. 2002;


Manrodt and Vitasek 2004; Esper et al. 2007), three cases(Choi and Hong 2002; Grutter et al. 2002), four cases (Bour-geois and Eisenhardt 1988; Smaros 2007), six cases (Closset al. 2008), eight cases (Wu and Choi 2005), and 11 cases(Ellram and Siferd 1998).

The data collected from both phases were analyzedaccording to a three-step process outlined by Miles and Hu-berman (1994), which is widely used in case study research(e.g., Wu and Choi 2005; Closs et al. 2008). Step 1 consistedof first-level coding to summarize and describe the data. Step2 consisted of pattern coding to reduce the data by groupingsimilar codes and descriptions. The coding criteria employedin step 2 are listed in Appendix B. Step 3 consisted ofarranging findings into empirical generalizations that explainwhy certain supply chain characteristics contribute or takeaway from supply resiliency.

We considered using qualitative software analysis pro-grams, such as NUD.IST (QSR International Pty Ltd, Mel-bourne, Australia), Atlas.ti (Atlas.ti Scientific SoftwareDevelopment GmbH, Berlin, Germany), and MAXQDA(2010 version; VERBI GmbH, Berlin, Germany), to facilitatethe coding process. There are numerous types of qualitativesoftware packages available depending on the primary objec-tive of the research (Lewins and Silver 2004). Code-based

theory-building software, for example, assists researchers indeveloping data categories and visualizing relationshipsbetween categories (Lewins and Silver 2004). Alternatively,more text-based qualitative software packages provideinsight into natural language processing and focus more ongrammatical and semantic information embedded in bothunstructured and structured text (Lewins and Silver 2004).To ensure our understanding of the data was not restrictedby a particular software program, we choose to code thedata according to guidelines outlined in Miles and Huber-man (1994). Furthermore, qualitative software packages mayrequire a high degree of familiarization and are unable toincorporate aspects of the surrounding context in the analy-sis of the data (Bezborodova and Bennett 2004). The codingprocess plays an instrumental role in qualitative data analy-sis and thus, in order to preserve to context surrounding thequotes and remain connected to the data, we followed guide-lines put forth by Miles and Huberman (1994) to develop adeeper and more holistic understanding of the data.

Although a distinct form of qualitative research, the casestudy method shares certain similarities with grounded the-ory. Data for case studies and grounded theory, for example,come from similar sources, such as interviews, observations,and archival records (Eisenhardt 1989; Yin 1994; Strauss

Table 2: Phase 2 interview participants—firms, executives, and company profiles

Firm

Informant’s

corporate title Company profile

PharMfg (pharmaceuticalmanufacturer)

Director of GlobalDistribution

International pharmaceutical company that manufacturesprescription medication, consumer health products, and vaccines

Workforce consists of 100,000+ employees with manufacturingfacilities in Ireland, Singapore, and the United Kingdom

Government agencies heavily regulate supply chainVertically integrated supply chainSupply chain security a major issueProduct: Long development times, small size, high costs

Retailer1 Senior Manager,Import Operations

U.S.-based mass merchandise retailer with 1,200+ stores in theUnited States

Imports 24% of merchandise from 60+ countriesExperiences large seasonal demand fluctuations

Retailer2 Chief Logistics Officer Large discount retailer with 2,000+ stores in the United StatesWorkforce consists of 25,000+ employeesImports 50% of merchandise from overseas, majority (80%) ofimports come from China

Retailer3 Vice President,International SupplyChain

U.S.-based retailer with 1,500+ stores in the United StatesImports 10% of merchandise from 35 counties, majority (80%) ofimports come from the Asia basin

LogProvider 1 (logisticsprovider 1)

Chief OperatingOfficer

International logistics provider with 300+ warehouse and officelocations

Workforce consists of 10,000+ employeesProvides distribution, freight forwarding, warehousing, and supplychain management consulting ⁄ assistance services

LogProvider 2 (logisticsprovider 2)

Manager of LatinAmerican Operations

International end-to-end logistics provider who operates in 214counties

Provides wide range of services from single package shipments tothird-party logistic solutions


and Corbin 1998). While the primary objective of groundedtheory is to develop theory (Glaser and Strauss 1967; Corbinand Strauss 2008), case studies can also be employed to gen-erate theory (Eisenhardt 1989; Yin 1994). The grounded the-ory approach to qualitative research can be seen as lying ona continuum. On one end, researchers collect and analyzedata without any a priori assumptions (Glaser and Strauss1967; Glaser 1992; Locke 1996). With this approach togrounded theory, the phenomenon under investigation com-pletely shapes the theorizing process (Glaser and Strauss1967; Glaser 1992; Locke 1996). On the other end of thecontinuum, it is believed that researchers can gain deeperinsights into the data by allowing a priori theory to helpguide the data collection and analysis (Locke 1996; Straussand Corbin 1998). The latter approach to grounded theory ismore structured and shares numerous similarities with thecase study method, which was employed in this research.

RESULTS

The results of the analysis yielded six sets of empirical gener-alizations in two major areas. The first three sets of empiricalgeneralizations are related to factors that reduce the impactof a disruption and thus contribute to supply resiliency. As areminder, we refer to these as supply resiliency enhancers.The last three sets of empirical generalizations are related tofactors that amplify the impact of a disruption and thus takeaway from supply resiliency. Again, we refer to these as sup-ply resiliency reducers. When presenting the results we enfoldliterature (Eisenhardt 1989) into the discussion when appro-priate. This allows the research findings to be compared withextant literature to provide a deeper and more holistic under-standing (Eisenhardt 1989).

Resiliency enhancers

Analysis of the data revealed a variety of factors that canenhance supply resiliency (Table 3). We divided these resil-iency enhancers into three main categories including humancapital resources (Becker 1964), organizational and interor-ganizational capital resources (Tomer 1987), and physicalcapital resources (Williamson 1975), which is consistent withthe RBV of the firm (Barney 1991).

Human capital resourcesThe data analysis culminated in an assortment of human cap-ital factors (Becker 1964) that can increase supply resiliency.These factors involved the knowledge and training of employ-ees, understanding the total cost of supply chain manage-ment, and the ability to conduct an effective postdisruptionanalysis. Education and training of supply chain employeeswas identified by six of the seven firms as playing a major rolein increasing supply resiliency. LogProvider1, for example,discussed how supply chains are extremely complex systemsand thus managers must be ‘‘fluent’’ in all aspects of the sup-ply chain. Similarly, Retailer2 stated that educated employeesare ‘‘key’’ for effectively managing supply chain disruptions.The proper education and training of supply chain managers

equips them with the necessary skills to know when it isappropriate to take action. The action performed may becommunicating with firms in other areas of the supply chain,locating inventory throughout the supply chain, or imple-menting employee overtime. The most effective action consid-ering a variety of factors (i.e., the type of disruption,characteristics of the supply chain, resources available, etc.) isdetermined by managers employing knowledge acquiredthrough training and drawing on their past experiences. Whena disruption occurs, for example, trained and experiencedmanagers will probably stabilize the supply chain morequickly than managers without training and experience.

Four of the seven firms noted the importance of employ-ees having a comprehensive understanding of cost ⁄benefittrade-offs when managing risks in a supply chain. AutoMfgspecifically noted that supply chain managers must under-stand cost ⁄benefit trade-offs in order to effectively handlesupply chain disruptions, which has also been highlighted inthe literature (e.g., Miemczky and Holweg 2004; Hale andMoberg 2005; Meepetchdee and Shah 2007; Manuj andMentzer 2008b). AutoMfg, for example, attempted to bufferagainst shipments not arriving on time by holding six weeksof inventory for all globally sourced parts. Although holdingexcess inventory is a commonly used approach to guardagainst disruptions (Ho 1992), there is certainly a hefty costassociated with this mitigation strategy. In today’s economy,supply chain managers have limited resources and thus mustcarefully decide how and where to allocate them (Chopraand Sodhi 2004). Obtaining a holistic understanding of howcosts are related will enable managers to systematically com-bine financial resources and thus be able to more effectivelymanage supply chain disruptions.

Postdisruption analysis was identified by four of the firmsas a primary factor in enhancing supply resiliency. When adisruption is handled successfully, firms should probe beyondthe surface to understand how and why it was handled in asuccessful manner. Managers, for instance, should ask them-selves was communication effective throughout the supplychain and what mitigation strategies were employed? Con-versely, when a disruption is handled in a suboptimal man-ner, lessons should be captured and disseminated throughoutthe supply chain. Firms that learn from postdisruption feed-back are generally better equipped to handle future supplychain disruptions.

Hence:EMPIRICAL GENERALIZATION 1: Human capital

enhancers are positively related to supply resiliency.

Empirical generalization 1a: Supply chain education andtraining are positively related to supply resiliency.

Empirical generalization 1b: Understanding cost ⁄benefittrade-offs are positively related to supply resiliency.

Empirical generalization 1c: Postdisruption feedback is posi-tively related to supply resiliency.

Organizational and interorganizational capital resourcesIn addition to human capital resources, organizational andinterorganizational capital resources (Tomer 1987) can also


increase supply resiliency. These factors center on intangibleassets, such as interactions between groups within the firmand relationships between the focal firm and other firmswithin the supply chain, such as suppliers (Barney 1991).Organizational and interorganizational capital resourcesrevealed during the data analysis include well-defined com-munication networks, cross-functional risk managementteams, predefined contingency plans, partnering with cus-toms programs and creating port diversification plans, anddeveloping supplier relationship management programs.

Six of the seven firms stressed the need to have communi-cation protocols predefined so when a disruption occursmanagers are cognizant of who to contact and how to com-

municate (i.e., phone, email, fax). Having well-defined com-munication channels eliminates confusion and can helpprevent costly delays in deploying mitigation tactics as infor-mation is quickly and efficiently distributed (Fugate et al.2009). AutoMfg, for example, discussed the importance ofwell-defined communication protocols.

Likewise, cross-functional risk management teams wereidentified by six firms as playing a large role in enhancingsupply resilience. Risk management teams that are cross-functional in nature are able to optimize the entire supplychain rather than small portions of the supply chain andthus eliminate potential bottlenecks in the system. Cross-functional risk management teams also help avoid a ‘‘silo

Table 3: Supply resiliency enhancers: coding

Supply resiliency enhancers PharMfg AutoMfg Retailer1 Retailer2 Retailer3 LogProvider1 LogProvider2

Human capital resourcesEducation and training ofemployees to execute supplychain contingency plans

X X X X X X

Employee’s understanding ofcost ⁄benefit trade-offs whenmanaging risk in a supplychain

X X X X

Ability to performpostdisruption analysis

X X X X

Organizational and interorganizationalcapital resourcesDefined communicationprotocols

X X X X X X

Cross-functional supply chainrisk management teams

X X X X X X

Predefined and ⁄or self-executingcontingency plans

X X X X X

Partnering with customsprograms (such as C-TPAT)and ⁄or developing portdiversification plans

X X X X X

Developing supplier relationshipmanagement programs

X X X X X X X

Physical capital resourcesUse of safety stock X X X X XIncreased visibility in the supplychain

X X X X X X X

Exception reporting systems andpredictive tools for earlyawareness of impendingdisruptions

X X X X X X

Risk monitoring systems foreach node (i.e., firm) in thesupply chain

X X X X X

Ability to quickly redesign thesupply chain

X X X X

Note: PharMfg, pharmaceutical manufacturer; AutoMfg, automobile manufacturer; LogProvider 1, logistics provider 1; LogProvider 2, logis-

tics provider 2.


mentality’’ and enable firms to stabilize their supply chainafter a disruption quickly and more efficiently.

Five of the seven firms discussed the value of developingand practicing contingency plans in anticipation of a disrup-tion as means to help reduce response time. Several firmsalso mentioned self-executing plans, which are plans that areautomatically triggered when a disruption occurs withouthuman intervention. Practicing contingency plans can helpreduce mistakes and developing self-executing plans enablefirms to deploy mitigation strategies quickly, thereby increas-ing supply resiliency.

Five firms also discussed the importance of utilizing cus-toms programs such as Customs-Trade Partnership AgainstTerrorism (C-TPAT) to reduce the variability and length oftime it takes for goods to pass through customs. As a meansto avoid stringent customs policies and to help diversify riskin case of port closure or congestion, some firms were in theprocess of developing port diversification plans. Retailer1,for example, implemented a port diversification plan toreduce the likelihood of disruptions in the flow of goodsthrough U.S. ports. Partnering with customs programs anddeveloping port diversification plans can help streamline thethroughput of goods into and out of countries and thuseliminate potential bottlenecks in a supply chain.

All firms stressed the need to develop supplier relationshipmanagement programs to mitigate the exposure of risks fromsuppliers. When discussing sources of risk in a supply chain,AutoMfg stated their suppliers are their ‘‘number oneworry.’’ Some firms discussed developing higher levels oftrust with their key suppliers while other firms stressed theimportance of understanding supplier capacity restrictions aswell as options for alternative suppliers.

Hence:EMPIRICAL GENERALIZATION 2: Organizational

and interorganizational capital enhancers are positivelyrelated to supply resiliency.

Empirical generalization 2a: Defined communication net-works are positively related to supply resiliency.

Empirical generalization 2b: Cross-functional risk manage-ment teams are positively related to supply resiliency.

Empirical generalization 2c: Developing and practicing con-tingency plans (including self-executing plans) are posi-tively related to supply resiliency.

Empirical generalization 2d: Partnering with customs pro-grams and developing port diversification plans are posi-tively related to supply resiliency.

Empirical generalization 2e: Developing supplier relationshipmanagement programs are positively related to supplyresiliency.

Physical capital resourcesPhysical capital resources (Williamson 1975), which consistsof tangible assets, also contribute to supply resilience. Thesefactors include the use of safety stock, technologies thatincrease visibility within the supply chain, systems that moni-tor the supply chain and predict weak areas, the ability tomanage risks at individual nodes (i.e., firms), and the capa-

bility to quickly redesign the supply chain when disruptionsoccur.

Five of the seven firms discussed holding safety stockthroughout the supply chain as a primary strategy to miti-gate the impact of disruptions (Ho 1992). Pharmaceuticalmanufacturer (PharMfg) noted that they rely heavily onsafety stock to reduce the likelihood of lost sales due tostock outs, which are extremely costly because of the highprofit margins on many pharmaceutical products. One man-ager stated:

…Making a sale has always been more important thanthe amount of inventory you hold because of the marginswe work with. The margins are so huge that every saleadds dollars to the bottom line.

While carrying high levels of inventory throughout theentire supply chain can actually decrease resiliency (Giunip-ero and Eltantawy 2004), understanding where inventoryshould be strategically placed, in what form it should beheld, and how much is necessary can give a company a com-petitive advantage and can increase their supply resiliency.

All seven firms discussed the need for increased visibilitywithin the supply chain. AutoMfg noted that managing asupply chain with limited visibility is a particularly challeng-ing task (Lorentz et al. 2007) and stated:

…There’s one thing I know that we struggle with—thatis visibility into our Tier 2 suppliers.…We know whenthey’re shipping their seats to us…but we don’t knowwho the supplier buys the leather from or some of theelectronics…we don’t know where they are…and if theirTier 2 has a serious failure…

Increasing the visibility within a supply chain can revealwhere resources are located, where risk is present, and howdisruptions propagate throughout the supply chain. Under-standing these factors can help firms manage disruptionsmore effectively because they know how the supply chain isdesigned, how the system reacts to various external influ-ences, and where inventory is located.

Six firms highlighted the need for systems that help themmonitor their supply chains in real-time to be able to makestrategic decisions to avoid impending disruptions. Retailer3,for example, noted that a ‘‘real-time, end-to-end supplychain wide monitoring system’’ would be particularly valu-able in helping them manage their supply chains. Implement-ing control limits or exception reporting (i.e., flaggingactivities when they fall outside acceptable limits) was dis-cussed as a means to helps firms engage in reactive disrup-tion discovery because supply chains are simply too largeand complex to track each individual event. Conversely,Retailer2 monitors their supply chain and attempts to predictdisruptions by closely following issues, such as labor negotia-tions, trade contracts, and regulatory changes. A managerfrom Retailer2 stated:

My expectation for my staff is that they are well readand they stay up with what is going on in the


industry—what is happening with labor negotiations inthe east coast? What is happening with trade relationswith China?

Five of the seven firms noted the importance of monitor-ing individual nodes within a supply chain in order to pre-dict disruptions. Both Retailer1 and PharMfg, for example,receive weekly updates from each firm in their supply chainson issues related to transportation carriers, seaports, security,and regulations in attempt to anticipate disruptions. Moni-toring the entire supply chain, as well as individual nodes,can reveal where a supply chain is most susceptible to risksand can help firms predict disruptions before they occur.Strategies that help firms discover disruptions or are able togive warning signs before a disruption occurs allow firms torecover faster and enable them to employ tactics to avoiddisruptions altogether.

Four firms discussed the need to be able to quickly rede-sign their supply chains to reduce the impact of disruptions.AutoMfg highlighted a particular incident when a tornadodamaged one of their manufacturing plants. They wereunable to quickly reroute materials from upstream suppliersto other manufacturing facilities, which significantlyincreased the detrimental impact of the disruption to thesupply chain. To effectively handle disruptions, a firm mustunderstand the design of their supply chain from beginningto end, which was noted when one manager stated, ‘‘When anode becomes a bottleneck, you need to be able to quicklyre-route and ramp up at other nodes.’’

The ability to quickly redesign a supply chain facilitates afirm’s recovery from disruptions and thus may increase thesupply chain’s resiliency.

Hence:EMPIRICAL GENERALIZATION 3: Physical capital

enhancers are positively related to supply resiliency.

Empirical generalization 3a: Safety stock is positively relatedto supply resiliency.

Empirical generalization 3b: Visibility in the supply chain ispositively related to supply resiliency.

Empirical generalization 3c: Monitoring systems and otherpredictive risk tools are positively related to supply resil-iency.

Empirical generalization 3d: The ability to monitor risk atindividual nodes (i.e., firms) in the supply chain is posi-tively related to supply resiliency.

Empirical generalization 3e: The ability to quickly redesign asupply chain is positively related supply resiliency.

Resiliency reducers

In addition to discovering factors that can increase supplyresiliency, factors having the opposite effect were alsorevealed (Table 4). We divided these resiliency reducers intothree main categories including flow activities (i.e., actionsrequired to move materials from one node to another)(Buckley 1967), flow units (i.e., items ⁄materials that flowthrough a supply chain) (Bertalanffy 1950), and source of

flow units (i.e., the node from where the flow unit originated)(Scott and Davis 2003) (Table 4).

Flow activitiesFactors related to the flow of material between nodes may sig-nificantly reduce supply resilience (Buckley 1967; Svensson2003). These factors include the number of nodes in the supplychain, presence of regulation and security issues, and conges-tion of ports and vessel capacity restrictions in the supplychain.

Four of the seven firms noted that the number of nodes ina supply chain and the system’s resiliency are inverselyrelated. That is, as the number of nodes increase, the supplychain becomes longer and more complex, which increases theamount of time it takes for materials to flow through thesupply chain and thus reduces the system’s responsivenessand resiliency (Rahman 2002).

Five firms noted that the presence of security and customsregulations also have a negative impact on supply resiliencybecause additional precautions (i.e., security check points)are typically put in place that increase the amount of time ittakes for materials to flow through a supply network. Log-Provider2, for example, revealed the primary bottleneck andmain source of disruptions in the flow of products into coun-tries is getting goods through customs and stated, ‘‘One ofthe biggest drivers of supply chain disruptions is customsissues around the world.’’

Four of the seven firms discussed how port congestion andvessel capacity restrictions can reduce supply resiliency. Retai-ler2, for example, noted that vessel capacity during peak sea-son out of China has become a critical problem and stated:

During a typical retail peak season, because there is somuch merchandise being shipped out of China, some-times there are problems associated with vessel capacityand availability—it becomes cumbersome to get space.

As ports and vessels reach their maximum capacities, itwill take longer to get items into a country and it willbecome more challenging to move materials through the sys-tem, which may reduce supply resiliency.

Hence:EMPIRICAL GENERALIZATION 4: Flow activity

reducers are negatively related to supply resiliency.

Empirical generalization 4a: The number of nodes in thesupply chain is negatively related to supply resiliency.

Empirical generalization 4b: Stringent security and customsregulations are negatively related to supply resiliency.

Empirical generalization 4c: Port and vessel capacity restric-tions are negatively related to supply resiliency.

Flow unitsFactors related to the product itself (i.e., flow unit) can alsoreduce supply resiliency (Bertalanffy 1950). These factorsinclude the complexity of the product as well as stringentstorage and quality requirements, which make it more diffi-cult to source and move products through a supply chain.


Six of the seven firms discussed how complex products(i.e., products that are difficult to produce, unique or chal-lenging to source, and ⁄or required some sort of proprietarytechnology) make it more difficult to manage risk in a supplychain. Characteristics of complex products result in a longerand typically more difficult recovery from a supply chain dis-ruption. AutoMfg specifically noted the challenges associatedwith complex parts:

Unfortunately in automotive a lot of our stuff is likethat: can’t be resourced, long lead time, there is no alter-native and it’s extremely complex…I would say anexample that—think about the instrument panel of thefront dash board of your car.…so they’re (suppliers) aredoing a lot of module assembly and they just give youthe module and how do you go to somebody else to getthat done if there’s a problem. You can’t. And they mayhave the patent, some legal barriers and you run into thisoverseas—if you’re buying something specialized youknow all bets are off.

Four firms noted that stringent storage and ⁄or qualityrequirements of materials can significantly reduce supplyresiliency. A particularly vulnerable component or a partthat requires cold storage, for instance, is more difficult tomove in a supply chain than one with no requirements.Rigid storage and quality requirements may make it more

difficult to recover from supply chain disruptions due to theadded precautions and specific needs of the product.

Hence:EMPIRICAL GENERALIZATION 5: Flow unit reducers

are negatively related to supply resiliency.

Empirical generalization 5a: Product complexity is negativelyrelated to supply resiliency.

Empirical generalization 5b: Stringent storage and qualityrequirements are negatively related to supply resiliency.

Source of flow unitsFactors related to the source of the flow unit can negativelyimpact supply resilience (Svensson 2000; Scott and Davis2003). These factors include the volatility of the supplier’slocation and issues related to labor and manufacturingcapacity at the supplier’s facility.

Five of the seven firms noted that suppliers who arelocated in risk prone areas and ⁄or are geographically clus-tered increase the likelihood of disruptions within a supplychain. Retailer2, for example, stated that the clustering ofsuppliers increased the severity of disruptions within theirsupply chain. Several firms noted that suppliers are enticedby factors such as cheap labor to locate in geographicallyunstable areas of the world (Manuj and Mentzer 2008a).AutoMfg discussed how they are more concerned about

Table 4: Supply resiliency reducers: coding

Supply resiliency reducers PharMfg AutoMfg Retailer1 Retailer2 Retailer3 LogProvider1 LogProvider2

Flow activitiesNumber of nodes in the supplychain

X X X X

Stringent customs regulationsand security issues

X X X X X

Port congestion and vesselcapacity restrictions

X X X X

Flow unitsProduct complexity (number ofand uniqueness of parts andneed for proprietarytechnology)

X X X X X X

Stringent quality and storagerequirements

X X X X X X X

Source of flow unitsVolatility of supplier’s location(e.g., political uncertainty ornatural disasters, such astsunamis) and ⁄or supplierclusters

X X X X X

Limitations on suppliermanufacturing capacity andlabor availability

X X X X

Note: PharMfg, pharmaceutical manufacturer; AutoMfg, automobile manufacturer; LogProvider 1, logistics provider 1; LogProvider 2, logis-

tics provider 2.


regional disruptions, which affect a cluster of suppliers,rather than disruptions that impact individual suppliers.Regional disruptions can manifest themselves in variousforms, such as extreme weather conditions (i.e., tsunamis) orpolitical uncertainty, which was specifically highlighted byAutoMfg as a source of recent regional disruptions withintheir supply chain. One manager stated:

I would say that concentration (of suppliers) in a point isimportant…You have a lot of suppliers in one region andthen something happens to that region. It really is regionaldisruptions you worry about more than a specific supplier.

Four firms discussed that restrictions on a supplier’s abil-ity to produce goods, which includes manufacturing capacityand labor availability, negatively impacts supply resiliency.Retailer1, for example, noted that all their suppliers are pre-qualified and are required to specify their maximum manu-facturing capacity in order to reduce the likelihood ofdisruptions. Likewise, Retailer2 discussed the importance ofmonitoring labor issues in the supply chain.

Hence:EMPIRICAL GENERALIZATION 6: Source reducers

are negatively related to supply resiliency.

Empirical generalization 6a: The volatility of a supplier’slocation and supplier clusters are negatively related tosupply resiliency.

Empirical generalization 6b: Limitations on supplier capacityand labor availability are negatively related to supplyresiliency.

DISCUSSION AND CONCLUSIONS

In today’s highly competitive global business environment,having a resilient supply chain should give companies a com-petitive advantage and therefore building a resilient supplychain should be a strategic initiative (Sheffi and Rice 2005).A number of scholars note the importance of increasing

resiliency within a supply chain (e.g., Zsidisin and Ellram2003; Zsidisin et al. 2004, 2005a; Sheffi and Rice 2005), butthe literature on how to increase resiliency is fragmented andprovides a more general overview of supply resiliency.Because a systematic investigation that operationalizes theconcept of resiliency has not been performed, specific strate-gies that increase supply resiliency are unknown and thuscompanies have little guidance on which tactics are mosteffective. This study attempts to fill that gap in the literatureand provides both research and managerial implications.

The main research contribution of this study is an empiri-cally derived framework of supply resiliency (Figure 1). Sys-tems theory (Bertalanffy 1951) and the RBV of the firm(Barney 1991) are used to ground this research and thus pro-vide a solid theoretical foundation for the framework. Sys-tems theory (Bertalanffy 1951) is employed to captureessential components of open systems including flows (Buck-ley 1967), flow units (Bertalanffy 1950), and sources of flowunits (Scott and Davis 2003). The components of open sys-tems both individually and collectively detract from supplyresiliency and thus may act as resiliency reducers. Firms maybe able to moderate the impact of resiliency reducers. How-ever, resiliency reducers may fall outside a firm’s control(such as customs regulations) and therefore it could be moreeffective for firms to focus on developing resiliency enhanc-ers. Resiliency enhancers are created by combining both tan-gible (i.e., physical capital resources) (Williamson 1975) andintangible resources (i.e., human capital) (Becker 1964) andorganizational and interorganizational capital resources(Tomer 1987), which is consistent with the RBV of the firm(Barney 1991). Several empirical generalizations were derivedfrom the research findings that address specific characteristicsthat may enhance or reduce supply resiliency. While it maybe possible to test all six sets of empirical generalizations in asingle large-scale empirical study, modeling the various inter-actions between variables could become extremely complexand could culminate in a lengthy survey instrument. Further,there may be moderating effects both within and betweeneach enhancer or reducer. Each resource, for example,

Enhancers ReducersSupply Resiliency

Education and Training

Cost/Benefit Knowledge

Post-Disruption Feedback

Human Capital

Resources

Physical Capital

Resources

Organizational &InterorganizationalCapital Resources

Sources of Flow Units

Communication Protocols

Cross-Functional Risk Management Teams

Contingency Plans

Customs Programs/Port Diversification Plans

Supplier Relationship Management

Safety Stock

Visibility Tools

Node Monitoring/Exception Tools

Redesign Tools

Number of Nodes

Stringent Security and Customs Regulations

Port/Vessel Capacity Restrictions

Product Complexity

Stringent Storage/Quality Requirements

Volatility of Supplier’s Location

Supplier Capacity/Labor Restrictions

Flow Units

Flow Activities

+ -

Figure 1: Framework of supply resiliency.


consists of factors (e.g., education and training, supply chainknowledge, and postdisruption feedback) that interact to cre-ate individual resource categories (i.e., human capitalresources). The resource categories then interact amongthemselves to enhance supply resiliency. Consequently, inter-actions between factors within one resource category mayaffect interactions between resource categories. In sum, thecomplexity of the interaction among variables may make itchallenging, if not impossible, to capture all the interactionsin one broad study. Multiple investigations may be necessaryso researchers are able isolate and control certain variableswhile manipulating others. One study, for instance, couldfocus on human capital resources by building on the extantresearch on strategies to build and management knowledgein supply chains (e.g., Craighead et al. 2009; Fugate et al.2009).

We also believe a particularly fruitful investigation is toanalyze the level of resiliency within supply chains and per-formance via a longitudinal study. For example, researcherscould capture the level of resiliency within each supply chainat the beginning of the study. The supply chains could bemonitored over a certain period, which would allowresearchers to observe how resilient supply chains respond todisruptions compared to nonresilient supply chains.Researchers could also compare the outcome of specific dis-ruptions to quantify the value of supply resiliency.

While the resiliency framework (Figure 1) lays the ground-work for a series of research studies, we believe it also offerspractical implications. For example, managers could use theframework to assess the current level of supply resiliency intheir supply chains or, more realistically, segments of theirsupply chains. In essence, the framework could be convertedto a type of supply resiliency scorecard. Managers could thensummarize their resiliency assessments into a supply resil-iency matrix (a possible matrix is shown in Figure 2) thatcaptures the various levels of risk in their supply chain. Eachquadrant in the matrix captures a level of resiliency inherentin the supply chain, and more importantly, could serve as abasis to prioritize supply resiliency building efforts.

Vulnerable supply chains, which have low resiliency en-hancers and high resiliency reducers, are the most problem-atic and thus warrant the majority of a firm’s attention andresources. These supply chains are particularly vulnerableand even minor disruptions may have a severe impact on thefirm’s operations. Volatile supply chains have high resiliencyenhancers and high resiliency reducers, which makes themextremely unpredictable and hard to manage. When a dis-ruption occurs in a volatile supply chain, for example, thesystem may absorb the disruption one time and experiencesevere repercussions the next. Conversely, sensitive supplychains have low resiliency enhancers and low resiliencyreducers. While this may not appear to be problematic, sup-ply chains are complex systems (Levy 1994; Choi and Krause2006) and thus even small disruptions could increase inseverity and propagate both upstream and downstreamwithin the supply chain (this is consistent with the conceptof the sensitivity to initial conditions—cf. the literature oncomplexity ⁄ chaos theory). Resilient supply chains have highresiliency enhancers and low resiliency reducers, which is theideal situation. Resilient supply chains are able to absorbdisruptions and return to stable conditions quickly (Sheffiand Rice 2005), which could give companies a unique com-petitive advantage.

We believe that the framework (Figure 1) and matrix (Fig-ure 2) are versatile in that they are not industry specific andcan be employed to determine the level of supply resiliencyin certain segments of supply chains (i.e., for particularcountries or regions of the world) or for specific productflows (i.e., mission-critical items). The framework and matrixcan serve as tools for managerial decisions relative to supplyresiliency within a firm’s supply chain. Of course, supplychains are dynamic and therefore, the supply resiliencymatrix ⁄ typology should be revisited when necessary.

Clearly, much more research is needed to further investi-gate the insights provided by the framework—this frame-work should serve as a starting point for future research onsupply resiliency and we certainly do not claim that it iscomprehensive or conclusive. In addition to the futureresearch needs discussed above, future studies can branch offin a number of different directions. Some of the possibleresearch examinations include, but are not limited to thefollowing:

• The empirical generalizations proposed in this studyshould be further investigated across multiple serviceand ⁄or manufacturing sectors with the intent of captur-ing new insights on both supply resiliency enhancers andreducers. The empirical generalizations lend themselves tobe tested by a variety of research methods, several ofwhich were highlighted above.

• Future research should analyze various company andsupply chain contextual variables in light of our findings.It would be quite valuable to investigate if ⁄how thesestrategies ⁄practices affect our proposed supply resiliencyenhancers and reducers.

• In our study, we captured resources that impact resil-iency. Future research should examine the process,including cost ⁄benefit issues, of if ⁄how firms can close

Volatile Supply Chain Resilient Supply Chain

Vulnerable Supply Chain Sensitive Supply Chain Res

ilien

cy E

nhan

cers

Low

Hig

h

Resiliency ReducersHigh Low

Figure 2: Supply resiliency matrix.


resource gaps. As we previously discussed, resource stick-iness will likely be an issue.

• While our intent was to be more inclusive relative to theenhancers and reducers, future research should scrutinizeeach and every factor. It is likely that some factors willplay a much larger role in building resiliency than theothers.

• A factor that did not surface in the research findings, butcould be investigated in future research studies is the con-nectivity of nodes via information sharing. Researchers,for example, could investigate the impact informationsharing across cultures has on mitigating or enhancingrisk within a supply chain. Findings from this study areparticularly important in today’s global business environ-ment where firms are more likely to source from compa-nies in foreign counties and thus face cultural differenceson a daily basis both within and between firms.

• Related to the issue directly above, the ‘‘blank’’ spaces inTables 3 and 4 need to be thoroughly examined. Webelieve investigating this study’s findings from a ‘‘whynot’’ perspective could provide fascinating new insights,such as why certain resources were allocated in supplychains with various product characteristics (e.g., high-costproducts, products with stringent storage requirements,or highly complex products). A fruitful next step couldbe matching product characteristics and resource alloca-tion for optimal supply resiliency and thus enhanced sup-ply chain performance.

• Although not directly captured from the research find-ings, we suggest there may be interaction effects (shownby dotted-line in Figure 1) among both the reducers andenhancers. In the latter case, this would be consistentwith the concept of complementarity (i.e., synergy—cf.Zhu 2004) between a firm’s internal resources. Comple-mentarity between resources arises when enhancing oneresource also increases the value of the other resources(Zhu 2004). Further investigation is necessary to deter-mine if complementarities among a firm’s enhancementresources exist.

In sum, the findings from this study provide guidance onstrategies that may increase supply resiliency and lay thefoundation for future research, but certain limitations doexist. First, although interviews provide rich data, intervie-wees are susceptible to natural human biases (Berg 2003;Alvesson 2003). Conducting interviews with the automobilemanufacturer’s first-tier suppliers on-site at the manufac-turer’s location, for example, may have influenced the infor-mants to provide socially desirable answers. Providingsocially desirable responses, however, is a limitation inregard to all interviews (Berg 2003; Alvesson 2003) and isnot a unique limitation of this study. To reduce this poten-tial bias, a future research opportunity could be to conductinterviews with suppliers at their own facility or at a neutrallocation. Additionally, in the second phase of the study, weconducted interviews via telephone, rather than face-to-face.Removing the face-to-face interaction and allowing respon-dents to be interviewed in the privacy of their office mayhave reduced the desire of informants to provide socially

desirable responses. However, conducting interviews via tele-phone could be a potential limitation of the study becausewe were not able to observe nonverbal channels of communi-cation (Berg 2003).

Employing multiple data sources and data collection meth-ods helped mitigate these potential limitations and allowedthis study to provide a more holistic perspective of supplyresiliency that spans across industry sectors. Research onsupply chain disruptions and corresponding strategies thatincrease resiliency is still in its infancy and thus a consider-able amount of work remains to be done in this area (e.g.,Hallikas et al. 2002; Zsidisin and Ellram 2003; Braunscheideland Suresh 2009). We hope our efforts in this research helpto fill this gap and contribute the supply resilience body ofknowledge.

APPENDIX A: FINAL INTERVIEW PROTOCOL

1. Please provide a brief description of your internationalsupply chain design (location of overseas sources andgeneral flow of information and material flow).

2. What supply chain ⁄product characteristics amplify thepotential for, or impact of, the disruptions?

3. What characteristics are common among severe disrup-tions?

4. How does a firm quickly discover the disruption and ⁄orthe event that will trigger the disruption? What are thekey metrics available to detect early warning signals?

5. Given the discovery of the disruption ⁄ event, how does afirm efficiently assess what areas of the supply chain willbe affected by the disruption?

6. In general, what are the alternative activities and mecha-nisms that may be employed to deal with disruptions?

7. What are the primary barriers to an effective disruptionrecovery? What are the technology, personnel, and deci-sion-making processes enablers of an effective recoveryprogram?

8. How can the supply chain be redesigned to minimize thepotential for, and impacts of, disruptions?

APPENDIX B: CODING CRITERIA

Enhancer Categories (Barney 1991)

(P) Physical Capital Resources are the tangible assets ofthe firm including physical technology, facilities, equip-ment, and inventory (Williamson 1975).(H) Human Capital Resources are intangible assets of thefirm including the knowledge, training, experience, intui-tion, and relationships of individual managers within thefirm (Becker 1964).(O) Organizational and Interorganizational CapitalResources are intangible assets of the firm including theformal ⁄ informal reporting structure, the planning, con-trolling and coordinating systems, as well as relationshipsamong groups within the firm or relationships between the


focal firm and the firms within the environment (e.g., sup-pliers) (Tomer 1987).

Reducer Categories (Scott and Davis 2003)

(U) Flow Unit is the units (e.g., raw materials, subassem-blies, finished products, service parts) that are beingextracted from the environment as an input for the focalfirm (Bertalanffy 1950).(S) Source is the supply node in the environment wherethe flow units originated (Scott and Davis 2003).(F) Flow is associated with activities (e.g., transportation)related to the extraction of the flow units from the source(Buckley 1967).

REFERENCES

Alvesson, M. 2003. ‘‘Beyond Neopositivists, Romantics, andLocalists: A Reflective Approach to Interviews in Organi-zation Research.’’ Academy of Management Review28(1):13–33.

Barney, J. 1991. ‘‘Firm Resources and Sustained CompetitiveAdvantage.’’ Journal of Management 17(1):99–121.

Becker, G. 1964. Human Capital. New York: Columbia.Berg, B.L. 2003. Qualitative Research Methods for the Social

Sciences. 5th ed., Chapter 4. Needham Heights, MA:Allyn & Bacon.

Bertalanffy, L. 1950. ‘‘An Outline of General System Theory.’’The British Journal for the Philosophy of Science 1(2):134–65.

———. 1951. ‘‘General Systems Theory: A New Approach toUnity of Science.’’ Human Biology 3:23–29.

———. 1956. ‘‘General Systems Theory.’’ General Systems:Yearbook of the Society for the Advancement of GeneralSystems Research 1:1–10.

Bezborodova, E., and Bennett, B. 2004. ‘‘An InvestigationInto the Non-Usage of Qualitative Software in the Mar-keting Research Industry.’’ Irish Marketing Review17(1 ⁄ 2):49–52.

Blackhurst, J., Craighead, C., Elkins, D., and Handfield, R.2005. ‘‘An Empirically Derived Agenda of CriticalResearch Issues for Managing Supply-Chain Disrup-tions.’’ International Journal of Production Research43(19):4067–81.

Blackhurst, J., Scheibe, K., and Johnson, D. 2008. ‘‘SupplierRisk Assessment and Monitoring for the AutomotiveIndustry.’’ International Journal of Physical Distributionand Logistics Management 38(2):143–65.

Bourgeois, L.J., and Eisenhardt, K. 1988. ‘‘Strategic Deci-sion Processes in High Velocity Environment: Four Casesin the Microcomputer Industry.’’ Management Science34(7):816–35.

Boyer, K., Hallowell, R., and Roth, A. 2002. ‘‘E-Services:Operating Strategy—A Case Study and a Method forAnalyzing Operational Benefits.’’ Journal of OperationsManagement 20(2):175–88.

Braunscheidel, M., and Suresh, N. 2009. ‘‘The Organiza-tional Antecedents of a Firm’s Supply Chain Agility forRisk Mitigation and Response.’’ Journal of OperationsManagement 27(2):119–40.

Buckley, W. 1967. Sociology and Modern Systems Theory.Upper Saddle River, NJ: Prentice Hall.

Byrd, T.A., Pitts, J., Adrian, A., and Davidson, N. 2008.‘‘Examination of a Path Model Relating InformationTechnology Infrastructure With Firm Performance.’’Journal of Business Logistics 29(2):161–87.

Choi, T., and Hong, Y. 2002. ‘‘Unveiling the Structure ofSupply Networks: Case Studies in Honda, Acura, andDaimlerChrysler.’’ Journal of Operations Management20(5):469–93.

Choi, T., and Krause, D. 2006. ‘‘The Supply Base and ItsComplexity: Implications for Transaction Costs, Risks,Responsiveness, and Innovation.’’ Journal of OperationsManagement 24(5):637–52.

Chopra, S., and Sodhi, M.M.S. 2004. ‘‘Managing Risk toAvoid Supply-Chain Breakdown.’’ MIT Sloan Manage-ment Review 46(1):53–61.

Closs, D., Jacobs, M., Swing, M., and Webb, S. 2008.‘‘Toward a Theory of Competencies for the Managementof Product Complexity: Six Case Studies.’’ Journal ofOperations Management 26(5):590–610.

Corbin, J., and Strauss, A. 2008. Basics of QualitativeResearch: Techniques and Procedures for DevelopingGrounded Theory. 3rd ed. Newbury Park, CA: Sage.

Craighead, C., Blackhurst, J., Rungtusanatham, J., andHandfield, R. 2007. ‘‘The Severity of Supply Chain Dis-ruptions Design Characteristics and Mitigation Capabili-ties.’’ Decision Sciences 38(1):131–56.

Craighead, C., Tomas, G., Hult, M., and Ketchen, D.J.,Jr. 2009. ‘‘The Effects of Innovation-Cost Strategy,Knowledge and Action in the Supply Chain on FirmPerformance.’’ Journal of Operations Management 27(5):405–21.

Deane, J., Craighead, C., and Ragsdale, C. 2009. ‘‘Mitigat-ing Environmental and Density Risk in Global Sourcing.’’International Journal of Physical Distribution and LogisticsManagement 39(10):861–83.

Deeter-Schmelz, D. 1997. ‘‘Applying Teams to Logistics Pro-cesses: Information Acquisition and the Impact of TeamRole Clarity and Norms.’’ Journal of Business Logistics18(1):159–78.

Dierickx, I., and Cool, K. 1989. ‘‘Asset Stock Accumulationand Sustainability of Competitive Advantage.’’ Manage-ment Science 35(12):1504–11.

Dill, W. 1958. ‘‘Environments as an Influence on ManagerialAutonomy.’’ Administrative Science Quarterly 2(4):409–43.

Eisenhardt, K. 1989. ‘‘Building Theories From Case StudyResearch.’’ Academy of Management Review 14(4):532–50.

Elkins, D., Handfield, R., Blackhurst, J., and Craighead, C.2005. ‘‘18 Ways to Guard Against Disruption.’’ SupplyChain Management Review 9(1):46–53.

Ellram, L. 1996. ‘‘The Use of the Case Study Method inLogistics Research.’’ Journal of Business Logistics17(2):93–138.


Ellram, L., and Siferd, S. 1998. ‘‘Total Cost of Ownership:A Key Concept in Strategic Cost Management Deci-sions.’’ Journal of Business Logistics 19(1):55–84.

Esper, T., Fugate, B., and Davis-Sramek, B. 2007. ‘‘LogisticsLearning Capability: Sustaining the Competitive Advan-tage Gained Through Logistics Leverage.’’ Journal ofBusiness Logistics 28(2):57–81.

Frankel, R., Bolumole, Y., Eltantawy, R., Paulraj, A., andGundlach, G. 2008. ‘‘The Domain and Scope of SCM’sFoundation Disciplines—Insights and Issues to AdvanceResearch.’’ Journal of Business Logistics 29(1):1–30.

Fugate, B., Stank, T., and Mentzer, J. 2009. ‘‘LinkingImproved Knowledge Management to Operational andOrganizational Performance.’’ Journal of Operations Man-agement 27(3):247–64.

Gibbert, M., Ruigrok, W., and Wicki, B. 2008. ‘‘WhatPasses as a Rigorous Case Study?’’ Strategic ManagementJournal 29(13):1465–74.

Giunipero, L., and Eltantawy, R.A. 2004. ‘‘Securing theUpstream Supply Chain: A Risk ManagementApproach.’’ International Journal of Physical Distributionand Logistics Management 34(9):698–713.

Glaser, B. 1992. Basics of Grounded Theory Analysis. 1st ed.Mill Valley, CA: Sociology Press.

Glaser, B., and Strauss, A. 1967. The Discovery of GroundedTheory. 1st ed. Chicago: Aldine Transaction.

Grant, R. 1991. ‘‘The Resource-Based Theory of Competi-tive Advantage: Implications for Strategy Formulation.’’California Management Review 33(3):114–35.

Grutter, A., Field, J., and Faull, N. 2002. ‘‘Work Team Per-formance Over Time: Three Case Studies of South Afri-can Manufacturers.’’ Journal of Operations Management20(5):641–57.

Hale, T., and Moberg, C. 2005. ‘‘Improving Supply ChainDisaster Preparedness: A Decision Process for Secure SiteLocation.’’ International Journal of Physical Distributionand Logistics Management 35(3):195–207.

Hallikas, J., Virolainen, V.-M., and Tuominen, M. 2002.‘‘Risk Analysis and Assessment in Network Environ-ments: A Dyadic Case Study.’’ International Journal ofProduction Economics 78(1):45–55.

Handfield, R., and Melnyk, S. 1998. ‘‘The Scientific Theory-Building Process: A Primer Using the Case of TQM.’’Journal of Operations Management 16(4):321–39.

Hendricks, K. 2003. ‘‘The Effect of Supply Chain Glitcheson Shareholder Wealth.’’ Journal of Operations Manage-ment 21(5):501–22.

———. 2005a. ‘‘An Empirical Analysis on the Effect of Sup-ply Chain Disruptions on Long-Run Stock Price Perfor-mance and Equity Risk of the Firm.’’ Production andOperations Management 14(1):35–52.

———. 2005b. ‘‘Association Between Supply Chain Glitchesand Operating Performance.’’ Management Science51(5):695–711.

Hendricks, K., Singhal, V., and Zhang, R. 2008. ‘‘The Effectof Operational Slack, Diversification, and Vertical Relat-edness on the Stock Market Reaction on Supply ChainDisruptions.’’ Journal of Operations Management 27(3):233–46.

Ho, C.-J. 1992. ‘‘An Examination of a Distribution ResourcePlanning Problem: DRP System Nervousness.’’ Journal ofBusiness Logistics 13(2):125–52.

Jick, T. 1979. ‘‘Mixing Qualitative and Quantitative Meth-ods: Triangulation in Action.’’ Administrative ScienceQuarterly 24(4):602–11.

Katz, D., and Kahn, R. 1978. The Social Psychology ofOrganizations. 2nd ed. New York: John Wiley.

Kleindorfer, P., and Saad, G. 2005. ‘‘Managing DisruptionRisk in Supply Chains.’’ Production and Operations Man-agement 14(1):53–68.

Knight, R., and Pretty, D. 1998. ‘‘Value at Risk: The Effectsof Catastrophes on Share Price.’’ Risk Management45(5):39–41.

———. 2003. ‘‘Understanding the Risks Behind Sudden Shiftsin Shareholder Value.’’ Corporate Finance Review 7(5):5–12.

Latour, A. 2001. ‘‘Trial by Fire: A Blaze in AlbuquerqueSets Off Major Crisis for Cell-Phone Giants—Nokia Han-dles Supply Shock With Aplomb as Ericsson of SwedenGets Burned—Was SISU the Difference?’’ Wall StreetJournal, January 29, A1.

Levy, D. 1994. ‘‘Chaos Theory and Strategy: Theory, Appli-cation, and Managerial Implications.’’ Strategic Manage-ment Journal 15: 167–78.

Lewins, A., and Silver, C. 2004. ‘‘Choosing a CAQDASPackage.’’ CAQDAS Networking Project 1–32.

Locke, K. 1996. ‘‘Rewriting the Discovery of GroundedTheory After 25 Years?’’ Journal of Management Inquiry5(3):239–45.

Lorentz, H., Wong, C.Y., and Hilmola, O.-P. 2007. ‘‘Emerg-ing Distribution Systems in Central and Eastern Europe:Implications From Two Case Studies.’’ International Jour-nal of Physical Distribution and Logistics Management37(8):670–97.

Mahapatra, S., Narasimhan, R., and Barbieri, P. 2010.‘‘Strategic Interdependence, Governance Effectiveness andSupplier Performance: A Dyadic Case Study Investigationand Theory Development.’’ Journal of Operations Man-agement 28(6):537–52.

Manrodt, K., and Vitasek, K. 2004. ‘‘Global Process Stan-dardization: A Case Study.’’ Journal of Business Logistics25(1):1–23.

Manuj, I., and Mentzer, J. 2008a. ‘‘Global Supply ChainRisk Management Strategies.’’ International Journal ofPhysical Distribution and Logistics Management38(3):192–223.

———. 2008b. ‘‘Global Supply Chain Risk Management.’’Journal of Business Logistics 29(1):133–55.

McCutcheon, D., and Meredith, J. 1993. ‘‘Conducting CaseStudy Research in Operations Management.’’ Journal ofOperations Management 11(3):239–56.

Meepetchdee, Y., and Shah, N. 2007. ‘‘Logistical NetworkDesign With Robustness and Complexity Consider-ations.’’ International Journal of Physical Distribution andLogistics Management 37(3):201–22.

Mentzer, J., Min, S., and Bobbitt, L.M. 2004. ‘‘Toward aUnified Theory of Logistics.’’ International Journal of


Physical Distribution and Logistics Management34(8):606–27.

Meredith, J. 1998. ‘‘Building Operations Management The-ory Through Case and Field Research.’’ Journal of Opera-tions Management 16(4):441–54.

Miemczky, J., and Holweg, M. 2004. ‘‘Building Cars toCustomer Order—What Does it Mean for InboundLogistics Operations?’’ Journal of Business Logistics25(2):171–97.

Miles, M., and Huberman, M. 1994. Qualitative Data Analy-sis. 2nd ed. Thousand Oaks, CA: Sage.

Norrman, A., and Jansson, U. 2004. ‘‘Ericsson’s ProactiveSupply Chain Risk Management Approach After a Seri-ous Sub-Supplier Accident.’’ International Journal ofPhysical Distribution and Logistics Management34(5):434–56.

Rahman, S.-U. 2002. ‘‘The Theory of Constraints’ ThinkingProcess Approach to Developing Strategies in SupplyChains.’’ International Journal of Physical Distribution andLogistics Management 32(10):809–28.

Rice, J., and Caniato, F. 2003. ‘‘Building a Secure and Resil-ient Supply Network.’’ Supply Chain Management Review7(5):22–30.

Scott, R., and Davis, G. 2003. Organizations and Organizing:Rational, Natural and Open System Perspectives. 5th ed.Upper Saddle River, NJ: Pearson Education, Inc.

Sheffi, Y., and Rice, J.B. 2005. ‘‘A Supply Chain View of theResilient Enterprise.’’ MIT Sloan Management Review47(1):41–48.

Skipper, J., Craighead, C., Byrd, T., and Rainer, K. 2008.‘‘Towards a Theoretical Foundation of Supply NetworkInterdependence and Technology-Enabled CoordinationStrategies.’’ International Journal of Physical Distributionand Logistics Management 38(1):39–56.

Smaros, J. 2007. ‘‘Forecasting Collaboration in the Euro-pean Grocery Sector: Observations From a Case Study.’’Journal of Operations Management 25(3):702–16.

Strauss, A., and Corbin, J. 1998. Basics of QualitativeResearch: Techniques and Procedures for DevelopingGrounded Theory. 2nd ed. Thousand Oaks, CA: Sage.

Stuart, I., McCutcheon, D., Handfield, R., McLachlin, R.,and Samson, D. 2002. ‘‘Effective Case Research in Opera-tions Management: A Process Perspective.’’ Journal ofOperations Management 20(5):419–33.

Svensson, G. 2000. ‘‘A Conceptual Framework for the Anal-ysis of Vulnerability in Supply Chains.’’ InternationalJournal of Physical Distribution and Logistics 30(9):731–49.

———. 2003. ‘‘The Principle of Balance Between Companies’Inventories and Disturbances in Logistics Flows.’’ Inter-national Journal of Physical Distribution and LogisticsManagement 33(9):765–84.

Teece, D., Pisano, G., and Shuen, A. 1997. ‘‘Dynamic Capa-bilities and Strategic Management.’’ Strategic Manage-ment Journal 18(7):509–33.

Tomer, J. 1987. Organizational Capital: The Path to HigherProductive and Well-Being. New York: Praeger. inFriedrichs Grangsjo, Y. 2003. ‘‘Destination Networking:Co-Opetition in Peripheral Surroundings.’’ International

Journal of Physical Distribution and Logistics 33(5):427–448.

Towill, D., Naim, M., and Wikner, J. 1992. ‘‘IndustrialDynamics Simulation Models in the Design of SupplyChains.’’ International Journal of Physical Distribution andLogistics Management 22(5):3–14.

Voss, C., Tsikriktsis, N., and Mark, F. 2002. ‘‘CaseResearch in Operations Management.’’ InternationalJournal of Physical Distribution and Logistics 22(2):195–219.

Wagner, S., and Bode, C. 2008. ‘‘An Empirical Examinationof Supply Chain Performance Along Several Dimensionsof Risk.’’ Journal of Business Logistics 29(1):307–25.

Wernerfelt, B. 1984. ‘‘A Resource-Based View of the Firm.’’Strategic Management Journal 5(2):171–80.

Williamson, O. 1975. Markets and Hierarchies. New York:Free Press.

Wu, Z., and Choi, T. 2005. ‘‘Supplier–Supplier Relationshipsin the Buyer–Supplier Triad: Building Theories FromEight Case Studies.’’ Journal of Operations Management24(1):27–52.

Yin, R. 1994. Case Study Research: Design and Methods.2nd ed. Newbury Park, CA: Sage.

Zhu, K. 2004. ‘‘The Complementarity of Information Tech-nology Infrastructure and E-Commerce Capability: AResourced-Based Assessment of Their Business Value.’’Journal of Management Information Systems 21(1):167–202.

Zsidisin, G., and Ellram, L. 2003. ‘‘An Agency TheoryInvestigation of Supply Risk Management.’’ Journal ofSupply Chain Management 39(3):15–27.

Zsidisin, G., Ellram, L., Carter, J., and Cavinato, J. 2004.‘‘An Analysis of Supply Risk Assessment Techniques.’’International Journal of Physical Distribution and LogisticsManagement 34(5):397–413.

Zsidisin, G., Melnyk, S., and Ragatz, G. 2005a. ‘‘An Institu-tional Theory Perspective of Business Continuity Planningfor Purchasing and Supply Management.’’ InternationalJournal of Production Research 43(16):3401–20.

Zsidisin, G., Ragatz, G., and Melnyk, S. 2005b. ‘‘The DarkSide of Supply Chain Management.’’ Supply Chain Man-agement Review 9(2):46–52.

SHORT BIOGRAPHIES

Jennifer Blackhurst (PhD University of Iowa) is an Associ-ate Professor in the College of Business at Iowa State Uni-versity. Her research interests include: Supply Chain Riskand Disruption; Supply Chain Coordination; and SupplierAssessment and Selection. Her publications have appeared insuch journals as Journal of Operations Management, Produc-tion and Operations Management, and IEEE Transactions onEngineering Management.

Kaitlin S. Dunn (MS in Information and Telecommuni-cation Systems) is a Doctoral Candidate in Supply Chainand Information Systems in the Smeal College of Business atThe Pennsylvania State University. Her research interestsinclude: Procurement, Strategic Sourcing, and Supply Riskand Disruptions. She graduated with a Master’s degree in


Information and Telecommunication Systems for Businessfrom Johns Hopkins University and has a undergraduatedegree in Finance from the University of Florida.

Christopher W. Craighead (PhD Clemson University)joined the Smeal College of Business at The PennsylvaniaState University in 2008. He has articles in Journal of Opera-tions Management, Production and Operations Management,

Decision Sciences, International Journal of Physical Distribu-tion and Logistics Management, International Journal ofLogistics Management, Supply Chain Management Review,and other journals. He is an Associate Editor of the Journalof Operations Management, an Area Editor for OperationsManagement Research, and serves on the Review Board ofProduction and Operations Management.


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An Empirically Derived Framework for Global SC Resilience

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