Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex...

Decision SciencesVolume 38 Number 4November 2007

C© 2007, The AuthorJournal compilation C© 2007, Decision Sciences Institute

Complexity and Adaptivity in SupplyNetworks: Building Supply Network TheoryUsing a Complex AdaptiveSystems Perspective∗

Surya D. Pathak†

Engineering Management Program, School of Engineering, Vanderbilt University, VU StationB 351831, 2301 Vanderbilt Place, Nashville, TN 37235, e-mail: [email protected]

Jamison M. DayDepartment of Decision and Information Sciences, Bauer College of Business, University ofHouston, Melcher Hall 290D, Houston, TX 77204, e-mail: [email protected]

Anand NairDepartment of Management Science, Moore School of Business, University of South Carolina,Columbia, SC 29208, e-mail: [email protected]

William J. SawayaDepartment of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall,Ithaca, NY 14853, e-mail: [email protected]

M. Murat KristalOperations Management and Information Systems Department, Schulich School of Business,York University, 4700 Keele Street Toronto, Ontario, Canada M3J 1P3,e-mail: [email protected]

ABSTRACT

Supply networks are composed of large numbers of firms from multiple interrelatedindustries. Such networks are subject to shifting strategies and objectives within adynamic environment. In recent years, when faced with a dynamic environment, severaldisciplines have adopted the Complex Adaptive System (CAS) perspective to gain in-sights into important issues within their domains of study. Research investigations in thefield of supply networks have also begun examining the merits of complexity theory andthe CAS perspective. In this article, we bring the applicability of complexity theory andCAS into sharper focus, highlighting its potential for integrating existing supply chainmanagement (SCM) research into a structured body of knowledge while also providinga framework for generating, validating, and refining new theories relevant to real-worldsupply networks. We suggest several potential research questions to emphasize how a

∗We sincerely thank Professors Thomas Choi (Arizona State University), David Dilts (Vanderbilt Uni-versity), and Kevin Dooley (Arizona State University) for their help, guidance, and support.

†Corresponding author.

547

548 Complexity and Adaptivity in Supply Networks

CAS perspective can help in enriching the SCM discipline. We propose that the SCMresearch community adopt such a dynamic and systems-level orientation that brings tothe fore the adaptivity of firms and the complexity of their interrelations that are ofteninherent in supply networks.

Subject Areas: Adaptivity, Complex Adaptive System, Complexity, ComplexityTheory, Decision Making, Supply Chain Management, and Supply Networks.

INTRODUCTION

Today, supply chain management (SCM) involves adapting to changes in a com-plicated global network of organizations. A typical supply network consists ofinterfirm relationships that may connect multiple industries. As a result, supplynetwork decisions often require consideration of a large number of factors frommultiple dimensions and perspectives. Two emergent themes that managers fre-quently encounter when making these decisions are (i) the structural intricacies oftheir interconnected supply chains (Choi & Hong, 2002) and (ii) the need to learnand adapt their organization in a constantly changing environment to ensure itslong-term survival (Brown & Eisenhardt, 1998).

Complex interconnections between multiple suppliers, manufacturers, as-semblers, distributors, and retailers are the norm for industrial supply networks.When decision making in these networks is based on noncomplex assumptions(e.g., linearity, a buyer–supplier dyad, sparse connectivity, static environment,fixed and nonadaptive individual firm behavior), problems are often hidden, leavingplenty of room for understanding and improving the underlying processes. Considerthe recent implementation of complexity-oriented decision making by AmericanAir Liquide, a firm based in Houston, Texas. The following information was ac-quired through multiple employee interviews, associated document examinations,and observations of the Operations Control Center at American Air Liquide. Thecompany produces industrial and medical gases such as nitrogen, oxygen, and hy-drogen at about 100 manufacturing locations in the United States and delivers tonearly 6,000 customer sites using a mix of pipelines, railcars, and more than 400trucks. In the past, its distribution routing was based on analytical optimizationmethods. However, this approach had a difficult time integrating environmentalvolatility, feedback from truck drivers, and dynamic sourcing opportunities. Af-ter working with NuTech Solutions (formerly Bios Group), they created a newcomplexity-based solution that leverages neural networks and agent-based mod-eling (with ant-foraging algorithms) to integrate decisions across their multinodaland multimodal supply network. Most important, the new solution method solvesboth sourcing and routing together in the optimization process. Charles Harper,director of National Supply & Pipeline and Supply Operations, summarizes thebenefits of their complexity-based approach:

After switching over, we drive less miles, we don’t do stupid things, and wemove people to different jobs that didn’t exist before. All those things add upto savings. It’s been mind-blowing to see how much opportunity there was.The knowledge we gained from implementing the complexity-based solutionhelped us realize what the real-time incremental cost of the liquid going intocustomers’ tanks really was. Our supply network can now flexibly adapt to

Pathak et al. 549

volatility in the environment due to differentials in power prices or even hurri-canes. Complexity-based solutions are extremely applicable and people needto start using them or they’re going to lose out.

American Air Liquide is far from being the only firm that is using thestructural complexity (interconnectedness of firms) and adaptivity (dynamic learn-ing of individual firms) principles of Complex Adaptive Systems (CAS). Boe-ing has effectively used CAS principles to redesign their 787 Dreamliner sup-ply network, reducing the risk of expensive cascading supply network delays(Global Logistics and Supply Chain Strategies, 2007). Similarly, using CASprinciples, Citibank Credit Risk uncovered $200 million in hidden expenses,Proctor and Gamble reduced supply network inventory by 25% and saved 22%on distribution expenses, and Southwest Airlines saved $2 million annually intheir freight delivery operations (Kelly & Allison, 1999; Waldrop, 2003; GlobalLogistics and Supply Chain Strategies, 2007). As seen in these examples, a CAS-oriented approach can help firms reap benefits such as increased efficiency, rapidflexibility, better preparedness for external uncertainties, increased awareness ofmarkets and competition, and improved decision making (Abell, Serra, & Wood,1999).

Along with managing the complexity inherent in the interconnectivity oftheir supply networks, organizations have also started to learn the benefits of beingadaptive in their behavior. Sheffi and Rice (2005) present an illustration of adaptivefirm behavior in a cellular telephone supply network. They highlight the differentapproaches that Nokia and Ericsson took when a fire disrupted the supply fromPhilips, the sole supplier for a particular chip common to both manufacturers.While Ericsson suffered an estimated $2.34 billion loss, Nokia engaged directlywith Philips to restore supply using alternate supply options. They modified designsof the handsets where possible and secured worldwide manufacturing capacity fromPhilips to ensure a steady supply of the chips. Meanwhile, the direct interactionbetween top management of Nokia and Philips further enhanced the ability ofNokia to adapt in the future. Wollin and Perry (2004) provide another example ofhow Honda adapted to the changing automotive sector environment by leveragingthe notions of learning and path dependency of adaptive systems. They used theirAccord and Civic platforms as the basis of several of their most recent sport utilityvehicles, and, as a result, they gained significant market share in that segment eventhough they were slow to enter the four-wheel-drive market.

The pioneering article by Choi, Dooley, and Rungtusanatham (2001) exam-ined how properties of CAS are embodied by supply networks. Since this article,there have been only a handful of papers that use the CAS view of supply networks,signaling that the SCM discipline has yet to enthusiastically embrace the CAS per-spective. The intent of this position paper is to draw attention to recent developmentsin CAS theory from across multiple disciplines and articulate how this knowledgecan be leveraged to enrich the operations management (OM) and SCM disciplines.We suggest leveraging the conceptualizations of Complex Adaptive Supply Net-works (CASN), such as those found in Choi et al. (2001) and Surana, Kumara,Greaves, and Raghavan (2005), to lay a foundation for both integrating existingwork and developing new theories within the SCM body of knowledge. Specifically,


we discuss how CAS principles can be useful for identification and organizationof complex and adaptive phenomena in supply networks, such as individual firmadaptation, self-organization and emergence, buyer–supplier relationships, supplynetwork performance, environmental change, and feedback mechanisms. Finally,we examine the challenges associated with CASN theory development and providesuggestions for future research efforts and CASN theory development.

A CAS VIEW OF SUPPLY NETWORKS

Because organizations exhibit adaptivity and can exist in a complex environmentwith myriad relationships and interactions, it is a natural step to identify a supplynetwork as a CAS. Choi et al. (2001) argue that supply networks should be recog-nized as CAS by providing a detailed mapping of each property of CAS to a supplynetwork. In a similar way, subsequent research has recognized this same inherentcomplexity of supply networks (Surana et al., 2005). For brevity, we use Anderson(1999) and Choi et al. (2001) to offer an overview of CAS and its framing of SCMresearch.

A CAS is an interconnected network of multiple entities (or agents) thatexhibit adaptive action in response to changes in both the environment and the sys-tem of entities itself (Choi et al., 2001). Collective system performance or behavioremerges as a nonlinear and dynamic function of the large number of activities madein parallel by interacting entities. For example, the individual decisions made byfirms facing imperfect information and variable demand lead to a globally observedphenomenon (i.e., the bullwhip effect) (Lee, Padmanabhan, & Whang, 1997). An-derson (1999) outlined four common properties of such systems.

First, a CAS consists of entities that interact with other entities and withthe environment by following a set of simple decision rules (i.e., schema). Theseentities may evolve over time as entities learn from their interactions. In contrastto relational modeling, which tries to use one set of variables to explain variationin another set of variables, CAS examines how changes in an individual entity’sschema lead to different aggregate outcomes.

Second, a CAS is self-organizing. Self-organization is a consequence of in-teractions between entities. Self-organization is defined as a process in which newstructures, patterns, and properties emerge without being externally imposed onthe system. Because the behavior in complex systems comes from dynamic inter-actions among the agents and between the environment and the agents, the changestend to be nonlinear with respect to the original changes in the system. Thus, theremay be small changes that have a dramatic effect on the system, or, conversely,large changes that have relatively little effect. Choi et al. (2001, p. 357) state, “thebehavior of a complex system cannot be written down in closed form; it is notamenable to prediction via the formulation of a parametric model, such as a statis-tical forecasting model.” Even though it may not be possible to predict the futurein an exact manner, the future may exhibit some underlying regularity. While thechanges that are made to a system may be dramatic and unpredictable, there maybe patterns of behavior that can be considered prototypical. Appropriate analysesmay yield some knowledge of key patterns of behavior that are likely to developin the system over time.

Pathak et al. 551

Third, a CAS coevolves to the edge of chaos. Choi et al. (2001) explaincoevolution, positing that a CAS reacts to and creates its environment so that asthe environment changes it may cause the agents within it to change, which, inturn, cause other changes to the environment. These actions and reactions can betriggered by external events such as natural disasters (e.g., Hurricane Katrina) orthe actions of agents (e.g., a decision to implement an enterprise resource planningsystem). A CAS exhibits dynamism as changes occur in the environment; thisdynamism affects the system. Environmental factors may cause changes to whichthe agents must adapt, influencing the way agents perceive their environment or theschema used by the agents themselves. Thus, the rules followed by the individualentities organize the system, because individual entities are not privy to the objectivefunction of the system as a whole. The coevolution of the system happens in therugged fitness landscapes in which the CAS exists. The concept of landscape wasfirst introduced by biologist Sewell Wright (1932). It refers to the mapping from anorganism’s genetic structure to its fitness level. In management research, the idea oflandscape is analogous to the domain of social and economic phenomena (Levinthal& Warglien, 1999). Specifically, these landscapes may be thought of in terms ofan analogy of a range of mountains that represents an objective function (i.e.,performance function) that is filled with hills and valleys (Kauffman, 1995). Thehills or peaks represent the desired optimal states, in which a rugged landscape hasmany peaks surrounded by deep valleys. For instance, in the Toyota supply network,the flow of goods between its Camry plant and the Johnson Controls seat-framemanufacturing plant controlled via a tightly coupled kanban system would reactdifferently to an external event than the flow of goods between Johnson Controls’seat-frame manufacturing plants and their raw materials suppliers.

Fourth, a CAS is recursive by nature, and it recombines and evolves overtime. For example, going back to the bullwhip effect (Lee et al., 1997), the inter-firm orders could be characterized as orders from one organizational function toanother organizational function, orders from an individual employee of one firm toan employee of another, or any combination of the involved individuals, functions,or firms. Furthermore, from a macroeconomic viewpoint, it can be posited thatindustry supply networks are interrelated within a national or international contextand interact together as a CAS in a larger context (Arthur, Durlauf, & Lane, 1997).Thus, a CAS is often composed of entities that can themselves be characterizedas CASs composed of smaller constituents (a nested hierarchy of smaller-scalecomplex systems). Changes in these smaller systems and even in individual entitiescan cause the entire system to change over time.

Building on these properties, Choi et al. (2001) outline three key foci forsupply chain research: internal mechanisms, the environment, and coevolution.For internal mechanisms, the key elements are agents (entities) and schema, self-organization and emergence, network connectivity, and network dimensionality.In the context of supply networks, an entity may be an organization, a division, ateam, or an individual, or even a function of an individual’s job. The key feature isthat agents have the ability to make decisions in response to the environment and tothe action of other entities. In supply networks, schemas are the rules that the orga-nizations, or the decision makers within organizations, use to make the decisionsfor, and guide the actions of, the organization. Self-organization and emergence


occur as a result of decisions that are made by the individual agents that cause thesystem to change and the collective system behavior to emerge over time. Networkconnectivity is the connection among the agents that determines the complexityof the network. As the connectivity among the agents increases, the interrelation-ships among the agents increase, in turn causing increases in the complexity of thenetwork. In the case of supply-network relationships these connections are real,physical connections between organizations such as telephone lines, fax numbers,electronic data interchange systems, and so on. Dimensionality is the degree towhich agents can act in an autonomous fashion without influencing other agents.Therefore, as the degree of connectivity increases, the dimensionality decreasesas the actions of a given agent has a greater impact on those with which it isconnected.

As an example, Choi et al. (2001) present the interconnectivity of an aircraftengine manufacturer (Honeywell) with a university hospital (Metro UniversityHospital). Honeywell depends on mining companies for supplies of raw materi-als such as steel, copper, aluminum, and other composite materials. These miningcompanies source equipment that relies on the latest material extraction techniquesdeveloped by various firms and agencies. The material extraction techniques relyon pattern recognition technologies that aid in interpretations of X-ray scans ofpotential material vein and enable a firm to make appropriate decisions regardingextraction locations. It is conceivable that the required pattern recognition tech-nology is developed in a completely unrelated sector, such as health care. Forexample, a university hospital might develop a new pattern recognition techniquefor the purposes of medical treatment that could have potential application in mate-rial extraction. Over time, the knowledge gets passed on to the material extractioncompany via research conferences. This example illustrates complex interconnec-tivities among firms and the impact of decisions made by one firm on others in thenetwork. We present the decisions and information flows among firms in Figure 1.

Since the initial article on supply chains as CAS by Choi et al. (2001),there have been numerous developments in the CAS and network-related liter-ature across a wide range of disciplines, such as industrial engineering, computerscience, physics, organizational science, new product development, and strategicmanagement. In the next section, we highlight these advancements and discusshow knowledge gained from these research studies can be beneficial for supplynetwork research.

NEW DEVELOPMENTS IN CAS AND THEIR APPLICABILITYTO SCM RESEARCH

Research endeavors using the CAS perspective have been undertaken in diversefields such as physics, biology, mathematics, computer science, engineering, psy-chology, political science, sociology, economics, and organizational behavior. Tosystematically approach this wide range of literature, we adopted the data trian-gulation approach. As a first step, we sought expert opinion regarding the state ofrecent research pertaining to CAS. This step provided an initial reference list and

Pathak et al. 553

Figure 1: Example of decision making in supply networks as complex adaptivesystems (Based on the example in Choi et al., 2001).

Honeywell (Aircraft engine

manufacturer)

Mining companies

Mining equipment

manufacturers

Firms/agencies engaged in

development of new material extraction

techniques

University HospitalPattern recognition

technique developed in the medical field

Information flow: Via research conferences and journal articles

Decision: Purchase raw materials such as steel, copper, aluminum, and other composite materials

Information flow:Shortages, costs, delivery schedules

Information flow:Technology, capabilities of equipments, cost

Decision: Purchase mining equipment

Information flow: Competing technological option requirements Decision: Choice of

material extraction technique

guided our subsequent search process. In the next step, we undertook an extensivesearch of selected peer-reviewed journals (e.g., Academy of Management Journal;Management Science; Organizational Science; Non-Linear Dynamics, Psychology& Life Sciences; Emergence; and Complexity) by using the ABI/INFORMS andBusiness Source Premier databases. In the search process, we included keywordssuch as supply network, CAS, complexity theory, adaptation, adaptivity, chaos,SCM, and nonlinear time series analysis. From the results obtained, we selectedmore than 100 articles that were directly related to CAS and undertook an in-depthexamination of these articles to identify significant theoretical, methodological,and technical developments related to all the major aspects of a CAS-based supplychain as described in Choi et al. (2001).

Researchers across multiple disciplines have significantly advanced the theo-retical boundaries of CAS-based systems (Zhang, 2002; Fonseca & Zeidan, 2004;Richardson, 2004, 2005, 2007), especially focusing on organizational adaptation(Dooley, Corman, McPhee, & Kuhn, 2003), individual entity learning (Downs, Du-rant, & Carr, 2003), and network connectivity models (Barabaasi, 2002; Newman,2003). Methodological advancements such as sophisticated agent-based model-ing (Chatfield, Kim, Harrison, & Hayya, 2004; Sawaya, 2006; Pathak, Dilts, &Biswas, 2007), cellular automata (Wolfram, 2002; Mizraji, 2004), dynamical sys-tems theory (Surana et al., 2005), dynamic networks analysis (Carley, forthcoming),and empirical and case-study methods (Varga & Allen, 2006) have been appliedto problems ranging from nursing and health care domains (Anderson, Issel, &McDaniel, 2003) to supply networks (Thadakamalla, Raghavan, Kumara, &Albert, 2004). Analysis techniques used within these articles include chaos theory(Strogatz, 1994), computational and statistical mechanics (Shalizi, 2001), and non-linear time series methods (Williams, 1997). Table 1 summarizes some of these


Tabl

e1:

Adv

ance

men

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ning

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sche

mas

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si(2

002)

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iche

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2),

Zha

ng(2

002)

,Alle

nan

dSt

rath

ern

(200

3),

Doo

ley

etal

.(20

03),

Dow

nset

al.(

2003

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asle

ttan

dO

sbor

ne(2

003)

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man

(200

3),

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2003

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agni

no(2

004)

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dZ

eida

n(2

004)

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hard

son

(200

4),

Ald

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Rob

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005)

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(200

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icha

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007)

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Pathak et al. 555

Tabl

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Tabl

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Sne

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rmat

ion

Pathak et al. 557

research developments and advancements over the last 6 years across multipledifferent areas.

On careful examination, we note an interesting trend. Almost all of the re-search contributions and advancements listed in Table 1 have occurred predomi-nantly outside the OM and SCM discipline. This observation is further supportedby the observation that the special issue of Management Science on ComplexityTheory (Amaral & Uzzi, 2007) does not carry a single article that deals purelywith supply chain issues. Thus, it is clear that, while other areas such as industrialengineering, computer science, physics, organizational science, research and devel-opment, and strategic management, to name a few, are strongly pursuing researchbased on CAS perspectives, OM and SCM research is not keeping pace.

One of the greatest contributions of the CAS perspective may be its abil-ity to incorporate increasing realism and empirical data into research models thatcan be understood in a practical business setting (Anderson, 1999). This has beendemonstrated with CAS research both in diverse applications (ecology, social re-tirement models, and zoology) with high realism (Van Winkle, Rose, & Chambers,1993; Grimm, 1999; Axtell, 2003) and in uses of empirical data from businessorganizations (Nilsson & Darley, 2006; Sawaya, 2006).

Consider the parallels that exist between work by Albert, Jeong, and Barabasi(2000) on error and attack tolerance of complex networks and research by Hen-dricks and Singhal (2003) regarding supply network resilience under disruption.Findings indicate that the heterogeneous dyads in scale-free networks, such asthose found in the Internet, biological-cell, and social-network connectivity, ex-hibit higher tolerance to random errors but lower tolerance to targeted attack thanthe more homogenous, exponential-style networks. These findings can be leveragedto hypothesize how different supply-network topologies give rise to different levelsof supply-network resiliency under disruptions related to either random failure ortargeted attack, potentially leading to important implications for industry manage-ment decisions. In fact, Thadakamalla et al. (2004) have shown how knowledgecan be generated about survivability and resiliency of supply networks using con-cepts shown in the work of Albert et al. (2000). The work of Braha and Bar-Yam(2007) utilizes statistical properties of a complex network to show how the struc-tural information flows in distributed product development networks have similarproperties to other social, biological, and technological networks. It would be inter-esting to follow Braha and Bar-Yam’s suggestion regarding applying their findingsabout statistical properties of intraorganizational product development network toa supply network context, as this may result in new insights on how interfirm andintrafirm properties connect and evolve.

Recent advancements made by Rivkin and Siggelkow (2007) toward extend-ing CAS research of organizations (Levinthal, 1997; McKelvey, 1999) using theNK model of fitness from theoretical biology (Kauffman & Levin, 1987; Kauffman& Weinberger, 1989) to questions of adaptability in individual organizations couldhave important lessons for the study of supply chains. Rivkin and Siggelkow (2007)leverage empirical research demonstrating patterns of interactions within decisionprocesses to show that the number of local optima is highly correlated with thedecision-interaction patterns. Therefore, if there are many local optima, the relativevalue of exploration decreases. The implication is that the value of exploration of


opportunities versus the exploitation of existing opportunities varies depending onhow rugged and dynamic the landscape is.

From a supply chain management perspective, the results and findings onadaptability and use of NK models have been demonstrated for supply base man-agement (Choi & Krause, 2006). Also important are the number of suppliers (N)and the level of interrelationships among the suppliers (K) and the degree of dif-ferentiation of these suppliers. In particular, the significance of interrelationshipscould have further implications for buyer–buyer or supplier–supplier coopetition(simultaneous competition and cooperation) in supply networks (Bengtsson &Kock, 2000; Choi, Zhaohui, Ellram, & Koka, 2002). For instance, supplier firmsare typically under the control of the buying company through established workroutines and contractual terms, yet they are able to make decisions on their ownbehalf. In this regard, the tension between control and emergence might be applica-ble to supplier–supplier relationships and thus may provide an interesting contextfor CASN studies.

Another use of NK models can be found in the manufacturing-strategy litera-ture. Levinthal and Warglien (1999) show how Japanese automotive manufacturersuse robust design to achieve single-peaked landscapes (landscapes with very lowinteraction levels among agents as compared to the total number of agents). Theystate that “in die change operations, using pear-shaped clamps that can be smoothlybrought to fit in only one way thereby driving even approximate movements intothe right direction, reduces errors on the production line. The landscape in thiscase is designed by the physical shape of the task environment” (p. 346). Thisexample illustrates how NK models can be conceptualized to reduce variability ina production network. If we apply this concept to SCM, one can argue that qualitymanagement practices can use similar concepts from NK models for managingbuyer–supplier relationships in order to reduce variability of the quality of theproducts that the suppliers send to their buyers, thus leading to a single-peakedlandscape as suggested by Levinthal and Warglien (1999). For instance, whenHonda uses a consistent supplier-management approach not only with their first-tier suppliers but also with their second- and third-tier suppliers (Choi & Hong,2002), one might view this as an attempt to create a single-peaked landscape in thesupply network.

Discussions and examples so far suggest that the CAS perspective holdspromise for enriching and extending the current body of knowledge in the OM andSCM disciplines. We provide a detailed discussion of potential research directionslater in the article, but we first discuss some underlying issues and challenges.

CRITICAL ISSUES AND CHALLENGES IN CASN RESEARCH

For more than 50 years, research studies have enriched our understanding of variousOM and SCM issues (Beamon, 1998). The use of analytical models, simulationmethods, and empirical approaches have greatly enhanced knowledge and im-proved decision-making processes. Analytical modeling-based studies have ma-tured from their initial years into explicit considerations of various operationaldecisions, the stochastic nature of demand, and the combinatorial possibilities ofavailable scenarios and options. Empirical research has grown to provide insights

Pathak et al. 559

regarding strategic issues, managerial perceptions, and measurements of key op-erational issues. Undoubtedly, the scope of problems being investigated in extantliterature is becoming richer and scholars are attacking complicated issues that werepreviously outside the scope of investigation for tractability reasons (Vonderembse,Uppal, Huang, & Dismukes, 2006). Addressing complicated issues, however, doesnot equate to addressing complexities.

Complexity vs. Complicatedness

The distinction between complicated research and complexity-oriented researchis important for ensuring a broad-based research agenda. Cilliers (2000) suggeststhat something that is complicated can be intricate, but the relationship betweenthe components is fixed and well defined. For instance, a jumbo jet is a complicatedsystem that is amenable to taking individual components apart and putting themback together. In contrast, a complex system is characterized in terms of the nonlin-ear dynamic interactions of the individual parts. Furthermore, while a complicatedsystem can be viewed as the sum of its parts, a complex system cannot be viewedthat way; one cannot predict the behavior of a complex system by examining thebehavior of its individual parts. These emergent properties of complex systems aredue to the nonlinear dynamic relationship between the individual components.

In a recent special issue on complex systems in Management Science, Amaraland Uzzi (2007) provide the following commentary that further illuminates thedifferences between complicatedness and complexity (p. 1033):

In contrast to simple systems, such as the pendulum, which has a small numberof well-understood components, or complicated systems, such as Boeing jet,which have many components that interact through predefined coordinationrules (Perrow, 1999), complex systems typically have many components thatcan autonomously interact through emergent rules. In management contexts,complex systems arise whenever there are populations of interacting agentsthat can act on their limited and local information. The agents and the largersystem in which they are embedded operate by trading their resources withoutthe aid of a central control mechanism or event a clear understanding of howactions of (possibly distant) agents can affect them.

Amaral and Uzzi (2007) comment on the complexity in the supply chain arenaand emphasize the increasingly decentralized decision making, networkwide dis-semination of innovations, and the need to find approaches to make lean supplychains robust against random failures and targeted breakdowns. The authors pro-pose a complexity-based perspective for future investigations of various businessissues.

Parallel to the investigation of complicated issues that continue to be exam-ined, research initiatives are needed that examine complexity in OM and SCM.This endeavor can potentially illuminate several critical issues, such as intercon-nected supply networks and learning and adaptivity within supply networks thatare currently rare in SCM literature.

Challenges of Theory Development with a CAS Perspective

In general, theory building requires careful application of structural methodsto identify phenomena. Once identified, the phenomena must be validated by


designing and conducting research studies (Meredith, 1998). Throughout this pro-cess, careful attention must be given to the level of rigor such that the research ad-heres to appropriate methodological guidelines. The results obtained, as well as anyrelevant insights, must have clear application to the phenomena within the boundaryconditions and be generalizable for the theory to be integrated into a wider body ofknowledge. Here, we examine some of the unique theory-development challengesthat must be overcome if a coherent body of knowledge is to be developed aroundCAS principles.

First, the complexity of supply networks will press limits on researchers’ability to understand the internal interactions between constructs and mechanismsof larger-scope phenomena. For example, operations research has successfullyleveraged game theory to understand competitive and cooperative phenomena bothwithin and between organizations (Cachon & Lariviere, 1999). Although theseinvestigations provide insight into optimal monopolistic or duopolistic decisions,there are limits to modeling the nonlinear dynamics and adaptations inherent inthe oligopoly or free-market structures that dominate our economy. As discussedpreviously, when several locally optimal policies interact in a complex supplynetwork, the resulting nonlinear dynamics of global behavior can be unpredictable.Therefore, game-theoretic studies can be enriched by adopting the CAS perspectiveto help examine the applicability, impact, and robustness of their findings within thelarger, more realistic supply network contexts in which game theory is intractable.One reason for the growing popularity of CAS across several disciplines is itsability to incorporate more realism in building theories, providing opportunityfor greater relevance, and supplying an understanding of the way phenomena actin otherwise intractable environments. CAS provides an approach to rigorouslyexamine situations that closely map reality, yet simultaneously requires continuousextension and refinement to unravel unexpected behaviors that supply chains andnetworks are capable of producing.

A second challenge is that OM and SCM as disciplines currently lack metricsfor evolution and dynamism in supply networks. For example, many phenomenain supply networks occur over time, and it will be crucial to examine the evolutionof the supply network over an extended time horizon. Such a behavior could bemeasured and depicted using attractors and the corresponding lags at which attrac-tors are reconstructed (Williams, 1997). Furthermore, because phenomena in anevolving supply chain occur at different levels, they must be captured at the firm,topology, and systems levels. For example, investigation of supply chain disruptionswould require simultaneous consideration of agent-level metrics such as capacityand fitness, topology-level metrics such as degree distribution and path length, andsystem-level metrics such as robustness and efficiency. Given that empirical datacollection can be problematic whenever real organizations are involved, empiricalstudies aimed at examining dynamic and evolutionary behavior inherent in sup-ply networks will require resourceful approaches to operationalize and integrateunderlying constructs based on data collected from multiple system levels.

Third, developing robust theories in the presence of adaptation presents aformidable task. In a system of entities with changing policies, careful analysis ofthe impact of interactions among these policies will be required. For example, Texasand California are preparing to restructure their power markets from zonal to nodal

Pathak et al. 561

models next year (Alaywan, Wu, & Papalexopoulos, 2004; Ercot, 2007). Powergenerators and wholesalers are planning to adapt their policies (e.g., trade strategies,scheduling, risk management) to take advantage of almost continuous shifts inpricing and transmission congestion across 3,000–4,000 locations. Attempting toascertain common overarching principles in such CASNs may require approachesuncommon to operations and supply chain research like longitudinal data collectionand data analysis without resorting to linearity assumptions. Research design andvalidation techniques will require resourcefulness when exploring both new andpreviously identified phenomena in the presence of dynamically changing andinteracting entity behaviors.

It may be possible to glean supply network information from publicly avail-able data or company archival data sources in order to understand factors affectingthe dynamic behavior of the network. Such information, assuming it can be found,can be used to inform model development and validate models of supply networks.Because of the dynamic nature of CASN, rich longitudinal data of both quanti-tative and qualitative nature are important to accurately assess entity adaptationand its impact on system-level behavior. This likely requires close collaborationbetween academic researchers and practitioners who are dedicated to understand-ing the complexities that affect organizations in a supply network in order to makethe commitment to this type of research effort. For example, structure, schema,and performance of various constituent organizations of a supply network mightbe sampled at regular intervals over time in order to understand the dynamic andemergent behavior of the system.

Finally, while borrowing concepts and ideas developed in other disciplinescan be innovative and useful, one must remember to take great care when relatinga phenomenon found in a few studies to a wider range of situations. As seen inphysics, abstraction of phenomena to larger- or smaller-scale systems does notalways hold true, and any attempt to do so must be done thoughtfully and withgreat care (Feynman & Weinberg, 1986). Likewise, the impact of complexity andadaptation observed in one system may not hold true when applied in other systems.Such CASN characteristics make research in this area difficult, but, fortunately, OMand SCM disciplines could learn from other disciplines, such as organizationalscience, economics, computer science, and evolutionary biology, to name but afew. These disciplines have been extremely careful in generalizing their results andhave intelligently combined a diverse range of methods and tools (as summarizedin Table 1) to effect a slow paradigm shift.

FUTURE DIRECTIONS OF CASN RESEARCH

One key way in which CASN ideas and theories might be leveraged is in bridgingthe research–reality gap. For instance, tapping existing CAS research and apply-ing it to supply network contexts will move the field beyond a static, isolateddyadic buyer–supplier framework. As indicated previously in this article, Brahaand Bar-Yam (2007) studied the statistical properties of organizational networksthat focus on product development. They show that structure of information-flownetworks have properties that are similar to those displayed by other social, biolog-ical, and technological networks. They conclude their study by suggesting that the


intraorganizational properties they studied might be applied to an interorganiza-tional level at which business organizations form the networks (i.e., supply net-works). Thus, by shifting the unit of analysis to the firm level, existing knowledgefrom an external discipline can be used for researching supply network problems.

In this section, we attempt to highlight some of the issues that must be ad-dressed in order to develop a useful CASN research framework. We start by sug-gesting a CASN definition. We then elaborate on how supply network theory may bedeveloped, building on CAS phenomenon. We finish by discussing some uniqueCASN research design, measurement, and methodological issues for validationpurposes and list some potential CASN research questions.

Defining CASN

A formal definition of CASN is one step toward furthering the use of CAS principlesin examining supply networks. Formulating such a definition is not a trivial taskand will require an iterative process with inputs, from a variety of experiencedresearchers. What we propose here should be taken as a starting point for a formaldiscussion from which an acceptable definition might emerge.

A CASN is a system of interconnected autonomous entities that make choicesto survive and, as a collective, the system evolves and self-organizes over time.CASN consists of four key elements: (i) organizational entities exhibiting adap-tivity, (ii) a topology with interconnectivity between multiple supply chains, (iii)self-organizing and emergent system performance, and (iv) an external environ-ment that coevolves with the system. Each of these fundamental elements withina CASN can maintain several properties, such as capacity and service level (en-tity); path length, redundancy, and clustering (topology); efficiency and flexibility(system); and demand, dynamism, and risk (environment). The properties of theseelements can be used to describe the state of a CASN at a moment in time orover a finite span of time. It is the interactions across these entities over time andthe evolution of their properties that the SCM discipline seeks to understand morefully. Some of these properties may already have well-accepted measurements ormetrics, such as a firm’s inventory holding costs, while others, such as supply chainagility, may require additional refinement.

Building SCM Theory by IdentifyingCAS Phenomena

A theory states how interrelated constructs are impacted by mechanisms creating aphenomenon (Schmenner & Swink, 1998). Future development of CASN theory-building efforts likewise should begin by viewing the properties associated withentities, topology, system, and environment as interrelated constructs. Mechanismsthat alter these constructs are initiated by entities residing both inside and outsidethe CASN. For example, participating entity decisions such as supplier selection,shifting priorities (allocation of resources), or procedural modifications may im-pact not only internal constructs such as capacity, service level, or inventory butalso system constructs like supply network efficiency, flexibility, and redundancy.Similarly, entities that exist in the external environment of the CASN can initiate

Pathak et al. 563

mechanisms such as modification of infrastructure or changes in regulatory policythat may impact CASN constructs.

The constructs associated with each of the fundamental CASN elements areclearly interrelated. Changes in any one entity construct may lead to alterationof topology that impacts overall system properties, which, in turn, may lead tochanges in the surrounding environment. Ultimately, the states of the entity, topol-ogy, system, and environmental constructs impact decision making within eachparticipating entity. Individual-entity decision making may spawn changes that cy-cle through the CASN and eventually lead to an altered system and environmentthat impacts future decisions. Therefore, theory development about how variousCASN elements interact can improve understanding of the impact of decisionsmade within each entity as well as their impact on other elements in the supply net-work. For example, the vertical integration decision taken by an original equipmentmanufacturer (OEM) determines the components or subcomponents that it wouldoutsource. Furthermore, a firm could decide to sole-source or engage several sup-pliers. These decisions would directly affect the network topology. The sourcingstrategy and the associated network topology impact the OEM’s flexibility to caterto potential demand fluctuations. In the event that the OEM is unable to satisfy aportion of demand due to supply shortages (e.g., due to capacity constraints at thesole supplier), the service level of the OEM gets adversely affected. This illustrateshow entity decisions, network topology, system characteristics, and environmentalcharacteristics are closely intertwined with each other.

Unique CASN Research Design Issues

While physical and temporal scales are often quite naturally defined and addressedin fixed and well-delineated relationships in complicated research, the nonlineardynamic relationships in a CAS often span multiple scales. Defining the appropri-ate system scale is essential if the CASN behavior under study is to be observedconsistently. Also, any constructs external to both the entities and the topologicalrelationships constituting the system that impact the behavior must be integratedinto the theoretical model, while superfluous variables must be eliminated. In ad-dition to the system scale, defining the environmental scope of the system is alsoparamount. Properly specifying these various types of scales enhances the valueof the research and also helps to focus the emphasis of study on key factors.

System scale and unit of analysis

Because of the recursive nature of systems both within and outside the CASN, it isimportant to select the appropriate physical scale or unit of analysis within whichthe theory is valid. Just as physics has discovered (Feynman & Weinberg, 1986)where, at the nano-scale level, normal laws of Newtonian physics break down,attempting to analyze a CASN phenomenon in too small or too large a contextmay yield comparatively perplexing results. Descriptions of the physical scalemust specify the range of entities that constitute the system as well as the types ofrelationships that are considered to form the interrelations within the topology.

In addition to defining the physical scale of the system, the proper scalingof time is important as well. Different types of phenomena may occur over longer


or shorter periods of time; therefore, certain research designs may require either alengthier period of study or more frequent measurements than others. For example,examining how changes in fuel-efficiency regulations impact supplier selectionpolicies in the automobile industry might require a longer time period of study thaninvestigating interfirm behavior in online reverse auctions. Clearly, there must bemultiple scales and potential units of analysis for systems as complicated as supplynetworks. An illustration of this is the problem with multiple levels of validationthat are common to interorganizational and agent-based models in general (Carley,2003). Even here, one key feature is the systems-level behavior that emerges overtime. Therefore, while there may be many factors that are important at an entitylevel, systems-level behavior must include observation of the system’s behaviorthat is creatively derived from the state and behavior of the constituent entities.

Environmental scope

As discussed previously, the system and its surrounding environment coevolve overtime (Lewin, Long, & Carroll, 1999). Changes in either of these elements impacthow decisions are made by CASN entities. Therefore, it is important to considerboth the properties of the system and the environmental constructs that are related tothe phenomenon of interest in any theory set forth. For example, using agent-basedsimulation, Siggelkow and Rivkin (2005) studied how environmental turbulenceand complexity affect the formal design of the organizations. From an empiricalperspective, Anderson and Tushman (2001) studied the effect of environmentalconstructs such as uncertainty, munificence, and structural complexity on firmsurvival. They found that uncertainty was the main reason that firms go out ofbusiness. These are examples of how inclusion of environmental constructs isimportant for research in CASN.

Just as it is important to determine the proper physical and temporal scales,finding the appropriate number and type of environmental constructs to includein a theory is important when balancing the needs for validity and tractability.Examples of potentially important constructs are demand, dynamism, uncertainty(both aleatory and epistemic), risk, munificence, and ecological factors. As inany research, however, caution must be exercised when selecting environmentalconstructs, as inclusion of too many may lead to models that are unwieldy whileinclusion of too few may yield insufficient explanatory power of the phenomena.

Leveraging models, measurements, and methodologies for validation

A model of CASN behavior should precisely state how to measure the relevantconstructs, how the constructs are related, and how certain mechanisms affectthose constructs. Only when these issues are clearly stated can the theory be val-idated and examined for consistency with the phenomena under study across awide range of situations. However, in addition to precise and internally consis-tent theoretical statement, a model should also allow for integration of other con-structs and mechanisms so that further theory refinement can make a significantimprovement. Different validation methodologies have various strengths and weak-nesses and some are more easily accepted within a discipline than others. In a fieldsuch as SCM, in which so many constructs are interrelated, this observation holds

Pathak et al. 565

particularly true. For example, in the 1980s just-in-time inventory movement high-lighted the inefficiencies of classic inventory models that were developed usingmathematical optimization techniques. The interrelationship of inventory levelswith other important operational aspects such as push/pull strategy, setup times,capital costs, multiskilled employees, and strong supplier relationships were notexplicitly considered in the classic inventory models, partly due to the constraintsplaced by the methodological orientation. Yet, in hindsight it is clear that an ex-plicit consideration of these interrelationships in research investigations pertainingto inventory models would have been a worthy undertaking much earlier. Whiletheories with a small number of constructs may lend themselves well to analyti-cal validation, integrating components across multiple theories or exploring singletheories with a large number of constructs may require empirical investigation.

Regardless of how a new or reformulated theory is created, it is importantto ensure the possibility of validation and refinement of the resultant theory. In-deed, when building CASN theories, such validation can be accomplished viamany different methodologies such as analytical, simulation-based, empirical, orarchival. For example, analytical models of interorganizational industrial systemshave existed for many years and have been the focus of many researchers’ ef-forts. Within small physical-scale models, closed-form mathematical equationshave been leveraged to expose detailed relationships between multiple variableswithin and across organizational boundaries. Mathematical programming opti-mization models have also been leveraged to provide insight for improved decisionmaking. However, analytical tractability for the most realistic situations (e.g., in aCASN) is often limited in its ability to obtain solutions for problems of reasonablesize.

Thus, analytical efforts of a CASN may require a different orientation fromthe optimization approach that is currently commonplace in studies investigatingsupply chain issues. The impact of uncertainties within a many-entity environmentmay overwhelm the limited robustness of small-scale globally optimal solutions.Furthermore, the adaptive nature of CASN entities must allow for reactive decisionmaking within, and in response to, their changing surroundings. New investigationsof analytical models that seek to mitigate risk and improve decisions throughmaintaining multiple alternative policies that can be implemented contingent uponspecific changes in larger-scale theoretical models should lead to improved supplychain performance.

Methodologically, computer-based simulations have been leveraged forinterorganizational supply network research as well (Lin & Shaw, 1998;Swaminathan, Smith, & Sadeh, 1998; Tan, 1999; Chatfield, 2001; Chatfield et al.,2004; Sawaya, 2006; Pathak et al., 2007). Some of the earliest work in the area wasperformed by Forrester (1961), who used simulation to examine system dynamicswithin a supply chain. Simulations of CASNs can allow for entities to adjust theirdecisions in response to their environments as well as the actions of other entities.Such a methodology is powerful in that it can generate results about larger-scalesystemic behavior in ways that are analytically intractable. Simulations also providea method for examining the dynamic behavior of systems in addition to potentialsteady-state behavior. Unfortunately, when compared to the specific results oftenobtained from analytical models via proofs or bounds, the ability of simulations


may be limited when definitively extrapolating the inner workings of large-scalesystems to the overall system behavior.

Consider the example of the beer game (Sterman, 1989) in which local firmsare making reordering decisions (small-scale decision change) that lead to thebullwhip effect due to excessive ordering at each tier in the supply network (large-scale performance change). Such an effect has been investigated using agent-basedcomputer simulation. One of the interesting effects that has been observed in thesesimulations has been an overall unstable behavior (in the form of wild order fluctua-tions) under certain simulation conditions in which the local agents have unlimitedmemory about the order fulfillment history of their suppliers and the order historyof their customers (Sawaya, 2006). This is due to the agents’ overreaction to lateorders, whereby the agents keep placing larger and larger orders as they adjust theirreorder point to compensate, leading to fluctuations and system instability.

The example highlights the possibility of generating extraneous system ef-fects due to a particular implementation of the simulation model with specific be-haviors. In this beer-game context, when the memory of the agents is limited, thesystem instability is reduced. It is challenging to use simulation to prove anything,but it allows researchers to understand something important about the likelihoodof different outcomes. Naturally, simulation is subject to many of the same limita-tions as analytical and other models, for example, lack of robust empirical data todrive or motivate the simulation, the inherent assumptions, or artifacts introducedbecause of the way the simulation has been implemented. Therefore, caution mustbe exercised and simulation studies probably need to be augmented with rigorousadditional research efforts via empirical and analytical methodologies that thor-oughly examine the connections between small-scale decisions and large-scaleperformance in a CASN.

Empirical methodologies are likely to be an important contributor to CASNtheory-development efforts as they establish a link to industry reality, providingvalidation and ensuring the practicability of model prescriptions. Because one ofthe advantages of the CASN view of supply networks is its ability to incorporateincreasing realism into models and theories of supply networks, empirical dataare essential for the development of CASN theory. Empirical methods will alwayscarry significant motivational weight in the OM and SCM disciplines. However,researchers often face challenges with data collection and with the complexities thatempirical data introduce into supply-network conceptualizations and models. Oneexample of empirical research comes from Choi and Hong (2002), in which theyuse an inductive case-study approach to build propositions about supply networks.In any case, as researchers become more familiar with the power of CASN, theywill perhaps be less hesitant to incorporate complicated real-world data into theoryand models of supply networks. It is also possible that, as various organizationsrecognize the benefits of more complex supply-network representations, they willbe more willing to allocate the necessary resources for detailed empirical datacollection and analysis.

Finally, archival data methodologies can aid in the collection of data to inves-tigate the evolution of supply networks. For example, Utterback (1994) determinedthe dynamics of industrial growth by using census data and Christensen (1997) usedarchival data on disk drives and their makers over time to develop the theory of

Pathak et al. 567

disruptive technology. Such data can be mined to examine how a particular industryevolved and to investigate what other evolutionary paths might have been followed.Within a CASN context, Pathak (2005) used archival demand data from the U.S.automobile industry to investigate factors affecting the evolution and growth in asupply network.

The complexity and multidimensionality of a CASN paradigm, as well as thediversity of research questions, rule out the use of a single approach. A combinationof approaches is necessary to adequately explore difficult issues such as multidi-rectional causalities, simultaneous and time-lagged effects among variables, non-linearities, cyclical feedback mechanisms, and path dependencies. Furthermore,the normal means of applying methodologies may require modification for appli-cation within the CASN context. Creatively combining the strengths of analytical,simulation, empirical, and archival methodologies will be essential when gener-ating, establishing, and refining theories within an integrated body of knowledge.As an example, consider leveraging multiple methodologies in developing newstrategies for bullwhip mitigation within a CASN context (Murray, 2007). Analyt-ical methodologies are capable of determining how order variance can be reducedby strategically leveraging negatively correlated demand streams or demand in-formation from multiple downstream supply network participants. Simulation canprovide verification of analytical results while extending them to examine the in-direct cost reductions that result at firms further upstream. Empirical studies couldbe used to investigate the applicability of these mitigation strategies in real-worldsupply networks or perhaps even identify where they are already in use. Further,archival data can be used to demonstrate the prevalence of the problem in an in-dustry.

Based on the discussions thus far, it is clear that future CASN research offersan exciting perspective to extend known problems and also a new set of problemsto address. In Table 2, we summarize sample research questions that could beaddressed by embracing the complexity and adaptivity perspective.

CONCLUSIONS AND IMPLICATIONS

SCM research examines the systems that span organizational boundaries. To date,the field has amassed a large and insightful collection of research that focuses ondyadic relations and phenomena that arise in tightly coupled, integrated systems(Beamon, 1998; Vonderembse et al., 2006). Largely absent from this body of workhas been research that examines the broader, network-level effects that exist in real-life supply networks. In such networks, cause and effect are not simple, behavior isdynamic, and the actions of any firm in the network can potentially affect any otherfirms in the network. Complexity science provides a conceptual and methodologicalframework that enables consideration of these network-level issues.

In this position paper we present a CASN perspective as a means to sup-plement and augment existing SCM theories and practices. For example, whilethe issue of visibility is central to research that examines collaborative plan-ning and inventory management among members of a supply chain, a CASNperspective would require researchers to extend the concept of visibility to anentire network of firms that may only be indirectly connected to the buying

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Pathak et al. 569

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Pathak et al. 571

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firm. Thus, as the practices of supply chain managers change over the futurefrom a dyadic-only perspective to more of a network perspective, new researchconcerning supplier selection and supplier relations should be conducted in or-der to identify new best practices emerging from such new types of decisionmaking.

To perform CASN research, we believe that supply chain researchers willneed to draw from a rich variety of research methodologies. Whereas most existingsupply chain research has focused on variance studies using surveys, discrete-eventsimulation, case studies of dyads, or analytical models, CASN research requiresagent-based and computational models, process models that are dynamic and gen-erative, and case studies of larger ensembles of firms. Both computational andqualitative methods provide means to capture complex cause and effect, nonlin-earity, ambiguity, and dynamism; however, these are difficult methodologies toimplement in a rigorous way, and so CASN researchers will possibly have to de-fine and uphold extremely high methodological standards in order for their workto be valid and have impact.

A CASN perspective has the potential to be particularly important to decision-making activities in a supply network. For a supply network manager, a CASNperspective offers a new language and a new mental model from which to viewthe business world, draw interesting insights, and make decisions. A CASN per-spective may aid a supply network manager in making decisions while keepingthe adaptivity of other firms, the complexity of the overall system, and the sur-rounding environment in mind. Furthermore, a CASN perspective will help enableresearchers to study the effects of decision making at the network level, as a supplynetwork is ultimately a complex web of decision making.

Supply networks today are being forced to take a growing amount of in-formation into account as more data continue to become available both from thesurrounding environmental context and from increased numbers of evolving sup-ply network partners. Organizations that are unable to interpret and leverage vastamounts of information from changing and interconnected sources may face legalliabilities and will likely fail to maintain adequate performance in the competitiveenvironment. Thus, information and decision-science researchers are likely to playan important role in helping to determine the future of decision making withinthese CASN contexts.

A paradigm shift toward embracing and integrating principles from complex-ity science has already occurred in many other disciplines. Recent SCM researchthat draws analogy between supply networks and CAS suggests this discipline maybe embarking on a similar change (Swaminathan et al., 1998; Choi et al., 2001;Surana et al., 2005). We urge the SCM research community to leverage the CASperspective for integrating existing knowledge and further investigating the com-plexity and adaptivity that inherently exist within supply networks. These effortswould benefit from a generally accepted foundation within which theories can becombined and on which future efforts can build. Creation of such a foundationis well beyond the scope of any single article such as this. What is required isboth authoritative identification of, and agreement on, the conceptually appropri-ate and empirically valid constructs that can be applied to supply network systemsframed as CAS. With such a foundation, the SCM field will be poised for both

Pathak et al. 573

integrating existing knowledge into a structured body of knowledge, thus extend-ing its relevance and applicability to real-world industry. [Invited.]

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Surya Pathak is a research associate and a lecturer in the engineering man-agement program at Vanderbilt University, School of Engineering, Nashville,TN. He received his PhD in interdisciplinary management of technology fromVanderbilt in 2005. He is currently conducting research in the area of complexadaptive supply networks, decision making under risk and uncertainty, supply net-work design, supply relationship management, and policy design for large-scalesystems. His methodological orientations include agent-based simulations and cel-lular automaton models on grid computing infrastructure along with mathematicalmodeling, robust and reliability-based design optimization, archival data analysis,and game theoretic modeling techniques for investigating policy implications indiverse domains, such as manufacturing and health care supply networks, trans-portation networks, and super networks. Dr. Pathak’s work has been published or isunder consideration in the IEEE Transactions on Engineering Management, Jour-nal of Operations Management, International Journal of Production Research, andTransportation Research Records.

Jamison M. Day is an assistant professor of supply chain management in theBauer College of Business at the University of Houston. Prior to obtaining his PhDin operations management and decision science at the Indiana University KelleySchool of Business, he served as the chief technology officer of Advanteq, LLC,a technology and business development firm. He has more than 12 years of ex-perience in information system and decision support technology, and his clientsinclude Microsoft, Pain Enterprises, Smith Research Center, the Journal of Amer-ican History, and Xylor Medical Systems. He has published articles appearing inpublications including European Journal of Operational Research, OMEGA, Inter-national Journal of Logistics Systems and Management, and World Energy MonthlyReview, and he has presented findings at several regional and national conferences.His research interests include complexity-based supply chain management strate-gies, improving disaster relief coordination, coordination of distributed solutionmethodologies, and intuition refinement.

Anand Nair is an assistant professor in the Department of Management Scienceat the University of South Carolina. He earned his PhD in business administrationfrom the Eli Broad Graduate School of Management at Michigan State Univer-sity. Professor Nair’s current research interests are in the areas of supply chainrelationship management, supply chain risk management, network analysis, qual-ity management, and technology management. His methodological orientationfor research includes qualitative and quantitative empirical methods, computa-tional experiments using complexity theory and complex adaptive systems ap-proach, discrete-event simulations, data envelopment analysis, and mathematical


modeling using optimal control theory and game theory. Professor Nair’s researcharticles have been published in Journal of Operations Management, EuropeanJournal of Operational Research, IEEE Transactions on Engineering Manage-ment, International Journal of Production Research, and other journals. ProfessorNair is an Area Editor for Operations Management Research and also serves onthe Editorial Review Board of the Journal of Operations Management.

William J. Sawaya III is a postdoctoral associate in the School of Civil and En-vironmental Engineering at Cornell University, Ithaca, NY. He earned his PhD inbusiness administration in the Department of Operations and Management Sciencein the Carlson School of Management at the University of Minnesota. His researchinterest spans many arenas of operations management with a focus on supply chainmanagement, supply chain risk management, and new product development. Hiscurrent research focuses on the impact of interorganizational information sharingwithin supply network contexts, and the economic impact of catastrophic supplydisruptions. Methodologically, he emphasizes the use of empirical data in modelsof operations systems, including agent-based simulation and other analytic models,and the application of a complex adaptive system paradigm in modeling organiza-tions.

Dr. M. Murat Kristal is an assistant professor of operations management atSchulich School of Business at York University, Toronto, Canada. He teaches in theareas of operations management/strategy, supply chain management, and statisticalmodels. Dr. Kristal graduated from the Operations Management Department in theKenan-Flagler Business School at the University of North Carolina at Chapel Hill.His research interests focus on the areas of supply chain, operations management,and strategy. His current research spans from how supply chains adapt to theircompetitive environments in order to survive in hyper competition to which factorsenable manufacturers to achieve mass customization capabilities and to variousstrategy problems that manufacturers face in their operations.

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