102-Complexity and Adaptivity in Supply Networks

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Complexity and Adaptivity in Supply

Networks: Building Supply Network

Theory Using a Complex AdaptiveSystems Perspective*

By Pathak, Surya D,Day, Jamison M,Nair, Anand,Sawaya, WilliamJ,Kristal, M Murat

Publication: Decision SciencesDate: Thursday, November 1 2007

INTRODUCTION

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 gaininsights into important issues within their domains of study. Research investigations inthe field of supply networks have also begun examining the merits of complexity theoryand the CAS perspective. In this article, we bring the applicability of complexity theoryand CAS into sharper focus, highlighting its potential for integrating existing supplychain management (SCM) research into a structured body of knowledge while alsoproviding a framework for generating, validating, and refining new theories relevant toreal-world supply networks. We suggest several potential research questions toemphasize how a CAS perspective can help in enriching the SCM discipline. We proposethat the SCM research community adopt such a dynamic and systems-level orientationthat brings to the fore the adaptivity of firms and the complexity of their interrelationsthat are often inherent in supply networks. Today, supply chain management (SCM)involves adapting to changes in a complicated global network of organizations. A typicalsupply network consists of interfirm relationships that may connect multiple industries.As a result, supply network decisions often require consideration of a large number offactors from multiple dimensions and perspectives. Two emergent themes that managersfrequently encounter when making these decisions are (i) the structural intricacies of theirinterconnected supply chains (Choi & Hong, 2002) and (ii) the need to learn and adapttheir organization in a constantly changing environment to ensure its long-term survival(Brown & Eisenhardt, 1998).

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Complex interconnections between multiple suppliers, manufacturers, assemblers,distributors, and retailers are the norm for industrial supply networks. When decisionmaking in these networks is based on noncomplex assumptions (e.g., linearity, a buyer-supplier dyad, sparse connectivity, static environment, fixed and nonadaptive individualfirm behavior), problems are often hidden, leaving plenty of room for understanding and

improving the underlying processes. Consider the recent implementation of complexity-oriented decision making by American Air Liquide, a firm based in Houston, Texas. Thefollowing information was acquired through multiple employee interviews, associateddocument examinations, and observations of the Operations Control Center at AmericanAir Liquide. The company produces industrial and medical gases such as nitrogen,oxygen, and hydrogen at about 100 manufacturing locations in the United States anddelivers to nearly 6,000 customer sites using a mix of pipelines, railcars, and more than400 trucks. 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. Afterworking with NuTech Solutions (formerly Bios Group), they created a new complexity-

based solution that leverages neural networks and agent-based modeling (with ant-foraging algorithms) to integrate decisions across their multinodal and multimodal supplynetwork. Most important, the new solution method solves both sourcing and routingtogether in the optimization process. Charles Harper, director of National Supply &Pipeline and Supply Operations, summarizes the benefits of their complexity-basedapproach:

After switching over, we drive less miles, we don't do stupid things, and we move peopleto different jobs that didn't exist before. All those things add up to savings. It's beenmind-blowing to see how much opportunity there was. The knowledge we gained fromimplementing the complexity-based solution helped us realize what the real-time

incremental cost of the liquid going into customers' tanks really was. Our supply networkcan now flexibly adapt to volatility in the environment due to differentials in powerprices or even hurricanes. Complexity-based solutions are extremely applicable andpeople need to start using them or they're going to lose out.

American Air Liquide is far from being the only firm that is using the structuralcomplexity (interconnectedness of firms) and adaptivity (dynamic learning of individualfirms) principles of Complex Adaptive Systems (CAS). Boeing has effectively used CASprinciples to redesign their 787 Dreamliner supply network, reducing the risk ofexpensive cascading supply network delays (Global Logistics and Supply ChainStrategies, 2007). Similarly, using CAS principles, Citibank Credit Risk uncovered $200million in hidden expenses, Proctor and Gamble reduced supply network inventory by25% and saved 22% on distribution expenses, and Southwest Airlines saved $2 millionannually in their freight delivery operations (Kelly & Allison, 1999; Waldrop, 2003;Global Logistics 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 of marketsand competition, and improved decision making (Abell, Serra, & Wood, 1999).

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Along with managing the complexity inherent in the interconnectivity of their supplynetworks, organizations have also started to learn the benefits of being adaptive in theirbehavior. Sheffi and Rice (2005) present an illustration of adaptive firm behavior in acellular telephone supply network. They highlight the different approaches that Nokiaand Ericsson took when a fire disrupted the supply from Philips, the sole supplier for a

particular chip common to both manufacturers. While Ericsson suffered an estimated$2.34 billion loss, Nokia engaged directly with Philips to restore supply using alternatesupply options. They modified designs of the handsets where possible and securedworldwide manufacturing capacity from Philips to ensure a steady supply of the chips.Meanwhile, the direct interaction between top management of Nokia and Philips furtherenhanced the ability of Nokia to adapt in the future. Wollin and Perry (2004) provideanother example of how Honda adapted to the changing automotive sector environmentby leveraging the notions of learning and path dependency of adaptive systems. Theyused their Accord and Civic platforms as me basis of several of their most recent sportutility vehicles, 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) examined howproperties of CAS are embodied by supply networks. Since this article, there have beenonly a handful of papers that use the CAS view of supply networks, signaling that theSCM discipline has yet to enthusiastically embrace the CAS perspective. The intent ofmis position paper is to draw attention to recent developments in CAS theory from acrossmultiple disciplines and articulate how this knowledge can be leveraged to enrich theoperations management (OM) and SCM disciplines. We suggest leveraging theconceptualizations of Complex Adaptive Supply Networks (CASN), such as those foundin Choi et al. (2001) and Surana, Kumara, Greaves, and Raghavan (2005), to lay afoundation for both integrating existing work and developing new theories within the

SCM body of knowledge. Specifically, we discuss how CAS principles can be useful foridentification and organization of complex and adaptive phenomena in supply networks,such as individual firm adaptation, self-organization and emergence, buyer-supplierrelationships, supply network performance, environmental change, and feedbackmechanisms. Finally, we examine the challenges associated with CASN theorydevelopment and provide suggestions for future research efforts and CASN theorydevelopment.

A CAS VIEW OF SUPPLY NETWORKS

Because organizations exhibit adaptivity and can exist in a complex environment withmyriad relationships and interactions, it is a natural step to identify a supply network as aCAS. Choi et al. (2001) argue that supply networks should be recognized as CAS byproviding a detailed mapping of each property of CAS to a supply network. In a similarway, subsequent research has recognized this same inherent complexity of supplynetworks (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 SCM research.

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A CAS is an interconnected network of multiple entities (or agents) that exhibit adaptiveaction in response to changes in both the environment and the system of entities itself(Choi et al., 2001). Collective system performance or behavior emerges as a nonlinearand dynamic function of the large number of activities made in parallel by interactingentities. For example, the individual decisions made by firms facing imperfect

information and variable demand lead to a globally observed phenomenon (i.e., thebullwhip effect) (Lee, Padmanabhan, & Whang, 1997). Anderson (1999) outlined fourcommon properties of such systems.

First, a CAS consists of entities that interact with other entities and with theenvironment by following a set of simple decision rules (i.e., schema). These entities mayevolve over time as entities learn from their interactions. In contrast to relationalmodeling, which tries to use one set of variables to explain variation in another set ofvariables, CAS examines how changes in an individual entity's schema lead to differentaggregate outcomes.

Second, a CAS is self-organizing. Self-organization is a consequence of interactionsbetween entities. Self-organization is defined as a process in which new structures,patterns, and properties emerge without being externally imposed on the system. Becausethe behavior in complex systems comes from dynamic interactions among the agents andbetween the environment and the agents, the changes tend to be nonlinear with respect tothe original changes in the system. Thus, there may be small changes that have a dramaticeffect on the system, or, conversely, large changes that have relatively little effect. Choiet al. (2001, p. 357) state, "the behavior of a complex system cannot be written down inclosed form; it is not amenable to prediction via the formulation of a parametric model,such as a statistical forecasting model." Even though it may not be possible to predict thefuture in an exact manner, the future may exhibit some underlying regularity. While the

changes that are made to a system may be dramatic and unpredictable, there may bepatterns of behavior that can be considered prototypical. Appropriate analyses may yieldsome knowledge of key patterns of behavior that are likely to develop in the system overtime.

Third, a CAS coevolves to the edge of chaos. Choi et al. (2001) explain coevolution,positing that a CAS reacts to and creates its environment so that as the environmentchanges it may cause the agents within it to change, which, in turn, cause other changesto the environment. These actions and reactions can be triggered by external events suchas natural disasters (e.g., Hurricane Katrina) or the actions of agents (e.g., a decision toimplement an enterprise resource planning system). A CAS exhibits dynamism aschanges occur in the environment; this dynamism affects the system. Environmentalfactors may cause changes to which the agents must adapt, influencing the way agentsperceive their environment or the schema used by the agents themselves. Thus, the rulesfollowed by the individual entities organize the system, because individual entities arenot privy to the objective function of the system as a whole. The coevolution of thesystem happens in the rugged fitness landscapes in which the CAS exists. The concept oflandscape was first introduced by biologist Sewell Wright (1932). It refers to the mappingfrom an organism's genetic structure to its fitness level. In management research, the idea

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of landscape is analogous to the domain of social and economic phenomena (Levinthal &Warglien, 1999). Specifically, these landscapes may be thought of in terms of an analogyof a range of mountains that represents an objective function (i.e., performance function)that is filled with hills and valleys (Kauffman, 1995). The hills or peaks represent thedesired optimal states, in which a rugged landscape has many peaks surrounded by deep

valleys. For instance, in the Toyota supply network, the flow of goods between its Camryplant and the Johnson Controls seat-frame manufacturing plant controlled via a tightlycoupled kanban system would react differently to an external event than the flow ofgoods between Johnson Controls' seat-frame manufacturing plants and their rawmaterials suppliers.

Fourth, a CAS is recursive by nature, and it recombines and evolves over time. Forexample, going back to the bullwhip effect (Lee et al., 1997), the interfirm orders couldbe characterized as orders from one organizational function to another organizationalfunction, orders from an individual employee of one firm to an employee of another, orany combination of the involved individuals, functions, or firms. Furthermore, from a

macroeconomic viewpoint, it can be posited that industry supply networks areinterrelated within a national or international context and interact together as a CAS in alarger context (Arthur, Durlauf, & Lane, 1997). Thus, a CAS is often composed ofentities that can themselves be characterized as CASs composed of smaller constituents(a nested hierarchy of smaller-scale complex systems). Changes in these smaller systemsand even in individual entities can cause the entire system to change over time.

Building on these properties, Choi et al. (2001) outline three key foci for supply chainresearch: internal mechanisms, the environment, and coevolution. For internalmechanisms, the key elements are agents (entities) and schema, self-organization andemergence, network connectivity, and network dimensionality. In the context of supply

networks, an entity may be an organization, a division, a team, or an individual, or even afunction of an individual's job. The key feature is that agents have the ability to makedecisions in response to the environment and to the action of other entities. In supplynetworks, schemas are the rules that the organizations, or the decision makers withinorganizations, use to make the decisions for, and guide the actions of, the organization.Self-organization and emergence occur as a result of decisions that are made by theindividual agents that cause the system to change and the collective system behavior toemerge over time. Network connectivity is the connection among the agents thatdetermines the complexity of the network. As the connectivity among the agentsincreases, the interrelationships among the agents increase, in turn causing increases inthe complexity of the network. In the case of supply-network relationships theseconnections are real, physical connections between organizations such as telephone lines,fax numbers, electronic data interchange systems, and so on. Dimensionality is the degreeto which agents can act in an autonomous fashion without influencing other agents.Therefore, as the degree of connectivity increases, the dimensionality decreases as theactions of a given agent has a greater impact on those with which it is connected.

As an example, Choi et al. (2001) present the interconnectivity of an aircraft enginemanufacturer (Honeywell) with a university hospital (Metro University Hospital).

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Honeywell depends on mining companies for supplies of raw materials such as steel,copper, aluminum, and other composite materials. These mining companies sourceequipment that relies on the latest material extraction techniques developed by variousfirms and agencies. The material extraction techniques rely on pattern recognitiontechnologies that aid in interpretations of X-ray scans of potential material vein and

enable a firm to make appropriate decisions regarding extraction locations. It isconceivable that the required pattern recognition technology is developed in a completelyunrelated sector, such as health care. For example, a university hospital might develop anew pattern recognition technique for the purposes of medical treatment that could havepotential application in material extraction. Over time, the knowledge gets passed on tothe material extraction company via research conferences. This example illustratescomplex interconnectivities among firms and the impact of decisions made by one firmon others in the network. We present the decisions and information flows among firms inFigure 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 literature across a wide range ofdisciplines, such as industrial engineering, computer science, physics, organizationalscience, new product development, and strategic management. In the next section, wehighlight these advancements and discuss how knowledge gained from these researchstudies can be beneficial for supply network research.

NEW DEVELOPMENTS IN CAS AND THEIR APPLICABILITY TO SCM

RESEARCH

Research endeavors using the CAS perspective have been undertaken in diverse fieldssuch as physics, biology, mathematics, computer science, engineering, psychology,

political science, sociology, economics, and organizational behavior. To systematicallyapproach this wide range of literature, we adopted the data triangulation approach. As afirst step, we sought expert opinion regarding the state of recent research pertaining toCAS. This step provided an initial reference list and guided our subsequent searchprocess. In the next step, we undertook an extensive search of selected peer-reviewedjournals (e.g., Academy of Management Journal; Management Science; OrganizationalScience; Non-Linear Dynamics, Psychology & Life Sciences; Emergence; andComplexity) by using the ABI/TNFORMS and Business Source Premier databases. In thesearch process, we included keywords such as supply network, CAS, complexity theory,adaptation, adaptivity, chaos, SCM, and nonlinear time series analysis. From the resultsobtained, we selected more than 100 articles that were directly related to CAS andundertook an in-depth examination of these articles to identify significant theoretical,methodological, and technical developments related to all the major aspects of a CAS-based supply chain as described in Choi et al. (2001).

IMAGE CHART1

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

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Researchers across multiple disciplines have significantly advanced the theoreticalboundaries 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, Durant, & Carr, 2003), andnetwork connectivity models (Barabaasi, 2002; Newman, 2003). Methodological

advancements such as sophisticated agent-based modeling (Chatfield, Kim, Harrison, &Hayya, 2004; Sawaya, 2006; Pathak, Dilts, & Biswas, 2007), cellular automata(Wolfram, 2002; Mizraji, 2004), dynamical systems theory (Surana et al., 2005), dynamicnetworks analysis (Carley, forthcoming), and empirical and case-study methods (Varga &Allen, 2006) have been applied to problems ranging from nursing and health caredomains (Anderson, Issel, & McDaniel, 2003) to supply networks (Thadakamalla,Raghavan, Kumara, & Albert, 2004). Analysis techniques used within these articlesinclude chaos theory (Strogatz, 1994), computational and statistical mechanics (Shalizi,2001), and nonlinear time series methods (Williams, 1997). Table 1 summarizes some ofthese research developments and advancements over the last 6 years across multipledifferent areas.

Table 1: Advancements in complex adaptive systems (CAS)-based research.

On careful examination, we note an interesting trend. Almost all of the researchcontributions and advancements listed in Table 1 have occurred predominantly outsidethe OM and SCM discipline. This observation is further supported by the observation thatthe special issue of Management Science on Complexity Theory (Amaral & Uzzi, 2007)does not carry a single article that deals purely with supply chain issues. Thus, it is clearthat, while other areas such as industrial engineering, computer science, physics,organizational science, research and development, and strategic management, to name afew, are strongly pursuing research based on CAS perspectives, OM and SCM research is

not keeping pace.

One of the greatest contributions of the CAS perspective may be its ability to incorporateincreasing realism and empirical data into research models that can be understood in apractical business setting (Anderson, 1999). This has been demonstrated with CASresearch both in diverse applications (ecology, social retirement models, and zoology)with high realism (Van Winkle, Rose, & Chambers, 1993; Grimm, 1999; Axtell, 2003)and in uses of empirical data from business organizations (Nilsson & Darley, 2006;Sawaya, 2006).

Consider the parallels that exist between work by Albert, Jeong, and Barabasi (2000) onerror and attack tolerance of complex networks and research by Hendricks and Singhal(2003) regarding supply network resilience under disruption. Findings indicate that theheterogeneous dyads in scale-free networks, such as those found in the Internet,biological-cell, and social-network connectivity, exhibit higher tolerance to randomerrors but lower tolerance to targeted attack than the more homogenous, exponential-stylenetworks. These findings can be leveraged to hypothesize how different supply-networktopologies give rise to different levels of supply-network resiliency under disruptionsrelated to either random failure or targeted attack, potentially leading to important

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implications for industry management decisions. In fact, Thadakamalla et al. (2004) haveshown how knowledge can be generated about survivability and resiliency of supplynetworks using concepts shown in the work of Albert et al. (2000). The work of Brahaand Bar-Yam (2007) utilizes statistical properties of a complex network to show how thestructural information flows in distributed product development networks have similar

properties to other social, biological, and technological networks. It would be interestingto follow Braha and Bar-Yam's suggestion regarding applying their findings aboutstatistical properties of intraorganizational product development network to a supplynetwork context, as this may result in new insights on how interfirm and intrafirmproperties connect and evolve.

Recent advancements made by Rivkin and Siggelkow (2007) toward extending CASresearch of organizations (Levinthal, 1997; McKelvey, 1999) using the NK model offitness from theoretical biology (Kauffman & Levin, 1987; Kauffman & Weinberger,1989) to questions of adaptability in individual organizations could have importantlessons for the study of supply chains. Rivkin and Siggelkow (2007) leverage empirical

research demonstrating patterns of interactions within decision processes to show that thenumber of local optima is highly correlated with the decision-interaction patterns.Therefore, if there are many local optima, the relative value of exploration decreases. Theimplication is that the value of exploration of opportunities versus the exploitation ofexisting opportunities varies depending on how rugged and dynamic the landscape is.

From a supply chain management perspective, the results and findings on adaptabilityand use of NK models have been demonstrated for supply base management (Choi &Krause, 2006). Also important are the number of suppliers (N) and the level ofinterrelationships among the suppliers (AT) and the degree of differentiation of thesesuppliers. In particular, the significance of interrelationships could have further

implications for buyer-buyer or supplier-supplier coopetition (simultaneous competitionand cooperation) in supply networks (Bengtsson & Kock, 2000; Choi, Zhaohui, Ellram,& Koka, 2002). For instance, supplier firms are typically under the control of me buyingcompany through established work routines and contractual terms, yet they are able tomake decisions on their own behalf. In this regard, me tension between control andemergence might be applicable to supplier-supplier relationships and thus may provide aninteresting context for CASN studies.

Another use of NK models can be found in the manufacturing-strategy literature.Levinthal and Warglien (1999) show how Japanese automotive manufacturers use robustdesign to achieve single-peaked landscapes (landscapes with very low interaction levelsamong agents as compared to the total number of agents). They state that "in the changeoperations, using pear-shaped clamps that can be smoothly brought to fit in only one waythereby driving even approximate movements into the right direction, reduces errors onthe production line. The landscape in this case is designed by the physical shape of thetask environment" (p. 346). This example illustrates how NK models can beconceptualized to reduce variability in a production network. If we apply this concept toSCM, one can argue that quality management practices can use similar concepts from NKmodels for managing buyer-supplier relationships in order to reduce variability of the

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quality of me products that the suppliers send to their buyers, thus leading to a single-peaked landscape as suggested by Levinthal and Warglien (1999). For instance, whenHonda uses a consistent supplier-management approach not only with their first-tiersuppliers but also with their second- and third-tier suppliers (Choi & Hong, 2002), onemight view this as an attempt to create a single-peaked landscape in the supply network.

Discussions and examples so far suggest that the CAS perspective holds promise forenriching and extending the current body of knowledge in the OM and SCM disciplines.We provide a detailed discussion of potential research directions later in the article, butwe 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 various OMand SCM issues (Beamon, 1998). The use of analytical models, simulation methods, andempirical approaches have greatly enhanced knowledge and improved decision-making

processes. Analytical modeling-based studies have matured from their initial years intoexplicit considerations of various operational decisions, the stochastic nature of demand,and the combinatorial possibilities of available scenarios and options. Empirical researchhas grown to provide insights regarding strategic issues, managerial perceptions, andmeasurements of key operational issues. Undoubtedly, the scope of problems beinginvestigated in extant literature is becoming richer and scholars are attacking complicatedissues that were previously outside the scope of investigation for tractability reasons(Vonderembse, Uppal, Huang, & Dismukes, 2006). Addressing complicated issues,however, does not equate to addressing complexities.

Complexity vs. Complicatedness

The distinction between complicated research and complexity-oriented research isimportant for ensuring a broad-based research agenda. Cilliers (2000) suggests thatsomething that is complicated can be intricate, but the relationship between mecomponents is fixed and well defined. For instance, a jumbo jet is a complicated systemthat is amenable to taking individual components apart and putting them back together. Incontrast, a complex system is characterized in terms of the nonlinear dynamicinteractions of the individual parts. Furthermore, while a complicated system can beviewed as the sum of its parts, a complex system cannot be viewed that way; one cannotpredict the behavior of a complex system by examining the behavior of its individualparts. These emergent properties of complex systems are due to the nonlinear dynamicrelationship between the individual components.

In a recent special issue on complex systems in Management Science, Amaral and Uzzi(2007) provide the following commentary that further illuminates me differences betweencomplicatedness and complexity (p. 1033):

In contrast to simple systems, such as the pendulum, which has a small number of w

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In contrast to simple systems, such as the pendulum, which has a small number of well-understood components, or complicated systems, such as Boeing jet, which have manycomponents that interact through predefined coordination rules (Perrow, 1999), complexsystems typically have many components that can autonomously interact throughemergent rules. In management contexts, complex systems arise whenever there are

populations of interacting agents that can act on their limited and local information. Theagents and the larger system in which they are embedded operate by trading theirresources without the aid of a central control mechanism or event a clear understandingof how actions of (possibly distant) agents can affect them.

Amaral and Uzzi (2007) comment on me complexity in the supply chain arena andemphasize the increasingly decentralized decision making, networkwide dissemination ofinnovations, and the need to find approaches to make lean supply chains robust againstrandom failures and targeted breakdowns. The authors propose a complexity-basedperspective for future investigations of various business issues.

Parallel to the investigation of complicated issues that continue to be examined, researchinitiatives are needed that examine complexity in OM and SCM. This endeavor canpotentially illuminate several critical issues, such as interconnected supply networks andlearning and adaptivity within supply networks that are currently rare in SCM literature.

Challenges of Theory Development with a CAS Perspective

In general, theory building requires careful application of structural methods to identifyphenomena. Once identified, me phenomena must be validated by designing andconducting research studies (Meredith, 1998). Throughout this process, careful attentionmust be given to the level of rigor such that the research adheres to appropriate

methodological guidelines. The results obtained, as well as any relevant insights, musthave clear application to the phenomena within me boundary conditions and begeneralizable for the theory to be integrated into a wider body of knowledge. Here, weexamine some of the unique theory-development challenges that must be overcome if acoherent body of knowledge is to be developed around CAS principles.

First, the complexity of supply networks will press limits on researchers' ability tounderstand the internal interactions between constructs and mechanisms of larger-scopephenomena. For example, operations research has successfully leveraged game theory tounderstand competitive and cooperative phenomena both within and betweenorganizations (Cachon & Lariviere, 1999). Although these investigations provide insightinto optimal monopolistic or duopolistic decisions, there are limits to modeling thenonlinear dynamics and adaptations inherent in the oligopoly or free-market structuresthat dominate our economy. As discussed previously, when several locally optimalpolicies interact in a complex supply network, the resulting nonlinear dynamics of globalbehavior can be unpredictable. Therefore, game-theoretic studies can be enriched byadopting the CAS perspective to help examine the applicability, impact, and robustnessof their findings within the larger, more realistic supply network contexts in which gametheory is intractable. One reason for the growing popularity of CAS across several

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disciplines is its ability to incorporate more realism in building theories, providingopportunity for greater relevance, and supplying an understanding of me way phenomenaact in 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 and

networks are capable of producing.

A second challenge is that OM and SCM as disciplines currently lack metrics forevolution and dynamism in supply networks. For example, many phenomena in supplynetworks occur over time, and it will be crucial to examine the evolution of the supplynetwork over an extended time horizon. Such a behavior could be measured and depictedusing attractors and the corresponding lags at which attractors are reconstructed(Williams, 1997). Furthermore, because phenomena in an evolving supply chain occur atdifferent levels, they must be captured at the firm, topology, and systems levels. Forexample, investigation of supply chain disruptions would require simultaneousconsideration of agent-level metrics such as capacity and fitness, topology-level metrics

such as degree distribution and path length, and system-level metrics such as robustnessand efficiency. Given that empirical data collection can be problematic whenever realorganizations are involved, empirical studies aimed at examining dynamic andevolutionary behavior inherent in supply networks will require resourceful approaches tooperationalize and integrate underlying constructs based on data collected from multiplesystem levels.

Third, developing robust theories in the presence of adaptation presents a formidabletask. In a system of entities with changing policies, careful analysis of the impact ofinteractions among these policies will be required. For example, Texas and California arepreparing to restructure their power markets from zonal to nodal models next year

(Alaywan, Wu, & Papalexopoulos, 2004; Ercot, 2007). Power generators and wholesalersare planning to adapt their policies (e.g., trade strategies, scheduling, risk management) totake advantage of almost continuous shifts in pricing and transmission congestion across3,000-4,000 locations. Attempting to ascertain common overarching principles in suchCASNs may require approaches uncommon to operations and supply chain research likelongitudinal data collection and data analysis without resorting to linearity assumptions.Research design and validation techniques will require resourcefulness when exploringboth new and previously identified phenomena in the presence of dynamically changingand interacting entity behaviors.

It may be possible to glean supply network information from publicly available data orcompany archival data sources in order to understand factors affecting the dynamicbehavior of the network. Such information, assuming it can be found, can be used toinform model development and validate models of supply networks. Because of thedynamic nature of CASN, rich longitudinal data of both quantitative and qualitativenature are important to accurately assess entity adaptation and its impact on system-levelbehavior. This likely requires close collaboration between academic researchers andpractitioners who are dedicated to understanding the complexities that affectorganizations in a supply network in order to make the commitment to this type of

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research effort. For example, structure, schema, and performance of various constituentorganizations of a supply network might be sampled at regular intervals over time inorder to understand the dynamic and emergent behavior of the system.

Finally, while borrowing concepts and ideas developed in other disciplines can be

innovative and useful, one must remember to take great care when relating a phenomenonfound in a few studies to a wider range of situations. As seen in physics, abstraction ofphenomena to larger- or smaller-scale systems does not always hold true, and any attemptto do so must be done thoughtfully and with great care (Feynman & Weinberg, 1986).Likewise, the impact of complexity and adaptation observed in one system may not holdtrue when applied in other systems. Such CASN characteristics make research in this areadifficult, but, fortunately, OM and SCM disciplines could learn from other disciplines,such as organizational science, economics, computer science, and evolutionary biology,to name but a few. These disciplines have been extremely careful in generalizing theirresults and have intelligently combined a diverse range of methods and tools (assummarized in 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 bridging theresearch-reality gap. For instance, tapping existing CAS research and applying it tosupply network contexts will move the field beyond a static, isolated dyadic buyer-supplier framework. As indicated previously in this article, Braha and Bar-Yam (2007)studied the statistical properties of organizational networks that focus on productdevelopment. They show that structure of information-flow networks have properties thatare similar to those displayed by other social, biological, and technological networks.They conclude their study by suggesting that the intraorganizational properties they

studied might be applied to an interorganizational level at which business organizationsform the networks (i.e., supply networks). Thus, by shifting the unit of analysis to thefirm level, existing knowledge from an external discipline can be used for researchingsupply network problems.

In this section, we attempt to highlight some of the issues that must be addressed in orderto develop a useful CASN research framework. We start by suggesting a CASNdefinition. We then elaborate on how supply network theory may be developed, buildingon CAS phenomenon. We finish by discussing some unique CASN research design,measurement, and methodological issues for validation purposes and list some potentialCASN research questions.

DefiningCASN

A formal definition of CASN is one step toward furthering the use of CAS principles inexamining supply networks. Formulating such a definition is not a trivial task and willrequire an iterative process with inputs, from a variety of experienced researchers. Whatwe propose here should be taken as a starting point for a formal discussion from which anacceptable definition might emerge.

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A CASN is a system of interconnected autonomous entities that make choices to surviveand, as a collective, the system evolves and self-organizes over time. CASN consists offour key elements: (i) organizational entities exhibiting adaptivity, (ii) a topology withinterconnectivity between multiple supply chains, (iii) self-organizing and emergentsystem performance, and (iv) an external environment that coevolves with the system.

Each of these fundamental elements within a CASN can maintain several properties, suchas capacity and service level (entity); path length, redundancy, and clustering (topology);efficiency and flexibility (system); and demand, dynamism, and risk (environment). Theproperties of these elements can be used to describe the state of a CASN at a moment intime or over 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 more fully.Some of these properties may already have well-accepted measurements or metrics, suchas a firm's inventory holding costs, while others, such as supply chain agility, may requireadditional refinement.

Building SCM Theory by Identifying CAS Phenomena

A theory states how interrelated constructs are impacted by mechanisms creating aphenomenon (Schmenner & Swink, 1998). Future development of CASN theory-buildingefforts likewise should begin by viewing the properties associated with entities, topology,system, and environment as interrelated constructs. Mechanisms that alter theseconstructs are initiated by entities residing both inside and outside the CASN. Forexample, participating entity decisions such as supplier selection, shifting priorities(allocation of resources), or procedural modifications may impact not only internalconstructs such as capacity, service level, or inventory but also system constructs likesupply network efficiency, flexibility, and redundancy. Similarly, entities that exist in theexternal environment of the CASN can initiate mechanisms such as modification of

infrastructure or changes in regulatory policy that may impact CASN constructs.

The constructs associated with each of the fundamental CASN elements are clearlyinterrelated. Changes in any one entity construct may lead to alteration of topology thatimpacts overall system properties, which, in turn, may lead to changes in me surroundingenvironment. Ultimately, the states of the entity, topology, system, and environmentalconstructs impact decision making within each participating entity. Individual-entitydecision making may spawn changes that cycle through the CASN and eventually lead toan altered system and environment that impacts future decisions. Therefore, theorydevelopment about how various CASN elements interact can improve understanding ofthe impact of decisions made within each entity as well as their impact on other elementsin the supply network. For example, the vertical integration decision taken by an originalequipment manufacturer (OEM) determines the components or subcomponents that itwould outsource. Furthermore, a firm could decide to sole-source or engage severalsuppliers. These decisions would directly affect the network topology. The sourcingstrategy and the associated network topology impact the OEM's flexibility to cater topotential demand fluctuations. In the event that the OEM is unable to satisfy a portion ofdemand due to supply shortages (e.g., due to capacity constraints at the sole supplier), theservice level of the OEM gets adversely affected. This illustrates how entity decisions,

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network topology, system characteristics, and environmental characteristics are closelyintertwined with each other.

Unique CASN Research Design Issues

While physical and temporal scales are often quite naturally defined and addressed infixed and well-delineated relationships in complicated research, the nonlinear dynamicrelationships in a CAS often span multiple scales. Defining the appropriate system scaleis essential if the CASN behavior under study is to be observed consistently. Also, anyconstructs external to both the entities and the topological relationships constituting thesystem that impact the behavior must be integrated into the theoretical model, whilesuperfluous variables must be eliminated. In addition to the system scale, defining theenvironmental scope of the system is also paramount. Properly specifying these varioustypes of scales enhances the value of the research and also helps to focus the emphasis ofstudy 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 which thetheory is valid. Just as physics has discovered (Feynman & Weinberg, 1986) where, atthe nano-scale level, normal laws of Newtonian physics break down, attempting toanalyze a CASN phenomenon in too small or too large a context may yield comparativelyperplexing results. Descriptions of the physical scale must specify the range of entitiesthat constitute the system as well as the types of relationships that are considered to formthe interrelations within the topology.

In addition to defining the physical scale of the system, the proper scaling of time isimportant as well. Different types of phenomena may occur over longer or shorter periodsof time; therefore, certain research designs may require either a lengthier period of studyor more frequent measurements than others. For example, examining how changes infuel-efficiency regulations impact supplier selection policies in the automobile industrymight require a longer time period of study than investigating interfirm behavior in onlinereverse auctions. Clearly, there must be multiple scales and potential units of analysis forsystems as complicated as supply networks. An illustration of this is the problem withmultiple levels of validation that are common to interorganizational and agent-basedmodels in general (Carley, 2003). Even here, one key feature is the systems-levelbehavior that emerges over time. Therefore, while there may be many factors that areimportant at an entity level, systems-level behavior must include observation of thesystem's behavior that is creatively derived from the state and behavior of the constituententities.

Environmental scope

As discussed previously, the system and its surrounding environment coevolve over time(Lewin, Long, & Carroll, 1999). Changes in either of these elements impact how

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decisions are made by CASN entities. Therefore, it is important to consider both theproperties of the system and the environmental constructs that are related to thephenomenon of interest in any theory set forth. For example, using agent-basedsimulation, Siggelkow and Rivkin (2005) studied how environmental turbulence andcomplexity affect the formal design of the organizations. From an empirical perspective,

Anderson and Tushman (2001) studied the effect of environmental constructs such asuncertainty, munificence, and structural complexity on firm survival. They found thatuncertainty was the main reason that firms go out of business. These are examples of howinclusion of environmental constructs is important for research in CASN.

ust as it is important to determine the proper physical and temporal scales, finding theappropriate number and type of environmental constructs to include in a theory isimportant when balancing the needs for validity and tractability. Examples of potentiallyimportant constructs are demand, dynamism, uncertainty (both aleatory and epistemic),risk, munificence, and ecological factors. As in any research, however, caution must beexercised when selecting environmental constructs, as inclusion of too many may lead to

models that are unwieldy while inclusion of too few may yield insufficient explanatorypower 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 affect thoseconstructs. Only when these issues are clearly stated can the theory be validated andexamined for consistency with the phenomena under study across a wide range ofsituations. However, in addition to precise and internally consistent theoretical statement,a model should also allow for integration of other constructs and mechanisms so that

further theory refinement can make a significant improvement. Different validationmethodologies have various strengths and weaknesses and some are more easily acceptedwithin a discipline than others. In a field such as SCM, in which so many constructs areinterrelated, this observation holds particularly true. For example, in the 1980s just-in-time inventory movement highlighted the inefficiencies of classic inventory models thatwere developed using mathematical optimization techniques. The interrelationship ofinventory levels with other important operational aspects such as push/pull strategy, setuptimes, capital costs, multi skilled employees, and strong supplier relationships were notexplicitly considered in the classic inventory models, partly due to the constraints placedby the methodological orientation. Yet, in hindsight it is clear that an explicitconsideration of these interrelationships in research investigations pertaining to inventorymodels would have been a worthy undertaking much earlier. While theories with a smallnumber of constructs may lend themselves well to analytical validation, integratingcomponents across multiple theories or exploring single theories with a large number ofconstructs may require empirical investigation.

Regardless of how a new or reformulated theory is created, it is important to ensure thepossibility of validation and refinement of the resultant theory. Indeed, when buildingCASN theories, such validation can be accomplished via many different methodologies

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such as analytical, simulation-based, empirical, or archival. For example, analyticalmodels of inter organizational industrial systems have existed for many years and havebeen the focus of many researchers' efforts. Within small physical-scale models, closed-form mathematical equations have been leveraged to expose detailed relationshipsbetween multiple variables within and across organizational boundaries. Mathematical

programming optimization models have also been leveraged to provide insight forimproved decision making. However, analytical tractability for the most realisticsituations (e.g., in a CASN) is often limited in its ability to obtain solutions for problemsof reasonable size.

Thus, analytical efforts of a CASN may require a different orientation from theoptimization approach that is currently commonplace in studies investigating supplychain issues. The impact of uncertainties within a many-entity environment mayoverwhelm the limited robustness of small-scale globally optimal solutions. Furthermore,the adaptive nature of CASN entities must allow for reactive decision making within, andin response to, their changing surroundings. New investigations of analytical models that

seek to mitigate risk and improve decisions through maintaining multiple alternativepolicies that can be implemented contingent upon specific changes in larger-scaletheoretical models should lead to improved supply chain 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 was performed by Forrester(1961), who used simulation to examine system dynamics within a supply chain.Simulations of CASNs can allow for entities to adjust their decisions in response to theirenvironments as well as the actions of other entities. Such a methodology is powerful in

that it can generate results about larger-scale systemic behavior in ways that areanalytically intractable. Simulations also provide a method for examining the dynamicbehavior of systems in addition to potential steady-state behavior. Unfortunately, whencompared to the specific results often obtained from analytical models via proofs orbounds, the ability of simulations may be limited when definitively extrapolating theinner workings of large-scale systems to the overall system behavior.

Consider the example of the beer game (Sterman, 1989) in which local firms are makingreordering decisions (small-scale decision change) that lead to the bullwhip effect due toexcessive ordering at each tier in the supply network (large-scale performance change).Such an effect has been investigated using agent-based computer simulation. One of theinteresting effects that has been observed in these simulations has been an overallunstable behavior (in the form of wild order fluctuations) under certain simulationconditions in which the local agents have unlimited memory about the order fulfillmenthistory of their suppliers and the order history of their customers (Sawaya, 2006). This isdue to the agents' overreaction to late orders, whereby the agents keep placing larger andlarger orders as they adjust their reorder point to compensate, leading to fluctuations andsystem instability.

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The example highlights the possibility of generating extraneous system effects due to aparticular implementation of the simulation model with specific behaviors. In this beer-game context, when the memory of the agents is limited, the system instability isreduced. It is challenging to use simulation to prove anything, but it allows researchers tounderstand something important about the likelihood of different outcomes. Naturally,

simulation is subject to many of the same limitations as analytical and other models, forexample, lack of robust empirical data to drive or motivate the simulation, the inherentassumptions, or artifacts introduced because of the way the simulation has beenimplemented. Therefore, caution must be exercised and simulation studies probably needto be augmented with rigorous additional research efforts via empirical and analyticalmethodologies that thoroughly examine the connections between small-scale decisionsand large-scale performance in a CASN.

Empirical methodologies are likely to be an important contributor to CASN theory-development efforts as they establish a link to industry reality, providing validation andensuring the practicability of model prescriptions. Because one of the advantages of the

CASN view of supply networks is its ability to incorporate increasing realism intomodels and theories of supply networks, empirical data are essential for the developmentof CASN theory. Empirical methods will always carry significant motivational weight inthe OM and SCM disciplines. However, researchers often face challenges with datacollection and with the complexities that empirical data introduce into supply-networkconceptualizations and models. One example of empirical research comes from Choi andHong (2002), in which they use an inductive case-study approach to build propositionsabout supply networks. In any case, as researchers become more familiar with the powerof CASN, they will perhaps be less hesitant to incorporate complicated real-world datainto theory and models of supply networks. It is also possible that, as variousorganizations recognize the benefits of more complex supply-network representations,

they will be 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 investigate theevolution of supply networks. For example, Utterback (1994) determined the dynamics ofindustrial growth by using census data and Christensen (1997) used archival data on diskdrives and their makers over time to develop the theory of disruptive technology. Suchdata can be mined to examine how a particular industry evolved and to investigate whatother evolutionary paths might have been followed. Within a CASN context, Pathak(2005) used archival demand data from the U.S. automobile industry to investigatefactors affecting the evolution and growth in a supply network.

The complexity and multidimensionality of a CASN paradigm, as well as the diversity ofresearch questions, rule out the use of a single approach. A combination of approaches isnecessary to adequately explore difficult issues such as multidirectional causalities,simultaneous and time-lagged effects among variables, nonlinearities, cyclical feedbackmechanisms, and path dependencies. Furthermore, the normal means of applyingmethodologies may require modification for application within the CASN context.Creatively combining the strengths of analytical, simulation, empirical, and archival

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methodologies will be essential when generating, establishing, and refining theorieswithin an integrated body of knowledge. As an example, consider leveraging multiplemethodologies in developing new strategies for bullwhip mitigation within a CASNcontext (Murray, 2007). Analytical methodologies are capable of determining how ordervariance can be reduced by strategically leveraging negatively correlated demand streams

or demand information from multiple downstream supply network participants.Simulation can provide verification of analytical results while extending them to examinethe indirect cost reductions that result at firms further upstream. Empirical studies couldbe used to investigate the applicability of these mitigation strategies in real-world supplynetworks or perhaps even identify where they are already in use. Further, archival datacan be used to demonstrate the prevalence of the problem in an industry.

Based on the discussions thus far, it is clear that future CASN research offers an excitingperspective to extend known problems and also a new set of problems to address. InTable 2, we summarize sample research questions that could be addressed by embracingthe complexity and adaptivity perspective.

CONCLUSIONS AND IMPLICATIONS

SCM research examines the systems that span organizational boundaries. To date, thefield has amassed a large and insightful collection of research that focuses on dyadicrelations and phenomena that arise in tightly coupled, integrated systems (Beamon, 1998;Vonderembse et al., 2006). Largely absent from this body of work has been research thatexamines the broader, network-level effects that exist in real-life supply networks. Insuch networks, cause and effect are not simple, behavior is dynamic, and the actions ofany firm in the network can potentially affect any other firms in the network. Complexityscience provides a conceptual and methodological framework that enables consideration

of these network-level issues.

In this position paper we present a CASN perspective as a means to supplement andaugment existing SCM theories and practices. For example, while the issue of visibility iscentral to research that examines collaborative planning and inventory managementamong members of a supply chain, a CASN perspective would require researchers toextend the concept of visibility to an entire network of firms that may only be indirectlyconnected to the buying firm. Thus, as the practices of supply chain managers changeover the future from a dyadic-only perspective to more of a network perspective, newresearch concerning supplier selection and supplier relations should be conducted inorder to identify new best practices emerging from such new types of decision making.

Table 2: Potential research issues and questions for building complex adaptive supplynetwork (CASN) theory.

To perform CASN research, we believe that supply chain researchers will need to drawfrom a rich variety of research methodologies. Whereas most existing supply chainresearch has focused on variance studies using surveys, discrete-event simulation, casestudies of dyads, or analytical models, CASN research requires agent-based and

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computational models, process models that are dynamic and generative, and case studiesof larger ensembles of firms. Both computational and qualitative methods provide meansto capture complex cause and effect, nonlinearity, ambiguity, and dynamism; however,these are difficult methodologies to implement in a rigorous way, and so CASNresearchers will possibly have to define and uphold extremely high methodological

standards in order for their work to be valid and have impact.

A CASN perspective has the potential to be particularly important to decision makingactivities in a supply network. For a supply network manager, a CASN perspective offersa new language and a new mental model from which to view the business world, drawinteresting insights, and make decisions. A CASN perspective may aid a supply networkmanager in making decisions while keeping the adaptivity of other firms, the complexityof the overall system, and the surrounding environment in mind. Furthermore, a CASNperspective will help enable researchers to study the effects of decision making at thenetwork level, as a supply network is ultimately a complex web of decision making.

Supply networks today are being forced to take a growing amount of information intoaccount as more data continue to become available both from the surroundingenvironmental context and from increased numbers of evolving supply network partners.Organizations that are unable to interpret and leverage vast amounts of information fromchanging and interconnected sources may face legal liabilities and will likely fail tomaintain adequate performance in the competitive environment. Thus, information anddecision-science researchers are likely to play an important role in helping to determinethe future of decision making within these CASN contexts.

A paradigm shift toward embracing and integrating principles from complexity sciencehas already occurred in many other disciplines. Recent SCM research that draws analogy

between supply networks and CAS suggests this discipline may be embarking on asimilar change (Swaminathan et al., 1998; Choi et al., 2001; Surana et al., 2005). We urgethe SCM research community to leverage the CAS perspective for integrating existingknowledge and further investigating the complexity and adaptivity that inherently existwithin supply networks. These efforts would benefit from a generally acceptedfoundation within which theories can be combined and on which future efforts can build.Creation of such a foundation is well beyond the scope of any single article such as this.What is required is both authoritative identification of, and agreement on, theconceptually appropriate and empirically valid constructs that can be applied to supplynetwork systems framed as CAS. With such a foundation, the SCM field will be poisedfor both integrating existing knowledge into a structured body of knowledge, thusextending its relevance and applicability to real-world industry.

FOOTNOTE

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

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AUTHOR_AFFILIATION

Surya D. Pathak[dagger]

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

Jamison M. Day

Department of Decision and Information Sciences, Bauer College of Business, Universityof Houston, Melcher Hall 290D, Houston, TX 77204, e-mail: [email protected]

Anand Nair

Department of Management Science, Moore School of Business, University of SouthCarolina, Columbia, SC 29208, e-mail: [email protected]

William J. Sawaya

Department of Civil and Environmental Engineering, Cornell University, 220 HollisterHall, Ithaca, NY 14853, e-mail: [email protected]

M. Murat Kristal

Operations Management and Information Systems Department, Schulich School ofBusiness, York University, 4700 Keele Street Toronto, Ontario, Canada M3J 1P3, e-mail:[email protected]

Surya Pathak is a research associate and a lecturer in the engineering managementprogram at Vanderbilt University, School of Engineering, Nashville, TN. He received hisPhD in interdisciplinary management of technology from Vanderbilt in 2005. He iscurrently conducting research in the area of complex adaptive supply networks, decision

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making under risk and uncertainty, supply network design, supply relationshipmanagement, and policy design for large-scale systems. His methodological orientationsinclude agent-based simulations and cellular automaton models on grid computinginfrastructure along with mathematical modeling, robust and reliability-based designoptimization, archival data analysis, and game theoretic modeling techniques for

investigating policy implications in diverse domains, such as manufacturing and healthcare supply networks, transportation networks, and super networks. Dr. Pathak's work hasbeen published or is under consideration in the IEEE Transactions on EngineeringManagement, Journal of Operations Management, International Journal of ProductionResearch, and Transportation Research Records.

Jamison M. Day is an assistant professor of supply chain management in the BauerCollege of Business at the University of Houston. Prior to obtaining his PhD inoperations management and decision science at the Indiana University Kelley School ofBusiness, he served as the chief technology officer of Advanteq, LLC, a technology andbusiness development firm. He has more than 12 years of experience in information

system and decision support technology, and his clients include Microsoft, PainEnterprises, Smith Research Center, the Journal of American History, and Xylor MedicalSystems. He has published articles appearing in publications including European Journalof Operational Research, OMEGA, International Journal of Logistics Systems andManagement, and World Energy Monthly Review, and he has presented findings atseveral regional and national conferences. His research interests include complexity-based supply chain management strategies, improving disaster relief coordination,coordination of distributed solution methodologies, and intuition refinement.

Anand Nair is an assistant professor in the Department of Management Science at theUniversity of South Carolina. He earned his PhD in business administration from the Eli

Broad Graduate School of Management at Michigan State University. Professor Nair'scurrent research interests are in me areas of supply chain relationship management,supply chain risk management, network analysis, quality management, and technologymanagement. His methodological orientation for research includes qualitative andquantitative empirical methods, computational experiments using complexity theory andcomplex adaptive systems approach, discrete-event simulations, data envelopmentanalysis, and mathematical modeling using optimal control theory and game theory.Professor Nair's research articles have been published in Journal of OperationsManagement, European Journal of Operational Research, IEEE Transactions onEngineering Management, International Journal of Production Research, and otherjournals. Professor Nair is an Area Editor for Operations Management Research and alsoserves on the Editorial Review Board of the Journal of Operations Management.

William J. Sawaya III is a postdoctoral associate in the School of Civil andEnvironmental Engineering at Cornell University, Ithaca, NY. He earned his PhD inbusiness administration in the Department of Operations and Management Science in theCarlson School of Management at the University of Minnesota. His research interestspans many arenas of operations management with a focus on supply chain management,supply chain risk management, and new product development. His current research

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focuses on the impact of interorganizational information sharing within supply networkcontexts, and the economic impact of catastrophic supply disruptions. Methodologically,he emphasizes the use of empirical data in models of operations systems, including agent-based simulation and other analytic models, and the application of a complex adaptivesystem paradigm in modeling organizations.

Dr. M. Murat Kristal is an assistant professor of operations management at SchulichSchool of Business at York University, Toronto, Canada. He teaches in the areas ofoperations management/strategy, supply chain management, and statistical models. Dr.Kristal graduated from the Operations Management Department in the Kenan-FlaglerBusiness School at the University of North Carolina at Chapel Hill. His research interestsfocus on the areas of supply chain, operations management, and strategy. His currentresearch spans from how supply chains adapt to their competitive environments in orderto survive in hyper competition to which factors enable manufacturers to achieve masscustomization capabilities and to various strategy problems that manufacturers face intheir operations.

102-Complexity and Adaptivity in Supply Networks

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