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Decision Sciences
Volume 38 Number 4
November 2007
C 2007, The Author
Journal 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: surya.pathak@vanderbilt.edu
Jamison M. DayDepartment of Decision and Information Sciences, Bauer College of Business, University ofHouston, Melcher Hall 290D, Houston, TX 77204, e-mail: jmday@uh.edu
Anand NairDepartment of Management Science, Moore School of Business, University of South Carolina,Columbia, SC 29208, e-mail: nair@moore.sc.edu
William J. Sawaya
Department of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall,Ithaca, NY 14853, e-mail: wjs32@cornell.edu
M. Murat KristalOperations Management and Information Systems Department, Schulich School of Business,York University, 4700 Keele Street Toronto, Ontario, Canada M3J 1P3,e-mail: MKristal@schulich.yorku.ca
ABSTRACT
Supply networks are composed of large numbers of firms from multiple interrelated
industries. Such networks are subject to shifting strategies and objectives within adynamic environment. In recent years, when faced with a dynamic environment, several
disciplines have adopted the Complex Adaptive System (CAS) perspective to gain in-
sights into important issues within their domains of study. Research investigations in the
field of supply networks have also begun examining the merits of complexity theory and
the CAS perspective. In this article, we bring the applicability of complexity theory and
CAS into sharper focus, highlighting its potential for integrating existing supply chain
management (SCM) research into a structured body of knowledge while also providing
a framework for generating, validating, and refining new theories relevant to real-world
supply 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
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548 Complexity and Adaptivity in Supply Networks
CAS perspective can help in enriching the SCM discipline. We propose that the SCM
research community adopt such a dynamic and systems-level orientation that brings to
the fore the adaptivity of firms and the complexity of their interrelations that are often
inherent in supply networks.
Subject Areas: Adaptivity, Complex Adaptive System, Complexity, Complexity
Theory, 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 of
interfirm relationships that may connect multiple industries. As a result, supply
network 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 of
their interconnected supply chains (Choi & Hong, 2002) and (ii) the need to learn
and adapt their organization in a constantly changing environment to ensure its
long-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 buyersupplier dyad, sparse connectivity, static environment,
fixed and nonadaptive individual firm behavior), problems are often hidden, leavingplenty of room for understanding and improving theunderlying processes. Consider
the recent implementation of complexity-oriented decision making by American
Air 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. The
company produces industrial and medical gases such as nitrogen, oxygen, and hy-
drogen at about 100 manufacturing locations in the United States and delivers to
nearly 6,000 customer sites using a mix of pipelines, railcars, and more than 400
trucks. In the past, its distribution routing was based on analytical optimization
methods. 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 new
complexity-based solution that leverages neural networks and agent-based mod-
eling (with ant-foraging algorithms) to integrate decisions across their multinodal
and multimodal supply network. Most important, the new solution method solves
both sourcing and routing together in the optimization process. Charles Harper,
director of National Supply & Pipeline and Supply Operations, summarizes the
benefits of their complexity-based approach:
After switching over, we drive less miles, we dont do stupid things, and wemove people to different jobs that didnt exist before. All those things add upto savings. Its 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
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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 theyre going to lose out.
American Air Liquide is far from being the only firm that is using the
structural complexity (interconnectedness offirms) 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 CAS
principles, 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 in
their 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, rapid
flexibility, better preparedness for external uncertainties, increased awareness of
markets and competition, and improved decision making (Abell, Serra, & Wood,
1999).
Along with managing the complexity inherent in the interconnectivity of
their supply networks, organizations have also started to learn the benefits of being
adaptive in their behavior. Sheffi and Rice (2005) present an illustration of adaptive
firm behavior in a cellular telephone supply network. They highlight the different
approaches that Nokia and 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 alternate supply options. They modified designs
of the handsets where possible and secured worldwide manufacturing capacity from
Philips to ensure a steady supply of the chips. Meanwhile, the direct interaction
between top management of Nokia and Philips further enhanced the ability of
Nokia to adapt in the future. Wollin and Perry (2004) provide another example of
how Honda adapted to the changing automotive sector environment by leveraging
the 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 utility
vehicles, and, as a result, they gained significant market share in that segment even
though 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 intentof this position paper is to draw attention to recent developments
in CAS theory from across multiple disciplines and articulate how this knowledge
can 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 existing
work and developing new theories within the SCM body of knowledge. Specifically,
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550 Complexity and Adaptivity in Supply Networks
we discuss how CAS principles can be useful for identification and organization
of complex and adaptive phenomena in supply networks, such as individual firm
adaptation, self-organization and emergence, buyersupplier relationships, supply
network performance, environmental change, and feedback mechanisms. Finally,we examine the challenges associated with CASN theory development and provide
suggestions for future research efforts and CASN theory development.
A CAS VIEW OF SUPPLY NETWORKS
Because organizations exhibit adaptivity and can exist in a complex environment
with myriad relationships and interactions, it is a natural step to identify a supply
network 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 supply
network. 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 SCM
research.
A CAS is an interconnected network of multiple entities (or agents) that
exhibit 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 behavior
emerges as a nonlinear and dynamic function of the large number of activities made
in parallel by interacting entities. For example, the individual decisions made by
firms facing imperfect information and variable demand lead to a globally observed
phenomenon (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 with
the environment by following a set of simple decision rules (i.e., schema). These
entities may evolve over time as entities learn from their interactions. In contrast
to relational modeling, which tries to use one set of variables to explain variation
in another set of variables, CAS examines how changes in an individual entitys
schema 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 on
the system. Because the behavior in complex systems comes from dynamic inter-
actions among the agents and between the environment and the agents, the changes
tend to be nonlinear with respect to the original changes in the system. Thus, there
may 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, the
behavior of a complex system cannot be written down in closed form; it is not
amenable 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 future
in an exact manner, the future may exhibit some underlying regularity. While thechanges that are made to a system may be dramatic and unpredictable, there may
be patterns of behavior that can be considered prototypical. Appropriate analyses
may yield some knowledge of key patterns of behavior that are likely to develop
in the system over time.
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Pathak et al. 551
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 environment changes it may cause the agents within it to change, which, in
turn, cause other changes to the environment. These actions and reactions can betriggered by external events such as natural disasters (e.g., Hurricane Katrina) or
the actions of agents (e.g., a decision to implement an enterprise resource planning
system). A CAS exhibits dynamism as changes occur in the environment; this
dynamism affects the system. Environmental factors may cause changes to which
the agents must adapt, influencing the way agents perceive their environment or the
schema used by the agents themselves. Thus, the rules followed by the individual
entities organize thesystem, because individual entities are notprivy to the objective
function of the system as a whole. The coevolution of the system happens in the
rugged fitness landscapes in which the CAS exists. The concept of landscape was
first introduced by biologist Sewell Wright (1932). It refers to the mapping from anorganisms genetic structure to its fitness level. In management research, the idea 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 analogy of 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 the desired 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 Camry plant and the Johnson Controls seat-frame
manufacturing 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 over
time. 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 to
another organizational function, orders from an individual employee of one firm to
an employee of another, or any combination of the involved individuals, functions,
or firms. Furthermore, from a macroeconomic viewpoint, it can be posited that
industry supply networks are interrelated within a national or international context
and 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 characterized
as CASs composed of smaller constituents (a nested hierarchy of smaller-scale
complex systems). Changes in these smaller systems and 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 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, a
team, 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 to
the 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 decisions
for, and guide the actions of, the organization. Self-organization and emergence
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552 Complexity and Adaptivity in Supply Networks
occur as a result of decisions that are made by the individual agents that cause the
system to change and the collective system behavior to emerge over time. Network
connectivity is the connection among the agents that determines the complexity
of the network. As the connectivity among the agents increases, the interrelation-ships among the agents increase, in turn causing increases in the complexity of the
network. 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 to
which agents can act in an autonomous fashion without influencing other agents.
Therefore, as the degree of connectivity increases, the dimensionality decreases
as the actions 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
engine 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 mining
companies source equipment that relies on the latest material extraction techniques
developed by various firms and agencies. The material extraction techniques rely
on pattern recognition technologies that aid in interpretations of X-ray scans of
potential material vein and enable a firm to make appropriate decisions regarding
extraction locations. It is conceivable that the required pattern recognition tech-
nology is developed in a completely unrelated sector, such as health care. For
example, 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 extraction
company via research conferences. This example illustrates complex interconnec-
tivities among firms and the impact of decisions made by one firm on others in the
network. 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, computer
science, physics, organizational science, new product development, and strategicmanagement. In the next section, we highlight these advancements and discuss
how knowledge gained from these research studies 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
fields such as physics, biology, mathematics, computer science, engineering, psy-chology, political science, sociology, economics, and organizational behavior. To
systematically approach this wide range of literature, we adopted the data trian-
gulation approach. As a first step, we sought expert opinion regarding the state of
recent research pertaining to CAS. This step provided an initial reference list and
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Pathak et al. 553
Figure 1: Example of decision making in supply networks as complex adaptive
systems (Based on the example in Choi et al., 2001).
Honeywell(Aircraftengine
manufacturer)
Miningcompanies
Miningequipment
manufacturers
Firms/agenciesengaged in
development of newmaterial extraction
techniques
University HospitalPattern recognition
technique developedin the medical field
Information flow: Via research
conferences and journal articles
Decision: Purchase rawmaterials such as steel,copper, aluminum, and othercomposite materials
Information flow:Shortages, costs,delivery schedules
Information flow:Technology,capabilities ofequipments, cost
Decision: Purchasemining equipment
Information flow: Competingtechnological optionrequirementsDecision: Choice of
material extractiontechnique
guided our subsequent search process. In the next step, we undertook an extensive
search 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 and
Business Source Premier databases. In the search process, we included keywords
such as supply network, CAS, complexity theory, adaptation, adaptivity, chaos,
SCM, and nonlinear time series analysis. From the results obtained, we selected
more than 100 articles that were directly related to CAS and undertook 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).
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 applied
to 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
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Table1:Adv
ancementsincomplexadaptivesystems(CAS)-basedrese
arch.
Research
Contribution
SignificantDevelopment
ContributionRelatedto
RepresentativePublications
Theoretical
Organizationaladaptation,in
novation,
intervention,andlearning
Agents
andschemas
Barabaasi(2
002),L
issackandLetiche(2002
),
Zhang(20
02),AllenandStrathern(2003),
Dooleyet
al.(
2003),Downsetal.(
2003),
Haslettan
dOsborne(2003),Newman(2003),
Anderson
etal.(
2003),Dagnino(2004),
Fonsecaa
ndZeidan(2004),R
ichardson(2004),
Aldunate,
Pena-Mora,andRobinson(2005),
Richardso
n(2005),Burke,Fournier,andP
rasad
(2006),C
hoiandKrause(2006),Peltoniemi
(2006),Twomey(2006),R
ichardson(2007)
Communicationmechanisms
inCAS
Self-organization
CASPerspectiveusedfortheorybuilding
(evolutionaryeconomictheo
ryandconsumer
choicetheory)
Connectivity
Similaritiesbetweencomplexityandsystem
theories
Feedba
ck
Distributeddecisionmaking
andentity
coordinationinCAS
Ruggedfitness
landscape
Networkemergence,scale-fr
ee,andsmallworld
networks
Coevolution
Entitylearningandemergentstrategy
development
Adapta
tion
CAS-basedmodelingofbusinessecosystems
Emergence
Designingemergence
Learning
Supplybasemanagement
C
ontinued
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Pathak et al. 555
Table1:(Continued)
Research
Contribution
SignificantDevelopment
Contrib
utionRelatedto
RepresentativePublications
Methodologica
l
Systemdynamicsandqueuingtheory
Learn
ingand
adaptation
LinandShaw(1998),Swaminathanetal.(
1998);
Tan(1999),C
hatfield(2001),Iwanagaan
d
Namatam
e(2002),R
ivkinandSiggelkow
(2002),W
olfram(2002),Andersonetal.(2003),
Skvoretz
(2003),Stiller(2003),C
hatfield
etal.
(2004),C
hiles,Meyer,andHench(2004)
,
Mizraji(2004),Thadakamallaetal.(
2004),
Hordijka
ndKauffman(2005),Suranaet
al.
(2005),C
arlisleandMcMillan(2006),
Lichtenstein,Dooley,andLumpkin(2006),
McCarthy,Tsinopoulos,A
llen,and
Rose-Anderssen(2006),Sawaya(2006),
Varga
andAllen(2006);Goldberg,Sastry,andLlora
(2007),L
ichtenstein,Carter,Dooley,and
Gartner(2007),Pathak,Dilts,andBiswas
(2007),S
toica-KluVerandKluVer(2007)
Cellularautomata
Self-organization
Agent-basedmodelingoforganizationsand
supplynetworks
AgentsandSchemas
GeneticalgorithmsonCASDesign
Fitnesslandscapes
Fitnessmodeling,NKmod
els
Case-studyapproachforin
vestigating
organizationalstrategy,innovation,evolution,
fluctuation,positivefeedback,stabilization,and
recombinationandnewpr
oductdevelopment
EmpiricalstudyofCASan
daction-based
research
Neuralnetworkmodelingofagentschemas
Agentlearningmechanism
s
Heterogeneousagentdecis
ionmodels
Dynamicnetworkmodelin
g
Logisticalequationmodelingofinnovation
dynamism
C
ontinued
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556 Complexity and Adaptivity in Supply Networks
Table1:(Continued)
Research
Contribution
SignificantDevelopment
ContributionRelatedto
RepresentativePublications
Technical
Nonlineartimeseriesanalysis
Emergenceofpatterns
Shalizi(2001),Kumara,Ranjan,Surana
,and
Narayanan
(2003),
Suranaetal.
(2005),
Bhan
andMjolsness(2006),
BrahaandYaneer(2007),
SchillingandPhelps(2007)
Computationalmechanicsan
d-machines
Attractorreconstruction
Bifurcationdiagramsandchaosanalysis
Chaosidentification
Applicationofstatisticalmechanicsformodeling
andanalyzingCASnetworks
Allianc
eformation
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Pathak et al. 557
research developments and advancements over the last 6 years across multiple
different 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 supported
by the observation that the 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 clear that, while other areas such as industrial
engineering, computer science, physics, organizational science, research and devel-
opment, and strategic management, to name a few, 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 abil-
ity to incorporate increasing realism and empirical data into research models that
can 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 business
organizations (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 as
those found in the Internet, biological-cell, and social-network connectivity, ex-hibit higher tolerance to random errors but lower tolerance to targeted attack than
the more homogenous, exponential-style networks. These findings can be leveraged
to hypothesize how different supply-network topologies give rise to different levels
of supply-network resiliency under disruptions related to either random failure or
targeted attack, potentially leading to important implications for industry manage-
ment decisions. In fact, Thadakamalla et al. (2004) have shown how knowledge
can 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-Yams suggestion regarding applying theirfindings
about statistical properties of intraorganizational product development network to
a supply network context, as this may result in new insights on how interfirm and
intrafirm properties connect and evolve.
Recent advancements made by Rivkin and Siggelkow (2007) toward extend-
ing CAS research 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 important lessons for the study of supply chains. Rivkin and Siggelkow (2007)leverage empirical research demonstrating patterns of interactions within decision
processes to show that the number of local optima is highly correlated with the
decision-interaction patterns. Therefore, if there are many local optima, the relative
value of exploration decreases. The implication is that the value of exploration of
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558 Complexity and Adaptivity in Supply Networks
opportunities versus the exploitation of existing opportunities varies depending on
how rugged and dynamic the landscape is.
From a supply chain management perspective, the results and findings on
adaptability 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 interrelationships
could have further implications for buyerbuyer or suppliersupplier coopetition
(simultaneous competition and cooperation) in supply networks (Bengtsson &
Kock, 2000; Choi, Zhaohui, Ellram, & Koka, 2002). For instance, supplier firms
are typically under the control of the buying company through established work
routines and contractual terms, yet they are able to make decisions on their own
behalf. In this regard, the tension between control and emergence might be applica-
ble to suppliersupplier 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 manufacturers
use robust design to achieve single-peaked landscapes (landscapes with very low
interaction levels among agents as compared to the total number of agents). They
state that in die change operations, using pear-shaped clamps that can be smoothly
brought to fit in only one way thereby driving even approximate movements into
the right direction, reduces errors on the production line. The landscape in this
case is designed by the physical shape of the task environment (p. 346). Thisexample illustrates how NK models can be conceptualized to reduce variability in
a production network. If we apply this concept to SCM, one can argue that quality
management practices can use similar concepts from NK models for managing
buyersupplier relationships in order to reduce variability of the quality of the
products that the suppliers send to their buyers, thus leading to a single-peaked
landscape as suggested by Levinthal and Warglien (1999). For instance, when
Honda 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 the
supply network.Discussions and examples so far suggest that the CAS perspective holds
promise for enriching 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, 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 various
OM and SCM issues (Beamon, 1998). The use of analytical models, simulation
methods, 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 operational
decisions, the stochastic nature of demand, and the combinatorial possibilities of
available scenarios and options. Empirical research has grown to provide insights
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Pathak et al. 559
regarding strategic issues, managerial perceptions, and measurements of key op-
erational issues. Undoubtedly, the scope of problems being investigated in extant
literature is becoming richer and scholars are attacking complicated issues 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
is important for ensuring a broad-based research agenda. Cilliers (2000) suggests
that something that is complicated can be intricate, but the relationship between
the components is fixed and well defined. For instance, a jumbo jet is a complicated
system that is amenable to taking individual components apart and putting them
back together. In contrast, a complexsystem is characterized in terms of the nonlin-
ear dynamic interactions of the individual parts. Furthermore, while a complicated
system can be viewed as the sum of its parts, a complex system cannot be viewed
that way; one cannot predict the behavior of a complex system by examining the
behavior of its individual parts. These emergent properties of complex systems are
due to the nonlinear dynamic relationship between the individual components.
In a recent special issue on complex systems inManagement Science, Amaral
and Uzzi (2007) provide the following commentary that further illuminates the
differences 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 supply
chains robust against random failures and targeted breakdowns. The authors pro-
pose a complexity-based perspective for future investigations of various business
issues.
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 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 identify phenomena. Once identified, the phenomena must be validated by
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560 Complexity and Adaptivity in Supply Networks
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 any
relevant insights, must have clear application to the phenomena within the boundaryconditions and be generalizable for the theory to be integrated into a wider body of
knowledge. Here, we examine some of the unique theory-development challenges
that must be overcome if a coherent body of knowledge is to be developed around
CAS principles.
First, the complexity of supply networks will press limits on researchers
ability to understand the internal interactions between constructs and mechanisms
of larger-scope phenomena. For example, operations research has successfully
leveraged game theory to understand competitive and cooperative phenomena both
within and between organizations (Cachon & Lariviere, 1999). Although these
investigations provide insight into optimal monopolistic or duopolistic decisions,there are limits to modeling the nonlinear dynamics and adaptations inherent in
the oligopoly or free-market structures that dominate our economy. As discussed
previously, when several locally optimal policies interact in a complex supply
network, the resulting nonlinear dynamics of global behavior can be unpredictable.
Therefore, game-theoretic studies can be enriched by adopting the CAS perspective
to help examine the applicability, impact, and robustness of theirfindings within the
larger, more realistic supply network contexts in which game theory is intractable.
One reason for the growing popularity of CAS across several disciplines is its
ability to incorporate more realism in building theories, providing opportunityfor greater relevance, and supplying an understanding of the way phenomena act
in otherwise intractable environments. CAS provides an approach to rigorously
examine situations that closely map reality, yet simultaneously requires continuous
extension 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
for evolution and dynamism in supply networks. For example, many phenomena
in supply networks occur over time, and it will be crucial to examine the evolution
of the supply network over an extended time horizon. Such a behavior could be
measured and depicted using attractors and the corresponding lags at which attrac-tors are reconstructed (Williams, 1997). Furthermore, because phenomena in an
evolving supply chain occur at different levels, they must be captured at the firm,
topology, and systems levels. For example, investigation of supply chain disruptions
would require simultaneous consideration 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 robustness and efficiency. Given that empirical data
collection can be problematic whenever real organizations are involved, empirical
studies aimed at examining dynamic and evolutionary behavior inherent in sup-
ply networks will require resourceful approaches to operationalize and integrate
underlying constructs based on data collected from multiple system levels.Third, developing robust theories in the presence of adaptation presents a
formidable task. In a system of entities with changing policies, careful analysis of
the impact of interactions among these policies will be required. For example, Texas
and California are preparing to restructure their power markets from zonal to nodal
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Pathak et al. 561
models next year (Alaywan, Wu, & Papalexopoulos, 2004; Ercot, 2007). Power
generators and wholesalers are planning to adapt their policies (e.g., trade strategies,
scheduling, risk management) to take advantage of almost continuous shifts in
pricing and transmission congestion across 3,0004,000 locations. Attempting toascertain common overarching principles in such CASNs may require approaches
uncommon to operations and supply chain research like longitudinal data collection
and data analysis without resorting to linearity assumptions. Research design and
validation techniques will require resourcefulness when exploring both new and
previously identified phenomena in the presence of dynamically changing and
interacting 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 affecting
the 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 adaptation
and its impact on system-level behavior. This likely requires close collaboration
between academic researchers and practitioners who are dedicated to understand-
ing the complexities that affect organizations in a supply network in order to make
the commitment to this type of research effort. For example, structure, schema,
and performance of various constituent organizations of a supply network might
be sampled at regular intervals over time in order 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 phenomenon found in a few studies to a wider range of situations. As seen in
physics, abstraction of phenomena to larger- or smaller-scale systems does not
always hold true, and any attempt to 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 hold true when applied in other systems.
Such CASN characteristics make research in this area difficult, but, fortunately, OM
and SCM disciplines could learn from other disciplines, such as organizational
science, economics, computer science, and evolutionary biology, to name but afew. These disciplines have been extremely careful in generalizing their results and
have intelligently combined a diverse range of methods and tools (as summarized
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
the researchreality gap. For instance, tapping existing CAS research and apply-
ing it to supply network contexts will move the field beyond a static, isolated
dyadic buyersupplier framework. As indicated previously in this article, Braha
and Bar-Yam (2007) studied the statistical properties of organizational networks
that focus on product development. They show that structure of information-flow
networks have properties that are similar to those displayed by other social, biolog-
ical, and technological networks. They conclude their study by suggesting that the
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562 Complexity and Adaptivity in Supply Networks
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 knowledge
from 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 be
developed, building on CAS phenomenon. We finish by discussing some unique
CASN research design, measurement, and methodological issues for validation
purposes and list some potential CASN research questions.
Defining CASN
Aformaldefinition of CASN is one step toward furthering the use of CAS principlesin examining supply networks. Formulating such a definition is not a trivial task
and will require an iterative process with inputs, from a variety of experienced
researchers. What we propose here should be taken as a starting point for a formal
discussion from which an acceptable definition might emerge.
A CASN is a system of interconnected autonomous entities that make choices
to 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 within
a 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 these
elements can be used to describe the state of a CASN at a moment in time or
over a finite span of time. It is the interactions across these entities over time and
the 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, such as a firms inventory holding costs, while others, such as supply chain
agility, may require additional refinement.
Building SCM Theory by IdentifyingCAS Phenomena
A theory states how interrelated constructs are impacted by mechanisms creating a
phenomenon (Schmenner & Swink, 1998). Future development of CASN theory-
building efforts likewise should begin by viewing the properties associated with
entities, topology, system, and environment as interrelated constructs. Mechanisms
that alter these constructs are initiated by entities residing both inside and outside
the 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 but
also system constructs like supply network efficiency, flexibility, and redundancy.
Similarly, entities that exist in the external environment of the CASN can initiate
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Pathak et al. 563
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
clearly interrelated. Changes in any one entity construct may lead to alterationof topology that impacts overall system properties, which, in turn, may lead to
changes in the surrounding environment. Ultimately, the states of the entity, topol-
ogy, system, and environmental constructs impact decision making within each
participating entity. Individual-entity decision making may spawn changes that cy-
cle through the CASN and eventually lead to an altered system and environment
that impacts future decisions. Therefore, theory development about how various
CASN elements interact can improve understanding of the impact of decisions
made 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 equipment
manufacturer (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 sourcing
strategy and the associated network topology impact the OEMs flexibility to cater
to potential demand fluctuations. In the event that the OEM is unable to satisfy a
portion of demand due to supply shortages (e.g., due to capacity constraints at the
sole supplier), the service level of the OEM gets adversely affected. This illustrates
how entity decisions, network topology, system characteristics, and environmental
characteristics are closely intertwined with each other.
Unique CASN Research Design Issues
While physical and temporal scales are often quite naturally defined and addressed
in fixed and well-delineated relationships in complicated research, the nonlinear
dynamic 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 observed
consistently. Also, any constructs external to both the entities and the topological
relationships constituting the system that impact the behavior must be integrated
into the theoretical model, while superfluous variables must be eliminated. In ad-
dition to the system scale, defining the environmental scope of the system is also
paramount. 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 is
important to select the appropriate physical scale or unit of analysis within which
the 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 context
may yield comparatively perplexing results. Descriptions of the physical scalemust specify the range of entities that constitute the system as well as the types of
relationships that are considered to form the interrelations within the topology.
In addition to defining the physical scale of the system, the proper scaling
of time is important as well. Different types of phenomena may occur over longer
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564 Complexity and Adaptivity in Supply Networks
or shorter periods of time; therefore, certain research designs may require either a
lengthier period of study or more frequent measurements than others. For example,
examining how changes in fuel-efficiency regulations impact supplier selection
policies in the automobile industry might require a longer time period of study thaninvestigating interfirm behavior in online reverse auctions. Clearly, there must be
multiple scales and potential units of analysis for systems as complicated as supply
networks. An illustration of this is the problem with multiple levels of validation
that are common to interorganizational and agent-based models in general (Carley,
2003). Even here, one key feature is the systems-level behavior that emerges over
time. Therefore, while there may be many factors that are important at an entity
level, systems-level behavior must include observation of the systems behavior
that is creatively derived from the state and behavior of the constituent entities.
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 decisions are made by CASN entities. Therefore, it is important to consider
both the properties of the system and the environmental constructs that are related to
the phenomenon of interest in any theory set forth. For example, using agent-based
simulation, Siggelkow and Rivkin (2005) studied how environmental turbulence
and complexity affect the formal design of the organizations. From an empirical
perspective, Anderson and Tushman (2001) studied the effect of environmental
constructs such as uncertainty, munificence, and structural complexity on firmsurvival. They found that uncertainty was the main reason that firms go out of
business. These are examples of how inclusion of environmental constructs is
important 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 include
in 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 in
any research, however, caution must be exercised when selecting environmental
constructs, 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 relevant
constructs, how the constructs are related, and how certain mechanisms affect
those constructs. Only when these issues are clearly stated can the theory be val-
idated and examined for consistency with the phenomena under study across a
wide 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 significant
improvement. Different validation methodologies have various strengths and weak-
nesses and some are more easily accepted within a discipline than others. In a field
such as SCM, in which so many constructs are interrelated, this observation holds
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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 using
mathematical optimization techniques. The interrelationship of inventory levels
with other important operational aspects such as push/pull strategy, setup times,capital costs, multiskilled employees, and strong supplier relationships were not
explicitly considered in the classic inventory models, partly due to the constraints
placed by the methodological orientation. Yet, in hindsight it is clear that an ex-
plicit consideration of these interrelationships in research investigations pertaining
to inventory models would have been a worthy undertaking much earlier. While
theories with a small number of constructs may lend themselves well to analyti-
cal validation, integrating components across multiple theories or exploring single
theories with a large number of constructs may require empirical investigation.
Regardless of how a new or reformulated theory is created, it is important
to ensure the possibility of validation and refinement of the resultant theory. In-deed, when building CASN theories, such validation can be accomplished via
many different methodologies such as analytical, simulation-based, empirical, or
archival. For example, analytical models of interorganizational industrial systems
have existed for many years and have been the focus of many researchers ef-
forts. Within small physical-scale models, closed-form mathematical equations
have been leveraged to expose detailed relationships between multiple variables
within and across organizational boundaries. Mathematical programming opti-
mization models have also been leveraged to provide insight for improved decision
making. However, analytical tractability for the most realistic situations (e.g., in aCASN) is often limited in its ability to obtain solutions for problems of reasonable
size.
Thus, analytical efforts of a CASN may require a different orientation from
the optimization approach that is currently commonplace in studies investigating
supply chain issues. The impact of uncertainties within a many-entity environment
may overwhelm the limited robustness of small-scale globally optimal solutions.
Furthermore, the adaptive nature of CASN entities must allow for reactive decision
making within, and in response to, their changing surroundings. New investigations
of analytical models that seek to mitigate risk and improve decisions through
maintaining multiple alternative policies that can be implemented contingent uponspecific changes in larger-scale theoretical models should lead to improved supply
chain performance.
Methodologically, computer-based simulations have been leveraged for
interorganizational 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 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 provide
a method for examining the dynamic behavior of systems in addition to potential
steady-state behavior. Unfortunately, when compared to the specific results often
obtained from analytical models via proofs or bounds, the ability of simulations
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566 Complexity and Adaptivity in Supply Networks
may be limited when definitively extrapolating the inner workings of large-scale
systems to the overall system behavior.
Consider the example of the beer game (Sterman, 1989) in which local firms
are 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-based
computer simulation. One of the interesting effects that has been observed in these
simulations has been an overall unstable behavior (in the form of wild orderfluctua-
tions) under certain simulation conditions in which the local agents have unlimited
memory about the order fulfillment history of their suppliers and the order history
of their customers (Sawaya, 2006). This is due to the agents overreaction to late
orders, whereby the agents keep placing larger and larger orders as they adjust their
reorder 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, the
system instability is reduced. It is challenging to use simulation to prove anything,
but it allows researchers to understand something important about the likelihood
of 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 to
drive or motivate the simulation, the inherent assumptions, or artifacts introduced
because of the way the simulation has been implemented. Therefore, caution must
be 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-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 and ensuring the practicability of model prescriptions. Because one of
the advantages of the CASN view of supply networks is its ability to incorporate
increasing realism into models and theories of supply networks, empirical data
are essential for the development of CASN theory. Empirical methods will always
carry significant motivational weight in the OM and SCM disciplines. However,researchers often face challenges with data collection and with the complexities that
empirical data introduce into supply-network conceptualizations and models. One
example of empirical research comes from Choi and Hong (2002), in which they
use an inductive case-study approach to build propositions about supply networks.
In any case, as researchers become more familiar with the power of CASN, they
will perhaps be less hesitant to incorporate complicated real-world data into theory
and models of supply networks. It is also possible that, as various organizations
recognize the benefits of more complex supply-network representations, they will
be more willing to allocate the necessary resources for detailed empirical data
collection 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) determined
the dynamics of industrial growth by using census data and Christensen (1997) used
archival data on disk drives and their makers over time to develop the theory of
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Pathak et al. 567
disruptive technology. Such data can be mined to examine how a particular industry
evolved 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 the
diversity of research questions, rule out the use of a single approach. A combination
of 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 new
strategies for bullwhip mitigation within a CASN context (Murray, 2007). Analyt-
ical methodologies are capable of determining how order variance can be reduced
by strategically leveraging negatively correlated demand streams or demand in-
formation from multiple downstream supply network participants. Simulation can
provide verification of analytical results while extending them to examine the in-
direct cost reductions that result at firms further upstream. Empirical studies could
be used to investigate the applicability of these mitigation strategies in real-world
supply 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 offers
an exciting perspective to extend known problems and also a new set of problems
to address. In Table 2, we summarize sample research questions that could be
addressed 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 on
dyadic relations 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 that examines the broader, network-level effects that exist in real-
life supply networks. In such networks, cause and effect are not simple, behavior is
dynamic, and the actions of any firm in the network can potentially affect any other
firms in the network. Complexity science 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 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 CASN
perspective would require researchers to extend the concept of visibility to an
entire network of firms that may only be indirectly connected to the buying
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568 Complexity and Adaptivity in Supply Networks
Table2:Pote
ntialresearchissuesandquestionsforbuildingcomplexadaptivesupplynetwork(CAS
N)theory.
PotentialArea
ofContribution
PotentialCASNRese
archIssues
AssociatedCASNResearchQuestions
Theoretical
Interfirminteractio
nsaffectingCASNtopology
HowdodifferentC
ASNtopologiesgiveriseto
StatisticalpropertiesofCASNtopologies
supplynetworkres
iliencyunderdisruptions?
Fitnessofindividu
alentities;fitnessofCASN
Howdointerfirma
ndintrafirmpropertiesconnect
Effectofenvironm
entonCASNevolutionbothat
andevolveinaCA
SN?
individualandsystemlevels
Howcanconcepts
offitness,exploration,and
Multiplefeedback
loopsandtheireffectson
exploitationbeuse
dforstudyingcollaborative
evolutionandperformance
buyer-supplierrela
tionships?
Complexityandre
dundancyofinformationflow
Howcanpolicymakerssetgloballyoptimal
andtheireffectsonCASNevolutionand
policiesbyinfluencinglocalfirmbehaviorina
performance
CASN?
Decisionmakingcriteriaatthefirm,system,and
Whatarethekeyd
ecisioncriteriathatadecision
environmentlevelsthataffectCASNevolution
makerneedstoknowfromafirmsperspective,
PolicydesignforCASN
fromasystempers
pective,andfromaregulatory
Notionoflooseco
uplingamongfirmsinasupply
bodysperspective?
network
Whatistheroleofinformationsystemsinfostering
Coevolutionofsupplychainstrategyandsupply
loosecouplingamongfirmsinasupplynetwork?
networkstructure
Howdoescollabor
ativedecisionmakingsustain
Examinationofstrongandweaktiesamongfirms
andprosperinasu
pplynetwork?
inasupplynetwork
Whatisthesystem
-wideimpactofopportunistic
behaviorbyasinglefirminasupplynetwork?
Howdodivergentsupplynetworksindivergent
industriesimpacte
achother?
Whataretheimplicationsforlong-termstrategy
processinlightofthecomplexandadaptive
natureofsupplyne
tworks?
C
ontinued
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Pathak et al. 569
Table2:(Continued)
PotentialArea
ofContribution
PotentialCASNRese
archIssues
AssociatedCASNResearchQuestions
Methodologica
l
Analyticalmethod
s
Howcandynamic
systemsmodelinganddynamic
Dynamicsystem
smodeling
networkanalysisb
eusedforstudying
Dynamicnetworkanalysisandmodeling
time-dependentevolutionofCASN?
Hamiltonian-basedoptimization,genetical-
Canoptimization-basedapproachesbeusedfor
gorithms,andreliability-baseddesign-optimization
studyingtime-dependentbehaviorand
methods
representingevolutiontrajectories?Whatarethe
Statisticalphysics
limitations?Canth
isbeovercomebycombining
Evolutionaryga
metheory
multiplemethodologies?
Empiricalmethod
s
Howcanonemodelsophisticatedagentsthatcan
Survey
learn,adapt,andre
spondtouncertaintiesinherent
Casestudy
inaCASN?
Econometrics
Couldepistemicuncertaintiespresentwithina
Archivaldataan
alysis
firmbemodeled,q
uantified,andanalyzedsoas
Longitudinalstu
dy
toimprovetheeffe
ctivenessofdecisions?
Ethnography
Howcouldcellularautomatonsbeusedfor
Actionresearch
investigatingcoopetitivedynamicsinaCASN?
Behavioralexpe
riment
Couldasystems-dynamicsapproachbecombined
withreliability-bas
eddesignoptimization
methodsforinvestigatingoptimalpolicy-design
issuesinCASN?
WhataretherelevantscalesforcoreCASN
constructs,suchas
complexity,adaptivity,and
dynamism,t
hatcanbeusedinsurveyresearch? C
ontinued
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570 Complexity and Adaptivity in Supply Networks
Table2:(Continued)
PotentialArea
ofContribution
PotentialCASNRese
archIssues
AssociatedCASNResearchQuestions
Simulationmethods
Whatarepotential
approachestocollectdatathat
Agent-basedsim
ulation
areamenableforanalyzingcomplexadaptive
Cellularautomaton
behaviorofsupply
networks?
Systemsdynamics
Whatarepotential
testsforvalidityofCASN
Evolutionarygametheory
results?
Neuralnetworks
Evolutionaryalg
orithms
Technical
Stabilityanalysis
HowcouldLyapun
ovanalysisbeusedfor
Causalityanalysis
addressingstabilityissuesinCASN?
Couldbifurcationdiagramsbeusedforanalyzing
thepresenceofchaosinanevolvingCASN
(numberoffirmsa
swellasthelinkageschange)? C
ontinued
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Pathak et al. 571
Table2:(Continued)
PotentialArea
ofContribution
PotentialCASNRese
archIssues
AssociatedCASNResearchQuestions
Attractorreconstruction
Howcancomputationalmechanicsand
causal-stateidentificationalgorithmsbeusedfor
identifyingcausalcomponentsinanevolving
CASN?
Couldeconometric
toolssuchasGranger
causalityanalysisandvectorautoregression
modelsbeusedforanalyzinglongitudinaldata
generatedbyCASNresearch?
Howcouldattracto
rsbeusedfordesigning
optimaldecisionsforafirm?Couldagent-based
modelingandoptimizationmethodologiesbe
combinedfordesigningoptimalpoliciesaround
attractorsthatarepresentinaCASN?
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572 Complexity and Adaptivity in Supply Networks
firm. Thus, as the practices of supply chain managers change over the future
from a dyadic-only perspective to more of a network perspective, new research
concerning 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 will
need to draw from a rich variety of research methodologies. Whereas most existing
supply chain research has focused on variance studies using surveys, discrete-event
simulation, case studies of dyads, or analytical models, CASN research requires
agent-based and computational models, process models that are dynamic and gen-
erative, and case studies of larger ensembles of firms. Both computational and
qualitative methods provide means to capture complex cause and effect, nonlin-
earity, ambiguity, and dynamism; however, these are difficult methodologies to
implement in a rigorous way, and so CASN researchers will possibly have to de-fine 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-
making activities in a supply network. For a supply network manager, a CASN
perspective offers a new language and a new mental model from which to view
the business world, draw interesting insights, and make decisions. A CASN per-
spective may aid a supply network manager in making decisions while keeping
the 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 supply
network 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 the
surrounding environmental context and from increased numbers of evolving sup-
ply network partners. Organizations that are unable to interpret and leverage vast
amounts of information from changing and interconnected sources may face legal
liabilities and will likely fail to maintain adequate performance in the competitive
environment. Thus, information and decision-science researchers are likely to play
an 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 research
that draws analogy between supply networks and CAS suggests this discipline may
be 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 CAS
perspective for integrating existing knowledge and further investigating the com-
plexity and adaptivity that inherently exist within supply networks. These efforts
would benefit from a generally accepted foundation within which theories can be
combined 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 is
both authoritative identification of, and agreement on, the conceptually appropri-
ate and empirically valid constructs that can be applied to supply network systems
framed as CAS. With such a foundation, the SCM field will be poised for both
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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.]
REFERENCES
Abell, B., Serra, R., & Wood, R. (1999). Strategic thinking and the new science
(Book review). Emergence, 1(2), 7179.
Alaywan, Z., Wu, T., & Papalexopoulos, A. D. (2004). Transitioning the Califor-
nia market from a zonal to a nodal framework: An operational perspective.
Presentation made at IEEE Power Engineering Society, Power Systems Con-
ference and Exposition, New York.
Albert, R., Jeong, H., & Barabasi, A. L. (2000). Error and attack tolerance ofcomplex networks. Nature, 406, 378382.
Aldunate, R. G., Pena-Mora, F., & Robinson,G. E. (2005). Collaborative distributed
decision making for large scale disaster relief operations: Drawing analogies
from robust natural systems. Complexity, 11(2), 2838.
Allen, P. M., & Strathern, M. (2003). Evolution, emergence, and learning in com-
plex systems. Emergence, 5(4), 833.
Amaral, L. A. N., & Uzzi, B. (2007). Complex systems-A new paradigm for the
integrative study of management, physical, and technological systems. Man-
agement Science, 53, 10331035.
Anderson, P. (1999). Complexity theory and organization science. Organization
Science, 10, 216232.
Anderson, P., & Tushman, M. L. (2001). Organizational environments and industry
exit: The effects of uncertainty, munificence and complexity. Industrial and
Corporate Change, 10, 675711.
Anderson, P. E., Jensen, H. J., Oliveira, L. P., & Sibani, P. (2004). Evolution in
complex systems. Complexity, 10(1), 4956.
Anderson, R., Issel, L., & McDaniel, R., Jr. (2003). Nursing homes as complex
adaptive systems: Relationship between management practice and resident
outcomes. Nursing Research Policy, 52(1), 1221.
Arthur, W. B., Durlauf, N. B., & Lane, D. (1997).Economy as an evolving complex
system II process and emergence in the economy. Santa Fe, NM: Santa Fe
Institute.
Axtell, R. A. (2003). Toward behavioral realism in retirement models: From micro
simulation to agent-based modeling. Presentation made at the Conference
on Improving Social Insurance Programs, University of Maryland, College
Park, MD.
Barabaasi, A.-L. (2002). Linked: The new science of networks. Cambridge, MA:
Perseus Books.
Beamon, B. M. (1998). Supply chain design and analysis: Models and methods.
International Journal of Production Economics, 55, 281294.
7/28/2019 Complexity and Adaptivity in Supply Networks
28/34
574 Complexity and Adaptivity in Supply Networks
Bengtsson, M., & Kock, S. (2000). Coopetition in business networksto co-
operate and compete simultaneously. Industrial Marketing Management, 29,
411426.
Bhan, A., & Mjolsness, E. (2006). Static and dynamic models of biological net-works. Complexity, 11(6), 5763.
Braha, D., & Yaneer, B.-Y. (2007). The statistical mechanics of complex prod-
uct development: Empirical and analytical results. Management Science, 53,
11271145.
Brown, S. L., & Eisenhardt, K. M. (1998). Competing on the edge: Strategy as
structured chaos. Boston: Harvard Business School Press.
Burke, M. A., Fournier, G. M., & Prasad, K. (2006). The emergence of local norms
in networks. Complexity, 11(5), 6583.
Cachon, G., & Lariviere, M. (1999). Capacity choice and allocation: Strategic
behavior and supply chain performance. Management Science, 45, 1091
1108.
Carley, K. (2003). Validating computational models. CASOS working paper,
Carnegie Mellon University, Pittsburgh, PA.
Carley, K. M. (forthcoming). Dynamic network analysis in the summary of the
NRC workshop on social network modeling and analysis. In R. Breiger & K.
M. Carley (Eds.), National Research Council.
Carlisle, Y., & McMillan, E. (2006). Innovation in organizations from a complexadaptive systems perspective. E:CO, 8(1), 29.
Chatfield, D. C. (2001). SISCO and SCMLSoftware tools for supply chain sim-
ulation modeling and information sharing. Doctoral dissertation, The Penn-
sylvania State University, State College, PA.
Chatfield, D. C., Kim, J. G., Harrison, T. P., & Hayya, J. C. (2004). The bullwhip
effectimpact of stochastic lead time, information quality, and information
sharing: A simulation study. Production and Operations Management, 13,
340353.
Chiles, T., Meyer, A., & Hench, T. (2004). Organizational emergence: The originand transformation of Branson, Missouris musical theaters. Organization
Science, 15, 499520.
Choi, T. Y., Dooley, K. J., & Rungtusanatham, M. (2001). Supply networks and
complex adaptive systems: Control versus emergence.Journal of Operations
Management, 19, 351366.
Choi, T. Y., & Hong, Y. (2002). Unveiling the structure of supply networks: Case
studies in Honda, Acura, and Daimler Chrysler. Journal of Operations Man-
agement, 20, 469493.
Choi, T. Y., & Krause, D. R. (2006). The supply base and its complexity: Implica-tions for transaction costs, risks, responsiveness, and innovation. Journal of
Operations Management, 24, 637652.
7/28/2019 Complexity and Adaptivity in Supply Networks
29/34
Pathak et al. 575
Choi, T. Y., Zhaohui, W., Ellram, L., & Koka, B. R. (2002). Supplier-supplier
relationships and their implications for buyer-supplier relationships. IEEE
Transactions on Engineering Management, 49, 119130.
Christensen, C. M. (1997).Innovators dilemma. Boston: Harvard Business SchoolPress.
Cilliers, P. (2000). Rules and complex systems. Emergence, 2(3), 4050.
Dagnino, G. B. (2004). Complex systems as key drivers for the emergence of a
resource- and capability-based interorganizational network. E:CO Special
Double Issue, 6(12), 6169.
Dooley, K., Corman, S., McPhee, R., & Kuhn, T. (2003). Modeling high-resolution
broadband discourse in complex adaptive systems.Nonlinear Dynamics, Psy-
chology, & Life Sciences, 7(1), 6185.
Downs, A., Durant, R., & Carr, A. N. (2003). Emergent strategy development for
organizations. Emergence, 5(2), 528.
Ercot. (2007). Ercot nodal transition plan, accessed September 12, 2007, available
at http://nodal.ercot.com/docs/po/index.html.
Feynman, R., & Weinberg, S. (1986).Elementary particles and the laws of physics:
The 1986 Dirac Memorial Lectures. New York: Cambridge University Press.
Fonseca, M. G. D., & Zeidan, R. M. (2004). Epistemological considerations on
agent-based models in evolutionary consumer choice theory. E:CO, 6(3),
48.Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.
Goldberg, D. E., Sastry, K., & Llora, X. (2007). Toward routine billion-variable
optimization using genetic algorithms. Complexity, 12(3), 2729.
Global Logistics and Supply Chain Strategies. (2007). Supply chain com-
plexity masters: Boeing. For Boeing, a new aircraft means a revamped
supply chain. 11(3), 3841, accessed September 12, 2007, available at
http://glscs.texterity.com/glscs/200703/?pg = 38.
Grimm, V. (1999). Ten years of individual-based modelling in ecology: What have
we learned and what could we learn in the future.Ecological Modelling, 115,129148.
Haslett, T., & Osborne, C. (2003). Local rules: Emergence on organizational
landscapes. Nonlinear Dynamics, Psychology, and Life Sciences, 7(1), 87
98.
Hendricks, K., & Singhal, V. (2003). The effect of supply chain glitches on share-
holder wealth. Journal of Operations Management, 21, 501522.
Hordijk, W., & Kauffman, S. A. (2005). Correlation analysis of coupled fitness
landscapes. Complexity, 10(6), 4149.
Iwanaga, S., & Namatame, A. (2002). The complexity of collective decision. Non-
linear Dynamics, Psychology,