DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

key realization to tackle this problem is that supply chain networks should not just be treated as a
“system”, but as a “Complex Adaptive System” (CAS). The study of CAS augments the systems
theory and provides a rich set of tools and techniques to model and analyze the complexity arising
in systems encompassing science and technology. In this paper we take this perspective in
dealing with supply chains and show how various advances in the realm of CAS, provide novel
and effective ways to characterize, understand and manage their emergent dynamics.
A similar viewpoint has been emphasized in (Choi et al. 2001). The focus of Choi et al. was to
demonstrate how supply networks should be managed if we recognize them as CAS. The concept
of CAS allows one to understand how supply networks as living systems co-evolve with the
rugged and dynamic environment in which they exist and identify patterns that arise in such an
evolution. The authors conjecture various propositions stating how the patterns of behavior of
individual agents in a supply network can be related to the emergent dynamics of the network.
One of the important deductions made is that when managing supply networks, managers must
appropriately balance how much to control and how much to let emerge. However, no concrete
framework has been suggested under which such conjectures can be verified and generalized. It is
the onus of this paper to show how the theoretical advances made in the realm of CAS can be
used to study such issues systematically and formally in the context of supply chain networks.
This paper is divided into eight sections, including the introduction. In Section 2, we give a
brief introduction to complex adaptive systems in which we discuss the architecture and
characteristics of complex systems in diverse areas encompassing biology, social systems,
ecology and technology. In Section 3 we discuss characteristics of supply chain network and
argue that they should be understood in terms of a CAS. We also present some emerging trends in
supply chains and the increasing critical role of information technology in supply chain
management in the light of these trends. In Section 4 we give a brief overview of the main
techniques that have been used for modeling and analysis of supply chains and then discuss how
the science of complexity provides a genuine extension and reformulation of these approaches.
Like any CAS, the study of supply chains, should involve a proper balance of simulation and
theory. System dynamics based and recently agent based simulation models (inspired from
complexity theory) have been extensively used to make theoretical investigations of supply
chains feasible and to support decision-making in real world supply chains. System dynamics
approach often leads to models of supply chains, which can be described in the form of a
dynamical system. Dynamical systems theory provides a powerful framework for rigorous
analysis of such models and thus can be used to supplement the system dynamics simulation
approach. We illustrate this in Section 5, using some nonlinear models, which consider the effect
of priority, heterogeneity, feedback, delays and resource sharing on the performance of supply
chain. Furthermore, the large volumes of data, generated from simulations can be used to
understand and comprehend the emergent dynamics of supply chains. Even though an exact
understanding of the dynamics is difficult in complex systems, archetypal behavior patterns can
often be recognized, using techniques from complexity theory like Nonlinear Time Series
Analysis and Computational Mechanics, which are discussed in Section 6. The benefits of
integrated supply chain concepts are widely recognized, but the analytical tools that can exploit
those benefits are scarce. In order to study supply chains as a whole it is critical to understand the
interplay of organizational structure and functioning of supply chains. Network dynamics an
extension of nonlinear dynamics to networks, provides a systematic framework to deal with such
issues and is discussed in Section 7. We conclude in Section 8, with the recommendations for
future research.
2. Complex Adaptive Systems
Many natural systems and increasingly many artificial (man-made) systems as well, are
characterized by apparently complex behaviors that arise as the result of nonlinear spatiotemporal
interactions among a large number of components or subsystems. We would use the