Supply-chain networks: a complex adaptive systems perspective

TLDR

Information technology and the emerging complexity revolution have made supply chains as complex as biological systems, yet principles governing their emergence and development remain unclear, prompting study of complex adaptive systems to uncover general laws. This paper examines how information technology and complexity affect supply chains, highlights the challenge of coordinating adaptive behavior, and proposes treating supply chains as complex adaptive systems. The authors propose using CAS concepts, tools, and techniques—rooted in nonlinear dynamics, statistical physics, and information theory—to characterize and model supply‑chain networks.

Abstract

In this era, information technology is revolutionizing almost every domain of technology and society, whereas the 'complexity revolution' is occurring in science at a silent pace. In this paper, we look at the impact of the two, in the context of supply-chain networks. With the advent of information technology, supply chains have acquired a complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply-chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behaviour in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality are attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctions among complex systems arising in diverse domains (like biology, social systems, ecology and technology) that might lead to a better understanding of how complexity occurs, whether it follows any general scientific laws of nature, and how it might be related to simplicity. In this paper, we argue that supply chains should be treated as a CAS. With this recognition, we propose how various concepts, tools and techniques used in the study of CAS can be exploited to characterize and model supply-chain networks. These tools and techniques are based on the fields of nonlinear dynamics, statistical physics and information theory.