A complex network approach to supply chain network theory

Abstract

Purpose: The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains. Design/methodology/approach: The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena. Findings: This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution. Research limitations/implications: The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context. Practical implications: If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms. Originality/value: This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.