“Smart parts” supply networks as complex adaptive systems: analysis and implications

Abstract

Purpose: The purpose of this paper is to critically analyze whether supply networks may be validly treated as complex adaptive systems (CAS). Finding this to be true, the paper turns into the latest concerns of complexity science like Pareto distributions to explain well‐known phenomena of extreme events in logistics, like the bullwhip effect. It aims to introduce a possible solution to handle these effects. Design/methodology/approach: The method is a comparative analysis of current literature in the fields of logistics and complexity science. The discussion of CAS in supply networks is updated to include recent complexity research on power laws, non‐linear dynamics, extreme events, Pareto distribution, and long tails. Findings: Based on recent findings of complexity science, the paper concludes that it is valid to call supply networks CAS. It then finds that supply networks are vulnerable to all the nonlinear and extreme dynamics found in CAS within the business world. These possible outcomes have to be considered in supply network management. It is found that the use of a neural network model could work to manage these new challenges. Practical implications: Since, smart parts are the future of logistics systems, managers need to worry about the combination of human and smart parts, resulting design challenges, the learning effects of interacting smart parts, and possible exacerbation of the bullwhip effect. In doing so, the paper suggests several options concerning the design and management of supply networks. Originality/value: The novel contribution of this paper lies in its analysis of supply networks from a new theoretical approach: complexity science, which the paper updates. It enhances and reflects on existing attempts in this field to describe supply networks as CAS through the comprehensive theoretical base of complexity science. More specifically, it suggests the likely vulnerability to extreme outcomes as the “parts” in supply networks become smarter. The paper also suggests different ways of using a neural network approach for their management – depending on how smart the logistics parts actually are.