Article
“Smart Parts” Supply Networks as Complex Adaptive Systems: Analysis and Implications
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.