An agent based model of the evolution of supplier networks

Abstract

We view supply chains as a type of complex adaptive system and develop an agent based computer simulation model of the evolution and performance of supply chains based on Stuart Kauffman’s NK models of fitness landscapes. Firms operate in networks in which they supply products to some firms and source inputs from others. They seek to maximize their own performance but they cooperate with other firms to gain access to inputs. We model firm performance in terms of the fit of its product with market demand and the contribution from first tier suppliers. The model uses genetic algorithms to mimic the way firms learn and adapt their products and supplier networks for more and less complex products and different switching conditions. We find that (a) as the complexity of the product increases, firms perform less well; and (b) firms build supplier networks with higher average in-degree, greater density, and significantly greater clustering to cope with product complexity. Our findings suggest that firms using highly specific assets or that face high switching costs are likely to pursue a supplier strategy that relies more on multiple suppliers and more clustered supply networks. Also, in industries characterized by highly specialized training, plants and machinery dedicated to specific products and other high product-specific transaction costs, we should observe more specialization at low levels of product complexity but less at high levels. The model contributes to our understanding of the evolution of supply networks, which is an under-researched topic, provides the basis for further extensions of the model and the development of more realistic models of actual supply chains. The model also provides a conceptual and methodological tool to assist firms and policymakers to better understanding the nature of supply chains and to identify and test strategies and policies.