Modeling defaults of companies in multi-stage supply chain networks

The interest in supply chain networks and their analysis as complex systems is rapidly growing. The physical approach to the topic draws on the concept of heterogenous interacting agents. The interaction among agents is considered as a repeated process of orders and production. The dynamics of production in the supply chain network which we observe is nonlinear due to the random failures in processes of orders and production. We introduce an agent-based model of a supply chain network which represents in more detail the real economic environment in which firms operate. We focus on the influence of local processes on the global economic behavior of the system and study how the proposed modifications change the general properties of the model. We observe collective bankruptcies of firms, which lead to self-emerging network structures. Our results give insight into the dynamics of default processes in supply chain networks, which have important implications both for risk managers and policy makers. Based on the simulations we show that agent-based modeling is a powerful tool for optimization of supply chain networks.