Managing Complexity in Supply Chains: A Discussion of Current Approaches on the Example of the Semiconductor Industry

Abstract

The aim of this paper is to analyze the state of the art of complexity management in the area of supply chain management. In this regard, the suitability of Complex Adaptive System (CAS) modeling for making complexity-optimizing supply chain decisions is discussed on the example of the semiconductor supply chain. New global markets, lower manufacturing costs, and sourcing activities have led to a global dispersion of supply chains. However, manufacturers have discovered an unpleasant side effect of global manufacturing: Rising complexity. In practice, supply chain managers react intuitively to the complexity of processes, products and IT. This is partly due to the fact that so far, only little effort has been made to develop tools for quantifying supply chain complexity. But supply chain managers are in need of these methods enabling them to make complexity-optimized supply chain decisions. The quantitative impact of complexity –its value and its costs– has to be effectively calculated to enable supply chain managers to make complexity-optimized supply chain decisions. A promising approach for managing supply chain complexity is the interpretation of a supply chain as a complex adaptive system (CAS). CAS are systems far from equilibrium, characterized by a large number of interacting and evolving agents, who adapt and learn and thus could be able to solve the complexity dilemma.