Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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A Supply Network (SN) is a collection of firms that maintain local autonomy but who interact together to fulfill customer requirements. SN researchers have focused on the need to understand the reasons behind the diversity in the number and types of supply networks, as well as how these diverse networks interact, change and adapt over time. My research focuses on the dynamic growth aspect of SN's and addresses two fundamental questions: (1) how do Supply Networks grow and emerge and (2) are there simple rules and conditions that control the growth and emergence process? The dissertation presents an investigative framework for studying these and other questions concerning the growth and evolution of complex supply networks. An inductive approach is used to answer these questions. It starts by creating a new theory-based unified model of supply network (called UMSN) that incorporates four theoretical lenses, namely (1) industrial growth theory, (2) network growth theory, (3) game & market structure theory and (4) complex adaptive systems theory. The UMSN provides a holistic view for modeling growth and emergence in SNs. A generic rule-based modeling framework and a simulation-based computational framework using software agent technology was developed to operationalize and implement the UMSN. For investigating the growth phenomenon data and parameters from the US automobile industry over the last 80 years were utilized. The results and analysis show similar growth trends as empirically published data of well-structured industries such as the US automobile industry. The SN system grew and emerged as a complex adaptive system. The research also presents statistically significant results that supply networks grow and emerge based on interactive effects of local decision-making rules and environmental conditions, and that there is an underlying order to the emergence process. This research also develops chaos theory analysis techniques for predicting the SN system behavior over time; showing how such techniques can generate insights for policy makers and managers. My research contributes by extending the current state of knowledge of SN's as a dynamic system (network) and developing novel classification and analysis techniques. The insights drawn can aid managers/decision makers towards a better understanding of how SN emerge and grow.