Agent-assisted supply chain management: Analysis and lessons learned

Authors:
William Groves;John Collins;Maria Gini;Wolfgang Ketter
Affiliations:
Department of Computer Science and Engineering, University of Minnesota, United States;Department of Computer Science and Engineering, University of Minnesota, United States;Department of Computer Science and Engineering, University of Minnesota, United States;Rotterdam School of Management, Erasmus University, The Netherlands
Venue:
Decision Support Systems
Year:
2014

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Abstract

This work explores ''big data'' analysis in the context of supply chain management. Specifically we propose the use of agent-based competitive simulation as a tool to develop complex decision making strategies and to stress test them under a variety of market conditions. We propose an extensive set of business key performance indicators (KPIs) and apply them to analyze market dynamics. We present these results through statistics and visualizations. Our testbed is a competitive simulation, the Trading Agent Competition for Supply-Chain Management (TAC SCM), which simulates a one-year product life-cycle where six autonomous agents compete to procure component parts and sell finished products to customers. The paper provides analysis techniques and insights applicable to other supply chain environments.