Agent-assisted supply chain management: Analysis and lessons learned

William Groves, John Collins, Maria L Gini, Wolfgang Ketter

Research output: Contribution to journalArticle

25 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)274-284
Number of pages11
JournalDecision Support Systems
Volume57
Issue number1
DOIs
StatePublished - Jan 1 2014

Keywords

  • Decision support systems
  • Economic simulation
  • Key performance indicators
  • Software agents
  • Supply chain management
  • Trading Agent Competition

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