Artificial intelligence in supply chain management: theory and applications
- 24 March 2009
- journal article
- review article
- Published by Taylor & Francis in International Journal of Logistics Research and Applications
- Vol. 13 (1), 13-39
- https://doi.org/10.1080/13675560902736537
Abstract
Artificial intelligence (AI) was introduced to develop and create othinking machineso that are capable of mimicking, learning, and replacing human intelligence. Since the late 1970s, AI has shown great promise in improving human decision-making processes and the subsequent productivity in various business endeavors due to its ability to recognise business patterns, learn business phenomena, seek information, and analyse data intelligently. Despite its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). To fully exploit the potential benefits of AI for SCM, this paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM. In so doing, this paper reviews the past record of success in AI applications to SCM and identifies the most fruitful areas of SCM in which to apply AI.Keywords
This publication has 95 references indexed in Scilit:
- Application of machine learning techniques for supply chain demand forecastingEuropean Journal of Operational Research, 2008
- Supply chain management: a modular Fuzzy Inference System approach in supplier selection for new product developmentJournal of Intelligent Manufacturing, 2007
- Multi-agent-oriented approach to supply chain planning and scheduling in make-to-order manufacturingInternational Journal of Electronic Business, 2007
- Reducing the Bullwhip effect in a supply chain with fuzzy logic approachInternational Journal of Integrated Supply Management, 2007
- Solving unequal area facility layout problems using genetic algorithmInternational Journal of Logistics Systems and Management, 2006
- An agent‐oriented and knowledge‐based system for strategic e‐procurementExpert Systems, 2004
- Agent-Based Modelling — Intelligent Customer Relationship ManagementBT Technology Journal, 2003
- Genetic algorithms in bus network optimizationTransportation Research Part C: Emerging Technologies, 2002
- A hybrid genetic algorithm for the container loading problemEuropean Journal of Operational Research, 2001
- Modeling rolling batch planning as vehicle routing problem with time windowsComputers & Operations Research, 1998