Intelligent Supply Chain Management


Fuzzy Logic is used to derive the optimal inventory policies in the Supply Chain (SC) numbers. We examine the performance of the optimal inventory policies by cutting the costs and increasing the supply chain management efficiency. The proposed inventory policy uses multi-agent and Fuzzy logic, and provides managerial insights on the impact of the decision making in all the SC numbers. In particular, we focus on the way in which our agent purchases components using a mixed procurement strategy (combining long and short term planning) and how it sets its prices according to the prevailing market conditions and its own inventory level (because this adaptivity and flexibility are key to its success). In modern global market, one of the most important issues of the supply chain (SC) management is to satisfy changing customer demands and enterprises should enhance the long-term advantage through the optimal inventory control. In this paper an intelligent multi-agent system to simulate supply chain management has been developed.

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Khan, M. , Al-Mushayt, O. , Alam, J. and Ahmad, J. (2010) Intelligent Supply Chain Management. Journal of Software Engineering and Applications, 3, 404-408. doi: 10.4236/jsea.2010.34045.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Collins, R. Arunachalam, et al., “The Supply Chain Management Game for the 2005 Trading Agent Competi-tion,” Technical Report CMU-ISRI-04-139, School of Computer Science, Carnegie Mellon University, Pittsburgh, December 2004.
[2] S. D. Levi, P. Kaminsky and S. E. Levi, “Designing and Managing the Supply Chain,” McGraw-Hill, Illinois, 2000.
[3] D. Pardoe and P. Stone, “TacTex-03: A supply Chain Management Agent,” SIGecom Exchanges: Special Issue on Trading Agent Design and Analysis, Vol. 4, No. 3, 2004, pp. 19-28.
[4] K. Kumar, “Technology for Supporting Supply-Chain Management,” Communications of the ACM, Vol. 44, No. 6, pp. 58-61, 2001.
[5] D. Pardoe and P. Stone, “Predictive Planning for Supply Chain Management,” Proceedings of International Conference on Automated Planning and Scheduling, to appear.
[6] M. Sugeno, “An Introductory Survey of Fuzzy Control,” Information Sciences, Vol. 36, 1985, pp. 59-83.
[7] M. Wellman, J. Estelle, S. Singh, et al., “Strategic Inte-ractions in a Supply Chain Game,” Computational Intel-ligence, Vol. 21, No. 1, 2005, pp. 1-26.
[8] M. He, H. F. Leung and N. R. Jennings, “An ARTMAP Based Bidding Strategy for Autonomous Agents in Con-tinuous Double Auctions,” IEEE Transactions on Know-ledge and Data Engineering, Vol. 15, No. 6, 2003, pp. 1345-1363.
[9] R. Arunachalam and N. Sadeh, “The Supply Chain Trad-ing Agent Competition,” Electronic Commerce Research and Applications, Vol. 4, No. 1, 2005, pp. 63-81.
[10] J. Collins, R. Arunachalam, N. Sadeh, J. Ericsson, N. Finne and S. Janson, “The Supply Chain Management Trading Agent Competition,” Technical Report CMU- ISRI-04-139, Carnegie Mellon University, Pittsburgh, 2004.
[11] M. He, N. R. Jennings and H. Leung, “On Agent-Mediated Electronic Commerce,” IEEE Transac-tions on Knowledge and Data Engineering, Vol. 15, No. 4, 2003, pp. 985-1003.

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