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Building knowledge to improve enterprise performance from inventory simulation models

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  • Diaz, Rafael
  • Bailey, Mike
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    Abstract

    This paper describes the process of building knowledge to improve enterprise performance. This allows managers both to identify unknown risks and to develop solutions that mitigate these risks. One of the most critical risks that the enterprise faces involves the unidentified presence of serial-correlation components on the demand patterns. Depending upon the levels of such correlation, inventory control policies can be appreciably inaccurate. We propose to use a knowledge management portfolio that allows managers to capture and build knowledge from their complex systems. We find that the error generated from ignoring identified risk factors exponentially grows as the autocorrelation increases. We construct an enhanced simulated annealing algorithm that provides superior solutions to this type of problem.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0925527311002416
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 134 (2011)
    Issue (Month): 1 (November)
    Pages: 108-113

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    Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:108-113

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    Web page: http://www.elsevier.com/locate/ijpe

    Related research

    Keywords: Knowledge management Stochastic inventory Simulation-based optimization Error characterization;

    References

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    1. Lin, Shih-Wei & Ying, Kuo-Ching & Lu, Chung-Cheng & Gupta, Jatinder N.D., 2011. "Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times," International Journal of Production Economics, Elsevier, vol. 130(2), pages 246-254, April.
    2. Tang, Ou, 2004. "Simulated annealing in lot sizing problems," International Journal of Production Economics, Elsevier, vol. 88(2), pages 173-181, March.
    3. Warren H. Hausman & Nesim K. Erkip, 1994. "Multi-Echelon vs. Single-Echelon Inventory Control Policies for Low-Demand Items," Management Science, INFORMS, vol. 40(5), pages 597-602, May.
    4. Kahn, James A, 1987. "Inventories and the Volatility of Production," American Economic Review, American Economic Association, vol. 77(4), pages 667-79, September.
    5. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    6. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    7. Perrott, Bruce E., 2007. "A strategic risk approach to knowledge management," Business Horizons, Elsevier, vol. 50(6), pages 523-533.
    8. Minner, Stefan, 2003. "Multiple-supplier inventory models in supply chain management: A review," International Journal of Production Economics, Elsevier, vol. 81(1), pages 265-279, January.
    9. Urban, Timothy L., 2005. "A periodic-review model with serially-correlated, inventory-level-dependent demand," International Journal of Production Economics, Elsevier, vol. 95(3), pages 287-295, March.
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