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Bayesian Forecastings For Automobile Parts Using Stochastic Simulation

Author

Listed:
  • Muñoz Negrón, David F.

    () (Instituto Tecnológico Autónomo de México)

  • Muñoz Medina, Diego F.

    () (Stanford University)

Abstract

This article presents the development and application of a simulation model that was used to forecast the demand of automobile parts using information from a car dealer in Mexico, D. F. In particular, this work illustrates, using a simple model, how stochastic simulation and Bayesian statistics can be combined to model and solve complex forecasting problems. The proposed framework is general enough to be applied to very detailed models of the system under study. The results obtained demonstrate how uncertainty on the parameters of the model can be incorporated, and the application using real data shows how a large sample size produces a posterior distribution that has little influence from the prior distribution.

Suggested Citation

  • Muñoz Negrón, David F. & Muñoz Medina, Diego F., 2009. "Bayesian Forecastings For Automobile Parts Using Stochastic Simulation," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 14(27), pages 7-20.
  • Handle: RePEc:ris:joefas:0008
    as

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    File URL: http://www.esan.edu.pe/publicaciones/2009/12/07/journal_of_economics_finance_and_administrative_science/1-david-munoz.pdf
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    References listed on IDEAS

    as
    1. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    2. Bartezzaghi, Emilio & Verganti, Roberto & Zotteri, Giulio, 1999. "A simulation framework for forecasting uncertain lumpy demand," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 499-510, March.
    3. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    4. Enrique de Alba & Manuel Mendoza, 2007. "Bayesian Forecasting Methods for Short Time Series," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 41-44, Fall.
    5. Zotteri, Giulio & Kalchschmidt, Matteo, 2007. "A model for selecting the appropriate level of aggregation in forecasting processes," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 74-83, July.
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