Pronósticos bayesianos para repuestos de automóviles usando simulación estocástica

Authors

  • David F. Muñoz Negrón Instituto Tecnológico Autónomo de México. Ph. D. en Investigación de Operaciones de Stanford University, California.
  • Diego F. Muñoz Medina Estudiante del Master of Science en Management Science and Engineering, Stanford University. Ingeniero industrial, Instituto Tecnológico Autónomo de México (México)

DOI:

https://doi.org/10.46631/jefas.2009.v14n27.01

Keywords:

Forecasts, repair forecasts, bayesian inferences, reorder points, level of service

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.

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Published

2009-12-30

How to Cite

Muñoz Negrón, D. F. ., & Muñoz Medina, D. F. . (2009). Pronósticos bayesianos para repuestos de automóviles usando simulación estocástica. Journal of Economics, Finance and Administrative Science, 14(27), 7–20. https://doi.org/10.46631/jefas.2009.v14n27.01