BAYESIAN FORECASTINGS FOR AUTOMOBILE PARTS USING STOCHASTIC SIMULATION

David F. Muñoz Negrón () and Diego F. Muñoz Medina ()
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David F. Muñoz Negrón: Instituto Tecnológico Autónomo de México
Diego F. Muñoz Medina: Stanford University

Journal of Economics, Finance and Administrative Science, 2009, vol. 14, issue 27, 7-20

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.

Keywords: Forecasts; repair forecasts; bayesian inferences; reorder points; level of service. (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:ris:joefas:0008

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