Pronósticos bayesianos para repuestos de automóviles usando simulación estocástica


  • 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)



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


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.


Download data is not yet available.


Alba, E. de, & Mendoza, M. (2007). Bayesian forecasting methods for short time series. Foresight, 8, 41-44.

Asmussen, S., & Glynn, P. W. (2007). Stochastic simulation algorithms and analysis. New York: Springer.

Bartezzaghi, E., Verganti, R., & Zotteri, G. (1999). A simulation framework for forecasting uncertain lumpy demand.

International. Journal of Production Economics, 59, 499-510.

Berger, J. O., Bernardo, J. M., & Sun, D. (2009). The formal definition of reference priors. annals of Statistics,

(2), 905-938.

Bernardo, J. M., & Smith, A. F. M. (2000). bayesian theory. Chichester: John Wiley.

Caniato, F., Kalchschmidt, M., Ronchi, E., Veganti, R., & Zotteri, G. (2005). Clustering customers to forecast demand.

Production Planning & Control, 16, 32-43.

Cheng, R. C. H., & Holland, W. (2004). Calculation of confidence intervals for simulation output. aCm transactions on

modeling and Computer Simulation, 14(4), 344-362.

Chopra, S., & Meindl, P. (2004). Supply chain management. (2.ª ed.). New Jersey: Prentice Hall.

Chung, K. L. (1974). a course in probability theory. (2.ª ed.). San Diego: Academic Press.

Croston, J. D. (1972). Forecasting and stock control for intermediate demands. operational research Quarterly,

, 289-303.

Kalchschmidt, M., Verganti, R., & Zotteri, G. (2006). Forecasting demand from heterogeneous customers. International Journal of operations & Production management, 26(6), 619-638.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications. (3.ª ed.). New

York: John Wiley.

Muñoz, D. F. (2009). Pronósticos bayesianos usando simulación

estocástica. Cuadernos de Difusión. 14(26), 7-26.

Rao, A. V. (1973). A comment on forecasting and stock control for intermediate demands. operational research

Quarterly, 24, 639-640.

Schmeiser, B. (1982). Batch size effects in the analysis of simulation output. operations research, 30(3), 556-568.

Serfling, R. J. (1980). approximation theorems of mathematical statistics. New York: John Wiley.

Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International. Journal of Production Economics, 71, 457-466.

Wacker, J. G., & Sprague, L. G. (1998). Forecasting accuracy:

comparing the relative effectiveness of practices between seven developed countries. Journal of operations management, 16, 271-290.

Willemain, T. R., Smart, C. N., & Schwarz, H. F. (2004). A new approach to forecasting intermittent demand for

service parts inventories. International Journal of Forecasting, 20, 375-387.

Zotteri, G., & Kalchschmidt, M. (2007a). Forecasting practices: Empirical evidence and a framework for research.

International. Journal of Production Economics, 108,84-99.

Zotteri, G., & Kalchschmidt, M. (2007b). A model for selecting the appropriate level of aggregation in forecasting

processes. International Journal of Production Economics, 108, 74-83.




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.