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Bayesian Forecasting of Spare Parts Using Simulation

  • David F. Muñoz
  • Diego F. Muñoz
Chapter

Abstract

Forecasting demand plays a major role in many service and manufacturing organizations. Forecasts help in the scheduling of taskforce, obtaining higher service levels for the customer, and determining resource requirements among many others (Makridakis et al. 1998). Forecasting accuracy is an increasingly important objective in most firms and, in particular, plays a key role in forecasting lumpy demand. According to many authors (e.g., Wacker and Sprague 1998; Zotteri and Kalchsdmidt 2007a), the accuracy of forecasts depends sensitively on the quantitative technique used, thus, this chapter has been motivated by an increasing need for applying and formulating new tools for demand forecasting, and in particular for the case of lumpy demand. As suggested by the work of Caniato et al. (2005) and Kalchsdmidt et al. (2006), it is necessary to propose forecasting techniques that not only take into account the time series, but also the structure of the demand-generating process (non-systematic variability). For this reason, in this chapter we illustrate how to apply simulation techniques and Bayesian statistics in a model that takes into account particular characteristics of the system under study.

Keywords

Markov Chain Monte Carlo Service Level Parameter Uncertainty Point Estimator Posterior Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research has received support from the Asociación Mexicana de Cultura A.C. Jaime Galindo and Jorge Luquin have also participated. They both shared their knowledge on the auto-parts sector. As such, the authors want to express their most sincere gratitude.

References

  1. Asmussen S (2003) Applied probability and queues. Springer, New YorkMATHGoogle Scholar
  2. Asmussen S, Glynn P (2007) Stochastic simulation algorithms and analysis. Springer, New YorkMATHGoogle Scholar
  3. Bartezzaghi E, Verganti R, Zotteri G (1999) A simulation framework for forecasting uncertain lumpy demand. Int J Prod Econ 59:499–510CrossRefGoogle Scholar
  4. Berger JO, Bernardo JM, Sun D (2009) The formal definition of priors. Ann Stat 37:905–938 Google Scholar
  5. Bernardo JM, Smith AFM (2000) Bayesian theory. Wiley, ChichesterMATHGoogle Scholar
  6. Caniato F, Kalchschmidt M, Ronchi E, Veganti R, Zotteri G (2005) Clustering customers to forecast demand. Prod Plan Control 16:32–43CrossRefGoogle Scholar
  7. Chopra S, Meindl P (2004) Supply chain management, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
  8. Chung KL (1974) A course in probability theory, 2nd edn. Academic Press, San DiegoMATHGoogle Scholar
  9. Croston JD (1972) Forecasting and stock control for intermittent demands. Oper Res Q 23:289–303CrossRefMATHGoogle Scholar
  10. de Alba E, Mendoza M (2007) Bayesian forecasting methods for short time series. Foresight 8:41–44Google Scholar
  11. Kalchschmidt M, Verganti R, Zotteri G (2006) Forecasting demand from heterogeneous customers. Int J Oper Prod Manag 26:619–638CrossRefGoogle Scholar
  12. Makridakis S, Wheelwright SC, Hyndman RJ (1998) Forecasting: methods and applications, 3rd edn. Wiley, New YorkGoogle Scholar
  13. Muñoz DF (2010) On the validity of the batch quantile method in Markov chains. Oper Res Lett 38:222–226Google Scholar
  14. Muñoz DF, Muñoz DG (2008) A Bayesian framework for the incorporations of priors and sample data in simulation experiments. Open Oper Res J 2:44–51CrossRefMathSciNetGoogle Scholar
  15. Rao AV (1973) A comment on forecasting and stock control for intermediate demands. Oper Res Q 24:639–640CrossRefMATHGoogle Scholar
  16. Schmeiser B (1982) Batch size effects in the analysis of simulation output. Oper Res 30:556–568CrossRefMATHMathSciNetGoogle Scholar
  17. Serfling RJ (1980) Approximation theorems of mathematical statistics. Wiley, New YorkCrossRefMATHGoogle Scholar
  18. Song TW, Chih M (2008) Implementable mse-optimal dynamic partial-overlapping batch means estimators for steady-state simulations. In: Mason SJ, Hill RR, Mönch L, Rose O, Jefferson T, Fowler JW (eds) Proceedings of the 2008 winter simulation conference, IEEE, New Jersey, 426–435Google Scholar
  19. Syntetos AA, Boylan JE (2001) On the bias of intermittent demand estimates. Int J Prod Econ 71:457–466CrossRefGoogle Scholar
  20. Wacker JG, Sprague LG (1998) Forecasting accuracy: comparing the relative effectiveness of practices between seven developed countries. J Oper Manag 16:271–290CrossRefGoogle Scholar
  21. Willemain TR, Smart CN, Schwarz HF (2004) A new approach to forecasting intermittent demand for service parts inventories. Int J Forecast 20:375–387CrossRefGoogle Scholar
  22. Zotteri G, Kalchsdmidt M (2007a) Forecasting practices: empirical evidence and a framework for research. Int J Prod Econ 108:84–99CrossRefGoogle Scholar
  23. Zotteri G, Kalchsdmidt M (2007b) A model for selecting the appropriate level of aggregation in forecasting processes. Int J Prod Econ 108:74–83CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Instituto Tecnológico Autónomo de MéxicoMexicoMexico
  2. 2.Stanford UniversityStanfordUSA

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