Bayesian Forecasting of Spare Parts Using Simulation
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.
KeywordsMarkov Chain Monte Carlo Service Level Parameter Uncertainty Point Estimator Posterior Density
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.
- Berger JO, Bernardo JM, Sun D (2009) The formal definition of priors. Ann Stat 37:905–938 Google Scholar
- Chopra S, Meindl P (2004) Supply chain management, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
- de Alba E, Mendoza M (2007) Bayesian forecasting methods for short time series. Foresight 8:41–44Google Scholar
- Makridakis S, Wheelwright SC, Hyndman RJ (1998) Forecasting: methods and applications, 3rd edn. Wiley, New YorkGoogle Scholar
- Muñoz DF (2010) On the validity of the batch quantile method in Markov chains. Oper Res Lett 38:222–226Google Scholar
- 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