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A Review of Bootstrapping for Spare Parts Forecasting

  • Marilyn Smith
  • M. Zied Babai
Chapter

Abstract

This chapter’s primary focus is limited to the bootstrap statistical method of forecasting and its application to spare parts inventory. Following a rough chronological order, first, the chapter describes Efron’s bootstrap method for estimating a sampling distribution based on an observed sample, as well as some of his extensions of his initial work. Since the bootstrap uses a Monte Carlo simulation to generate the distributions, and one of the most widely recognized applications of the bootstrap is a commercial software package (SmartForecasts), computer technology has been key to the implementation of the bootstrap. The chapter briefly describes the landmark model Croston developed for intermittent inventory demand, because most of the work using bootstrap compares results to Croston. After describing several models that use bootstrap for spare parts inventory forecasting, a few areas for more work are mentioned. Details related to the implementation of the bootstrapping methods discussed in this chapter are presented in Appendix A.

Keywords

Spare Part Absolute Percentage Error Exponential Smoothing Economic Order Quantity Reorder Point 
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.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Winthrop UniversityRock Hill,USA
  2. 2.BEM Bordeaux Management SchoolBordeaux,France

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