A Review of Bootstrapping for Spare Parts Forecasting

  • Marilyn Smith
  • M. Zied Babai


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.


Spare Part Absolute Percentage Error Exponential Smoothing Economic Order Quantity Reorder Point 
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  1. Aczel (1995) Interview with Bradley Efron. Irwin/McGraw-Hill Learning Aids Accessed 8 June 2009
  2. Bookbinder J, Lordahl A (1989) Estimation of inventory re-order levels using the bootstrap statistical procedure. IIE Trans 21(4):302–312CrossRefGoogle Scholar
  3. Bunn D, Wright G (1991) Interaction of judgemental and statistical forecasting methods: issues and analysis. Manag Sci 37(5):501–518CrossRefGoogle Scholar
  4. Croston J (1972) Forecasting and stock control for intermittent demands. Oper Res Q 23(3):289–303CrossRefMATHGoogle Scholar
  5. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26CrossRefMATHMathSciNetGoogle Scholar
  6. Efron B (1987) Better bootstrap confidence intervals. J Am Stat Assoc 82(397):171–185CrossRefMATHMathSciNetGoogle Scholar
  7. Efron B (1990) More efficient bootstrap computations. J Am Stat Assoc 85(409):79–89CrossRefMATHMathSciNetGoogle Scholar
  8. Efron B (2000) The bootstrap and modern statistics. J Am Stat Assoc 95(452):1293–1295CrossRefMATHMathSciNetGoogle Scholar
  9. Efron B, Gong G (1983) A leisurely look at the bootstrap, the jackknife, and cross validation. Am Stat 37(1):36–48CrossRefMathSciNetGoogle Scholar
  10. Efron B, Tibshirani R (1991) Statistical data analysis in the computer age. Science 253(5018):390–395CrossRefGoogle Scholar
  11. Elikai F, Badaranathi R, Howe V (2002) A review of 52 forecasting software packages. J Bus Forecast Summer 2002:19–27Google Scholar
  12. Gardner E, Koehler A (2005) Comments on a patented bootstrapping method for forecasting intermittent demand. Int J Forecast 21:617–618CrossRefGoogle Scholar
  13. Hua Z, Zhang B, Yang J, Tan D (2006) A new approach of forecasting intermittent demand for spare parts inventories in the process industries. J Oper Res Soc. doi: 10.1057/palgrave.jors.2602119
  14. Johnson R (2009) Statistical quotes and humor. Accessed 2 June 2009
  15. Johnston FR, Boylan JE, Shale EA (2003) An examination of the size of orders from customers, their characterization and the implications for inventory control of slow moving items. J Oper Res Soc 54:833–837CrossRefMATHGoogle Scholar
  16. Kennedy W, Patterson J, Fredendall L (2002) An overview of recent literature on spare parts inventories. Int J Prod Econ 76(2):201–215CrossRefGoogle Scholar
  17. Lawrence M (2004) Commentary on: a new approach to forecasting intermittent demand for service parts inventories. Int J Forecast 20:389–390CrossRefGoogle Scholar
  18. McNeill W, Fontanella J, Ruggles K (2007) Service parts planning and optimization landscape: saving millions through inventory reductions and increased service levels. Accessed 15 June 2009
  19. Miller R (1974) The jackknife—a review. Biometrika 61:1–15MATHMathSciNetGoogle Scholar
  20. Porras E, Dekker R (2008) An inventory control system for spare parts at a refinery: an empirical comparison of different re-order point methods. Eur J Oper Res. doi: 10.1016/j.ejor.2006.11.008
  21. Sanders N, Manrodt K (2003) Forecasting software in practice: use, satisfaction, and performance. Interfaces 33(5):90–93CrossRefGoogle Scholar
  22. Smart C (2002) Accurate intermittent demand/inventory forecasting: new technologies and dramatic results. In: 2002 international conference proceedings, American Production and Inventory Control SocietyGoogle Scholar
  23. Smart C (2002) Accurate intermittent demand forecasting for inventory planning: new technologies and dramatic results. Accessed 27 May 2009
  24. Smart Software (Oct 26, 1999) Smart Software brings new forecasting technologies to market. Business Wire: 0406Google Scholar
  25. Smart Software (2001) U.S. patent no. 6,205,431 B1. US Patent and Trademark Office, Washington, DCGoogle Scholar
  26. Smart Software. Intermittent Demand Planning and Service Parts Forecasting. Accessed 27 May 2009
  27. Smart C, Willemain T (2000) A new way to forecast intermittent demand. Perform Adv June 2000:64–68Google Scholar
  28. Snyder R (2002) Forecasting sales of slow and fast moving inventories. Eur J Oper Res 140:684–699CrossRefMATHGoogle Scholar
  29. Snyder R, Koehler A, Ord J (1999) Lead time demand for simple exponential smoothing: an adjustment factor for standard deviation. J Oper Res Soc 50:1079–1082MATHGoogle Scholar
  30. Snyder R, Koehler A, Ord J (2002) Forecasting for inventory control with exponential smoothing. J Forecast 18:5–18CrossRefGoogle Scholar
  31. Syntetos A (2001) Forecasting of intermittent demand. PhD Dissertation. Brussels UniversityGoogle Scholar
  32. Syntetos A, Boylan J (2005) The accuracy of intermittent demand estimates. Int J Forecast 21:303–314CrossRefGoogle Scholar
  33. Syntetos AA, Boylan JE, Croston JD (2005) On the categorization of demand patterns. J Oper Res Soc 56:495–503CrossRefMATHGoogle Scholar
  34. Syntetos A, Boylan J, Disney S (2009a) Forecasting for inventory planning: a 50 year review. J Oper Res Soc 60:S149–S160CrossRefMATHGoogle Scholar
  35. Syntetos A, Nikolopoulos K, Boylan J, Fildes R, Goodwin P (2009b) The effects of integrating management judgment into intermittent demand forecasts. Int J Prod Econ 118:72–81CrossRefGoogle Scholar
  36. Teunter R, Duncan L (2009) Forecasting intermittent demand: a comparative study. J Oper Res Soc 60:321–329CrossRefGoogle Scholar
  37. Varghese V, Rossetti M (2008) A parametric bootstrapping approach to forecast intermittent demand. In: Proceedings of the 2008 industrial engineering research conference, pp 857–862Google Scholar
  38. Wang M, Rao S (1992) Estimating reorder points and other management science applications by bootstrap procedure. Eur J Oper Res 56:332–342CrossRefMATHGoogle Scholar
  39. Willemain T (1994) Bootstrap on a shoestring: resampling using spreadsheets. Am Stat 48(1):40CrossRefGoogle Scholar
  40. Willemain T, Smart C, Shockor J, DeSautels P (1994) Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston’s method. Int J Forecast 10(4):529–538CrossRefGoogle Scholar
  41. Willemain T, Smart C, Schwartz H (2004) A new approach to forecasting intermittent demand for service parts inventories. Int J Forecast 20:375–387CrossRefGoogle Scholar
  42. Willemain T, Smart C, Schwartz H (2005) Author’s response to Koehler and Gardner. Int J Forecast 21:619–620CrossRefGoogle Scholar

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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