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A two-step method for forecasting spare parts demand using information on component repairs

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  • Romeijnders, Ward
  • Teunter, Ruud
  • van Jaarsveld, Willem

Abstract

Forecasting spare parts demand is notoriously difficult, as demand is typically intermittent and lumpy. Specialized methods such as that by Croston are available, but these are not based on the repair operations that cause the intermittency and lumpiness of demand. In this paper, we do propose a method that, in addition to the demand for spare parts, considers the type of component repaired. This two-step forecasting method separately updates the average number of parts needed per repair and the number of repairs for each type of component. The method is tested in an empirical, comparative study for a service provider in the aviation industry. Our results show that the two-step method is one of the most accurate methods, and that it performs considerably better than Croston’s method. Moreover, contrary to other methods, the two-step method can use information on planned maintenance and repair operations to reduce forecasts errors by up to 20%. We derive further analytical and simulation results that help explain the empirical findings.

Suggested Citation

  • Romeijnders, Ward & Teunter, Ruud & van Jaarsveld, Willem, 2012. "A two-step method for forecasting spare parts demand using information on component repairs," European Journal of Operational Research, Elsevier, vol. 220(2), pages 386-393.
    • Handle: RePEc:eee:ejores:v:220:y:2012:i:2:p:386-393
    DOI: 10.1016/j.ejor.2012.01.019
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    References listed on IDEAS

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    1. A A Syntetos & J E Boylan & J D Croston, 2005. "On the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 495-503, May.
    2. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    3. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
    4. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    5. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    6. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    7. Kennedy, W. J. & Wayne Patterson, J. & Fredendall, Lawrence D., 2002. "An overview of recent literature on spare parts inventories," International Journal of Production Economics, Elsevier, vol. 76(2), pages 201-215, March.
    8. Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
    9. M N Jalil & R A Zuidwijk & M Fleischmann & Jo A E E van Nunen, 2011. "Spare parts logistics and installed base information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 442-457, March.
    10. Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
    11. Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
    12. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    13. Petrovic, D. & Petrovic, R., 1992. "SPARTA II: Further development in an expert system for advising on stocks of spare parts," International Journal of Production Economics, Elsevier, vol. 24(3), pages 291-300, March.
    14. R H Teunter & L Duncan, 2009. "Forecasting intermittent demand: a comparative study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 321-329, March.
    15. Guide, V. Daniel R. & Srivastava, Rajesh, 1997. "Repairable inventory theory: Models and applications," European Journal of Operational Research, Elsevier, vol. 102(1), pages 1-20, October.
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