IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v220y2012i2p386-393.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712000392
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2012.01.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    2. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    3. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    4. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    5. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    6. 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.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    9. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    10. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    11. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    12. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.
    13. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    14. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    15. Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020. "Spare parts inventory control based on maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    16. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
    17. 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.
    18. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    19. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
    20. Mariusz Doszyn, 2020. "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 912-930.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:220:y:2012:i:2:p:386-393. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.