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Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence

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  • Zied Babai, Mohamed
  • Syntetos, Aris
  • Teunter, Ruud

Abstract

Intermittent demand items account collectively for considerable proportions of the total stock value of any organization. Forecasting the relevant inventory requirements constitutes a very difficult task and most work in this area is based on Croston’s estimator that relies upon exponentially smoothed demand sizes and inter-demand intervals. This method has been shown to be biased and a number of variants have been introduced in the literature, including the recently proposed TSB method that updates the demand probability instead of the demand interval and in doing so reacts faster to decreasing demand. The TSB has been shown theoretically to be unbiased (for all points in time), but its empirical performance has not been investigated yet and this constitutes one of the objectives of our work. More generally, we explore the empirical performance of forecasting methods used in an intermittent demand context, paying particular attention to the effects and implications of the smoothing constant values employed for updating purposes. We do so by means of experimentation on large empirical datasets from the military sector and automotive industry. The results enable insights to be gained into the sensitivity of the various methods’ forecasting and stock control performance to the smoothing constant values used. The paper concludes with an agenda for further research.

Suggested Citation

  • Zied Babai, Mohamed & Syntetos, Aris & Teunter, Ruud, 2014. "Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence," International Journal of Production Economics, Elsevier, vol. 157(C), pages 212-219.
  • Handle: RePEc:eee:proeco:v:157:y:2014:i:c:p:212-219
    DOI: 10.1016/j.ijpe.2014.08.019
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    References listed on IDEAS

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    10. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
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    3. Kamal Sanguri & Kampan Mukherjee, 2021. "Forecasting of intermittent demands under the risk of inventory obsolescence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1054-1069, September.
    4. 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.
    5. 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.
    6. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    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. 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).
    9. Bai, Qingguo & Xu, Jianteng & Gong, Yeming & Chauhan, Satyaveer S., 2022. "Robust decisions for regulated sustainable manufacturing with partial demand information: Mandatory emission capacity versus emission tax," European Journal of Operational Research, Elsevier, vol. 298(3), pages 874-893.
    10. 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.
    11. 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.
    12. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
    13. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    14. Sarlo, Rodrigo & Fernandes, Cristiano & Borenstein, Denis, 2023. "Lumpy and intermittent retail demand forecasts with score-driven models," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1146-1160.
    15. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.
    16. Sgarbossa, Fabio & Peron, Mirco & Lolli, Francesco & Balugani, Elia, 2021. "Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand," International Journal of Production Economics, Elsevier, vol. 233(C).

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