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Spare parts demand forecasting: a review on bootstrapping methods

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  • M. Hasni
  • M.S. Aguir
  • M.Z. Babai
  • Z. Jemai

Abstract

Accurate demand forecasts are essential to the inventory control of spare parts. There is a plethora of statistical methods developed in the academic literature to deal with the forecasting of spare parts demand. These methods belong to the parametric and the non-parametric approaches. Within the second approach, the bootstrapping methods are the most considered ones. Despite that bootstrapping methods have shown a good empirical performance in comparison with their parametric counterparts, none of the available studies highlight the necessity to bring together its related state of knowledge and critically review the relevant research advancements. The present paper bridges this gap by reviewing the literature that deals with the bootstrapping approach and by discussing some of its statistical properties. This yields a better understanding of its framework, and hence, retrieves more robust explanations of the observed mixed-performances of the available bootstrap-based forecasting methods. This paper reviews as well the service level models associated with the bootstrapping approach with an emphasis on the fill rate models.

Suggested Citation

  • M. Hasni & M.S. Aguir & M.Z. Babai & Z. Jemai, 2019. "Spare parts demand forecasting: a review on bootstrapping methods," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4791-4804, August.
  • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:15-16:p:4791-4804
    DOI: 10.1080/00207543.2018.1424375
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    Citations

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    Cited by:

    1. 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.
    2. Pedro Luis Camuñas García-Miguel & Donato Zarilli & Jaime Alonso-Martinez & Manuel García Plaza & Santiago Arnaltes Gómez, 2024. "Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties," Sustainability, MDPI, vol. 16(7), pages 1-22, March.
    3. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    4. 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.
    5. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    6. Priscila Espinosa & Jose M. Pavía, 2023. "Automation in Regional Economic Synthetic Index Construction with Uncertainty Measurement," Forecasting, MDPI, vol. 5(2), pages 1-19, April.
    7. Amniattalab, Ayda & Frenk, J.B.G. & Hekimoğlu, Mustafa, 2023. "On spare parts demand and the installed base concept: A theoretical approach," International Journal of Production Economics, Elsevier, vol. 266(C).
    8. Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
    9. Babai, M.Z. & Chen, H. & Syntetos, A.A. & Lengu, D., 2021. "A compound-Poisson Bayesian approach for spare parts inventory forecasting," International Journal of Production Economics, Elsevier, vol. 232(C).
    10. John P. Saldanha & Bradley S. Price & Douglas J. Thomas, 2023. "A nonparametric approach for setting safety stock levels," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1150-1168, April.

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