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Spare parts demand forecasting: a review on bootstrapping methods

Author

Listed:
  • Zied Jemai

    (LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec)

  • M. Hasni

    (OASIS - Optimisation des systèmes industriels et de services [Tunis] - ENIT - Ecole Nationale d'Ingénieurs de Tunis - UTM - Université de Tunis El Manar)

  • M.S. Aguir
  • Mohamed Zied Babai

    (Kedge Business School [Talence])

Abstract

No abstract is available for this item.

Suggested Citation

  • Zied Jemai & M. Hasni & M.S. Aguir & Mohamed Zied Babai, 2018. "Spare parts demand forecasting: a review on bootstrapping methods," Post-Print hal-01742198, HAL.
  • Handle: RePEc:hal:journl:hal-01742198
    DOI: 10.1080/00207543.2018.1424375
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    Citations

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    Cited by:

    1. Chao Wu & Guolong Wang & Simon Hu & Yue Liu & Hong Mi & Ye Zhou & Yi-ke Guo & Tongtong Song, 2020. "A data driven methodology for social science research with left-behind children as a case study," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-28, November.
    2. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
    3. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.

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