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Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice

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  • Bacchetti, Andrea
  • Saccani, Nicola
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    Abstract

    This paper investigates the gap between research and practice in spare parts management, with specific reference to durable goods addressed to private or professional customers. The paper provides a critical literature review of theoretical contributions about spare parts classification and demand forecasting for stock control. The discussion of ten case studies, then, allows to analyze the reasons for this gap, by addressing the limitations of models developed in literature, the role of contextual factors and the maturity in companies' spare parts management practices. Four main directions for research are proposed in order to bridge the gap, namely: to develop integrated approaches to spare parts management; to define contingency-based managerial guidelines, to favor the knowledge accumulation process in companies, and to supplement theoretical models with practical relevance.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 40 (2012)
    Issue (Month): 6 ()
    Pages: 722-737

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    Handle: RePEc:eee:jomega:v:40:y:2012:i:6:p:722-737

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    Related research

    Keywords: Spare parts; Classification; Demand forecasting; Inventory management; Research–practice gap;

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    Cited by:
    1. Bacchetti, A. & Plebani, F. & Saccani, N. & Syntetos, A.A., 2013. "Empirically-driven hierarchical classification of stock keeping units," International Journal of Production Economics, Elsevier, vol. 143(2), pages 263-274.
    2. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    3. Lengu, D. & Syntetos, A.A. & Babai, M.Z., 2014. "Spare parts management: Linking distributional assumptions to demand classification," European Journal of Operational Research, Elsevier, vol. 235(3), pages 624-635.

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