Spare parts management: Linking distributional assumptions to demand classification

Lengu, D; Syntetos, A; Babai, MZ

Abstract

Spare parts are known to be associated with intermittent demand patterns and such patterns cause considerable problems with regards to forecasting and stock control due to their compound nature that renders the normality assumption invalid. Compound distributions have been used to model intermittent demand patterns; there is however a lack of theoretical analysis and little relevant empirical evidence in support of these distributions. In this paper, we conduct a detailed empirical investigation on the goodness of fit of various compound Poisson distributions and we develop a distribution-based demand classification scheme the validity of which is also assessed in empirical terms. Our empirical investigation provides evidence in support of certain demand distributions and the work described in this paper should facilitate the task of selecting such distributions in a real world spare parts inventory context. An extensive discussion on parameter estimation related difficulties in this area is also provided.

Citation

Lengu, D., Syntetos, A., & Babai, M. (2014). Spare parts management: Linking distributional assumptions to demand classification. European Journal of Operational Research, 235(3), 624-635. https://doi.org/10.1016/j.ejor.2013.12.043

• Journal Article Type: Article
• Acceptance Date: Dec 28, 2013
• Publication Date: Jan 6, 2014
• Deposit Date: May 6, 2014
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 235
• Issue: 3
• Pages: 624-635
• DOI: https://doi.org/10.1016/j.ejor.2013.12.043
• Keywords: Inventory; Demand distributions; Intermittent demand; Spare parts
• Publisher URL: http://dx.doi.org/10.1016/j.ejor.2013.12.043
• Related Public URLs: http://www.journals.elsevier.com/european-journal-of-operational-research/