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Spare parts demand: Linking forecasting to equipment maintenance

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  • Wang, Wenbin
  • Syntetos, Aris A.

Abstract

Demand for spare parts is typically intermittent and forecasting the relevant requirements constitutes a very challenging exercise. Why is the demand for spare parts intermittent and how can we use models developed in maintenance research to forecast such demand? We attempt to answer these questions; we present a novel idea to forecast demand that relies upon the very sources of the demand generation process and we compare it with a well-known time-series method. We conclude that maintenance driven models are associated with a better performance under certain conditions. We also outline an inter-disciplinary agenda for further research in this area.

Suggested Citation

  • Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
    • Handle: RePEc:eee:transe:v:47:y:2011:i:6:p:1194-1209
    DOI: 10.1016/j.tre.2011.04.008
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    1. Jones, B. & Jenkinson, I. & Wang, J., 2009. "Methodology of using delay-time analysis for a manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 111-124.
    2. Wenbin Wang, 2008. "Delay Time Modelling," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 14, pages 345-370, Springer.
    3. Robin P. Nicolai & Rommert Dekker, 2008. "Optimal Maintenance of Multi-component Systems: A Review," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 11, pages 263-286, Springer.
    4. John E. Boylan & Aris A. Syntetos, 2008. "Forecasting for Inventory Management of Service Parts," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 20, pages 479-506, Springer.
    5. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    6. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    7. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
    8. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    9. E A Shale & J E Boylan & F R Johnston, 2006. "Forecasting for intermittent demand: the estimation of an unbiased average," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 588-592, May.
    10. Kutanoglu, Erhan & Lohiya, Divi, 2008. "Integrated inventory and transportation mode selection: A service parts logistics system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(5), pages 665-683, September.
    11. Gutierrez, Rafael S. & Solis, Adriano O. & Mukhopadhyay, Somnath, 2008. "Lumpy demand forecasting using neural networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 409-420, February.
    12. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    13. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    14. Kennedy, W. J. & Wayne Patterson, J. & Fredendall, Lawrence D., 2002. "An overview of recent literature on spare parts inventories," International Journal of Production Economics, Elsevier, vol. 76(2), pages 201-215, March.
    15. Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
    16. Rustenburg, W. D. & van Houtum, G. J. & Zijm, W. H. M., 2001. "Spare parts management at complex technology-based organizations: An agenda for research," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 177-193, May.
    17. de Smidt-Destombes, Karin S. & van der Heijden, Matthieu C. & van Harten, Aart, 2007. "Availability of k-out-of-N systems under block replacement sharing limited spares and repair capacity," International Journal of Production Economics, Elsevier, vol. 107(2), pages 404-421, June.
    18. Cho, Danny I. & Parlar, Mahmut, 1991. "A survey of maintenance models for multi-unit systems," European Journal of Operational Research, Elsevier, vol. 51(1), pages 1-23, March.
    19. R H Teunter & L Duncan, 2009. "Forecasting intermittent demand: a comparative study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 321-329, March.
    20. Zohrul Kabir, A. B. M. & Al-Olayan, Ahmed S., 1996. "A stocking policy for spare part provisioning under age based preventive replacement," European Journal of Operational Research, Elsevier, vol. 90(1), pages 171-181, April.
    21. 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.
    22. Vincent P. Simpson, 1978. "Optimum Solution Structure for a Repairable Inventory Problem," Operations Research, INFORMS, vol. 26(2), pages 270-281, April.
    23. A H C Eaves & B G Kingsman, 2004. "Forecasting for the ordering and stock-holding of spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(4), pages 431-437, April.
    24. Wenbin Wang, 2008. "Condition-based Maintenance Modelling," Springer Series in Reliability Engineering, in: Complex System Maintenance Handbook, chapter 5, pages 111-131, Springer.
    25. A H Christer, 1999. "Developments in delay time analysis for modelling plant maintenance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1120-1137, November.
    26. de Smidt-Destombes, Karin S. & van der Heijden, Matthieu C. & van Harten, Aart, 2009. "Joint optimisation of spare part inventory, maintenance frequency and repair capacity for k-out-of-N systems," International Journal of Production Economics, Elsevier, vol. 118(1), pages 260-268, March.
    27. Wang, Wenbin & Banjevic, Dragan & Pecht, Michael, 2010. "A multi-component and multi-failure mode inspection model based on the delay time concept," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 912-920.
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    Cited by:

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      • 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. Romeijnders, Ward & Teunter, Ruud & van Jaarsveld, Willem, 2012. "A two-step method for forecasting spare parts demand using information on component repairs," European Journal of Operational Research, Elsevier, vol. 220(2), pages 386-393.
    6. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    7. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
    8. Antti Puurunen & Jukka Majava & Hanna Kropsu-Vehkapera & Pekka Kess, 2013. "Applicaability of the (Q,R)-Model in Maintenance Meterials Management," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    9. 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.
    10. Chen Bing & Sun Shouqun & Liu Gang, 2012. "An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory," Modern Applied Science, Canadian Center of Science and Education, vol. 6(6), pages 1-12, June.
    11. Wenhan Fu & Sheng Jing & Qinming Liu & Hao Zhang, 2023. "Resilient Supply Chain Framework for Semiconductor Distribution and an Empirical Study of Demand Risk Inference," Sustainability, MDPI, vol. 15(9), pages 1-14, April.
    12. Bjarnason, Erik T.S. & Taghipour, Sharareh & Banjevic, Dragan, 2014. "Joint optimal inspection and inventory for a k-out-of-n system," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 203-215.
    13. Van der Auweraer, Sarah & Zhu, Sha & Boute, Robert N., 2021. "The value of installed base information for spare part inventory control," International Journal of Production Economics, Elsevier, vol. 239(C).
    14. Zahedi-Hosseini, Farhad & Scarf, Philip & Syntetos, Aris, 2017. "Joint optimisation of inspection maintenance and spare parts provisioning: a comparative study of inventory policies using simulation and survey data," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 306-316.
    15. 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.
    16. Boliang Lin & Jiaxi Wang & Huasheng Wang & Zhongkai Wang & Jian Li & Ruixi Lin & Jie Xiao & Jianping Wu, 2017. "Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    17. Boram Choi & Jong Hwan Suh, 2020. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    18. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    19. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
    20. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    21. Sharma, Pankaj & Kulkarni, Makarand S & Yadav, Vikas, 2017. "A simulation based optimization approach for spare parts forecasting and selective maintenance," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 274-289.
    22. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.

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