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On the use of installed base information for spare parts logistics: a revieuw of ideas and industry practice

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Listed:
  • Dekker, R.
  • Pinçe, C.
  • Zuidwijk, R.A.
  • Jalil, M.N.

Abstract

Demand for spare parts is often difficult to forecast using historical data only. In this paper, we give an overview of installed based information and provide several ways in which installed base forecasting can be used. We discuss cases of installed based forecasting at four companies and list the issues involved. Moreover, we provide some models to assess the value of installed base information and conclude that forecasts of spare parts demand and return can be made considerably more timely and accurate by using installed base information.

Suggested Citation

  • Dekker, R. & Pinçe, C. & Zuidwijk, R.A. & Jalil, M.N., 2010. "On the use of installed base information for spare parts logistics: a revieuw of ideas and industry practice," Econometric Institute Research Papers EI 2010-73, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:21942
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    Cited by:

    1. Jin, Tongdan & Tian, Zhigang & Xie, Min, 2015. "A game-theoretical approach for optimizing maintenance, spares and service capacity in performance contracting," International Journal of Production Economics, Elsevier, vol. 161(C), pages 31-43.
    2. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
    3. 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).
    4. Fabian Taigel & Anselme K. Tueno & Richard Pibernik, 2018. "Privacy-preserving condition-based forecasting using machine learning," Journal of Business Economics, Springer, vol. 88(5), pages 563-592, July.
    5. Kati Stormi & Anni Lindholm & Teemu Laine & Tuomas Korhonen, 2020. "RFM customer analysis for product-oriented services and service business development: an interventionist case study of two machinery manufacturers," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 24(3), pages 623-653, September.
    6. 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.
    7. Yongquan, Sun & Xi, Chen & He, Ren & Yingchao, Jin & Quanwu, Liu, 2016. "Ordering decision-making methods on spare parts for a new aircraft fleet based on a two-sample prediction," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 40-50.
    8. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    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. 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).
    11. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    12. Euna Lee & Myungwoo Nam & Hongchul Lee, 2022. "Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    13. Kim, T.Y. & Dekker, R. & Heij, C., 2016. "Spare part demand forecasting for consumer goods using installed base information," Econometric Institute Research Papers EI2016-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. 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.
    15. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    16. Qin, Xuwei & Jiang, Zhong-Zhong & Sun, Minghe & Tang, Liang & Liu, Xiaoran, 2021. "Repairable spare parts provisioning for multiregional expanding fleets of equipment under performance-based contracting," Omega, Elsevier, vol. 102(C).
    17. José M. González-Varona & David Poza & Fernando Acebes & Félix Villafáñez & Javier Pajares & Adolfo López-Paredes, 2020. "New Business Models for Sustainable Spare Parts Logistics: A Case Study," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    18. 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.
    19. Hekimoğlu, Mustafa & Karlı, Deniz, 2023. "Modeling repair demand in existence of a nonstationary installed base," International Journal of Production Economics, Elsevier, vol. 263(C).
    20. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    21. Stip, J. & Van Houtum, G.J., 2020. "On a method to improve your service BOMs within spare parts management," International Journal of Production Economics, Elsevier, vol. 221(C).

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