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Inventory control of spare parts using a Bayesian approach: a case study

Author

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  • Aronis, K-P.
  • Magou, I.
  • Dekker, R.
  • Tagaras, G.

Abstract

This paper presents a case study of applying a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an (S-1,S) inventory system for controlling spare parts of electronic equipment. First, the problem and the current policy are described. Then, the basic elements of the Bayesian approach are introduced and the procedure for calculating the appropriate parameter S is illustrated. Finally, we present the results of applying the Bayesian approach in an innovative way to determine the stock levels of three types of circuit packs at several locations. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.

Suggested Citation

  • Aronis, K-P. & Magou, I. & Dekker, R. & Tagaras, G., 1999. "Inventory control of spare parts using a Bayesian approach: a case study," Econometric Institute Research Papers EI 9950-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1622
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    Cited by:

    1. Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
    2. Nenes, George & Panagiotidou, Sofia & Tagaras, George, 2010. "Inventory management of multiple items with irregular demand: A case study," European Journal of Operational Research, Elsevier, vol. 205(2), pages 313-324, September.
    3. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    4. S Taskin & E J Lodree,, 2011. "A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1098-1108, June.
    5. Qianru Ge & Willem van Jaarsveld & Zümbül Atan, 2020. "Optimal redesign decisions through failure rate estimates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 254-271, June.
    6. 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).
    7. Mauricio Varas & Franco Basso & Armin Lüer-Villagra & Alejandro Mac Cawley & Sergio Maturana, 2019. "Managing premium wines using an $$(s - 1,s)$$ ( s - 1 , s ) inventory policy: a heuristic solution approach," Annals of Operations Research, Springer, vol. 280(1), pages 351-376, September.
    8. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    9. Selçuk, B., 2013. "An adaptive base stock policy for repairable item inventory control," International Journal of Production Economics, Elsevier, vol. 143(2), pages 304-315.
    10. D Louit & R Pascual & D Banjevic & A K S Jardine, 2011. "Optimization models for critical spare parts inventories—a reliability approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 992-1004, June.
    11. Seongmin Moon & Ui Jun Kim, 2017. "The Development of a Concurrent Spare-Parts Optimization Model for Weapon Systems in the South Korean Military Forces," Interfaces, INFORMS, vol. 47(2), pages 122-136, April.
    12. Dekker, Rommert & Pinçe, Çerağ & Zuidwijk, Rob & Jalil, Muhammad Naiman, 2013. "On the use of installed base information for spare parts logistics: A review of ideas and industry practice," International Journal of Production Economics, Elsevier, vol. 143(2), pages 536-545.
    13. Dolgui, Alexandre & Pashkevich, Maksim, 2008. "Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance," International Journal of Production Economics, Elsevier, vol. 112(2), pages 885-894, April.

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