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Customer Differentiated End-of-Life Inventory Problem

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  • Pourakbar, M.
  • Dekker, R.

Abstract

This paper deals with the service parts end-of-life inventory problem in a circumstance that demands for service parts are differentiated. Customer differentiation might be due to criticality of the demand or based on various service contracts. In both cases, we model the problem as a finite horizon stochastic dynamic program and characterize the structure of the optimal policy. We show that when customers are differentiated based on the demand criticality then the optimal structure consists of time and state dependent threshold levels for inventory rationing. In case of differentiation based on service contracts, we show that in addition to rationing thresholds we also need contract extension thresholds by which the system decides whether to offer an extension to an expiring contract or not. By numerical experiments in both cases, we identify the value of incorporating such decisions in service parts end-of-life inventory management with customer differentiation. Moreover, we show that these decisions not only result in cost efficiency but also decrease the risk of part obsolescence drastically.

Suggested Citation

  • Pourakbar, M. & Dekker, R., 2011. "Customer Differentiated End-of-Life Inventory Problem," Econometric Institute Research Papers EI 2011-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:23784
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    References listed on IDEAS

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    Cited by:

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    2. Shen, Yuelin & Willems, Sean P., 2014. "Modeling sourcing strategies to mitigate part obsolescence," European Journal of Operational Research, Elsevier, vol. 236(2), pages 522-533.
    3. Shi, Zhenyang & Liu, Shaoxuan, 2020. "Optimal inventory control and design refresh selection in managing part obsolescence," European Journal of Operational Research, Elsevier, vol. 287(1), pages 133-144.
    4. 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.
    5. Ozyoruk, Emin & Erkip, Nesim Kohen & Ararat, Çağın, 2022. "End-of-life inventory management problem: Results and insights," International Journal of Production Economics, Elsevier, vol. 243(C).
    6. Hur, Mansik & Keskin, Burcu B. & Schmidt, Charles P., 2018. "End-of-life inventory control of aircraft spare parts under performance based logistics," International Journal of Production Economics, Elsevier, vol. 204(C), pages 186-203.
    7. Li, Na & Zhang, Yue & Teng, De & Kong, Nan, 2021. "Pareto optimization for control agreement in patient referral coordination," Omega, Elsevier, vol. 101(C).
    8. Mohammad Najjartabar Bisheh & G. Reza Nasiri & Esmaeil Esmaeili & Hamid Davoudpour & Shing I. Chang, 2022. "A new supply chain distribution network design for two classes of customers using transfer recurrent neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2604-2618, October.
    9. 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.
    10. Roni, Mohammad S. & Jin, Mingzhou & Eksioglu, Sandra D., 2015. "A hybrid inventory management system responding to regular demand and surge demand," Omega, Elsevier, vol. 52(C), pages 190-200.
    11. Benedikt Schulte & Richard Pibernik, 2016. "Service differentiation in a single-period inventory model with numerous customer classes," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 921-948, October.

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