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Policies for inventory models with product returns forecast from past demands and past sales

Author

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  • Mabel C. Chou

    (National University of Singapore)

  • Chee-Khian Sim

    (University of Portsmouth)

  • Xue-Ming Yuan

    (Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A*STAR))

Abstract

Finite horizon periodic review backlog models are considered in this paper for an inventory system that remanufactures two types of cores: buyback cores and normal cores. Returns of used products as buyback cores are modelled to depend on past demands and past sales. We derive an optimal inventory policy for the model in which returns are forecast to depend on past demands, and analyze properties of the optimal cost and optimal policy we derived. As the structure of the optimal inventory policy for the model in which returns are forecast from past sales is unlikely to be tractable, we instead consider a feasible inventory policy with a nice structure for this model. We investigate how close this policy is to optimality and find that in the worst case, the difference in system costs between the feasible policy and the optimal inventory policy is bounded by a constant that is dependent only on cost parameters, mean demands and a discount factor, and is independent of the planning horizon and initial inventories. We also perform numerical experiments to study the difference between system costs under the feasible policy and those under the optimal policy.

Suggested Citation

  • Mabel C. Chou & Chee-Khian Sim & Xue-Ming Yuan, 2020. "Policies for inventory models with product returns forecast from past demands and past sales," Annals of Operations Research, Springer, vol. 288(1), pages 137-180, May.
    • Handle: RePEc:spr:annopr:v:288:y:2020:i:1:d:10.1007_s10479-020-03545-4
    DOI: 10.1007/s10479-020-03545-4
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    References listed on IDEAS

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    Cited by:

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    2. Lin, Yizhong & Leung, Janny M.Y. & Zhang, Lianmin & Gu, Jia-Wen, 2020. "Single-item repairable inventory system with stochastic new and warranty demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    3. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    4. Guangyong Yang & Guojun Ji & Kim Hua Tan, 2022. "Impact of artificial intelligence adoption on online returns policies," Annals of Operations Research, Springer, vol. 308(1), pages 703-726, January.

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