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Reverse supply chain coordination under stochastic remanufacturing capacity

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  • Heydari, Jafar
  • Govindan, Kannan
  • Sadeghi, Razieh

Abstract

In this paper, a two-stage reverse supply chain (RSC) is analyzed where the retailer pays rewards to customers to return obsolete products and the manufacturer refurbishes eligible returned items through remanufacturing process. Remanufacturing capacity is assumed as a stochastic variable. Under the uncertainty of remanufacturing capacity, it is possible that some inspected and eligible items could not be processed. If a bought item could not be processed, it should be salvaged at low prices and be considered a lost profit. In such situations, increasing the number of returned obsolete products is suitable where there is a high probability for enough capacity in the remanufacturing process. In this study, a stochastic model is developed to find the optimal paid reward to customers under both scenarios, including decentralized (where the retailer decides independently on reward amount) and centralized (where reward amount is determined based on the whole channel interest). By sharing the manufacturer’s capacity risk, a revenue sharing contract is proposed to convince the retailer to consider uncertainty of remanufacturing capacity in deciding reward amount. Under the proposed contract, a part of retailer revenue is postponed until the remanufacturing process is completed. Non-eligible items, as well as those that cannot be processed due to insufficient capacity are not involved in revenue sharing with the retailer. Our numerical investigations reveal that the proposed scheme is able to coordinate the investigated RSC under the uncertainty of remanufacturing capacity. Contrary to the decentralized scenario, the proposed model recommends fewer paid rewards to customers when there is a high possibility of insufficient capacity in the remanufacturing process.

Suggested Citation

  • Heydari, Jafar & Govindan, Kannan & Sadeghi, Razieh, 2018. "Reverse supply chain coordination under stochastic remanufacturing capacity," International Journal of Production Economics, Elsevier, vol. 202(C), pages 1-11.
  • Handle: RePEc:eee:proeco:v:202:y:2018:i:c:p:1-11
    DOI: 10.1016/j.ijpe.2018.04.024
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    Cited by:

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    4. Pietro Giovanni & Georges Zaccour, 2019. "A selective survey of game-theoretic models of closed-loop supply chains," 4OR, Springer, vol. 17(1), pages 1-44, March.
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    6. Manavalan Ethirajan & Thanigai Arasu M & Jayakrishna Kandasamy & Vimal K.E.K & Simon Peter Nadeem & Anil Kumar, 2021. "Analysing the risks of adopting circular economy initiatives in manufacturing supply chains," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 204-236, January.
    7. Ullah, Mehran & Sarkar, Biswajit, 2020. "Recovery-channel selection in a hybrid manufacturing-remanufacturing production model with RFID and product quality," International Journal of Production Economics, Elsevier, vol. 219(C), pages 360-374.
    8. Taleizadeh, Ata Allah & Sadeghi, Razieh, 2019. "Pricing strategies in the competitive reverse supply chains with traditional and e-channels: A game theoretic approach," International Journal of Production Economics, Elsevier, vol. 215(C), pages 48-60.
    9. Chan, Chi Kin & Fang, Fei & Langevin, André, 2018. "Single-vendor multi-buyer supply chain coordination with stochastic demand," International Journal of Production Economics, Elsevier, vol. 206(C), pages 110-133.
    10. Yigit Kazancoglu & Esra Ekinci & Sachin Kumar Mangla & Muruvvet Deniz Sezer & Yasanur Kayikci, 2021. "Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 71-91, January.

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