In-house versus outsourcing collection in a closed-loop supply chain with remanufacturing technology development

Abstract

This paper develops a game model of a closed-loop supply chain consisting of one manufacturer, one remanufacturer and one retailer and investigates the long-term collection strategies of the manufacturer who needs to cooperate with the remanufacturer to develop the remanufacturing technology. The game model is developed from a fully dynamic perspective on the remanufacturing technology development and analyzed using the system dynamics method. We identify the trade-off in the choice of collection strategies: the early entry in the remanufacturing industry versus the complete control in the later stage. The simulation results show that when the entry barrier to the remanufacturing industry is low, the manufacturer can develop the remanufacturing technology in a short time and the direct reverse channel outperforms the indirect reverse channel; when the entry barrier is high, the manufacturer can achieve more profits under the indirect reverse channel by acquiring the remanufacturing technology directly from the remanufacturer. Moreover, the indirect reverse channel is more likely to be superior for low-barrier remanufacturing industry when the cost advantage is high as the early entry in the remanufacturing industry becomes more important than the complete control in the later stage.

KEYWORDS:

• Closed-loop supply chain
• game theory
• remanufacturing
• reverse channel
• simulation

Acknowledgements

We would like to thank the anonymous reviewers for their constructive comments that helped us to improve the quality of the paper considerably. The authors are grateful for the financial support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data, models, and code generated or used during the study appear in the submitted article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grants numbers 72101273, 72101258, 71991463/71991460, and 71971219], the Humanities and Social Science General Foundation of Ministry of Education of China [grants number 21YJC630008], the 6th Young Elite Scientist Sponsorship Programme by CAST [No. YESS20200198], the Fund for Building World-class Universities (disciplines) of Renmin University of China [No. KYGJC2021017] and the China Scholarship Council [No. 201906375013].

Notes on contributors

Shuzhen Chen

Shuzhen Chen is an assistant professor in Business School, Central South University, Changsha, China. She has authored or coauthored in refereed journals such as Risk Analysis, International Journal of Production Research, IEEE SMC, IEEE Systems Journal, etc. Her research interests include operations management of banking services, risk analysis and optimisation.

Yuchen Pan

Yuchen Pan is an assistant professor in the School of Information Resource Management, Renmin University of China, Beijing, China. He has authored or coauthored in refereed journals such as Journal of Management Information, Decision Support Systems, Information Sciences, IEEE Systems Journal, etc. His research interests are big data analysis, big data mining and recommender systems.

Desheng Wu

Desheng Wu is with the School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China, and also with the Stockholm Business School, Stockholm University, Stockholm, Sweden. He has authored or coauthored more than 100 papers in refereed journals such as Production and Operations Management, Decision Support Systems, Decision Sciences, Risk Analysis, IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, etc. His research interests include enterprise risk management in operations, performance evaluation in financial industry and decision sciences. Dr Wu has been an Associate Editor/Guest Editor for the IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, Omega, etc. He is elected member of Academia Europaea and the member of European Academy of Sciences and Arts.

Alexandre Dolgui

Alexandre Dolgui is a Fellow of IISE, distingished Professor and Head of Department at the IMT Atlantique, France. His research focuses on manufacturing line design, production planning and supply chain optimisation under uncertainty. He is the co-author of 5, co-editor of 20 books, he published 252 refereed journal papers, 30 editorials and 32 book chapters as well as over 400 papers in conference proceedings. He is the Editor in Chief of the International Journal of Production Research.