Design of a Reverse Logistics System with Internet of Things for Service Parts Management

Daniel Y. Mo (), Chris Y. T. Ma, Danny C. K. Ho and Yue Wang
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Daniel Y. Mo: Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong, China
Chris Y. T. Ma: Department of Computing, The Hang Seng University of Hong Kong, Hong Kong, China
Danny C. K. Ho: Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong, China
Yue Wang: Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong, China

Sustainability, 2022, vol. 14, issue 19, 1-15

Abstract: Despite that reverse logistics of service parts enables the reuse of failed components to achieve greater environmental and economic benefits, the research and successful business cases are inadequate. This study designs a novel reverse logistics system that applies the Internet of Things (IoT) and business intelligence to streamline the reverse logistics process by identifying the appropriate components for sustainable operations of component reuse. Furthermore, an inventory classification scheme and an analytical model are developed to identify the failed components for refurbishment by considering return quantity of the failed component, repair rate of the failed component in the repairing center, reusable rate of refurbished parts, corresponding costs, and the benefit of refurbished parts. Moreover, a mobile application powered by the IoT technology is developed to streamline the process flow and avoid collection of fake components. Lastly, a case study of an electronic product company is conducted, and it is concluded that the proposed approach enabled the company to facilitate the reuse of components and achieve the benefit of cost saving. The results of this study demonstrate the importance of a reverse logistics system for companies to sustain after-market service operations.

Keywords: reverse logistics; service parts management; analytical model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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