IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12013-d922648.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daniel Y. Mo & Chris Y. T. Ma & Danny C. K. Ho & Yue Wang, 2022. "Design of a Reverse Logistics System with Internet of Things for Service Parts Management," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12013-:d:922648
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12013/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Y. P. Tsang & C. H. Wu & H. Y. Lam & K. L. Choy & G. T. S. Ho, 2021. "Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application," International Journal of Production Research, Taylor & Francis Journals, vol. 59(5), pages 1534-1556, March.
    2. Daniel Y. Mo & Yue Wang & Danny C. K. Ho & K. H. Leung, 2022. "Redeploying excess inventories with lateral and reverse transshipments," International Journal of Production Research, Taylor & Francis Journals, vol. 60(10), pages 3031-3046, May.
    3. Daniel Y. Mo & Yue Wang & Lawrence C. Leung & Mitchell M. Tseng, 2020. "Optimal service parts contract with multiple response times and on-site spare parts," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 3049-3065, May.
    4. Syntetos, A.A. & Babai, M.Z. & Davies, J. & Stephenson, D., 2010. "Forecasting and stock control: A study in a wholesaling context," International Journal of Production Economics, Elsevier, vol. 127(1), pages 103-111, September.
    5. Wenyuan Wang & Daniel Y. Mo & Yue Wang & Mitchell M. Tseng, 2019. "Assessing the cost structure of component reuse in a product family for remanufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 575-587, February.
    6. Wang, Wenyuan & Wang, Yue & Mo, Daniel & Tseng, Mitchell M., 2017. "Managing component reuse in remanufacturing under product diffusion dynamics," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 551-560.
    7. Shuai Zhang & Kai Huang & Yufei Yuan, 2021. "Spare Parts Inventory Management: A Literature Review," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    8. Nima Kazemi & Nikunja Mohan Modak & Kannan Govindan, 2019. "A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4937-4960, August.
    9. Luis Isasi-Sanchez & Jesus Morcillo-Bellido & Jose Ignacio Ortiz-Gonzalez & Alfonso Duran-Heras, 2020. "Synergic Sustainability Implications of Additive Manufacturing in Automotive Spare Parts: A Case Analysis," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    10. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    11. Murthy, D. N. P. & Solem, O. & Roren, T., 2004. "Product warranty logistics: Issues and challenges," European Journal of Operational Research, Elsevier, vol. 156(1), pages 110-126, July.
    12. Yasaman Mashayekhy & Amir Babaei & Xue-Ming Yuan & Anrong Xue, 2022. "Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey," Logistics, MDPI, vol. 6(2), pages 1-19, May.
    13. A H C Eaves & B G Kingsman, 2004. "Forecasting for the ordering and stock-holding of spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(4), pages 431-437, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bacchetti, A. & Plebani, F. & Saccani, N. & Syntetos, A.A., 2013. "Empirically-driven hierarchical classification of stock keeping units," International Journal of Production Economics, Elsevier, vol. 143(2), pages 263-274.
    2. Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data," European Journal of Operational Research, Elsevier, vol. 322(2), pages 414-426.
    3. Zhou, Xiaoyang & Wei, Xiaoya & Lin, Jun & Tian, Xin & Lev, Benjamin & Wang, Shouyang, 2021. "Supply chain management under carbon taxes: A review and bibliometric analysis," Omega, Elsevier, vol. 98(C).
    4. Rego, José Roberto do & Mesquita, Marco Aurélio de, 2015. "Demand forecasting and inventory control: A simulation study on automotive spare parts," International Journal of Production Economics, Elsevier, vol. 161(C), pages 1-16.
    5. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    6. Cristina Blasi Casagran & Colleen Boland & Elena Sánchez-Montijano & Eva Vilà Sanchez, 2021. "The Role of Emerging Predictive IT Tools in Effective Migration Governance," Politics and Governance, Cogitatio Press, vol. 9(4), pages 133-145.
    7. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    8. Salehi-Amiri, Amirhossein & Zahedi, Ali & Akbapour, Navid & Hajiaghaei-Keshteli, Mostafa, 2021. "Designing a sustainable closed-loop supply chain network for walnut industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    9. Chen, Jiguang & Hu, Qiying, 2015. "Optimal payment scheme when the supplier’s quality level and cost are unknown," European Journal of Operational Research, Elsevier, vol. 245(3), pages 731-742.
    10. He Tingting, 2021. "Comparing Money and Time Donation: What Do Experiments Tell Us?," Marketing of Scientific and Research Organizations, Sciendo, vol. 41(3), pages 65-94, September.
    11. Alberto Cerezo-Narváez & Andrés Pastor-Fernández & Manuel Otero-Mateo & Pablo Ballesteros-Pérez, 2022. "The Influence of Knowledge on Managing Risk for the Success in Complex Construction Projects: The IPMA Approach," Sustainability, MDPI, vol. 14(15), pages 1-30, August.
    12. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    13. Rafidah Md Noor & Nadia Bella Gustiani Rasyidi & Tarak Nandy & Raenu Kolandaisamy, 2020. "Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    14. Dominika Ehrenbergerová & Martin Hodula & Zuzana Gric, 2022. "Does capital-based regulation affect bank pricing policy?," Journal of Regulatory Economics, Springer, vol. 61(2), pages 135-167, April.
    15. Mehmet Talha Dulman & Surendra M. Gupta, 2018. "Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops," Logistics, MDPI, vol. 2(1), pages 1-22, January.
    16. Tanka Milkova, 2022. "A Stochastic Inventory Management Model with Consideration of Additional Information," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 11(1), pages 167-174, April.
    17. Nguyen Thi Nha Trang & Thanh-Thuy Nguyen & Hong V. Pham & Thi Thu Anh Cao & Thu Huong Trinh Thi & Javad Shahreki, 2022. "Impacts of Collaborative Partnership on the Performance of Cold Supply Chains of Agriculture and Foods: Literature Review," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
    18. M. Masanta & B. C. Giri, 2022. "A manufacturing–remanufacturing supply chain model with learning and forgetting in inspection under consignment stock agreement," Operational Research, Springer, vol. 22(4), pages 4093-4117, September.
    19. Jackson, Canek & Pascual, Rodrigo, 2008. "Optimal maintenance service contract negotiation with aging equipment," European Journal of Operational Research, Elsevier, vol. 189(2), pages 387-398, September.
    20. Mohammed Khaled Al-Hanawi & Rubayyat Hashmi & Sarh Almubark & Ameerah M. N. Qattan & Mohammad Habibullah Pulok, 2020. "Socioeconomic Inequalities in Uptake of Breast Cancer Screening among Saudi Women: A Cross-Sectional Analysis of a National Survey," IJERPH, MDPI, vol. 17(6), pages 1-13, March.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12013-:d:922648. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.