IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v6y2022i1p4-d719477.html
   My bibliography  Save this article

A Predictive Maintenance System for Reverse Supply Chain Operations

Author

Listed:
  • Sotiris P. Gayialis

    (Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Evripidis P. Kechagias

    (Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Grigorios D. Konstantakopoulos

    (Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Georgios A. Papadopoulos

    (Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece)

Abstract

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.

Suggested Citation

  • Sotiris P. Gayialis & Evripidis P. Kechagias & Grigorios D. Konstantakopoulos & Georgios A. Papadopoulos, 2022. "A Predictive Maintenance System for Reverse Supply Chain Operations," Logistics, MDPI, vol. 6(1), pages 1-14, January.
  • Handle: RePEc:gam:jlogis:v:6:y:2022:i:1:p:4-:d:719477
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/6/1/4/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2305-6290/6/1/4/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Jiawen & Chen, Piao, 2020. "Predictive maintenance of systems subject to hard failure based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    2. Wang, Jinjiang & Liang, Yuanyuan & Zheng, Yinghao & Gao, Robert X. & Zhang, Fengli, 2020. "An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples," Renewable Energy, Elsevier, vol. 145(C), pages 642-650.
    3. Liu, Xiangwei & He, Daijie & Lodewijks, Gabriel & Pang, Yusong & Mei, Jie, 2019. "Integrated decision making for predictive maintenance of belt conveyor systems," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 347-351.
    4. 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.
    5. Rahman, Shams & Subramanian, Nachiappan, 2012. "Factors for implementing end-of-life computer recycling operations in reverse supply chains," International Journal of Production Economics, Elsevier, vol. 140(1), pages 239-248.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vitor William Batista Martins & Denilson Ricardo de Lucena Nunes & André Cristiano Silva Melo & Rayra Brandão & Antônio Erlindo Braga Júnior & Verônica de Menezes Nascimento Nagata, 2022. "Analysis of the Activities That Make Up the Reverse Logistics Processes and Their Importance for the Future of Logistics Networks: An Exploratory Study Using the TOPSIS Technique," Logistics, MDPI, vol. 6(3), pages 1-17, August.
    2. Natalia Khan & Wei Deng Solvang & Hao Yu, 2024. "Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review," Logistics, MDPI, vol. 8(1), pages 1-23, February.

    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. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. José María López-Sanz & Azucena Penelas-Leguía & Pablo Gutiérrez-Rodríguez & Pedro Cuesta-Valiño, 2021. "Sustainable Development and Consumer Behavior in Rural Tourism—The Importance of Image and Loyalty for Host Communities," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    4. Prosman, Ernst Johannes & Cagliano, Raffaella, 2022. "A contingency perspective on manufacturing configurations for the circular economy: Insights from successful start-ups," International Journal of Production Economics, Elsevier, vol. 249(C).
    5. 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.
    6. Maria Maddalena Sirufo & Francesca De Pietro & Alessandra Catalogna & Lia Ginaldi & Massimo De Martinis, 2021. "The Microbiota-Bone-Allergy Interplay," IJERPH, MDPI, vol. 19(1), pages 1-14, December.
    7. Oleh Pasko & Mykola Hordiyenko & Fuli Chen & Yarmila Tkal & Yulia Abraham, 2021. "Mapping Global Research on International Financial Reporting Standards: A Scientometric Review," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 116-134, May.
    8. Yang, Jing & Zhang, Zhiyong & Yang, Mingwan & Chen, Jiayu, 2019. "Optimal operation strategy of green supply chain based on waste heat recovery quality," Energy, Elsevier, vol. 183(C), pages 599-605.
    9. Zhang, Tianyu & Dong, Peiwu & Zeng, Yongchao & Ju, Yanbing, 2022. "Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model," Journal of Business Research, Elsevier, vol. 139(C), pages 90-105.
    10. Vitor Hugo Ferreira & André da Costa Pinho & Dickson Silva de Souza & Bárbara Siqueira Rodrigues, 2021. "A New Clustering Approach for Automatic Oscillographic Records Segmentation," Energies, MDPI, vol. 14(20), pages 1-18, October.
    11. Maurizio Massaro & Francesca Dal Mas & Charbel Jose Chiappetta Jabbour & Carlo Bagnoli, 2020. "Crypto‐economy and new sustainable business models: Reflections and projections using a case study analysis," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(5), pages 2150-2160, September.
    12. Ines A. Ferreira & Rachel M. Gisselquist & Finn Tarp, 2021. "On the impact of inequality on growth, human development, and governance," WIDER Working Paper Series wp-2021-34, World Institute for Development Economic Research (UNU-WIDER).
    13. 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.
    14. Beatriz Calzada Olvera & Mario Gonzalez-Sauri & Federico Louvin & David-Alexander Harings Moya, 2021. "COVID-19 in Central America: effects of firm resilience and policy responses on employment," WIDER Working Paper Series wp-2021-166, World Institute for Development Economic Research (UNU-WIDER).
    15. 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.
    16. Iversen, Sara V. & Naomi, van der Velden & Convery, Ian & Mansfield, Lois & Holt, Claire D.S., 2022. "Why understanding stakeholder perspectives and emotions is important in upland woodland creation – A case study from Cumbria, UK," Land Use Policy, Elsevier, vol. 114(C).
    17. Kik, M.C. & Claassen, G.D.H. & Meuwissen, M.P.M. & Smit, A.B. & Saatkamp, H.W., 2021. "Actor analysis for sustainable soil management – A case study from the Netherlands," Land Use Policy, Elsevier, vol. 107(C).
    18. 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.
    19. 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.
    20. Yue Tan & Chunxiang Guo, 2019. "Research on Two-Way Logistics Operation with Uncertain Recycling Quality in Government Multi-Policy Environment," Sustainability, MDPI, vol. 11(3), pages 1-18, February.

    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:jlogis:v:6:y:2022:i:1:p:4-:d:719477. 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.