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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
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    References listed on IDEAS

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    Cited by:

    1. Zeinab Farshadfar & Tomasz Mucha & Kari Tanskanen, 2024. "Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    2. 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.
    3. 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.

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