Title: Industry 4.0: smart preventive maintenance with optimal planning and scheduling process of the SMEs

Authors: K. Velmurugan; S. Saravanasankar; P. Venkumar; K.P. Paranitharan; R. Sudhakarapandian

Addresses: Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoill – 626126, Tamil Nadu, India ' Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoill – 626126, Tamil Nadu, India ' Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoill – 626126, Tamil Nadu, India ' TVS Sensing Solutions Private Limited, Madurai-Melur Road, Vellaripatti Madurai – 625122, Tamil Nadu, India ' School of Mechanical Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India

Abstract: In the modern world, small and medium scale manufacturing industries face a lot of challenges to achieve the reliability, availability, and safety as important performance attributes of the shop floor. In which planning and scheduling of preventive maintenance activities are considered to be a major issue in SMEs. This study is to propose the optimal framework of the preventive maintenance (PM) planning and scheduling process in SMEs. The optimal maintenance parameters (failure rate and repair rate), availability variations of the systems have been predicted through the utilisation of the Markov birth-death approach. To overcome the drawbacks associated with the existing optimal PM plan, a new approach is proposed in this study to develop an optimal preventive maintenance plan for electronic actuating switch manufacturers through the digital ecosystem. This proposed method integrates manufacturing subsystem failure into smart digital ecosystems and also to estimate the actual remaining useful life of the machines.

Keywords: small and medium sized enterprises; preventive maintenance; Markov birth-death process; remaining useful life; optimal planning and scheduling.

DOI: 10.1504/IJVCM.2023.129267

International Journal of Value Chain Management, 2023 Vol.14 No.1, pp.12 - 33

Received: 18 Oct 2021
Accepted: 16 Jan 2022
Published online: 02 Mar 2023 *

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