Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs
Journal of Quality in Maintenance Engineering
ISSN: 1355-2511
Article publication date: 27 November 2023
Issue publication date: 23 February 2024
Abstract
Purpose
Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.
Design/methodology/approach
To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.
Findings
The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.
Originality/value
The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.
Keywords
Citation
Kumaresan, V., Saravanasankar, S. and Di Bona, G. (2024), "Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs", Journal of Quality in Maintenance Engineering, Vol. 30 No. 1, pp. 133-152. https://doi.org/10.1108/JQME-10-2022-0070
Publisher
:Emerald Publishing Limited
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