Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing. (August 2019)

• Record Type: Journal Article
• Authors: Soltanali, Hamzeh; Garmabaki, A.H.S; Thaduri, Adithya; Parida, Aditya; Kumar, Uday; Rohani, Abbas
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 4(2019:Aug.)
• Page Start: 682
• Page End: 697
• Publication Date: 2019-08
• Subjects: Automotive manufacturing -- conveying process -- opportunistic maintenance -- reliability -- availability -- and maintainability methodologies -- multi-attribute utility theory; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X18818266
• Languages: English
• ISSNs: 1748-006X

Abstract

Automotive manufacturing industries are required to improve their productivity with higher production rates at the lowest cost, less number of unexpected shutdowns, and reliable operation. In order to achieve the above objectives, the application of reliability, availability, and maintainability methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this article, we propose a framework for reliability, availability, and maintainability evaluation and maintenance optimization to improve the performance of conveying process of vehicle body in an automotive assembly line. The results of reliability, availability, and maintainability analysis showed that the reliability and maintainability of forklift and loading equipment are the main bottlenecks. To find the optimal maintenance intervals of each unit, a multi-attribute utility theory is applied for multi-criteria decision model considering reliability, availability, and costs. Due to the series configuration of conveying process in automotive assembly line, the optimized time intervals are obtained using opportunistic maintenance strategy. The results could be useful to improve operational performance and sustainability of the production process.