A fuzzy decision support system for managing maintenance activities of critical components in manufacturing systems. (July 2019)

• Record Type: Journal Article
• Authors: Erozan, İhsan
• Is Part Of: Journal of manufacturing systems. Volume 52(2019)Part A
• Page Start: 110
• Page End: 120
• Publication Date: 2019-07
• Subjects: Critical component management -- Maintenance management -- Reliability -- Fuzzy logic -- Decision support system; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2019.06.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. The method and decision support system will especially help to keep manufacturing systems working. This study proposes a fuzzy decision support system. Abstract: Management of critical components in manufacturing systems aims at managing components with very low reliability or the highest risk which can cause disruptions in manufacturing. This study presents a method for identifying critical components and a decision support tool for managing maintenance activities of critical components in manufacturing systems. Unlike the traditional reliability function, the proposed method uses the duty cycle, utilization rate of capacity, safety stock effect, and redundancy effect. It also has the ability to calculate some of the costs associated with the reliability and maintenance. In addition to the proposed method, an expert system as a decision support tool has also been proposed to assist in managing maintenance activities of critical components. The proposed method and the developed decision support system have been tested with a real data set taken from an industrial company and a randomly generated data set. The results have shown that the proposed method has a more realistic impact on component reliability compared to the traditional reliability function. The experimental results have validated the credibility of the proposed decision support system to manage maintenance activities of critical components. Besides, two comparison tables have shown that the proposed decision support system outperforms some approaches such as ANN, FMEA, FMECA, and AHP.