Analysis Journal International
FUZZY COPRAS METHOD FOR PERFORMANCE MEASUREMENT IN TOTAL PRODUCTIVE MAINTENANCE: A COMPARATIVE ANALYSIS
Abstract
Modern manufacturing firms should be supported by effective maintenance to become successful in their operations. One of the approaches for improving the performance
of maintenance activities is to implement a total productive maintenance (TPM) strategy.
Overall equipment effectiveness (OEE) is the key measure of TPM. According tot he results of the literature review, the performance elements measured by the OEE tool are not sufficient to describe the effectiveness of TPM implementation. Hence, we aim at developing and evaluating new performance measures oriented towards the quantification of TPM implementation effectiveness under fuzzy environment.
For the evaluation of each performance measure, at first, the nominal group technique has been used. Then to determine whether these performance measures are statistically significant, conjoint analysis based experimental design has been applied. In the second step, COmplex PRoportional ASsessment of alternatives with Grey relations (COPRAS-G) and the fuzzy COPRAS method has been developed to evaluate these performance measures in TPM.
Proposed fuzzy COPRAS method gives the reassuring results of ranking newly developed
performance measures in TPM.
Introduction
TPM is a new concept for maintenance that better optimizes the equipment effectiveness,
minimizes breakdowns and encourages operators to autonomous maintenance for day-to-day activities involving total workforce (Andersson 2015). TPM aims to improve equipment effectiveness during the lifetime of the equipment.
Nakajima (1988) initiated TPM concept in the 1980s, which brought measurable metric named OEE for measuring productivity of individual equipment in a factory. It explains and measures losses of significant sides of manufacturing specifically availability, performance, and quality rate.
In this study, it is aimed to develop new performance measures impact on TPM and using a multi criteria decision making method based on the concepts of COPRAS under fuzzy environment.
The rest of the paper is organized as follows. Section 1 explains the problem and literature review. Section 2 introduces the literature review and the fundamentals of COPRAS-G method. In Sections 3 and 4, an application of COPRASG and proposed fuzzy COPRAS method for evaluation of developed new performance measures in TPM are presented. In the last section, results and conclusion are given.
Structure of study
In this study, an outline for defining different types of performance measures impact on for TPM is proposed, as shown in Table 3.
Conclution
In this study, it is developed new performance measures oriented towards the quantification of TPM implementation effectiveness and evaluated the new performance measure in TPM under fuzzy environment. In the evaluation process, COPRAS-G is applied for evaluation of new performance measures in TPM. Then the fuzzy COPRAS method is developed for the evaluation of new performance measures in TPM. When developing the fuzzy COPRAS all calculations are made based on the fuzzy arithmetic and fuzzy ranking operations. Therefore, no fuzzy value is converted to a crisp value.
This study helps to operators and executives to visualize the results of the investments made in TPM efforts with newly developed performance measures of TPM. The limitation of the proposed ordinary fuzzy COPRAS is its need for a modification in case of new extensions of fuzzy sets. In the future research, the proposed performance measures are going to be tested in a real-world manufacturing company where the original OEE has been evaluated previously. Proposed fuzzy COPRAS method can also be extended using intuitionistic, hesitant fuzzy sets or neutrosophic sets to evaluate newly developed performance measures in TPM.
Conclution
In this study, it is developed new performance measures oriented towards the quantification of TPM implementation effectiveness and evaluated the new performance measure in TPM under fuzzy environment. In the evaluation process, COPRAS-G is applied for evaluation of new performance measures in TPM. Then the fuzzy COPRAS method is developed for the evaluation of new performance measures in TPM. When developing the fuzzy COPRAS all calculations are made based on the fuzzy arithmetic and fuzzy ranking operations. Therefore, no fuzzy value is converted to a crisp value.
This study helps to operators and executives to visualize the results of the investments made in TPM efforts with newly developed performance measures of TPM. The limitation of the proposed ordinary fuzzy COPRAS is its need for a modification in case of new extensions of fuzzy sets. In the future research, the proposed performance measures are going to be tested in a real-world manufacturing company where the original OEE has been evaluated previously. Proposed fuzzy COPRAS method can also be extended using intuitionistic, hesitant fuzzy sets or neutrosophic sets to evaluate newly developed performance measures in TPM.
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