Fuzzy assessment analysis and key improvements to a production system

Abstract

Purpose The framework of Six Sigma Define-Measure-Analyze-Improve-Control was applied in this study, and various tools and techniques were used at different stages to implement lean measures to ensure quality. The purpose of this paper is to develop a decision-making framework that assesses key quality performance to ensure that practitioners improve quality and control by modeling and optimizing production processes. Design/methodology/approach A model of a quality performance index system was established. The weights of factors and sub-factors, which were estimated using an FAHP, were used as a reference for the decision maker under fuzzy uncertainly to make a decision, and thus, results present the bottlenecks in processes. Furthermore, any other factors that may affect the key process bottlenecks must be considered. The critical to quality characteristics were determined, and factor levels were set. The interaction between the factors was analyzed, their significance was studied using the Design of experiments and the parameters were predicted. Finally, quality improvement decisions were made through failure mode and effects analysis. Findings The implementation results of this research prove that the proposed model could successfully determine the key processes and focus on the improvement of critical quality factors under limited resources. Originality/value This study establishes a set of performance appraisal methods for production systems, which can be used for improving productivity and quality.

Evaluation of machine tool substitute under data-driven quality management system: a hybrid decision-making approach

“…Dubois and Prade (1985) suggested the application of statistical decision techniques for modeling insufficient knowledge, but they found that these techniques do not cope with the ambiguity and impreciseness in human behavior. It is found that the approaches allied with fuzzy set theory can be used to consider human subjectivity in decision-making (Wang et al , 2019; Shiau and Chang, 2019). Perego and Rangone (1998) worked under the domain of selecting AMT and appraised to implicate Fuzzy-MADM premises for selecting AMT.…”

“…Fuzzy logic incorporates partial truth data and can abound human's languages (Shiau and Chang, 2019; Sahu et al , 2019). Yu et al (2016) and Zhou et al (2016) suggested that fuzzy numbers set can be effectively employed to handle multiple criteria in decision-making.…”