A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry

Abstract

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.

Production Planning and “Define, Measure and Analysis“ Tools in Automotive Industry as Prerequisite of Automation: A Case Study

“…Previous researches that included the application of DMAIC cycle for process improvement has shown significant increase in business performance, pointing out importance of reliable collected data for good quality management (Godina et al, 2021;Karout et al, 2017;Kaushik et al, 2009). After analysis and presentation of the collected data, which as pointed out in the discussion part of this paper, unequivocally confirm the existence of the deviation in process data, further research was conducted with the company's senior management, and the conclusion was drawn that due to size of the production system and the number of visible problems, there are not enough resources left to carry out a detailed research on data quality, so decisions are made on the basis of available data.…”

A risk assessment of scrubber use for marine transport by rule-based fuzzy FMEA

“…Although FMEA is the most extensively used and favored risk assessment approach, some academics point out that it has several shortcomings such as variability of findings based on minor changes in O, S, and D scores and ignoring the relationship between input parameters. [32][33][34][35][36][37] At this point, the realization of FMEA under a fuzzy environment (FFMEA) which ensures coping with the specified drawbacks using fuzzy logic (FL) has been recommended. [38][39][40] FL which was formed by Zadeh 41 allows for the integration of linguistic and numerical data.…”

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA

“…FMEA can be changed from a traditional approach to a fuzzy method to solve these problems and limitations [ 36 ]. An alternative method to mitigate the identified weaknesses is to integrate it with MCDM method discussed in the literature [ 37 , 38 ].…”