# A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry
## 5 Conclusions  
FMEA is a well established methodology for problem solving in Quality based on the analysis of failure modes and the determination of risks associated with each of them. Nevertheless, despite remaining a standard and robust method to address numerous quality issues and risks, the shortcomings of the traditional FMEA are explored by several authors (e.g., [12; 13; 14; 15; 16; 17]). In the case of qualitative defects in visual inspection, traditional FMEA proved inefficient and fuzzy FMEA is a way to enhance its applicability in this setting [42]. In virtue, in this research paper was proposed a modification of the classic FMEA using fuzzy logic, to solve the problems associated with the calculation of the classic RPN when applied in subjective and vague contexts. It was proposed to evaluate how the integration of a fuzzy FMEA inference system in the DMAIC cycle, could improve the analysis made to potential failure modes with a subjective character, identified in the visual inspection stations of the automotive industry. In this paper, a fuzzy FMEA system and a method based on the DMAIC cycle to assess risks in the visual inspection process of defects of automobile parts were proposed. The fuzzy FMEA system was proposed to deal with subjectivity and uncertainty inherent to the visual inspection process of qualitative defects. The fuzzy system permits increased detail when determining the risk associated with each type of failure. While FMEA allows the computing of RPN in a direct manner (multiplying S, O and D), it is not possible to match RPN values with evaluation criteria of the traditional RPN. Through the proposed fuzzy FMEA model, it is possible to classify the potential failure modes, with linguistic terms of the variable RPN fuzzy. Fuzzy FMEA allows the assignation of fuzzy sets of values to each level of S, O, and D; and, the combination of those sets is made through knowledge rules that enhance the scale of RPN in a systematic manner. As such, the value of the fuzzy FMEA model comes from the way that specialists are able to express their needs and obtain a response according to what they actually have as a priority. To deploy the fuzzy system, a method based on the DMAIC cycle was also suggested. The method permits structuring the problem-solving process, enhancing the identification of failures to thoroughly solving them by measuring the current process, assessing risks through fuzzy RPN computation, improving and solving causes of failure, and controlling the process in the future through continuous improvement. In the define and measure steps, it was possible, in an organized way, to identify the process, the failure modes that were relevant to the study, as well as the quantification of their occurrences according to the metric/KPI ppm. This concept proved to be a great asset, not only in the way of structuring the project but also in interpreting and replicating it. In the analyze phase, after the implementation and obtaining of results by the fuzzy FMEA system, these same results were compared for the failure modes under study, namely the classic RPN index with the fuzzy RPN. The proposed fuzzy FMEA system and DMAIC method were implemented in a case study performed in an automotive supplier production of injection coils. The inclusion of the fuzzy FMEA system in the DMAIC cycle allowed adequate identification of production problems in the define and measure stages. By measuring the ppm metric along the production process, it was possible to early prioritize failure modes that were used to apply the fuzzy FMEA. In the analyze stage, the fuzzy FMEA system was implemented. The fuzzy system allowed the computing of a more detailed version of RPN. In Table 6, a comparison between traditional and fuzzy FMEA was made. The fuzzy RPN enhanced the risk scale to better classify failures and root causes. Comparing traditional and fuzzy RPN, components 6, 7, and 8 were classified with the same value of RPN (72).