Application of Monte Carlo Simulation to Study the Probability of Confidence Level under the PFMEA’s Action Priority

Abstract

Failure mode and effects analysis (FMEA) is the most commonly used risk evaluation tool in industry and academia. After four revisions, the US Automotive Industry Action Groups (AIAG) and German Association of the Automotive Industry (VDA) issued the latest FMEA manual, called AIAG and VDA FMEA Handbook Edition 1, in June 2019. Risk priority number (RPN) in the old-edition FMEA is replaced with action priority (AP), where the numerical evaluation of severity (S), occurrence (O), and detection (D) are referred to in the AP form for judging high (H), medium (M), and low (L) priority in order to ensure appropriate actions for improving prevention or detection control. When evaluating design (D) or process (P) in FMEA, the FMEA team has to refer to the evaluation criteria for S, O, and D, so as to reduce the difference in the evaluation reference and fairness. Since the criteria evaluation form is the qualitative rating standard with semantic judgment, evaluation errors are likely to occur when the team judges S, O, and D. The FMEA cases in this study are preceded by the confidence level (CL) of the S, O, and D evaluation standards and the setting of a confidence interval (CI) for the actual evaluation events. With discrete nonuniform distribution as the simulation setting, Monte Carlo simulation is applied several times to evaluate the probability before and after the evaluation, which is compared with the AP form to confirm the probability values of high, medium, and low priority. It provides reference for the FMEA cross-functional team, improving the originally non-AP events. Finally, the AP calculated in the simulation is compared and analyzed with the RPN sequence to verify the judgment of better actions with AP.