LAUSR

Failure mode and effects analysis (FMEA) is a useful method to analyze and then prioritize failure, but it has many drawbacks. First of them is risk factors, severity, occurrence and detection, which are considered equally important but their scores may be not equal in real-life applications. Another is that the risk factor values of risk priority number of failures are usually assessed by team member in FMEA method in incomplete information and uncertainty situations. The last one is expert’s experience which is not incorporated in effective automation of the risk assessment. In this study, it is aimed to use adaptive neuro-fuzzy inference system (ANFIS) that is a soft computing method to eliminate these drawbacks. However, there are many numbers of parameters that affect the accuracy of the prediction in ANFIS structure and training phase of the model. For this purpose, the parameter values were determined using Taguchi method. A novel FMEA model using hybrid ANFIS–Taguchi method and FMEA model using ANN were applied in furniture manufacturing, and the results were compared with traditional FMEA. The accuracy of the novel FMEA model was 100% while the FMEA–ANN model was 94.118%. It is recommended to use the novel FMEA model because this model is used with insufficient and imprecise data and needs only one expert.