Failure Mode and Effect Analysis using Robust Data Envelopment Analysis (Case Study: Automobile Oil Filter)

Risk management improves and increases the speed of development and optimal implementation of the company's strategy to achieve a competitive advantage. Risk identification and assessment are known as one of the main tools of safety management, which helps the safety manager better select risk reduction measures and standardization of automobile oil filter by creating a suitable information platform. In this regard, evaluating and analyzing failure modes and their effects is an appropriate tool for risk management and improving product quality. Due to the weaknesses of the traditional method the complexities of the fuzzy method, a new type of risk non-priority is presented by assigning different weights to each of the risk factors under uncertainty and the proposed method is compared with the traditional and fuzzy methods simultaneously. The purpose of this study is to analyze the failure mode and risks in operation and various stages of manufacturing automobile oil filter, then to prioritize and compare risks by applying the fuzzy theory method and robust data envelopment analysis. Oil filter is an essential part of the automobile that its standardization reduces fuel consumption, improves engine performance and consequently decreases environmental pollution. This research has used the combination of Failure Mode and Effect Analysis( FMEA) method for analyzing the reliability of the oil filter and fuzzy theory has been used to record experts' opinions on failure modes and calculate the risk priority of each subsystem under uncertainty. In order to eliminate the existing defects, a new method is introduced for calculating the risk priority number in the failure mode and effect analysis based on the data envelopment analysis method. In this research, the robust optimization method covers the results of the data envelopment analysis (DEA) and is less complex than the fuzzy method has been used. The results of the case study indicate that the proposed model is more effective and reliable than the traditional and fuzzy Risk Priority Number (RPN) and also the proposed method has less has complexity than the fuzzy method. This method provides a complete ranking and convincing prioritization of failure modes. After calculating the RPNs, the operations related to the spiral tube, fiber folding, ring bending and cutting, and fiber folding are the highest number of RPNs, respectively, and their corrective actions were also determined.