Neutrosophic Failure Mode and Effect Analysis—Elimination and Choice Translating Reality Method for Prioritizing Failure Modes

Abstract

Operations managers and engineers in the automotive industry confront the key challenge in ensuring the reliability of the manufacturing process. To accurately classify failure modes, this study proposed a novel Multi-Criteria Decision-Making (MCDM) model integrated with Single-Valued Neutrosophic Sets (SVNSs) for operations management to prioritize actions in eliminating failure modes that had the greatest impact on the concerned reliability. The identification and evaluation of failure modes were grounded in the conventional Failure Mode and Effect Analysis (FMEA), while the relative importance of risk factors (RFs) was expressed through predefined linguistic terms modelled with the SVNSs. The assessment of these risk factors was formulated as a fuzzy group decision-making problem and the fuzzy weight vector was derived from the Order Weighted Averaging (OWA) operator. Failure rankings were conducted through a modified version of the Elimination and Choice Translating Reality (ELECTRE) method; being tested and validated with real-world data from an automotive company, the proposed FMEA-ELECTRE model could inspire stakeholders in various industries to explore this scientific contribution further.