In an increasingly dynamic and complex industrial landscape, the continuous enhancement of organizational performance has emerged as a critical imperative. To this end, structured quality assessment frameworks, such as the European Foundation for Quality Management (EFQM) Excellence Model, have been widely adopted as integrative tools for diagnosing, monitoring, and improving business performance. Despite its comprehensive nature, the EFQM model often requires the incorporation of additional quantitative methods to refine the evaluation of the relative significance of its criteria. In this study, the Analytic Hierarchy Process (AHP) method, extended with triangular fuzzy numbers, has been employed to determine the weighted importance of the EFQM model's criteria under conditions of uncertainty and expert subjectivity. This fuzzy extension of AHP allows for a more nuanced capture of linguistic judgments, thereby enhancing the robustness of decision-making in ambiguous environments. Expert assessments were elicited through structured interviews with quality managers from three manufacturing companies, enabling the construction of pairwise comparison matrices for each criterion. These matrices were then aggregated and analyzed to derive consensus-based priority weights. The findings reveal significant variations in the perceived importance of enabler and result criteria, underscoring the context-dependent applicability of the EFQM model. Furthermore, the results offer a more granular understanding of the internal structure of the model, providing a foundation for its adaptive use in quality management systems across the manufacturing sector. The integration of fuzzy logic into the hierarchical decision-making process is demonstrated to yield improved precision and flexibility, making it a valuable methodological enhancement for organizations pursuing excellence under uncertainty. The proposed approach also contributes to the broader discourse on multi-criteria decision analysis in quality management by addressing limitations in conventional crisp AHP applications.
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.
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