A Proportional Intuitionistic Fuzzy AHP–EDAS Framework for Symmetric Risk Assessment in Automotive Assembly Lines: A 12-Failure-Mode PFMEA Study

Process Failure Mode and Effects Analysis (PFMEA) is the standard technique for proactive risk assessment in automotive assembly lines. To support differentiated criterion weighting, hesitancy-aware linguistic evaluation and rank stability validation, this paper proposes a symmetric extension of PFMEA that integrates Proportional Intuitionistic Fuzzy Sets (PIFSs) with the Analytic Hierarchy Process (AHP) and the Evaluation Based on Distance from Average Solution method (PIF-EDAS). PIFSs introduce a proportional balance between membership μ, non-membership ν and hesitancy π that is mathematically symmetric under linguistic-pair interchange and that preserves a constant hesitancy budget (π = 1/(1 + k1 + k2)) across the nine-point linguistic scale. The framework is applied to an automotive Original Equipment Manufacturer (OEM) assembly line in Türkiye, on an inventory of twelve failure modes spanning torque, fastening, welding, panel alignment, harness, sealant, paint, ECU, trim, electrical connector, part variant and tightening sequence operations. Consistency of the PIF-AHP pairwise comparisons is confirmed (CR = 0.0017 ≪ 0.1), yielding criterion weights wS = 0.598, wO = 0.245 and wD = 0.156. Comparative cross-method analysis against PIF-TOPSIS, PIF-VIKOR and a classical-style ordinal RPN benchmark indicates strong cross-method agreement: Spearman rank correlations range from 0.865 to 0.972, and Wrong Torque Application remains the unanimous top-priority failure across all four methods. Five sensitivity scenarios (proposed weights, equal weights, and severity-, occurrence- and detection-dominant) confirm that FM1 (Wrong Torque) remains the top-priority failure and FM9 (Damaged Interior Trim) remains the lowest-priority failure across all five scenarios; the composition of the upper-priority set is criterion-sensitive, with FM10 rising under equal-weight, occurrence- and detection-dominant scenarios and FM8 rising under the severity-dominant scenario. The proposed framework incorporates differentiated criterion weights, expert hesitancy and rank stability validation within a symmetric PIFS-based MCDM structure. The contribution of this study is therefore three-fold: (i) a symmetric PIFS formulation that enforces mirror symmetry under linguistic-pair interchange and a constant hesitation budget on the nine-point scale; (ii) a case-based assessment of twelve automotive PFMEA failure modes; and (iii) a transparent rank stability protocol for symmetric MCDM benchmarking. The framework integrates directly with existing FMEA workflows and scales linearly in computational complexity with the number of failure modes.