Supply-chain DSSs must translate objectives into lifecycle- and configuration-specific priorities while retaining traceable decision authority. Fuzzy models provide structured prioritisation, but generative explanations may weaken grounding and reproducibility. We developed a hybrid DSS in which a zero-order Sugeno engine combines configuration baselines, Industry 5.0 5S alignment, and lifecycle relevance through 27 rules, while an optional LLM renders frozen evidence under output validation and deterministic fallback. Five controlled scenarios varied lifecycle stage and configuration under a fixed 5S profile. Evaluation comprised 1000 ¿±¿20% perturbations, three MCDA comparators, and a single-round elicitation with 15 experts conducted 10¿25 July 2026. Mean Kendall ¿b under perturbation was 0.898¿0.938; alignment with TOPSIS, weighted sum, and PROMETHEE was (Formula presented) ¿0.897 (all p¿<¿0.001). Expert agreement on rule consequents was strong (Fleiss¿ (Formula presented)); 25 of 27 cases achieved at least 80% majority agreement and 21 matched the engine. Membership anchors supported overlapping fuzzy regions, but baseline exact matches were 18.2¿25.0% and elicited weights did not justify recalibration. The system provides reproducible, model-agnostic prioritisation and failure-tolerant explanation with external support for its rule structure. Evidence supports an auditable prototype, not industrial, outcome, or user validation.
