<![CDATA[Automotive component supply chains are exposed to interconnected disruptions arising from supplier dependence, demand variability, production coordination, logistics, information exchange, and external economic conditions. This study develops a quantitative risk-prioritization framework using Failure Mode and Effects Analysis (FMEA) to identify critical failure modes and evaluate the robustness of their priorities within an automotive component supply chain. The analysis integrates supply-chain process mapping, systematic failure-mode identification, expert validation, multi-expert scoring, Risk Priority Number (RPN) calculation, domain-level aggregation, and Monte Carlo sensitivity analysis. Eight cross-functional experts assessed 27 failure modes across nine supply-chain domains using standardized Severity, Occurrence, and Detection scales. After data screening, 212 valid ratings were retained. The aggregated results produced a mean RPN of 144.24, with Severity showing the highest mean score (6.60), followed by Detection (5.35) and Occurrence (4.20). Demand fluctuation recorded the highest RPN (233.38), followed by delayed supplier delivery (230.73) and nonconforming supplier components (226.82). Supplier risk also exhibited the highest domain-level mean RPN (201.45). Monte Carlo sensitivity analysis demonstrated strong ranking stability, with a mean Spearman correlation of 0.961 across 1,000 iterations; the ten baseline top-ranked failure modes all exceeded the 0.70 robustness threshold. The findings establish a supported mitigation agenda spanning demand forecasting, supplier diversification, quality assurance, preventive maintenance, financial hedging, logistics flexibility, information-system integration, and procurement planning. The framework strengthens proactive risk governance by linking failure-mode prioritization with robustness assessment and targeted intervention.]]>
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