This paper examines the supply chain resilience of Taiwan Semiconductor Manufacturing Co. (TSMC) using a Bayesian network (BN) model developed from the Supply Chain Operations Reference (SCOR) framework. This hybrid model allows for an integrated analysis of various key performance indicators (KPIs) across TSMC’s supply chain, providing a comprehensive view of its resilience. By simulating multiple disruption scenarios, the study captures the dynamic interactions and cascading effects of disruptions, such as inventory shortages, transportation delays, and labor cost fluctuations. This approach offers a quantitative analysis of TSMC’s resilience under varied scenarios, revealing critical strengths, such as flexibility in resource allocation, as well as vulnerabilities, particularly in response to high-impact events like geopolitical tensions and natural disasters. Insights from this model highlight the areas where strategic improvements can further strengthen resilience. Overall, the research demonstrates the applicability of Bayesian networks as a powerful tool for resilience assessment, not only in TSMC’s context but also as a scalable solution for other high-complexity, high-dependency supply chains within the semiconductor industry. This study contributes valuable knowledge to the broader field of supply chain resilience and advances the methodologies available for industry practitioners and researchers alike.

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