Automated Risk Assessment and Mitigation via Dynamic Bayesian Network Optimization and Reinforcement Learning - A Hyper-Specific Application to Supply Chain Resilience in Semiconductor Manufacturing

**Abstract:** This paper proposes a novel framework for automated risk assessment and mitigation within semiconductor manufacturing supply chains, a domain experiencing increased fragility due to geopolitical tensions and natural disasters. Leveraging Dynamic Bayesian Networks (DBNs) for probabilistic risk modeling and Reinforcement Learning (RL) for adaptive mitigation strategies, our system, *ResilientChain*, dynamically assesses risk profiles, prioritizes mitigation actions, and optimizes resource allocation to maximize supply chain resilience. ResilientChain demonstrates superiority over traditional static risk assessment methods by integrating real-time data streams, accounting for cascading dependencies, and proactively adjusting mitigation policies based on evolving conditions. The solution's impact lies in minimizing production disruptions, reducing operational costs, and bolstering the competitive advantage of semiconductor manufacturers.