Integrating Real-Time KPI Dashboards with Monte Carlo Simulation for Optimizing Semiconductor Manufacturing Processes

Currently, wafer fabs in semiconductor applications rely heavily on experience. Errors in production scheduling strategies, maintenance plans, and resource allocation can lead to downtime, scrap, and delivery delays. This research proposes an integrated decision-making intelligent dashboard and hypothetical scenario simulation method to improve real-time decision-making capabilities in semiconductor manufacturing by optimizing key performance indicators such as overall equipment efficiency, yield, and cost. By combining digital twin technology with historical production data and simulation models, a system capable of simulating various production scenarios is constructed to assess the impact of changes in production planning, resource allocation, and equipment utilization on OEE, yield, and cost. This research contributes to improving manufacturing capacity utilization and supply chain reliability, shortening process change verification cycles, reducing downtime and scrap risks in the manufacturing industry, and adapting to multi-factory, multi-node manufacturing expansion.