This developed technology integrated big data analytics, meta model, and XAI algorithm to develop more accurate demand and cycle time prediction for supply chain and production planner to reduce production costs in semiconductor industry and enhance network resilience via analyzing big data of supply chain, capacity portfolio and production data enabling automated feature engineering.


The developed solution (Advanced Artificial-intelligence based forecasting technology) integrates big data analytics, metamodels by incorporating interpretable AI algorithms, advanced cycle time prediction models for future new process platform introductions and new capacity portfolios. The fundamental goals are as follows: (1) Achieve flexible decision-making, enabling models to self-adjust and learn in different planning scenarios. (2) Achieve system resilience in production environments facing discrete events through action selection, monitoring of fluctuations, and adjustment. (3) Achieve real-time planning and control of dynamic systems.



The technology includes (1) automated feature engineering techniques, (2) capacity configuration classifier, (3) integration of metamodels with XAI to interpret advanced prediction models, and (4) hybrid strategies to adapt linear models applicable to planning scenarios. By collecting supply chain information, capacity portfolio and production data, the technology automatically identifies key factors in semiconductor supply chain and manufacturing processes and provide more accurate forecasted demand and manufacturing cycle time (Enhance 30%, 23% accuracy), and thus enhances semiconductor supply chain resilience, while reducing manufacturing costs (30% inventory costs and 20% production costs).