This talk introduces Lean Automation for Cycle-Time Wins.In today’s semiconductor industry, cycle time is a decisive lever for competitiveness and market leadership. We explore how lean manufacturing principles, when combined with state-of-the-art AI and automation technologies, can deliver substantial gains in production efficiency.Key innovations include:

• Knowledge graphs for unifying heterogeneous shop-floor data and ensuring full lineage and traceability.
• Causal discovery via KG link prediction for interpretable, data-driven root-cause hypotheses.
• Graph learning and GNNs for intelligent route and resource optimization.
• Selective state-space sequence models for fast, accurate cycle-time forecasting and online rescheduling.

Together, these methods establish a closed loop from causal insight → optimized plan → executed intervention (in the digital twin or on the fab floor), driving reductions in both average and tail cycle times.

Presentation abstract (shorter):

This talk introduces Lean Automation for Cycle-Time Wins, showing how lean manufacturing principles can be supercharged by state-of-the-art AI. We highlight knowledge graphs for data fusion and traceability, causal discovery for interpretable root causes, graph neural networks for routing optimization, and selective sequence models for cycle-time forecasting. Together, these methods enable a closed loop from insight to action, delivering reductions in semiconductor fab cycle times.