UM-Model 1 identifies true root causes of defects, predicts equipment failures before they occur, and optimizes production schedules by understanding causal relationships—not just correlations.
Why causal reasoning prevents costly production errors
Observes: "Defect rate increased when Machine A temperature rose"
Lower Machine A temperature to reduce defects ✗
Confounding! Temperature rise was caused by ambient conditions. Real cause: raw material quality degradation from Supplier B.
Builds causal graph: Ambient Temp → Machine Temp, Material Quality → Defects
"What if we had maintained material quality?" Identifies true root cause
Switch suppliers and implement quality checks. Defects reduced 34% ✓
Explore causal factors affecting manufacturing quality
Proven results from production facilities
Identified material batch quality as root cause, not machine settings. Prevented $1.2M in defective products
Causal analysis distinguished actual equipment degradation from normal operational variance
Identified bottleneck was caused by scheduling conflicts, not machine capacity. Optimized workflow
Found energy spikes caused by startup sequences, not production load. Optimized scheduling saved $420K annually
Simulate production scenarios with causal analysis
Adjust parameters and click "Run Analysis" to see production predictions