Global Consumer Goods Manufacturer · Consumer Goods
One production line had produced quality variance that resisted every improvement initiative for seven years. Extensive sampling-based analysis had failed to identify a consistent root cause. The process engineers had concluded the variance was inherent to the product design. The data to identify the actual cause existed in the sensor stream — a subtle interaction between temperature, humidity, and line speed — but it only became statistically visible when all three sensor channels were analyzed together at full temporal resolution.
Full sensor coverage with LLM reasoning configured to identify multi-variable correlations across sensor channels at full temporal resolution. The model was tasked with finding patterns that explain defect occurrence — not just monitoring for known anomalies.
"Seven years of improvement initiatives, Six Sigma projects, and process redesigns. The answer was in the data the whole time — we just couldn't see it because we were working with a fraction of what the sensors were telling us."
Global Head of Manufacturing Excellence
The root cause was a three-way interaction: when ambient humidity exceeded 68%, line speed was above 94% of rated capacity, and the primary heat exchanger was in the second half of its maintenance cycle, defect probability increased by 340%. Each variable individually was within specification. The combination was the cause. This pattern required all three sensor channels at full temporal resolution to become statistically significant — it was completely invisible in sampled data.