Global Logistics Network · Logistics & Supply Chain
The network processed 2.4 million shipment events daily across 180 countries. Their exception management system was sampling approximately 15% of events, relying on rule-based thresholds to identify shipments at risk. The early warning signals for delays and exceptions — subtle patterns in scan sequences, carrier behavior, and route anomalies — required full event coverage to become statistically significant.
Full event stream coverage with LLM reasoning applied to every shipment event. The model identified predictive exception signatures 24-48 hours before the exception occurred, enabling proactive intervention.
"We were managing exceptions after they happened. Now we see them coming. The difference is full coverage — the signals were always there."
Chief Operations Officer
The highest-impact pattern identified was a specific sequence of carrier scan anomalies that predicted a delivery exception with 89% accuracy 36 hours in advance. This pattern was present in 100% of historical exception events in the training data — but required all scan events to be included in the analysis to achieve statistical significance.