Anomaly Detection

We partnered with a logistics company to build a machine learning system that detects anomalies in their operational data — flagging issues before they cascade into costly disruptions.

The challenge

The company was processing thousands of shipments daily across multiple warehouses. Equipment failures, routing bottlenecks, and inventory discrepancies were only caught after they caused delays. Their existing rule-based alerts generated too many false positives to be useful.

What we built

We trained an ensemble of anomaly detection models on their historical operational data, tuned to distinguish genuine anomalies from normal variance. The system monitors sensor readings, throughput metrics, and inventory movements in near real-time, surfacing only high-confidence alerts with contextual explanations.

Results

• 80% reduction in false-positive alerts compared to rule-based system
• Equipment failures detected an average of 6 hours before impact
• Anomaly explanations helped operators resolve issues 3x faster