Predictive Shipment Risk and Intelligent Dispatch

A regional logistics operator coordinates shipments across hubs, carriers, and customer locations. Dispatchers rely on spreadsheets, transport-management records, GPS feeds, and phone calls. Delays are often recognized only after a delivery misses its promised window, while re-planning consumes staff time.

• Shipment IDs, timestamps, and status definitions differ across systems.
• Traffic, weather, loading delays, and carrier performance affect ETA differently.
• GPS data can be missing or delayed, creating unreliable labels.
• Recommendations must respect capacity, driver hours, contractual priorities, and operating constraints.

Combine event normalization, ETA prediction, delay-risk scoring, and constraint-aware dispatch recommendations. Estimate arrival windows and risk categories, then present feasible interventions to dispatchers instead of changing plans without approval.

• Connect transport-management, GPS, hub, and approved external data via APIs or scheduled pipelines.
• Normalize events, detect duplicates, flag stale telemetry, and create route- and shipment-level features.
• Compare models against existing ETA methods and simple baselines.
• Generate recommendations using capacity, priority, cost, and operating constraints.
• Show contributing factors and record approvals, overrides, and reasons.
• Begin in shadow mode before controlled live use.

Use time-based holdout data to prevent future information leakage. Measure ETA MAE and median absolute error; assess late-delivery risk with precision, recall, calibration, and segment-level results. Review performance by route, hub, carrier, and shipment type.

• ETA error versus baseline.
• Late-delivery rate and missed service windows.
• Dispatcher time per shipment and re-planning time.
• Recommendation acceptance and override reasons.