ML-Powered Disruption Prevention
What we deliver
The Problem (Before)
Teams react after disruptions. Exception handling is manual and repetitive.
Logistics Manager, Supply Chain Manager, Buyer, Planner, Procurement, Customer Service
The Outcome (After)
Disruptions predicted and prevented. ML trained on network-wide patterns.
Exception handling is reactive: teams fix problems after they occur. ML-powered prevention predicts disruptions from network-wide patterns and Tracy acts before the disruption hits your shipments.
Tracy
How it works
ML models trained on network-wide disruption patterns generate predictive risk scores; Tracy acts on recommendations by executing preventive actions before disruptions impact shipments.
ML models trained on network-wide disruption patterns.
Predictive risk scoring from cross-company outcomes.
Pattern recognition across carrier, lane, facility, and time.
This intelligence exists because the Graph aggregates behavior across 882 enterprise shippers, 10,164 carriers, and 3.6 million facilities over 11 years.
Exception handling is reactive: teams fix problems after they occur. ML-powered prevention predicts disruptions from network-wide patterns and Tracy acts before the disruption hits your shipments. The intelligence layer is what creates the gap. Any analytics tool can query your data. Only FourSight queries the Graph: 11 years of cross-company intelligence that cannot be replicated with software alone.
Shipment Twin
Tracy executes the workflow. Every action recorded with full decision trace.
Cross-company intelligence powering every decision
Recommendation dashboard with alerts and suggested actions; Risk scoring visualization
Supply Chain Intelligence and Analytics
- Natural Language Supply Chain Analytics
- Personalized Supply Chain Performance Intelligence
- Embedded Supply Chain Intelligence in Your BI Stack
- Supply Chain Historical Benchmarking
- Unified Operations Command Center
Validation
Deployment Evidence
Expected Impact Range
Related outcomes
Carrier performance scored automatically. Save 8 hours per week on manual evaluation.
Programmatic API access for custom applications and automated reporting.
One billion hours of operational work completed by AI agents over the next decade.
