Real-Time Risk Prediction in Global Logistics
David Park
Solutions Architect
Explore how SupplyAI’s multimodal risk engine predicts port disruptions, weather delays, and geo-political events 72 hours ahead, saving $8.7M in demurrage and protecting 12% of annual EBIT.
Introduction
The Red-Sea missile attacks in January 2024 forced vessels to reroute around the Cape of Good Hope, adding 14 days transit and $1.8M fuel per sailing. SupplyAI’s risk engine ingested AIS transponder data, combined with satellite SAR imagery and sentiment from maritime forums, to flag the disruption 68 hours before major carriers published advisories—allowing customers to rebook 3,200 TEU on cheaper rail-landbridge routes and avoid $8.7M demurrage.
Data Sources
Multimodal ingestion: (1) AIS vessel positions every 30 seconds via Spire Maritime; (2) NOAA weather grids at 4 km resolution; (3) European Space Agency Sentinel-1 SAR for port congestion; (4) GDELT news stream in 110 languages; (5) Twitter FIREHOSE filtered for geopolitical keywords; (6) customs manifest data via DCSA APIs. All streams land in Kafka, joined on H3 hexagonal grid indices for sub-second spatial queries.
Model Architecture
Graph-neural-network models port-call sequences as dynamic graphs where nodes are berths and edges are queue dependencies. Transformer encoder fuses weather, news-sentiment, and historical delay distributions to output risk score 0–100 per vessel-call 72 hours ahead. Uncertainty captured via deep-ensemble; 95% CI guides confidence. Model trains on 4.2M port-call records reaching back to 2015, updated nightly.
Alert System
Risk scores surface in Control-Tower dashboard with color-coded map layers. When score >75, automated Playbooks trigger: procurement is advised to expedite air-freight for critical SKUs, finance hedges fuel exposure, customer-success proactively notifies end-customers of potential delays with revised ETAs. API webhooks push alerts into SAP TMS and Oracle WMS for downstream replanning.
Economic Impact
During Q1-2024 Red-Sea crisis, customers using the engine saved average $940 per TEU in demurrage and lost-sales avoidance. Across 9,400 affected TEUs, total savings reached $8.7M. Insurance underwriters reduced premium quotes by 0.8% for voyages where SupplyAI risk score <30, recognizing lower loss probability.
False Positive Management
Initial model flagged 22% false positives—healthy voyages incorrectly labeled high-risk. We introduced counterfactual explanation layer: for each alert, system shows top-3 contributing features (e.g., wind-gust >45 km/h, berth queue length >8 vessels, news-sentiment spike containing ‘missile’). Planners can override and feedback loop retrains model weekly, cutting false-positive rate to 7% within six weeks.
Scalability
System processes 1.4M vessel positions/minute on 240-core Kubernetes cluster. Inference latency <200 ms for single voyage, <3 seconds for global fleet of 54K vessels. Cost optimized via spot-instance mix and GPU inference only during ensemble step, keeping compute spend under $0.08 per voyage monitored.
Next Steps
Integrate drone-based berth cameras for real-time crane-count, add large-language-model summaries of maritime notices, and pilot smart-contract parametric insurance that auto-pays when risk score exceeds agreed threshold.