Transport & Logistics

Intelligent Transport Document Processing

The logistics industry generates a massive flow of documents every day: waybills, CMR notes, bills of lading, customs declarations, and acceptance certificates. Our computer vision and NLP models recognize and extract data from documents in any format — scans, photos, PDFs — with 97–99% accuracy. The system automatically validates data, cross-references it with the order database, and creates electronic records in ERP and TMS systems.

Customs documentation processing is an especially labor-intensive task that requires checking dozens of parameters for regulatory compliance. AI automates product classification under the Harmonized System (HS codes), verifies document completeness, and calculates customs duties, reducing border clearance time by 50–70%.

Integration with electronic document management (EDM) systems enables a fully paperless workflow with counterparties, cutting document management costs and eliminating manual data entry errors.

Predictive Diagnostics and Fleet Monitoring

Machine learning-based predictive diagnostics systems analyze telematics data, onboard sensor readings, and maintenance history to forecast breakdowns before they occur. Models detect anomalies in engine, transmission, braking system, and electrical equipment operation 2⁠–⁠4 weeks before a potential failure.

Implementing predictive maintenance reduces unplanned fleet downtime by 30–50% and cuts repair costs by 20–35%. The system generates an optimal maintenance schedule based on the actual condition of each vehicle, mileage, operating conditions, and workload.

Real-time fleet monitoring includes driving behavior analysis, fuel consumption tracking, temperature compliance (for refrigerated vehicles), and driver hours-of-service compliance. AI identifies patterns of inefficient vehicle use and generates recommendations that reduce fuel consumption by 10–15%.

Route Optimization and Transportation Planning

Routing algorithms based on combinatorial optimization and neural network models calculate optimal routes accounting for dozens of constraints: delivery time windows, payload capacity, dimensions, ADR requirements, traffic conditions, toll roads, and restricted zones. The Vehicle Routing Problem (VRP) for fleets of hundreds of vehicles is solved within minutes.

Dynamic real-time rerouting responds to traffic changes, urgent orders, and force majeure events, redistributing assignments among drivers with minimal losses. Integration with mapping services and traffic data providers ensures up-to-date information.

Route optimization reduces total mileage by 15–25% on average, decreases the number of vehicles needed by 10–15%, and raises the on-time delivery rate to 95–98%.

Demand Forecasting and Capacity Management

Accurate freight volume forecasting is a key factor in logistics business efficiency. Our models analyze historical data, seasonal patterns, macroeconomic indicators, and customer data to forecast transportation demand with 85–92% accuracy over a 1⁠–⁠4 week horizon.

Based on forecasts, the system generates capacity management recommendations: engaging additional vehicles during peak periods, optimizing warehouse space utilization, and planning staff shifts. This helps avoid both capacity shortages and excess spending on idle resources.

For less-than-truckload (LTL) shipping, AI optimizes shipment consolidation, maximizing vehicle load utilization and minimizing the number of trips. Average load utilization with AI optimization reaches 85–92%, compared to 60–70% with manual planning.

Benefits

• Transport document recognition with 97–99% accuracy
• 30–50% reduction in unplanned fleet downtime
• 15–25% reduction in total mileage through route optimization
• 10–15% fuel savings via driving behavior monitoring
• 85–92% accuracy in transportation demand forecasting
• Vehicle load utilization increased to 85–92%

Who It Is For

Transport companies, logistics operators, delivery services, freight forwarders, and fleet managers