Challenge
A regional logistics provider managing 200+ vehicles across three distribution zones was planning routes manually. Dispatchers relied on experience and static maps, leading to suboptimal routing, rising fuel costs, and frequent SLA misses. Delivery time SLA compliance had dropped to 82%, and fuel spend was increasing quarter over quarter with no operational improvements.
Approach
We built a real-time route optimization engine using machine learning, integrated directly into the client’s existing fleet management system:
- ML model - Trained gradient-boosted decision trees on 18 months of historical delivery data, incorporating traffic patterns, weather conditions, delivery time windows, and vehicle capacity constraints
- Real-time processing - Built a streaming pipeline with Apache Kafka and Python microservices that recalculates optimal routes in under 2 seconds when conditions change
- API integration - Exposed the engine as a REST API that plugs into the client’s dispatcher dashboard and mobile driver apps
- Feedback loop - Actual delivery outcomes feed back into the model for continuous improvement, with weekly retraining on fresh data
- Fallback logic - Manual override capability for dispatchers with automatic logging for model evaluation
Result
Average delivery times dropped 25%. Fuel consumption decreased 18% through optimized routing and reduced idle time. SLA compliance improved from 82% to 97%. The system processes route updates in under 2 seconds, enabling real-time rerouting for urgent deliveries without dispatcher intervention.