How It Works
The project opened with a field-study phase: two full weeks of riding along with drivers to map real-world friction points. From there, the platform was rolled out in three stages — installing IoT gateways across the fleet, building the four sub-systems in parallel, and an operational pilot on 30 trucks before extending to the full fleet.
The Route-Optimisation Engine
The heart of the platform is a route-optimisation engine that fuses live Google traffic data, weather feeds, industrial-zone peak hours, and shipment constraints (refrigerated, hazardous, etc.). The engine recomputes the optimal route every 3 minutes and pushes updates to the driver whenever a better path emerges. This component alone delivered 18% of the total fuel savings.
IoT Integration
Every truck is fitted with an IoT gateway that streams 14 distinct metrics every 5 seconds: location, speed, instantaneous fuel consumption, engine temperature, cargo temperature (for refrigerated loads), door state, and more. The data is analysed in real time to detect issues before they become failures — for example, an abnormal engine-temperature rise triggers an alert before a breakdown.
Security & Data Protection
Customer data and shipment details are commercially sensitive, so the security layer is built on a zero-trust model: every request is independently authenticated, and device-to-server traffic is encrypted with TLS 1.3 plus client certificates. The access log is complete and append-only.
Scalability
The platform is designed to support 5,000 trucks without architectural change. A microservices + Kubernetes setup lets each service scale independently under load. The system was load-tested against a synthetic 10,000-truck workload with no performance degradation.
Conclusion
The company moved from a manual, call-driven operation to an automated logistics platform running 24/7. Fuel savings alone pay back the platform in under a year, and the company's ability to grow is no longer limited by the size of the operations team.
