AI-Driven Fleet Optimisation for a 200+ Vehicle Logistics Company
Client: Singh Logistics Pvt. Ltd.
The Challenge
Singh Logistics operated a fleet of 200+ trucks across South India with route planning done manually by dispatchers using local knowledge. GPS tracking was basic--showing vehicle location but offering no analytics. Fuel costs consumed 38% of revenue, on-time delivery rates were 79%, and the operations team had no consolidated view of fleet performance. Driver allocation was inefficient, with some vehicles running 30% under capacity while others were overloaded.
Research & Discovery
We analysed 6 months of trip data (18,000+ trips), fuel purchase records, maintenance logs, and delivery SLA reports. GPS historical data revealed that 23% of routes included unnecessary detours. Fuel consumption analysis showed a 15% variance between the most and least efficient drivers on identical routes. Customer feedback indicated that late deliveries were the #1 complaint, cited in 41% of support tickets.
Strategy
We designed a three-layer solution: (1) a real-time fleet visibility layer with GPS tracking, geofencing, and live ETAs; (2) an AI route optimisation engine that factors in traffic patterns, weather, vehicle capacity, and delivery windows; and (3) a driver performance analytics module to identify coaching opportunities. The rollout was phased by region over 8 weeks.
Design Approach
The command-centre dashboard was designed for the operations team to manage 200+ vehicles from a single screen--with map-based visualisation, colour-coded vehicle status, and one-click drill-down into any vehicle's trip history and performance. The driver mobile app was kept intentionally simple--large buttons, turn-by-turn navigation, and a delivery confirmation workflow that takes under 10 seconds.
Development
The platform was built with React for the web dashboard, Node.js microservices for the backend, PostgreSQL for transactional data, and Redis for real-time caching. Google Maps API powered the mapping and geocoding layer. The AI route optimisation engine used historical trip data and real-time traffic feeds to generate optimal routes. The driver app was built with React Native. Everything was deployed on AWS with auto-scaling for the real-time tracking workload.
“Microtechnique IT built a fleet management system that cut our fuel costs by 22% and pushed on-time deliveries to 96%. The AI route optimisation alone paid for the entire project in 3 months.”
Technologies Used
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