Fordel StudiosReal-Time Fleet Monitoring and Route Optimization
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
No real-time visibility into a fleet of 400+ vehicles. Dispatch planning done manually from driver phone calls. 22% empty miles on return legs due to lack of demand visibility at the time of dispatch. No systematic tracking of delivery completion or vehicle utilization.
GPS telemetry at scale is noisy. At 400 vehicles reporting every 30 seconds, the ingestion pipeline handles approximately 48,000 events per hour at peak, with a subset of devices sending duplicate, out-of-order, or malformed messages due to network connectivity issues in low-coverage areas. The processing pipeline needed to handle all of these without producing corrupt position history or false idle/movement state transitions. Route optimization for a fleet this size under real-world constraints (time windows, vehicle capacity, driver hour limits) is an NP-hard problem — exact solutions are not computationally viable at dispatch frequency. The optimization approach had to produce good solutions in bounded time rather than optimal solutions. Geofencing for 200+ delivery zones required spatial indexing to keep proximity checks under 10ms per vehicle event.
How we
built it
- 01
Conducted a two-week instrumentation audit across the fleet to understand GPS hardware variability, connectivity gaps, and the actual data quality the system would need to handle — before designing the data ingestion pipeline.
- 02
Built a real-time vehicle tracking layer with automated anomaly detection for GPS signal loss, prolonged stops, and route deviations — surfacing operational exceptions rather than requiring dispatch staff to monitor raw position data.
- 03
Implemented a route optimisation engine that reruns on every significant condition change: new traffic data, vehicle breakdown, or demand update — rather than optimising once at dispatch and letting conditions diverge.
- 04
Designed the dispatch interface around operational decisions rather than map visualisation: which routes need attention, which vehicles have capacity, which deliveries are at risk — with the map as supporting context, not the primary screen.
This engagement built the core operational technology layer for a logistics operation that had been running on manual coordination. The system ingests GPS telemetry from 400+ vehicles via AWS IoT Core, streams it into a real-time processing pipeline, and surfaces a live operational view to the dispatch team through a Next.js dashboard. Route optimization runs as a scheduled service prior to each dispatch cycle, using demand data from the order management system and real-time traffic conditions from an external API. PostGIS handles all geospatial computation: vehicle positions, delivery zone assignments, proximity matching, and route geometry storage. The system went from zero to production in 16 weeks with a three-person team.
What we
delivered
GPS ingestion pipeline, real-time fleet monitoring dashboard, and ML-based route optimization with traffic-aware ETA computation. Dispatch team now works from a live operational view rather than voice coordination.
Measurable
outcomes
- Fuel costs reduced 18% through route optimisation and empty-mile reduction on return legs.
- GPS data uptime reached 96.5% across the fleet with anomaly detection identifying connectivity issues within minutes rather than discovering them on billing reconciliation.
- Empty miles reduced 14% as dispatch gained real-time visibility into vehicle location and capacity for opportunistic load matching.
“The empty mile reduction paid for the system within the first two months of operation. The dispatch team now has real information to make decisions from instead of relying on driver phone calls.”
— Operations Director, Logistics CompanyReady to build
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