Real-time Fleet Analytics Platform
Built a real-time analytics platform processing millions of IoT events daily, providing instant visibility into fleet operations, predictive maintenance alerts, and automated dispatch optimization.
The Challenge
Delayed data was costing millions in inefficiencies and customer complaints.
- Fleet data from 450 vehicles was processed in nightly batches—drivers didn't see issues until the next day
- A truck breakdown on Monday wasn't flagged until Tuesday's report, by which time three deliveries were missed
- Dispatch decisions were based on gut feel rather than real-time vehicle locations and traffic
- Customers called asking "where's my shipment?" and dispatchers couldn't give accurate answers
- Fuel costs were 18% above industry average due to poor route optimization
- Compliance reporting took a full-time employee three days per month to compile manually
The Solution
We created a real-time operations platform that turns IoT data into instant, actionable intelligence.
- Stream processing pipeline handling 5M+ events daily with sub-100ms latency using Cloudflare Workers
- Predictive maintenance model analyzing engine diagnostics to flag issues 48 hours before breakdown
- Dynamic route optimization considering real-time traffic, weather, and delivery windows
- Customer-facing tracking portal with accurate ETAs updated every 30 seconds
- Automated compliance reports generated continuously, eliminating month-end crunch
- Mobile app for drivers showing optimized routes and real-time alerts
- Executive dashboard with KPIs, trends, and anomaly detection
Implementation
Week 1-2: Data audit—mapping all IoT sources, formats, and integration points
Week 3-4: Stream processing infrastructure on Cloudflare Workers with D1 for state
Week 5-6: Core analytics engine and real-time dashboard MVP
Week 7-8: Predictive maintenance model training on historical breakdown data
Week 9-10: Route optimization algorithm development and testing
Week 11-12: Driver mobile app and customer tracking portal
Week 13-14: Integration with existing TMS and ERP systems
Week 15-16: Pilot with one region, validation, and full rollout
The Results
The logistics company went from reactive to predictive, cutting costs and delighting customers.
"We used to find out about problems after customers complained. Now we fix issues before drivers even notice. Last month, the system predicted a transmission failure 36 hours early—saved us $15K and a missed delivery. That's the ROI."