Real-Time Fleet Tracking & Predictive Maintenance on AWS

About the Company

Tradebridge Logistics is a Nigerian freight and last-mile delivery company operating a fleet of over 80 vehicles across Lagos, Ibadan, and Port Harcourt. Serving e-commerce merchants, FMCG distributors, and manufacturing clients, Tradebridge is built on the promise of reliable, on-time delivery. As their fleet and client base grew, maintaining the operational discipline and vehicle reliability that underpins that promise became increasingly difficult without real-time data and intelligent tooling.

Customer Challenge

Tradebridge managed their fleet almost entirely through phone calls, manual logs, and periodic maintenance schedules that bore little relationship to actual vehicle usage. Fleet managers had no real-time visibility into vehicle location, driver behavior, or engine health. When vehicles broke down — often mid-delivery — the operational fallout was severe: missed SLAs, unhappy clients, costly emergency repairs, and idle vehicles that should have been earning revenue.

Unplanned breakdowns were the single biggest cost driver in the business. Without predictive maintenance capabilities, the maintenance team was always reactive — fixing problems after they occurred rather than preventing them. At the same time, the inability to track vehicles in real time meant dispatchers made routing and assignment decisions blind, resulting in inefficient routes, excessive fuel consumption, and unnecessary driver overtime. The team knew that data could solve these problems, but had no way to collect or act on it at scale.

• No real-time vehicle location or driver behavior visibility
• Reactive maintenance model leading to frequent unplanned breakdowns mid-delivery
• Inefficient route assignments driven by guesswork rather than live data
• High fuel and overtime costs from poorly optimized dispatching
• Missed SLAs and client dissatisfaction from vehicle reliability failures

Partner's Solution

Tribe Networks Limited designed and deployed an end-to-end IoT-powered fleet intelligence platform on AWS that transformed Tradebridge's operational model from reactive and manual to predictive and data-driven. The solution connects every vehicle in Tradebridge's fleet to the cloud in real time, streaming live telemetry data — including GPS location, engine diagnostics, fuel levels, speed, and driver behavior metrics — through AWS IoT Core.

This continuous stream of vehicle data is ingested and processed in real time by Amazon Kinesis Data Streams, enabling the platform to detect anomalies, flag risky driving behavior, and identify vehicles showing early signs of mechanical stress before a breakdown occurs. Predictive maintenance alerts are automatically generated and routed to the maintenance team with enough lead time to schedule proactive servicing, eliminating the cycle of emergency repairs that had been draining the business.

A live fleet operations dashboard — built on Amazon QuickSight and integrated with the IoT data pipeline — gives dispatchers and fleet managers a real-time view of every vehicle's location, status, and health score. Historical telemetry stored in Amazon S3 powers trend analysis, enabling the operations team to continuously improve routing, scheduling, and driver coaching decisions based on actual performance data rather than assumptions.

Results and Benefits

• 60% reduction in unplanned vehicle breakdowns through predictive maintenance and proactive servicing
• Real-time GPS visibility across the entire fleet, eliminating blind dispatching and improving delivery reliability
• Significant reduction in fuel costs through optimized routing and driver behavior monitoring
• Faster incident response — when issues do occur, managers are alerted immediately rather than learning about them hours later
• Improved SLA performance and client satisfaction driven by more reliable, on-time deliveries
• Data-driven driver coaching reducing instances of harsh braking, speeding, and idling across the fleet
• Scalable architecture that onboards additional vehicles with no changes to the underlying platform