Logistics
The trucks were moving. The team couldn't see where.
A regional logistics operator managing a fleet across multiple routes relied on calls, WhatsApp updates and spreadsheets to track vehicles and deliveries. Operations teams had limited real-time visibility into where vehicles were, whether deliveries were on schedule, and when unexpected delays or route-level exceptions needed attention. As the fleet grew, the problem wasn't a lack of data. It was that the data wasn't connected or being used to act early.
The impact
- 60 hours/month
- saved on manual tracking
- Faster
- response to delivery exceptions
- Real-time
- fleet and delivery visibility
Client story
“Earlier, our teams were constantly calling drivers and checking WhatsApp just to understand what was happening on the road. Now we have a live view of the fleet and can focus our attention on the deliveries that actually need intervention.”
Inside the system
We connected the fleet through IoT and GPS devices, bringing vehicle, driver and delivery data into a live operations layer. AI then helps interpret this data, identify unusual patterns and surface exceptions that need attention, so the team doesn't have to constantly monitor every vehicle manually.
How it works
01
Fleet data comes in
IoT and GPS devices continuously capture vehicle location, movement and relevant delivery data across the fleet.
02
The operation gets a live view
Vehicle locations, routes, delivery status and expected movement come together in one operations view instead of being spread across calls, WhatsApp and spreadsheets.
03
AI watches for exceptions
AI analyses live fleet and delivery signals to identify unusual delays, route deviations and other situations that may require attention.
04
The right issue gets surfaced
Instead of the operations team watching every truck, automated alerts bring potential exceptions to their attention as they occur.
05
Teams act before delays escalate
Operations teams can quickly identify what happened, which vehicle or delivery is affected, and where intervention is needed.
06
The system keeps learning from the operation
Driver, route and delivery data creates a growing operational picture that can help teams identify recurring delays and improve future planning.
The operation, before and after
- Before LecxeAfter Lecxe
- Vehicle updates came through calls and WhatsAppLive fleet data from connected IoT and GPS devices
- Teams manually checked where vehicles wereReal-time fleet visibility
- Delivery status tracked across spreadsheetsDelivery information connected in one operations view
- Delays were discovered through manual follow-upsAI surfaces potential delays and exceptions
- Teams had to monitor vehicles constantlyAutomated alerts bring attention to issues that need action
- Route deviations and unexpected events were harder to spotExceptions identified from live operational data
- Significant time spent tracking the fleet manually60 hours/month saved on manual tracking
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