Logistix Fleet

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IoTReal-time TrackingAI

Fleet Tracking for a Mid-Size Delivery Company

Replaced paper-based dispatch with a live map and simple ML model that flags vehicles likely to need service within the next two weeks — for a fleet of 180 trucks.

Client

Logistix Fleet

Category

Logistics

Year

2023

Result

Unplanned breakdowns down 41% in 6 months

// The Challenge

What We Faced

Dispatchers tracked 180 trucks via phone calls and a whiteboard. Breakdowns were discovered only when drivers called in, often mid-route, causing cascading delays and expensive emergency repairs.

// Our Approach

How We Solved It

We installed low-cost OBD-II readers on each vehicle and built a dashboard that streams location and engine data into TimescaleDB. A gradient-boosted model trained on 18 months of service records predicts maintenance windows with a two-week horizon.

// The Outcome

Results Achieved

Unplanned breakdowns dropped 41% in the first 6 months. The ops team avoided 23 roadside incidents they can trace directly to early service flags. Fuel spend per kilometre fell 9% after route recommendations were adopted.

Fleet Tracking for a Mid-Size Delivery Company

Tech Stack

PythonTensorFlowReactTimescaleDBKafka

Key Metrics

Unplanned breakdowns down 41% in 6 months

Primary achievement

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