A regional freight carrier reduced fuel costs by 18%, cut maintenance events by 34%, and forecast demand with 91% accuracy.
A regional freight and logistics company with 300 employees and 180 vehicles was managing operations reactively: manual route planning, no demand forecasting, and fleet maintenance scheduled by calendar rather than condition.
Dispatchers planned routes in Excel using historical knowledge and gut feel. No traffic prediction. No demand visibility beyond the next day or two. The 180-vehicle fleet was maintained on a fixed schedule, with no ability to detect failures before they happened. Result: excess fuel costs, unplanned downtime, and inefficient capacity allocation.
The company was growing, but operational costs were growing faster. Profitability was flat. Scaling required more vehicles and more fuel waste.
Two years of shipment data, vehicle telematics, maintenance logs, and GPS tracking existed in separate systems. Combined and analyzed, that data could unlock three improvements: optimized routes that reduced distance and fuel, forecasted demand to match capacity ahead of time, and predictive alerts for maintenance before failures.
A machine learning approach could run continuously, adapting to weather, traffic, and seasonal patterns.
We built three connected models, each feeding into dispatcher and operations dashboards.
We spent 4 weeks understanding data quality, completeness, and alignment across 8 source systems. That upfront work uncovered missing telematics and incomplete maintenance logs, which we backfilled. Garbage in = garbage out.
Before deployment, we measured: average miles per delivery, fuel cost per mile, maintenance event frequency, forecasting accuracy of the manual process. These baselines made the impact quantifiable and visible to leadership.
Routes and maintenance alerts were recommendations, not mandates. Dispatchers could accept or override. Trust grew as the models proved accurate. Within 8 weeks, 92% adoption of suggestions.
We built drift detection and model retraining into the pipeline. When forecast accuracy degraded (seasonal shift), Airflow automatically retrained and alerted ops. The system stayed accurate and trusted.
Bring the problem. We'll come back with a written brief: what to build, what to defer, and where AI actually moves the number. No deck pitches.