Dispatchers have
a gut feeling. AI has data.

Good dispatchers. Poor tools.

YS Transporte had everything: experienced dispatchers with strong instincts, three years of telematics data, a modern TMS. And yet fuel consumption kept rising while on-time delivery fell. No one knew why. The data was there, but no one had the time to make sense of it.

The familiar question: "Do we need AI?" Our answer after discovery: "Yes, but not as a replacement for the dispatchers, as their new co-pilot."

Predicting instead of reacting.

We built a forecasting model from three years of historical data: expected driving times, risk spots for delays, optimal route combinations, fuel-consumption estimates per route. The model runs as a suggestion inside the existing TMS, the dispatchers still make the call. It shows how we put artificial intelligence to work — always tied to a concrete business process.

• Forecasting model based on historical telematics and order data
• Integration into the existing TMS, no tool switching for dispatchers
• "What-if" simulator for route adjustments
• Explainable suggestions: why the AI recommends what it does (no black box)
• Weekly reports for management

Money on the road. On time at the customer.

After 8 months in production the numbers are clear, and the dispatchers are sold. Instead of the feared threat to their jobs, the AI has become a relief: less after-the-fact explaining, more time for the genuinely difficult cases.

• −12% fuel costs on comparable routes
• +18% on-time delivery at the customer's site
• −30% empty runs through better route combination
• Payback in 11 months (calculated without the image effect)