AI Fuel Optimization: Lower Consumption, Higher Profit — No New Vehicles Required
Fuel accounts for 25–35 % of a transport company’s operating costs. For a fleet of 20 vehicles × 100,000 km/year × 30 l/100 km × €1.50/l = €900,000/year. Reducing consumption by 10 % = €90,000 saved annually.
Where Fuel Disappears
Unnecessary Idling (2–4 % of Total Consumption)
The engine is running, but the vehicle is stationary. Typically: waiting at a gate, long unloading, traffic stoppages. AI identifies drivers and locations with the most idling time.
Idling Report — April 2026
Fleet total: 48 hours of idling
Top 3 vehicles:
BA-123AB: 6.2 h/month → €28 of fuel wasted
BA-456CD: 5.8 h/month → €26
BA-789EF: 5.1 h/month → €23
Locations with longest idling:
1. Distribution Centre Senec — avg. 23 min waiting
2. Customer Kovospol — avg. 18 min waiting at entrance
Driving Style
Hard acceleration and braking increase fuel consumption by 15–25 %. AI calculates an eco-driving coefficient for each driver and compares it against the fleet average.
Driver comparison — average km/litre:
Peter Novak: 9.8 km/l ✅ (fleet avg: 9.2)
Jan Horvath: 7.6 km/l ❌ (-17 % below average)
Saving potential for Horvath: 23,000 km/year × 1.6 l extra/100 km × €1.50 = €552/year
Suboptimal Routes
AI compares actually driven routes with optimal ones. It identifies recurring deviations — a driver who routinely takes a longer road.
Load and Aerodynamics
Half-empty trucks running full routes. AI identifies inefficient load patterns and suggests load consolidation.
AI Action Plan
Every month the system generates:
- Top 3 measures with the greatest impact on consumption + estimated savings
- Driver leaderboard — gamified eco-driving (driver of the month)
- Route audit — routes with shortening potential
After 3 months of implementation: benchmark of actual savings vs. prediction.