AI Route Optimization for Last-Mile Delivery
How we cut delivery times by 38% and fuel costs by 40% for a logistics company operating a fleet of 45 vehicles.
Before
After
The Problem
Routes were planned manually each morning by dispatchers, relying on experience rather than data. This led to suboptimal paths, wasted fuel, and inconsistent delivery windows that frustrated customers.
Scheduling was reactive -- drivers waited for assignments instead of following optimized sequences. There was no real-time visibility into fleet position or delivery status.
Our Approach
We deployed an AI route optimization engine that factors in traffic patterns, delivery windows, vehicle capacity, and driver schedules to generate optimal routes in minutes instead of hours.
A predictive scheduling system was added to pre-assign deliveries based on forecasted demand, and a real-time fleet tracking dashboard gave dispatchers live visibility into every vehicle's location and progress.
Deliverables
- AI-powered route optimization engine with real-time traffic integration
- Predictive scheduling system with demand forecasting
- Real-time fleet tracking dashboard with ETA updates
- Driver mobile app with turn-by-turn optimized navigation
Results
Average delivery time dropped from 4.5 hours to 2.8 hours -- a 38% improvement. Daily deliveries increased from 120 to 185 without adding vehicles or drivers.
Fuel costs decreased from EUR 12,000 to EUR 7,200 per month, a 40% reduction. Customer satisfaction scores improved by 28% due to more predictable delivery windows.