Apex Logistics — AI Dispatch That Outperformed Humans by 3x

The Challenge

Apex Logistics runs one of Ontario's largest last-mile delivery operations, with a fleet of over 800 vehicles making thousands of deliveries daily across urban, suburban, and rural routes. Their dispatch operation was entirely manual — a team of 15 dispatchers spent the first three hours of every morning planning routes, assigning drivers, and making adjustments on the fly.

The manual process created compounding inefficiencies. Routes were planned based on dispatcher intuition rather than data, leading to suboptimal sequencing and unnecessary backtracking. When real-time conditions changed — traffic jams, driver call-outs, urgent priority orders — the dispatch team scrambled to re-route manually, often making decisions that optimized one route at the expense of three others.

The result was late deliveries, exhausted drivers working overtime, fuel costs that kept climbing, and a dispatch team that was overwhelmed and burning out. Apex's CTO, Sofia Delgado, had already been burned by two agencies who promised AI solutions but delivered glorified dashboards that dispatchers ignored.

The Approach

TechForge took a fundamentally different approach from the failed attempts. Instead of building a dashboard and hoping dispatchers would use it, TechForge built a system that replaced the manual routing process entirely while keeping human dispatchers in a supervisory role for exception handling.

• Historical data pipeline — ingested 18 months of delivery data (2.4M completed deliveries) into BigQuery for training, including delivery times, route patterns, traffic conditions, weather correlations, and driver performance metrics
• Multi-objective optimization model — built a TensorFlow-based model that optimizes simultaneously for delivery time, fuel efficiency, driver workload balance, and priority order sequencing — not just shortest distance
• Real-time traffic integration — feeds live traffic data and road condition updates into the model, triggering automatic re-optimization when conditions change significantly
• Driver mobile app — built a progressive web app that gives drivers turn-by-turn optimized routes, real-time schedule updates, and one-tap delivery confirmation with photo proof
• Dispatch oversight dashboard — a monitoring interface where the reduced dispatch team can see all routes in real-time, override AI decisions when needed, and handle exceptions the model flags as uncertain

Tech Stack

The Results

The system launched after a 12-week build cycle and was phased in over two weeks, starting with 100 vehicles before expanding fleet-wide. The results surpassed every benchmark Apex's leadership had set.

Delivery times improved by 22% on average. The AI model's routing decisions consistently outperformed the best human dispatchers by a factor of three, primarily because it could simultaneously optimize across all 800+ vehicles rather than planning routes in isolation. Routes that a dispatcher would spend 20 minutes planning were generated in under 3 seconds.

The dispatch team shrank from 15 to 4 — not through layoffs, but through redeployment. Eleven former dispatchers moved into driver management and customer success roles. The four remaining dispatchers handle exceptions, VIP accounts, and model oversight.

Fuel costs dropped 18% in the first quarter, driven by more efficient routing and reduced deadhead miles. Across the fleet, that translated to a six-figure annual saving that alone justified the project cost. Driver overtime dropped 31%, and driver satisfaction scores improved from 3.2/5 to 4.4/5 in quarterly surveys.

Timeline

12 weeks total. 2 weeks discovery and data pipeline setup, 2 weeks model architecture and training, 6 weeks application build (API, driver PWA, dispatch dashboard), 2 weeks phased rollout and stabilization.