35%
Faster Delivery
28%
Cost Reduction
4.8/5
Driver Satisfaction
01 / The Challenge
Identifying the Friction
DashEats operated in 15 cities with 8,000 drivers, but inefficient routing and unpredictable demand spikes led to 42-minute average delivery times. Driver churn was 35% monthly due to low earnings per hour.
02 / The Solution
Intelligent Intervention
We deployed a real-time logistics optimization engine using reinforcement learning. The system dynamically reroutes drivers, predicts demand hotspots, and pre-positions drivers before the dinner rush.
Real-time Route Optimization
Demand Hotspot Prediction
Driver Pre-positioning AI
Dynamic Fleet Dispatching
"Our drivers now earn 40% more per hour because they're not stuck in traffic waiting for orders. Churn dropped to 12%."
Michael Torres
VP of Operations, DashEats
Analysis Provided By
Dr. Elena Vance
Chief Data ScientistFormer ML Researcher at Stanford with 15+ years in predictive analytics. Elena leads our core algorithm team, focusing on demand sensing and reinforcement learning for supply chains.
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