AI Route Optimization Cuts Delivery Costs 34%
We built an AI logistics optimisation layer that uses ML-based route scoring, demand forecasting, and real-time re-routing to reduce cost-per-delivery by 34% and improve on-time performance.
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2 Weeks
Timeline
Project Overview
1
What We Built
- We built an AI logistics optimisation layer that uses ML-based route scoring, demand forecasting, and real-time re-routing to reduce cost-per-delivery by 34% and improve on-time performance.
2
The Challenge
- A third-party logistics provider was losing $2.3M annually to suboptimal routing, manual load planning, and reactive scheduling. Their legacy TMS offered no predictive capabilities.
3
Our Solution
- ML-based dynamic routing engine
- Demand forecasting (LSTM model)
- Real-time re-routing with traffic data
- Driver app integration
- Fleet utilisation dashboard