The Challenge
A global retailer with operations across 15 countries was hemorrhaging money on logistics inefficiencies. Their legacy planning system relied on static historical averages and manual route assignments, resulting in frequent stockouts in high-demand regions and excess inventory in others. Annual logistics waste was estimated at $120M.
Our Approach
We deployed a three-phase AI transformation:
Demand Forecasting: Built a predictive model analyzing historical shipping data, seasonal patterns, promotional calendars, and real-time market signals to forecast demand at the SKU-location level with 94% accuracy.
Route Optimization: Implemented an AI-powered routing engine that incorporated real-time traffic patterns, weather data, and carrier capacity to dynamically optimize delivery routes across the network.
Inventory Rebalancing: Created an intelligent rebalancing system that proactively redistributes inventory across warehouses based on predicted demand shifts — before stockouts occur.
The Results
- 25% reduction in logistics costs: Smarter routing and demand-matched inventory placement eliminated the most expensive inefficiencies.
- 94% forecast accuracy: Up from 67% with the legacy statistical model, dramatically reducing both stockouts and overstock.
- 40% reduction in delivery lead times: Dynamic routing and pre-positioned inventory shortened the last mile.
- $30M annual savings: Direct bottom-line impact in the first year of full deployment.