Nexus Supply Chain Intelligence
Built an ML-powered demand forecasting and route optimization system that cut logistics costs by $1.2M annually.
Nexus Logistics operated on spreadsheet-based demand forecasting and manual route planning. Inventory mismatches cost millions annually, and dispatchers spent 4+ hours daily on route optimization that was still suboptimal.
We built a demand forecasting model trained on 3 years of historical data, integrated with real-time IoT signals from fleet GPS and warehouse sensors. Route optimization uses a constraint-satisfaction algorithm that factors in driver hours, vehicle capacity, and delivery windows.
The system reduced overall logistics costs by $1.2M in its first year. Forecast accuracy improved from 71% to 94%, and route planning time dropped from 4 hours to under 15 minutes.