AI-Powered Supply Chain Optimization
Machine learning models that predict demand patterns and enable real-time inventory decisions across a global warehouse network.
Context
Demand forecasting relied on manual analysis and historical averages. Stockouts and overstock situations were costing millions annually. Warehouse operations lacked real-time visibility and cross-regional coordination.
Approach
How we solved it.
01
Strategy
Analyzed three years of demand data across 200+ SKU categories. Identified seasonal patterns, market signals, and supply chain dependencies.
02
Design
Designed an intuitive operations dashboard that surfaces AI recommendations without overwhelming warehouse managers with raw data.
03
Build
Developed ensemble ML models combining time-series forecasting with external signal processing. Built real-time data pipelines for continuous model updates.
04
Scale
Rolled out across 12 regional warehouses. Implemented feedback loops that improve model accuracy with every decision cycle.
Solution
Ensemble demand forecasting models
Real-time inventory optimization engine
Operations intelligence dashboard
Automated reorder and distribution workflows
Impact
Measurable outcomes.
94%
Prediction Accuracy
72%
Stockout Reduction
-28%
Inventory Costs
12 regions
Warehouses Served
Capabilities Used
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