Omnichannel Retail: From Fragmented Systems to Governed Decision Intelligence
Unified fragmented inventory and pricing decisions across 800+ stores with auditable AI agents
The Challenge
Large omnichannel retailer faced fragmented inventory decisions across online, distribution centers, and stores. Regional managers operated independently with no unified logic. Pricing decisions varied by 40% for identical products. Manual systems couldn't track 'why' each decision was made. Inventory costs were 8% of revenue; customer fill rates were unpredictable.
Our Approach
We didn't build a demand-forecasting model. Instead, we systematized the retailer's embedded judgment. We mapped decision domains (inventory allocation, markdown timing, promotion mix), encoded regional risk appetite into explicit boundary rules, and deployed agent-assisted systems that proposed daily recommendations within those guardrails. Every inventory decision logs: what was recommended, what the store manager decided, and (3 months later) the business outcome. Our approach: Decision Intelligence + Boundary Calibration + Outcome Attribution. Agents propose; humans decide; the system learns.
The Outcome
Inventory costs dropped 6% within 6 months (freeing $45M in working capital). Fill rates stabilized at 96% (from 88–94% variance). Regional managers now operate from unified decision rules—no more fragmentation. The system automatically flags outlier decisions for learning. Total transformation time: 16 weeks from decision mapping to full deployment.