Multi-Agent AI for Retail Supply Chain
Specialised agents continuously monitor demand, supply and sales signals — a central orchestrator turns them into availability decisions across 500 stores.
The challenge
A retailer with 500 stores had signals scattered across systems — and availability decisions that lagged reality:
- Demand shifts spotted too late to act
- Sales trends invisible across regions
- Supplier constraints discovered at order time
- Stockouts in high-performing stores
- Excess inventory trapped in low performers
How it works
Agents that watch, an orchestrator that decides
Each agent owns one signal. The evaluator owns the decision:
- 01
Demand agent: “plumbing material demand will rise +40%”
- 02
Sales agent: “sales already rising in North Carolina”
- 03
Supplier agent: “supplier can fulfil only 60% of the quantity”
- 04
Availability evaluator agent weighs all three signals together
- 05
Decisions: prioritise high-performing stores, reallocate from low performers, increase replenishment frequency
What we built
Key capabilities
Inventory recommendations
Concrete stocking actions per store, ranked by revenue impact.
Assortment changes
What to range where, adjusted as demand signals shift.
Deployment plans
An executable allocation plan — not a dashboard to interpret.
Continuous monitoring
Agents never stop watching; decisions update as signals change.
Business impact
What it changed
Reduced stockouts
High-velocity stores stay stocked through demand surges.
Higher on-shelf availability
Availability decisions made before shelves go empty.
Higher revenue capture
Demand spikes convert to sales instead of walkouts.
Lower excess inventory
Stock flows out of low performers instead of ageing there.
Better working capital
Inventory investment follows actual demand.
Faster demand response
Signal-to-decision time collapses from weeks to hours.
“The supply chain that senses together, decides together — one orchestrated position across 500 stores.”
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