RETAIL

Store inventory auto-replenishment for a global convenience chain

Store managers spend hours on ordering across ~2,500 SKUs. Wexa built and delivered a working replenishment app that forecasts demand and moves stock from the nearest store or distribution center.

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SKUs per store modelled
per day manager time targeted for release
pilot design: stores × categories × weeks

CLIENT: Global convenience-store chain
REGION: US / global
WHEN: 2025
ENGAGEMENT: Solution build
WEXA COMPONENTS: Tables · transaction-triggered process flows · custom UI

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The challenge

Manual ordering and stock balancing per store
Stock-outs on fast movers, waste on fresh food
Supplier exceptions handled by email

What Wexa built

Tables for DC stock, store inventory, transactions and transfer requests
Transaction-triggered process flows
Auto-replenishment from the nearest store or DC; manager confirm or override
Custom web application deployed on Wexa

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How it runs on Wexa

FigureHow this use case runs on Wexa: client systems connected, ontology discovered, context graph in the Wexa memory layer, agents and process flows, and governance. Highlighted components are central to this use case.

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Solution workflow

FigureReplenishment loop: point-of-sale data → AI forecasting → auto-orders → manager confirmation.

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Results

Measure	Before / without Wexa	With Wexa
Manager time on ordering per store per day (100 stores)	1.5 hours	15 minutes
Annual cost of the process	$1.4M	$225K
Time to implement the AI solution	6 months	4 weeks
AI build cost (one-time)	$480K	$30K
AI operating cost per year (models, infrastructure, upkeep, platform)	$312K	$166K
Total AI cost in year 1	$792K	$195K