UK omnichannel retailer
Demand forecasting platform synced 6,200 SKUs across 340 stores and digital — cutting overstock by £14M.
What was getting in the way.
A UK omnichannel retailer with 340 stores and a fast-growing digital channel was managing 6,200 active SKUs on a legacy ERP with weekly replenishment cycles. Overstock was running £52M — capital locked in slow-moving inventory — driven by poor promotional lift modelling and no visibility into store-level demand signals.
Stockout rates in top-selling lines averaged 8.4%, directly causing missed sales and brand switching. A team of 14 merchandise planners was making allocation decisions using spreadsheets — a process that had not fundamentally changed in a decade.
What we deployed.
Gennexa built a cloud-native demand forecasting platform on the client's existing Azure data estate, requiring no migration of core systems. The platform ingested point-of-sale data, web traffic, seasonal indices, promotional calendars, competitor price signals, and weather data to generate store-level, SKU-level daily forecasts with calibrated confidence intervals.
A replenishment engine translated forecasts into automated draft purchase orders and inter-store transfer proposals, surfaced to merchandise planners via a self-serve dashboard. Planners shifted from building forecasts to reviewing and approving them — dramatically compressing the planning cycle.
Promotion lift modelling was introduced for the first time, significantly reducing planned-event overstock that had been one of the largest sources of write-down.
What we measured.
Inventory turns improved 18% within two trading seasons. Overstock reduced from £52M to £38M — a £14M release of working capital. Stockout rate in top-100 SKUs fell from 8.4% to 2.9%.
Markdown rate fell 6 percentage points as better forecasting reduced end-of-season clearance volumes. Merchandise planner time spent on manual forecasting dropped from 70% to 22% of the working week, freeing the team to focus on range strategy and supplier partnerships. The platform paid back its full build cost in 11 months.
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