AI-driven demand forecasting cuts stockouts by a third
RetailCo Group (illustrative) — Retail
- Operational efficiency
- 3.2x
- Stockout reduction
- 33%
- Inventory carrying cost
- 19%
Improvement in forecast accuracy versus prior manual process
Reduction in stockout incidents across top-selling SKUs
Reduction in excess inventory carrying cost
Overview
RetailCo needed to modernize demand planning across 1,200 stores without disrupting an already lean merchandising team.
The Challenge
A national retailer relied on manual, spreadsheet-driven demand forecasting that struggled with seasonal volatility, leading to frequent stockouts and costly overstock.
- Forecasts were built manually in spreadsheets by a small planning team
- Seasonal and promotional volatility was poorly captured in existing models
- Store-level replenishment decisions lagged real demand signals by days
Our Approach
Data Unification
Consolidated POS, weather, and promotional data into a single feature pipeline.
Model Development
Built and validated SKU-level forecasting models against 3 years of historical data.
Replenishment Integration
Integrated forecasts directly into the automated replenishment system.
The Outcome
The planning team shifted from manual spreadsheet work to managing forecast exceptions, while stockouts on top-selling SKUs dropped by a third within two quarters.
“We went from reactive, manual planning to a system that tells us what’s about to happen — not just what already did.”
VP of Merchandising Operations
RetailCo Group
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