Seasonal demand swings caused frequent overstock and stockouts, eating into margins.
We built an ensemble forecasting system that combines historical sales data with external signals to predict demand at the SKU-location level, replacing spreadsheet-based buyer intuition with data-driven reorder decisions.
Client
A specialty retailer with...
Timeline
14 weeks
Team
2 engineers
Industry
Retail
A specialty retailer with multiple locations struggled with inventory planning. Products had strong seasonal patterns, but existing forecasting relied on spreadsheets and buyer intuition, leading to frequent stockouts of popular items and heavy markdowns on excess inventory. The margin erosion was unsustainable.
Integrated POS data, inventory levels, and external signals (weather, local events, holidays) into a unified data pipeline
Built ensemble forecasting models combining statistical methods with gradient boosting for SKU-level predictions
Created automated reorder point calculations with safety stock optimization based on lead times and demand variability
Developed a buyer dashboard with scenario planning so merchandising teams can run what-if analyses before major ordering decisions
30%
Less Overstock
50%+
Fewer Stockouts
2x
Faster Reorder Decisions
14 wks
Delivered In
Overstock reduced by 30%, recovering significant markdown losses
Stockout frequency dropped by over 50%
Inventory turnover improved noticeably within the first two quarters
Buyers spend significantly less time on routine reorder decisions
“We used to guess. Literally — our buyers would look at last year's numbers and adjust by feel. Now we have actual forecasts that account for things we never thought to factor in, like local events. The markdowns alone justify the investment.”
— Head of Merchandising, Specialty Retailer
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