15% reduction in stockouts
Retail ┬╖ Fount Cloud (AWS)
A major grocery chain with 500+ stores was losing revenue to poor demand forecasting. Traditional time series models failed during promotions and seasonal shifts. Using Fount's causal forecasting API, they built a system that models the causal impact of promotions, weather, and holidays on demand ΓÇö reducing stockouts by 15% and overstocking by 12%.
15% reduction in stockouts
12% decrease in overstocking
$2.3M annual savings
With 500+ stores and thousands of SKUs, the grocery chain was experiencing significant losses from inaccurate demand forecasting. Traditional time series models failed to account for the causal impact of promotions, seasonal events, and weather patterns ΓÇö leading to chronic overstocking of slow movers and stockouts of high-demand items. The data science team spent months building custom models that still couldn't generalize across store formats.
The team integrated Fount's causal forecasting API into their existing data pipeline. Fount's Large Causal Architecture (LCA) automatically discovered the causal relationships between promotions, weather, holidays, competitor pricing, and demand. The system models these as a directed acyclic graph (DAG), enabling accurate demand forecasts that adapt when market conditions change ΓÇö without retraining from scratch.
Within 8 weeks of deployment, the chain saw a 15% reduction in stockouts and a 12% decrease in overstocking across all store formats. The causal model maintained accuracy during Black Friday and holiday seasons ΓÇö periods where previous models consistently failed. Annual savings exceeded $2.3M from reduced waste and improved fill rates.
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