AI Demand Forecasting Engine
RetailNext operates 340 grocery stores across 12 states. Their demand forecasting relied on simple moving averages and buyer intuition — leading to $8M in annual waste from perishable overstock and $3M in lost revenue from stockouts. They needed ML-powered forecasting that accounts for the real world: weather, local events, holidays, and trends.
What wasn't working.
Inaccurate demand forecasts led to $8M in annual waste from overstock and missed revenue from stockouts.
What we built.
Trained ensemble ML models on 3 years of sales, weather, and event data with autonomous retraining agents.
What it delivered.
Forecast accuracy improved by 42%, reducing waste by 35% and increasing same-store revenue by 12%.
From kickoff to production.
Data Unification
Consolidated 3 years of POS data, supplier lead times, weather feeds, and local event calendars into a unified feature store with 200+ signals per SKU-store combination.
Model Development
Built an ensemble of XGBoost, Prophet, and LSTM models with a stacking meta-learner. Each model specializes in different demand patterns — trend, seasonality, and event-driven spikes.
Autonomous Retraining
Deployed agents that monitor prediction drift, trigger retraining on fresh data, and A/B test new model versions against production — all without human intervention.
Buyer Integration
Built a Streamlit dashboard where buyers can see AI forecasts, understand key drivers, override with domain knowledge, and track forecast-vs-actual performance.
The stack.
“ Our buyers went from skeptical to evangelical in about two weeks. When the AI correctly predicted a 3x spike in ice cream sales before a heat wave, they were sold.
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