Forecasting
AI demand forecast with 85–92% accuracy for 30–90 days
How it works
Historical data ingestion
The system imports 2+ years of daily sales data per SKU, grouped by store location. Handles gaps, returns, and promotional anomalies automatically — no manual cleaning required.
Model selection tournament
Four stochastic models (polynomial, sinusoidal, exponential, logarithmic) compete on your data. The system picks the best-fit model per SKU based on MAPE score, then generates P10/P50/P90 scenarios.
52-week seasonality mapping
Each product gets a weekly seasonality coefficient from a full year of patterns. This catches micro-seasonality that monthly averages miss — like Monday retail spikes or Friday electronics peaks.
Live forecast & alerts
Forecasts update automatically with every data sync. When actual demand deviates from predictions by >20%, the system triggers a demand surge or demand drop alert with recommended action.
Under the Hood
4 model candidates (polynomial, sinusoidal, exponential, logarithmic) compete per SKU. Weighted R² automatically selects the winner — no manual tuning.
P10/P50/P90 intervals calculated via Poisson distribution for reliable inventory planning under demand uncertainty.
Each product-week gets a coefficient from a full year of patterns. 5 types: peak, growth, off-season, discount, and spot.
Real-World Example
Real example
Omega-3 Fish Oil: the system detected weekly seasonality and predicted a 40% demand surge 3 weeks before it happened, preventing a stockout that would have cost $12K in lost sales.