StockSense applies structured time-series analysis to your historical sales data — separating trend, seasonality, and residual demand — so you always know what to stock, and when.
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Why StockSense
Upload your historical sales CSV and StockSense automatically separates trend, seasonality, and residual components — no manual configuration required. Each SKU gets its own model.
Every forecast includes a confidence interval — a statistically grounded upper and lower bound. Know the realistic range of demand, not just a single optimistic number.
Translate forecasts into concrete reorder decisions. StockSense calculates recommended restock quantities per SKU — factoring in lead time, safety stock, and demand variability.
How it works
Drop in historical sales data with date and SKU columns. StockSense handles the rest — no cleaning or formatting required.
The model identifies trend, seasonal patterns, and irregular demand signals across each product line.
View 30, 60, or 90-day demand projections per SKU — with upper and lower bounds to inform your planning range.
Get specific reorder quantities and timing suggestions, calibrated to your lead times and desired service level.