Stockful doesn’t force every product onto a straight-line forecast. For each variant at each location it backtests a few demand models on that SKU’s own history - a flat baseline, a trend model (demand rising or falling), and a day-of-week model (weekly buying patterns) - and adopts a richer model only when it clearly beats the baseline on recent weeks. Slow or sparse sellers stay on the simple baseline, so a forecast never gets worse by guessing at a pattern that isn’t there.On the inventory detail page, the forecast chart shows the chosen model’s projection: a curve rather than a straight line for SKUs with a real trend or weekly rhythm. Incoming shipments with an expected arrival date appear as a step up on the projection, so you can see stock replenish before it runs out.
Locations with the Hub (warehouse) role measure demand differently: a hub’s velocity reflects the total demand of every location it supplies, and its reorder points are sized for your supplier lead time. See hub locations for how roles are detected and overridden.
Each week Stockful records the forecast it made, then grades it against what actually sold. The inventory detail page shows a Forecast accuracy figure once there are at least a couple of weeks to compare. You can also ask the AI assistant “how accurate are my forecasts?” or “where did the forecast miss, and why?” for a plain-English breakdown - including whether a miss lined up with a sale or a switch in model. Weeks a SKU was out of stock are left out of the grade, since there was nothing to sell.
