Demand forecasting with a model fitted to every SKU
Planster tests Prophet, ETS, SARIMA and linear regression against each SKU’s own history, keeps the one that forecasts it best, and re-tests when the pattern changes. Your planners adjust the forecast; nobody maintains the models.
Four models tested on every SKU
Your best seller and the SKU you launched in March do not behave the same way, so they do not get the same model. Planster tests Prophet, ETS, SARIMA and linear regression against each SKU’s own history and keeps the best fit. When the pattern changes, it re-tests. There are no model parameters to tune.
Seasonality detected per product
Seasonality is detected per product, not applied as one curve across the catalogue. Sunscreen and electrolyte mix peak in the same months for different reasons and at different shapes, and a forecast that averages them is wrong for both.
Stockout weeks excluded from the history
A week with no sales because there was no stock is not a week of low demand. Planster excludes stockout periods and anomalies from the fit. Leaving them in is the most common way a spreadsheet forecast ends up ordering less of the product that keeps selling out.
A rolling 12–18 month forecast
The forecast runs 12 to 18 months out and re-generates on a schedule, so there is no quarterly exercise where someone rebuilds the model. It needs about a year of history — more than 52 weeks — before it can fit a seasonal pattern to a SKU.
What the agent does with demand forecasting
Every night
The agent re-checks every SKU against this forecast overnight, so a pattern that broke on Tuesday is on your list Wednesday morning rather than at the end of the month.
What it can’t do
It cannot change the forecast, override a model, or edit a cell. Model selection happens in the forecast, and hand edits belong to your team.
Example: a forecast pulled down by a stockout
Take a supplements brand with one spreadsheet model across 240 SKUs: a twelve-week trailing average with a seasonality factor typed in by hand. The model reads the two months its best seller was out of stock as a drop in demand, and the next order gets cut. Planster fits each SKU separately, excludes the stockout weeks, and re-tests the model when the pattern changes, so the SKU that sells out gets a larger order, not a smaller one.
How it fits together
Every capability runs on the same demand forecast, so each one makes the others more useful.
Exceptions
Set a tolerance per SKU and get told when one runs outside it — while there is still lead time to do something about it.
Explore ExceptionsDemand planningMulti-channel and multi-warehouse
The same forecast runs per channel: DTC, Amazon, retail, and wholesale planned separately, then combined.
Explore Multi-channelSee the forecast for your own SKUs
Connect your data and look at the forecast for the SKU your team debates most. Setup takes days, not months.