Every SKU gets a forecast for the periods you plan on, built from cleaned sales history, seasonality, price and promotion calendars, and supplier lead times. Slow movers are handled with rules rather than pretend precision, stock-outs are treated as censored demand instead of zero demand, and every forecast carries an error range so planners know which numbers to trust.
A three-month moving average is blind to seasonality, promotions and trend at once. It is wrong in a predictable direction every quarter, and everyone compensates with a gut adjustment nobody records.
A week with no stock reads as a week with no demand. Train anything on that and it will confidently under-forecast the products that sell best.
Plans are made, reality happens, and the two are never compared. Without an error number there is no way to know whether the new process is better than the old spreadsheet.
Stock-outs marked as censored, promotions and price changes flagged, one-off events separated from pattern, returns netted. This is unglamorous and it decides how good everything after it can be.
Seasonality, day-of-week and holiday effects, promotion calendar, price, new-product cannibalisation, and external drivers where they genuinely help rather than where they look impressive.
Fast movers get statistical and gradient-boosted models; intermittent and slow movers get methods built for sparse demand or explicit rules. Pretending a model can forecast an item that sells four times a year is how trust dies.
Accuracy is measured on held-out periods and compared with your current method, per SKU class. We publish where the forecast is reliable and where it is not, before anyone plans on it.
Forecasts land in your planning tool or ERP with an override field and a reason box. Human overrides are tracked, so within a quarter you can see where the model or the planner is systematically better.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
It depends on your demand pattern, and anyone quoting a percentage before seeing your data is guessing. We report error per SKU class against your current method on a held-out period, before you plan on it.
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