E-commerce demand forecasting: anticipating your stock needs
Between stockouts that lose sales and overstock that ties up cash, reliable forecasting turns your data into a competitive edge.
- Two to three years of sales history form a reliable statistical base
- Isolate seasonality, trend and commercial events separately
- A MAPE of 10% deviation is a good result for most SMBs
The foundation of efficient logistics
Without reliable anticipation, you swing between stockouts that lose customers and overstock that ties up your cash and inflates your warehousing costs.
This is especially true in multichannel, where a stockout on your side immediately pushes the customer toward a competitor.
Collect and clean your data
Extract your sales from the last two to three years (date, quantity, product, channel) and isolate the anomalies (promotions, past stockouts) that would skew the analysis.
If you are starting out with little history, use it despite its limits: accuracy will improve with every full season you accumulate.
Isolate seasonality, trend and events
Spot the recurring seasonal peaks, the overall trend (growth or decline) and the one-off events (sales, Black Friday) that distort the average and must be handled separately.
A simple moving average or a seasonal decomposition is enough for most SMBs; save advanced models (ARIMA) for very large catalogs.
Measure accuracy and adjust continuously
Track your MAPE (mean absolute percentage error) every quarter: a MAPE of 10% is excellent, 25% signals a model that needs rework.
Involve your sales and logistics teams: their field signals enrich the model well beyond raw numbers alone.
Making your stock forecasts reliable is part of our e-commerce support.