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Inventory & operations

How do I forecast Amazon demand accurately?

The most accurate simple forecast weights the last 30 days at 60% and the prior 60 days at 40%, then adjusts for seasonality and known promotions. For most private-label SKUs this beats gut-feel and matches what statistical models produce within a 10% margin. Rebuild the forecast every 2 to 4 weeks.

M
COO at Nova Analytics

Jul 26, 2026 ยท 3 min read

The simple weighted formula

Forecast = (last 30-day velocity x 0.6) + (60-day prior velocity x 0.4). Multiply by seasonality index if you have one (e.g. 1.4 for Q4 on gift SKUs). Add units for known promotions.

What to layer on top

Lead time buffer: multiply forecast by lead-time days plus a 15% safety margin. Stockout adjustment: exclude out-of-stock days from the velocity denominator. Return adjustment: subtract expected refund rate from forecasted units.

When to use a statistical model instead

SKUs with heavy seasonality (Halloween, Christmas), SKUs launching soon, and SKUs with wide day-to-day variance benefit from something more advanced. For mature stable SKUs, the weighted formula performs within 10% of statistical models.

Frequently asked questions

How far ahead should I forecast?

Cover lead time + 30 days as the minimum. For long-lead SKUs from Asia, forecast 120 days out.

How often should I rebuild the forecast?

Every 2 to 4 weeks for stable SKUs, weekly during launches or seasonality peaks.

What is the biggest forecasting mistake?

Extrapolating a Q4 spike into Q1 planning. Always deseasonalise before projecting forward.

Does Nova generate forecasts?

Nova exposes velocity, sell-through, and days of cover so you can build forecasts. For automated statistical forecasts, use dedicated forecasting tools alongside.

See this in Nova

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