Forecast Accuracy
Forecast accuracy is a measure of how close forecast demand was to actual demand, commonly expressed as 100% minus the mean absolute percentage error (MAPE) across SKUs or periods.
Why it matters for ecommerce brands
Forecast accuracy tells you how much to trust your demand plan, which sets safety stock, purchase quantities, and labor plans. Low accuracy is expensive either way: too many units or too few. Measure it at the level you buy, usually SKU by month, and compare it across products to see where planning effort pays off. For example, three SKUs with forecast errors of 10%, 15%, and 10% have a MAPE of about 11.7%, so forecast accuracy is about 88.3%.
Formula and calculator
MAPE = average of |actual − forecast| ÷ actual; forecast accuracy = 100% − MAPE
Common questions
- What is MAPE in forecasting?
- MAPE, or mean absolute percentage error, is the average of the absolute differences between forecast and actual, each divided by the actual. Lower MAPE means a more accurate forecast; it is unreliable for SKUs with very low or zero sales.
- How do you calculate forecast accuracy?
- For each SKU, divide the absolute difference between actual and forecast by actual. Average those errors to get MAPE, then subtract it from 100%, so errors of 10%, 15%, and 10% give about 88.3% accuracy.
- Why is MAPE misleading for slow sellers?
- Because it divides by actual demand, a SKU that sells 2 units against a forecast of 4 shows a 100% error. Weighted MAPE, which weights each SKU's error by its volume, gives a more useful total.