Demand forecasting
Forecast Accuracy
By the Supply Chain Manager team · Updated October 16, 2026
Forecast accuracy measures how close demand forecasts came to what actually sold, so you can judge which methods and SKUs can be trusted.
There is no single accuracy number. Common measures include MAPE, WAPE and MASE, and each answers a slightly different question. Bias is measured separately: a forecast can be accurate on average and still consistently too high.
Accuracy should be judged at the level decisions are made: per SKU per warehouse over the lead time, not as one company-wide total that lets errors cancel out.
How to calculate forecast accuracy
Forecast Error = Actual − Forecast Accuracy is then summarized with a measure such as WAPE, MAPE or MASE
Forecast accuracy example
Illustrative numbers: a forecast of 100 units a week for four weeks against actuals of 80, 120, 90 and 110. Total error cancels to zero, which looks perfect, but the absolute errors add up to 60 units on 400 sold, a WAPE of 15%.
Why forecast accuracy matters for distributors
Without measuring accuracy, nobody knows whether the forecast is helping. Measured accuracy also sets how much safety stock each SKU needs: the less accurate the forecast, the bigger the buffer.
Common forecast accuracy mistakes
- Reporting one aggregate number that hides poor SKU-level forecasts.
- Comparing accuracy across SKUs with MAPE when some have near-zero sales.
- Never comparing against a simple naive forecast to see whether the method adds value.
Forecast accuracy in Supply Chain Manager
Supply Chain Manager's Forecast Accuracy page reports WAPE and MASE for each SKU's chosen model, grouped by ABC and XYZ class.
Open the live demoExamples on this page use made-up round numbers to show the calculation. They are not benchmarks or results from real businesses.