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Forecast Accuracy Calculator

Enter actual and forecasted sales to get the accuracy.

How it is calculated

The tool measures how far the forecast was from what actually happened, relative to the actual. Percentage error is |actual − forecast| / actual × 100 and accuracy is 100 − error, never shown below 0%. The absolute value means over-forecasting and under-forecasting count the same, so the tool cannot tell you the direction of the miss. Actual must be above zero because it is the divisor. Both values must be in the same unit (units sold or revenue) and for the same period. The tool handles one period at a time; to get MAPE, calculate each period's error and average them yourself.

Worked example

You forecast 850 units for the month, but 1,000 units were actually sold. Difference: |1,000 − 850| = 150. Error: 150 / 1,000 × 100 = 15.00%. Accuracy: 100 − 15 = 85.00%. The forecast was 15% under the actual, though the result would look identical if you had over-forecast by the same 150 units.

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Common mistakes

  • Dividing by the forecast instead of the actual. This tool divides by actual, so the same miss gives a different percentage than a forecast-based formula.
  • Expecting a negative accuracy for a big miss. Accuracy is floored at 0%, so a 150% error shows 0.00% accuracy; read the error figure to see how large the miss was.
  • Averaging the accuracy of a good month and a bad month, but ignoring that over and under misses are not netted out here since each is absolute.
  • Comparing figures from different periods or units, such as a monthly forecast against weekly actuals.
  • Judging a forecast from one period. A single close month may be luck, so look at the error across many periods.

Sources

About this calculator

Forecast accuracy compares what actually happened to what was predicted, most commonly expressed as the absolute percentage error, |Actual − Forecast| / Actual × 100, or averaged across many periods and SKUs as the Mean Absolute Percentage Error (MAPE). This calculator shows that error and the resulting accuracy (100% minus the error, never below 0%); it ignores direction, so it does not tell you whether you over- or under-forecast. A low error means the forecasting model or planner is reliably close to reality; a persistently high or biased error signals a demand-planning process that needs recalibration.

Demand planners, supply chain analysts and retail/FMCG merchandisers track this figure to size inventory and production runs correctly — overforecasting ties up cash in unsold stock, while underforecasting causes stockouts — and it's a standard KPI reported up to S&OP (sales and operations planning) leadership.

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