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Demand Forecast Accuracy Calculator

Enter your forecast and actual demand figures to compute MAPE, bias, and weighted MAPE. See the formulas and benchmark your accuracy.

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How do you calculate demand forecast accuracy?

Demand forecast accuracy is measured by MAPE (Mean Absolute Percentage Error): for each period, divide the absolute difference between forecast and actual demand by the actual demand, then average across all periods and multiply by 100. A MAPE of 20% means your forecast is off by 20% on average. Bias — the signed average error — tells you whether the forecast consistently over-predicts or under-predicts. Demand OS computes both metrics automatically at the SKU level and retrains models when accuracy drifts.

Calculator

Enter forecast vs actual demand

ForecastActual

Formulas used

MAPE= (1/n) × Σ |Forecast − Actual| / Actual × 100
Bias= (1/n) × Σ (Forecast − Actual) / Actual × 100
Weighted MAPE= Σ |Forecast − Actual| / Σ Actual × 100

Results

Enter your forecast and actual values, then click “Calculate Results” to see your accuracy metrics.

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Frequently Asked Questions

Forecast accuracy, answered

What is a good MAPE for demand forecasting?+
A MAPE below 20% is generally considered good for consumer goods; below 30% is acceptable for most industries. Fast-moving SKUs with stable demand often achieve 10–15% MAPE. Intermittent or new-product SKUs may exceed 40%. What matters more than the absolute number is the trend — accuracy should improve over time as models learn from more data.
What is the difference between MAPE and forecast bias?+
MAPE measures the magnitude of forecast error (always positive — it tells you how far off you are on average). Bias measures the direction of error (positive bias means you consistently over-forecast; negative means under-forecast). A forecast can have low MAPE but high bias if errors are consistently in one direction. Both should be tracked: MAPE for overall accuracy, bias for systematic skew that inflates or depletes inventory.
Why is weighted MAPE better than simple MAPE?+
Simple MAPE gives equal weight to every SKU or period regardless of volume. A 50% error on a SKU selling 10 units counts the same as a 50% error on a SKU selling 10,000 units. Weighted MAPE (wMAPE) weights each period by its actual demand, so high-volume items drive the metric more than low-volume ones. This better reflects the revenue and inventory impact of forecast errors.
How does Demand OS improve forecast accuracy?+
Demand OS runs an ensemble of statistical and ML models per SKU, automatically selects the best-performing model based on cross-validated accuracy, and retrains when accuracy drifts. Demand sensing adds daily signal detection to catch near-term shifts before the next planning cycle. Explainable AI shows ranked factor attribution for every forecast so planners know why the number changed — not just that it changed.
Can MAPE be calculated when actual demand is zero?+
No — MAPE divides by actual demand, so zero actuals produce a division-by-zero error. For periods with zero demand, alternatives include MAE (Mean Absolute Error, not a percentage), SMAPE (Symmetric MAPE, which uses the average of forecast and actual in the denominator), or simply excluding zero-demand periods from the MAPE calculation and tracking them separately.

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Ready to automate forecast accuracy tracking?

Demand OS computes MAPE, bias, and accuracy drift at the SKU level across your entire catalogue — continuously, not once a quarter.

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