Which metrics are used to measure forecast accuracy?

• A. MAD, MAPE, RMSE, and bias
• B. Inventory turns
• C. Lead time
• D. Cost per unit

Answer: (A)

Forecast accuracy is assessed with error metrics that quantify how far forecasts are from what actually happened. The metrics like MAD, MAPE, RMSE, and bias are used because they each capture different aspects of forecast error.

MAD (mean absolute deviation) averages the absolute errors, giving a straightforward sense of typical error magnitude in the same units as the data. MAPE (mean absolute percentage error) expresses errors as a percentage of actual values, making it easy to compare accuracy across items with different scales. RMSE (root mean square error) squares the errors before averaging and then takes the square root, so larger errors weigh more heavily, highlighting the impact of outliers in the overall accuracy. Bias looks at the average forecast error, revealing whether forecasts tend to systematically overshoot or undershoot actual results.

Other options like inventory turns, lead time, or cost per unit relate to inventory efficiency, responsiveness, or cost, but they do not directly quantify how close forecasts are to actual outcomes. Using a combination of these accuracy metrics provides a fuller picture of forecast performance.