Supply Chain Forecast Accuracy is usually measured with Mean Absolute Percent Error or MAPE, the average of percentage errors. But there are several other metrics to consider. Here’s an example …

n = 8 periods

|x| = absolute unsigned value

^2 = squared

Average Error (**AE**) = (sum (Et))/n = -4/8 = -0.50

Mean Absolute Deviation (**MAD**) = (sum |Et|)/n = 16/8 = 2.00

Mean Square Error (**MSE**) = (sum (Et*Et))/n = 48/8 = 6.00

Standard Deviation (**SD**) = square root((sum(Et*Et))/(n-1)) = sqrt(48/7) = 2.62

Mean Absolute Percent Error (**MAPE**) = (sum(|Et|/Dt)*100 = 210/8 = 26.25%

Cumulative Forecast Error = sum Et = 16

Cumulative Forecast Error Ratio = (sum |Et|)/(average Ft)

Group Forecast Accuracy = aggregate forecast vs actual

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