Well, it depends on what is it that you wish to measure. Of course, we are talking about the forecasting accuracy measurements.
MAD stands for mean absolute deviation. This is an average of the absolute measure of error between the forecast & actual value without regard to whether it was overestimated or underestimated. Since this is an absolute measure, its value diminishes for any relativistic comparisons when the demand varies greatly among the products or seasons. An absolute error of 50 over a base demand of 500 may be just fine while the same error on a base demand of 100 probably needs some action!
Alternate measures like MAPD (mean absolute percent deviation) or MAPE (mean absolute percent error) present the error as a percent and therefore relate the size of the error to the base demand. This is an average the absolute error between the forecast & actual value computed as a percent.
MSE (mean squared error) is another measure used for measuring forecast accuracy. This measures the average of the squared individual errors. It measures the dispersion of forecast error, however, since it uses the squared values, it accentuates the larger errors more than the smaller ones.
The bias tracks the trending of the forecast and may signal problems with the forecasting algorithms or settings if the trend is significant. Therefore, this allows for correcting potential problems that may otherwise become substantial.
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© Vivek Sehgal, 2009, All Rights Reserved.