Calculating demand forecast accuracy: Difference between revisions

Revision as of 04:57, 17 February 2016

Revision as of 09:50, 7 March 2016

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A simpler and more elegant method to calculate MAPE across all the products forecasted is to divide the sum of the absolute deviations by the total sales of all products.

This calculation <math>\sum{(|A - F|)}\over\sum{A}</math>, where <math>A</math> is the actual value and <math>F</math> the forecast, is also known as WAPE, WeigthedWeighted Absolute Percent Error. Another interesting option is the weighted

<math>MAPE = \frac{\sum(w\cdot|A-F|)}{\sum(w\cdot A)}</math>. The advantage of this measure is that could weight errors, so you can define how to weight for your relevant business, ex gross profit or ABC. The only problem is that for seasonal products you will create an undefined result when sales = 0 and that is not symmetrical, that means that you can be much more inaccurate if sales are higher than if they are lower than the forecast. So sMAPE is also used to correct this, it is known as symmetric Mean Absolute Percentage Error.