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{(Sum (ActualA - ForecastF)}\over\sum{A}</math>, where <math>A</math> Sumis (Actual))the actual value and <math>F</math> the forecast, is also known as WAPE, Weighted Absolute Percent Error. Another interesting option is the weighted <math>MAPE = Sum(weight*ABS(\frac{\sum(w\cdot|A-F|))/Sum}{\sum(weight*A) wherew\cdot A=Actual & F=Forecast)}</math>. The advantage of this meassuremeasure is that could weight errors, so you can define how to weightedweight for your relevant business, ex gross proffitprofit or ABC. The only problem is that for seasonal products you will create and indeterminedundefined result when sales = 0 and that is not symetricalsymmetrical, that means that you can be much more unnacurateunacurate 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.
Last but not least, for intermitentintermittent demand patterns none of the above are really usefulluseful. So you can consider MASE (Mean Absolute Scaled Error) as a good KPI to use in those situations, the problem is that is not as intuitive as the ones mentioned before. You can find an interesting discussion here: http://datascienceassn.org/sites/default/files/Another%20Look%20at%20Measures%20of%20Forecast%20Accuracy.pdf
