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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 (Sum (Actual - Forecast) / Sum (Actual)) is also known as WAPE, Weighted Absolute Percent Error. Another interesting option is the weighted MAPE = Sum(weight*ABS((A-F))/Sum(weight*A) where A=Actual & F=Forecast. The advantage of this meassure is that could weight errors, so you can define how to weighted for your relevant business, ex gross proffit or ABC. The only problem is that for seasonal products (and we have a lot in Syngenta) you will create and indetermined result when sales = 0 and that is not symetrical, that means that you can be much more unnacurate 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 intermitent demand patterns none of the above are really usefull. 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
==Calculating forecast error==
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