Calculating demand forecast accuracy: Difference between revisions

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==Calculating the accuracy of supply chain forecasts==

Forecast accuracy in the supply chain is typically measured using the [[Mean absolute percentage error|Mean Absolute Percent Error]] or MAPE. Statistically [[Mean absolute percentage error|MAPE]] is defined as the average of percentage errors. Most practitioners, however, define and use the MAPE as the Mean Absolute Deviation divided by Average Sales. This is in effect a volume weighted MAPE. This is also referred to as the MAD/Mean ratio.

AMost simplerpractitioners, however, define and moreuse elegantthe methodMAPE toas the Mean Absolute Deviation divided by Average Sales, which is just a volume calculateweighted MAPE, acrossalso allreferred to as the productsMAD/Mean forecastedratio. This is tothe dividesame as dividing 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, Weighted Absolute Percent Error.

Another interesting option is the weighted

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, Weighted 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.