I have defined this in my lectures and workshops as the classic measure of cross-sectional forecast bias.Įquation 4 also measures forecast bias, but some what weakly. If you over-forecast all SKUs in your product portfolio, then your forecast bias will equal the MAPE. The other component of MAPE is SKU Mix Error. Forecast bias is just a component of total forecast error or MAPE. Please see a downloadable presentation at DemandPlanning.Net.Įquation 3 and 4 describe a family of measures that are used to calculate forecast bias. This can also be intuitively explained as the average absolute deviation relative to the average unit demand. In other words, this is the percent Mean absolute deviation or PMAD. MAPE can be defined as the volume weighted absolute error relative to the total actual demand. small numbers don’t heavily influence this calculation. So equation 1 for MAPE is not the recommended solution, although many academics use this as a model diagnostic.Įquation 2 gives you the correct MAPE as used by the Supply Chain practitioners. => This gives you the correct MAPE weighted by volume.Īveraging percentages can give you strange numbers. => This calculates the average of the Percentages. Here are the equations that you originally sent through email.
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