I am working on a demand forecasting project and I am puzzled by the client's standards of forecast evaluation. The MAPE (Mean Absolute Percentage Error) with the sample data Forecast = 300 and Demand = 100 is $$ \text{MAPE} =\frac{|300-100|}{100} =2 $$

However the client focuses on forecasting accuracy. It is defined as $$ \text{Accuracy}=\max(0,1-\text{MAPE}) $$

This means that a MAPE of 1, 3 or 3000 gives the same forecasting accuracy of 0. To me this does not make sense, because it is equivalent to restricting MAPE to $\text{MAPE}_r = \max(1,\text{MAPE})$.

However, it seems to be in line with the measurement of forecasts in the demand planning ecosystem http://demandplanning.net/MAPE.htm. Can someone please explain to me why this could be useful?

EDIT: I understand that someone can define anything. My only question is whether the definition makes sense for the demand planning / management purpose.

Referring to the text in the link, restricting any error metric does not make sense to me especially(!) in demand planning. If the true demand is 1 unit but I forecasted 300, then 300 times more raw materials or human resources were planned for this product in this period. This overestimation must cause substantially higher cost than a forecast of 2 units, although both forecasts would result in a forecasting accuracy of 0. This is implied by MAPE but not by accuracy.

So why should forecasting accuracy defined as above be relevant at all? Why do I need it when MAPE is there already? What value does it add? To me it seems to introduce bias - if anything at all.