There's a software product (call it Main) we use to allocate parts to various locations. The way it works is it sums up the demand history of all locations to the part level. For example, if part A has a historical parts distribution for Jan, Feb, Mar and Apr of (1,0,2,0) for location 1, and (3,1,0,0) for location 2, the software will sum up the demand to be (4,1,2,0) at the part level. From this it determines the best model from a list of 5, i.e., SES, DES, MA, WA and Croston, as the one with the smallest MSE. It then uses the chosen model to forecast down to the location level. So, if SES was chosen as the best model, then it will apply SES to the demand in location 1 and location 2 to build the forecast for May.
I realize that this isn't an ideal solution for forecasting parts and I have been asked to analyze the forecast accuracy of the method. I was going to roll-up the demand (as the software does) and use it to develop forecasts using a mean, naive and other commercial applications. My question is: Is it also all right to roll-up the forecasts that the Main software produces so that I have something to compare forecasts from my benchmark methods?