In reply to Robin parsons.

I definitely do not suggest using Max(Forecast,Actual) as the denominator in the Bias calculation -- that would make no sense at all. For Bias I use Sum(Forecasts)/Sum(Actuals) and expressed as a positive or negative percent. Thus, if Sum(Forecasts) = 1050 and Sum(Actuals) = 1000, then Bias = 100 * (1050/1000) - 100 = 5%. If instead the Sum(Forecasts) = 975, then Bias = 100 * (975/1000) - 100 = -2.5%.

The sole reason for using Max(Forecast,Actual) in the denominator of the Forecast Accuracy calculation is that it scales the value to always fall between 0 and 100%. This makes it so simple that even a high-ranking business executive could understand it.

Of course, forecast analysts, demand planners, and those responsible for various supply chain and inventory management decisions can (and should) use other metrics for their decision making purposes.

The value of using Max(Forecast,Actual) is in reporting to management -- minimizing the need to spend time explaining things. Everyone understands a performance chart scaled 0-100%, and I would disagree with your contention that it is "Much easier to explain that there is a point where the error is so large it's pointless to try to scale it." If I'm discussing the forecast with management, I don't want to waste time trying to explain that.

We agree that Max(Forecast,Actual) has many flaws, and that it imperfectly represents what is really going on. But as a practical matter, when dealing with forecasts (which themselves are always imperfect), I'm ok with it as a useful metric for management reporting.