# Computing Forecast Error: Should I divide by forecast or demand?

I often get the following question from my clients and trainees:

To express the forecast error as a percentage, should we divide the forecast error by the demand or the forecast?

Basically, we can compute the forecast error percentage by dividing the absolute error by the demand; or by the forecast.

Or

Both approaches will usually result in slightly different numbers; but in some cases, the difference can be massive. As we will discuss, using the forecast as the denominator is an open door to metric-hacking.

For example in the figure below, the forecast error is 75% if computed based on demand, and 88% if computed based on forecast. MAE% computed with demand as the denomiator = 75%. MAE% with forecast as denominator = 88%.

PS: remember never to use MAPE to track forecast errors. This is a bad practice. I guess you know this already — if not, see my article below for a detailed explanations.

## Demand Forecasting

When discussing demand forecasting in general and forecasting KPIs in particular, I like to keep a few statements in mind:

• Our objective is to forecast demand. In other words, (unconstrainted) demand is what we aim for when forecasting.
• The objective of a forecasting KPI is not to be easy to maximize/minimize but to promote useful forecasts.
For example: let’s imagine that KPI1 is aligned with your supply chain objectives…