What Is Forecast Bias? Forecast Bias can be described as a tendency to either over-forecast (forecast is more than the actual), or under-forecast (forecast is less than the actual), leading to a forecasting error. There are many reasons why such bias exists including systemic ones as discussed in a prior forecasting bias discussion.

The first distinction we have to make is the difference between the precision of a forecast and its bias: Bias represents the historical average error. Basically, will your forecasts be, on average, too high (i.e., you overshot the demand) or too low (i.e., you undershot the demand)? This will give you the overall direction of the error.

The bias metric only tells you whether the overall forecast was good or not. It can easily disguise very large errors. You can find an example of this in Table 1. Table 1: This example shows sales and forecasts for three items for a single week.

Rick Glover on LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows: BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast).