Understanding Forecast Error in Demand Forecasting

In the field of demand forecasting, it is crucial to measure how accurate our predictions are. The question asks for the term that describes the difference between what actually happened (the actual demand) and what was predicted (the forecast) for a specific time period.

Let's look at the options provided:

• Regression: Regression is a statistical technique used to establish a relationship between variables. It's a method used in some forecasting techniques, but it is not the term for the difference between the actual and forecast values.
• Mean absolute deviation: Mean absolute deviation (MAD) is a measure of forecast accuracy. It is calculated by taking the average of the absolute values of the forecast errors over a period of time. So, MAD uses forecast errors, but it is not the forecast error itself.
• Delphi forecasting: Delphi forecasting is a qualitative forecasting method that relies on collecting and summarizing expert opinions through a series of questionnaires. It is a technique for generating forecasts, not the measure of the difference between actual and forecast.
• Forecast error: This term directly describes the difference between the actual value observed and the value that was forecasted for the same period. It is the fundamental measure of how "wrong" a specific forecast was.

The formula for forecast error for a single period can be written as:

\( \text{Forecast Error} = \text{Actual Demand} - \text{Forecast Demand} \)

A positive forecast error means the forecast was too low, while a negative forecast error means the forecast was too high.

Based on the definitions, the difference between the actual demand for any time period and the forecast for the same period is termed as forecast error.