Forecast Performance Evaluation Criteria

Depending on the selection of processing options and on trends and patterns in the sales data, some forecasting methods perform better than others for a given historical data set. A forecasting method that is appropriate for one product might not be appropriate for another product. You might find that a forecasting method that provides good results at one stage of a product life cycle remains appropriate throughout the entire life cycle.

You can select between two methods to evaluate the current performance of the forecasting methods:

• Percent of accuracy (POA).

• Mean absolute deviation (MAD).

Both of these performance evaluation methods require historical sales data for a period that you specify. This period is called a holdout period or period of best fit. The data in this period is used as the basis for recommending which forecasting method to use in making the next forecast projection. This recommendation is specific to each product and can change from one forecast generation to the next.