The majority of Forecast Pro users rely on Expert Selection, the software’s built-in system which automatically creates forecasts. This approach works quite well in most cases; however, you must keep in mind that Expert Selection views your data as a series of numbers and takes a purely statistical approach to generating the forecasts. At times your knowledge of your products and future events may lead you to either adjust the Expert Selection forecast judgmentally or reject it completely and use an alternative forecasting method. Let’s examine several cases where overriding the Expert Selection forecast should be considered.
1. When a non-time series method is called for.
Expert selection only considers time series methods. A time series method is a forecasting technique which generates forecasts based solely on an item’s past demand history. Time series methods considered in Expert Selection mode include different forms of exponential smoothing, Box-Jenkins models, the Croston’s Intermittent Demand model, discrete data models and moving averages. Methods not considered in Expert Selection mode include event models (which provide adjustments for promotions, business interruptions or other irregular occurrences), dynamic regression models (which allow you to incorporate explanatory variables), top-down approaches (which allow you to use aggregate-level forecasts to improve lower level forecasts) and curve fitting. When one of these methods is called for, Expert Selection should not be used.
2. When you feel that Expert Selection has selected the wrong forecasting method.
There may be times when Expert Selection selects a forecasting method that you feel is inappropriate. For instance, it may elect to use a non-seasonal model to forecast data that you know are seasonal. In these cases you will want to override the Expert Selection forecasting method and use a technique you feel to be better suited. Often times electing to use a specific form of exponential smoothing to reflect the desired trend and seasonal pattern is a good solution. These cases tend to occur more frequently when working with short data sets where the ability to test the data statistically is limited–so keep a close eye on Expert Selection when forecasting short data sets.
3. When your knowledge of future events is not captured in the statistical model.
At times you may have knowledge of future events that are not captured in the forecasting model. For instance, there may be a planned promotion, a competitor entering the market or a pre-booked one-time sale. In these instances, you’ll want to treat the Expert Selection forecast as a baseline and judgmentally adjust the forecast to reflect the future event. Forecast Pro provides a convenient forecast adjustment facility to allow you to accomplish this. Event models can also assist in accounting for the forecaster’s practical knowledge about both the past and future demand.