Forecast creation

Forecasting creates a projected labor demand by day by predicting future sales or volume results using the relevant historical data. These daily predictions are spread among time intervals according to the distribution. The projections are divided by the productivity, typically the average transaction handling time, to generate staffing requirements for Schedule Optimization.

For testing purposes, a forecast can be created using the Forecasting screen. However, in a production setup, the forecasts are typically created by the job scheduler using one or more Batch Forecasting tasks. These tasks are usually scheduled on a recurring basis, for example once a week, to create forecasts for all required stores across the entire organization.

Forecast creation process

Forecasts are initially generated based on historical trends. You can then manually adjust the forecast to account for other events as necessary. This adjusted forecast becomes the manager-adjusted forecast.

The location’s forecast method determines which historical trends are used to create the forecast. These options are available:

Machine Learning - The machine learning forecasting model uses forecasting models created during the training process to predict results for the forecast period. These forecasting models are generated based on an analysis of historical actual results and other data that is relevant to the location.
Trend of Historic Averages - Data from the previous eight weeks of actual data is compared with the same weeks last year to calculate a day-by-day and year-over-year trend. The trend is then applied to the actual results from the forecast period in the previous year. If there are six or more days of data available within the eight weeks, then the highest week and the lowest week are dropped during the calculation.
Linear Regression - Data from the specified historical period is used.
Multiple Regression - A historical relationship trend is applied to a known imported forecast value to create the forecast.