Machine learning labor forecasting

Customers that have historical sales or other volume data spanning multiple years can leverage the power of machine learning in LFSO to create better labor forecasts. Machine learning forecasting creates forecast models for each driver by analyzing historical data to determine the impact of seasonality and day-of-the-week trends on sales or volume performance. Compared with algorithmic forecasting methodologies, such as trend of historic averages, the machine learning model leverages more than one year of historical volume data. Incorporating additional data allows machine learning forecasting to generate more precise forecasts that give customers a more accurate picture of the number of people required to staff a given store.

In addition to sales and volume data imported from Point of Sale (POS) systems, machine learning forecasting incorporates supplemental data points provided by customers or third-party services to refine its forecasting models. Customers can input past and future sales promotions in the Promotion Calendar, which are used to predict how future promotions will affect performance. Optionally, historical weather data from a third-party weather API can be integrated into forecasting models to predict the impact of expected future weather on sales and volume results.

Machine learning forecasts can be generated either using the Create a New Forecast maintenance form or using the Batch Forecasting job scheduler task. Before forecasts can be generated, the machine learning forecasting model performs a training process to generate forecasting models for locations. The training process is performed by the Batch Forecast Training job scheduler task. This task produces forecasting model files for each driver of the locations that are being trained.

Forecasting model files are saved to a file location specified in the FORECAST_MODEL_PATH registry parameter. This file location may be a local directory on the job scheduler server, network or cloud location, or an Amazon S3 bucket. It is important to note that the application server must be able to access this file location, or the forecasting model files must be copied to a directory with the same path on the application server, before forecasts can be generated using the Create a New Forecast maintenance form. The Batch Forecasting task creates its forecasts on the job scheduler, so it can be used to create forecasts if this file location cannot be accessed by the application server.

See "Forecasting with machine learning" in the Infor Workforce Management LFSO Implementation and Administration Guide.