Forecasting shipping quantities with artificial neuronal networks

In the context of a proof of concept, a forecast method is developed on the basis of an artificial neuronal network (multilayer perceptron). By including historical data from the data warehouse, dedicated forecast models are developed for selected logistics sites using tools like Weka and RStudio, with the aim of forecasting shipping quantities precisely to the day (forecast horizon: one day in advance). The modeling relies on extracting and selecting descriptive characteristics (e.g. shipping quantities in the past x days, bank holidays, school holidays etc.). After suitable characteristics are selected and after the fine adjustment, the models are evaluated for the specific sites for a specified forecast period (one month). Compared to the existing method, the models based on artificial neuronal networks exhibit fewer forecasting errors across all sites (MAPE, RMSE).