Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data

Abstract

Supply chain management depends heavily on uncertain point forecasts of product sales. In order to deal with such uncertainty and optimize safety stock levels, methods that can estimate the right part of the sales distribution are required. Given the limited work that has been done in the field of probabilistic product sales forecasting, we propose and test some novel methods to estimate uncertainty, utilizing empirical computations and simulations to determine quantiles. To do so, we use the M5 competition data to empirically evaluate the forecasting and inventory performance of these methods by making comparisons both with established statistical approaches and advanced machine learning methods. Our results indicate that different methods should be employed based on the quantile of interest and the characteristics of the series being forecast, concluding that methods that employ relatively simple and faster to compute empirical estimations result in better inventory performance than more sophisticated and computer intensive approaches.