In a pursuit to overcome the limitations of the rule-based approach, retailers addressed the experience of other industries. Once the exclusive domain of internet giants, machine learning (ML) has spread far beyond the walls of IT companies to production lines, bank offices, warehouses, and even crop fields. Leading retailers also have adopted these smart predictive algorithms that analyze huge volumes of data. Unlike rule-based planning systems, ML algorithms can "learn" from data and make predictions based not only on historical sales record but a variety of parameters: from promotions and advertising to public holidays and weather.
What is more important, machine-learning technologies allow to fully automate demand-forecasting routine. Integrated into existing business processes, ML-based predictive solutions deliver daily forecasts for every SKU on a store level (to learn more about demand forecasting with ML technologies, download our latest case study
on the project for Lenta, one of the biggest retail chains worldwide and the second-largest retail chain in Russia). To obtain highly accurate predictions, machine-learning algorithms that stem from gradient boosting over decision trees are used. At its core, the solution utilizes ensembles of models, every new of which improves the quality of the ensemble. These tools are set up to obtain better predictive performance than classical statistical approach and have proved to be equally suited for demand forecasting of basic stock items, promotions forecasting, and demand forecasting for new products when few historical data are available.