Overstock Prediction Using Machine Learning in Retail Industry

Success in supply-chain relies, in large part, on good stock management. It is quite simple to guess that there will be an increase in demand for a type of product, or rather reluctance over a period of time, but it becomes complicated to know in advance the exact or optimal number of products to order to avoid stock-outs and at the same time overstocking. This article shows how transactional data can be used with Machine Learning to forecast demand in the retail industry. To train the machine learning models, a sample of 5,115,472 records of receipt data was obtained from the French branch of one of the largest Belgian supermarket chains's data warehouse. The results revealed that the machine learning models manage to learn the seasonality effects and allow to make better predictions.