I. Pierros, E. Kouloumpris, D. Zaikis, I. Vlahavas. “Retail Demand Forecasting for 1 Million Products”, in Proceedings of the Intelligent Systems Design and Applications (ISDA 2023), Lecture Notes in Networks and Systems, vol 646, pp. 467–79, 2023. https://doi.org/10.1007/978-3-031-27440-4_45.
Retail chains without proper demand forecasting tools are susceptible to significant financial losses by missing out on sales due to stocked-out products or having to throw out expired products because they were overstocked. Extensive research has been carried out, comparing different forecasting methodologies and models, examining the influence of different factors, and highlighting the significance of intermittent forecasting. However, these approaches often struggle to scale up and crumble when dealing with larger retail chains. In this paper, we analyze the real case of a big retail chain with 300 stores, 200 product groups per store and over 1 million products in total. We propose an architecture made up of multiple Neural Network models that can generate forecasts in a timely manner, taking into account calendar features, promotions and the interactions between competing products. It produces daily predictions in under 3 hours and retrains weekly the models whose performance deteriorates in 12 hours, using an AutoML component to explore deeper and larger architectures. It is a critical component of the company’s Order Management System, achieving a Root Mean Squared Error of 4.48 units across each horizon that was defined by the company.
