An integrated approach to demand forecasting and inventory optimization in e-commerce

This study investigates demand forecasting and inventory optimization in an e-commerce environment with a large assortment of products and highly variable demand. The research focuses on SKU-level demand modeling based on transactional data from an online retail store. The proposed approach combines machine learning methods with a stochastic inventory model. The study demonstrates that integrating machine learning forecasting with stochastic inventory optimization provides an effective decision-support tool for e-commerce, enabling balanced consideration of demand uncertainty, service level, and financial constraints.