aspg-1-1804-1791417868 20261008000428 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group American Journal of Business and Operations Research AJBOR 2770-0216 2692-2967 2020 1 2 Samah I. Abdel Aal Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Sharkia, Zagazig, 44519, Egypt With the growth of e-commerce, accurate demand forecasting has become a critical aspect of successful business operations. Traditional demand forecasting techniques such as time-series analysis, moving averages, and exponential smoothing have been used for years, but they have limitations in capturing the complex and dynamic nature of e-commerce demand. In this paper, we explore innovative approaches to demand forecasting in e-commerce. Specifically, we discuss the use of tree-based Machine Learning (ML) techniques as well as advanced statistical models such as Bayesian networks and hierarchical models. We provide a case study of successful implementations of innovative demand forecasting techniques in e-commerce companies. The results show that our approach can significantly improve inventory management and logistics strategies, leading to increased profitability and customer satisfaction. 2020 77 83 1804 https://creativecommons.org/licenses/by/4.0/ 10.54216/AJBOR.010203 https://www.americaspg.com/journal/1/article/1804 https://www.americaspg.com/storage/51684363412.pdf