American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/1804 2018 2018 Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Sharkia, Zagazig, 44519, Egypt Samah I. Abdel aal 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 2020 77 83 10.54216/AJBOR.010203 https://www.americaspg.com/articleinfo/1/show/1804