An Intelligent Approach for Demand Forecasting in E-commerce

Samah I. Abdel aal

Department of Information Systems,

Faculty of Computers and Informatics,

Zagazig University, Sharkia,
Zagazig, 44519, Egypt
Emails: eng_samah2013@yahoo.com; SIAbdelaal@fci.zu..edu.eg

Abstract

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.

Keywords: Machine Learning (ML) ; Forcasting; Intelligent Systems; E-Commerce