2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 1 , Issue 2 , PP: 77-83, 2020 | Cite this article as | XML | Html | PDF
Samah I. Abdel aal 1 *
Doi: https://doi.org/10.54216/AJBOR.010203
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.
Machine Learning (ML) , Forcasting , Intelligent Systems , E-Commerce
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