Portal de Periódicos da CAPES

Revolutionizing Supply Chain Forecasting With Generative AI and Machine Learning

2025; IGI Global; Linguagem: Inglês

10.4018/979-8-3693-4433-0.ch016

2327-3437

James Kanyepe, Rudolph L. Boy, Munyaradzi Chibaro, Thuso Mphela, Katlego Mahupa Ketlhaetse,

Scheduling and Optimization Algorithms

This chapter examines the paradigm shift in supply chain forecasting brought about by generative AI and machine learning technologies. Through real-world examples and case studies, the proposed chapter explores how these technologies enhance forecast accuracy, streamline operations, and drive cost efficiency. The study employed systematic analysis of the literature, drawing upon prominent academic databases such as Google Scholar, Scopus, Web of Science, and IEEE Xplore. Academic publications, reports, and related materials were obtained via comprehensive keyword searches to serve as primary sources of data, with a focus on English-language literature to ensure consistency and accessibility. Through the synthesis of data extracted from selected studies, this chapter provides a structured overview of the literature, discussing implications for theory, practice, and future research in supply chain forecasting.