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Leveraging Machine Learning and AI for Demand Forecasting in Supply Chain Management
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Author(s): J. Dhanalakshmi (Department of Data Science and Business Systems, SRM Institute of Science and Technology, India)and A. Prabhu Chakkaravarthy (Department of Networking and Communications, SRM Institute of Science and Technology, India)
Copyright: 2026
Pages: 26
Source title:
Transformative Impact of AI in Supply Chain Management
Source Author(s)/Editor(s): Jabir Arif (Sidi Mohamed Ben Abdellah University, Fez, Morocco)and Fouad Jawab (Sidi Mohamed Ben Abdellah University, Fez, Morocco)
DOI: 10.4018/979-8-3373-0923-1.ch005
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Abstract
In supply chain management, demand forecasting is essential because it affects inventory control, production scheduling, and operational effectiveness. In order to analyse past data and forecast future demand trends, this study suggests applying sophisticated machine learning and artificial intelligence techniques, such as ensemble methods, neural networks, and time series analysis. The approach tackles important issues like controlling seasonality, minimizing supply chain interruptions, and adjusting to changes in demand. The report illustrates how AI-driven demand forecasting may enhance cost-effectiveness and service quality through case studies from industries like manufacturing, retail, and e-commerce. The results demonstrate how combining AI and ML improves supply networks' resilience and flexibility while also giving companies useful information. This research enables the development of intelligent, responsive, and adaptable supply chain ecosystems.
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