Optimizing Demand Forecasting and Inventory Management With AI-Driven Solutions

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Author(s): K. Vijayakumar (Panimalar Engineering College, India)and L. A. Anto Gracious (R.M.K. College of Engineering and Technology, India)
Copyright: 2026
Pages: 24
Source title: Innovations in Green and Energy-Efficient Warehousing
Source Author(s)/Editor(s): Mohamed Amine Frikha (King Faisal University, Saudi Arabia), Mohieddine Rahmouni (King Faisal University, Saudi Arabia)and Ben Othman Soufiane (King Faisal University, Saudi Arabia)
DOI: 10.4018/979-8-3373-3176-8.ch005

Keywords: Energy / Engineering Science Reference / Nuclear & Energy Engineering / Science & Engineering

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Abstract

This chapter explores the transformative role of Artificial Intelligence (AI) in optimizing demand forecasting and inventory management across various industries. Traditional methods, while useful, often fail to meet the demands of modern, dynamic supply chains, leading to inefficiencies such as stockouts and overstocking. AI-driven solutions, leveraging machine learning, deep learning, and predictive analytics, offer enhanced accuracy, efficiency, and cost reductions. By automating key processes and integrating with advanced technologies like IoT and blockchain, AI enables real-time decision-making and smarter resource allocation. The chapter also examines challenges in data quality, system integration, and workforce adaptation, while highlighting future research directions in AI adoption, optimization, and the integration of emerging technologies.

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Optimizing Demand Forecasting and Inventory Management With AI-Driven Solutions

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