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AI-Driven Demand Forecasting and Inventory Management
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Author(s): Nirali Dave (Indus University, India), Dhanraj P. Tambuskar (Sri Balaji University, Pune, India), Reena Partha Nath (Independent Researcher, India), P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), T. C. Manjunath (Rajarajeswari College of Engineering, India)and Puneet Kumar Gupta (The ICFAI University, Dehradun, India)
Copyright: 2026
Pages: 30
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.ch004
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Abstract
Overall, successful sustainability companies exist across all industries. Two of the most fundamental components of an inventory management system are deeply interconnected and are essential for balancing customer demand with product availability, minimising costs, improving customer satisfaction, and enhancing operational efficiency. Demand forecasting involves historical seasonal patterns, relevant data, and allows businesses to inform production planning, levels, staffing, and distribution. The ability to anticipate helps companies avoid both understocking, holding costs, waste, and obsolescence. With globalisation and increased competition, crucial to align their resources with market requirements and remain agile in a rapidly changing environment. Several methods and technologies are employed in demand forecasting, ranging from qualitative techniques like using time-series analysis, AI, and predictive significantly improve demand insights proactive.
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