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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

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Research and review articles are invited for publication in January 2026 (Volume 18, Issue 1)

Leveraging Artificial Intelligence for predictive supply chain management, focus on how AI- driven tools are revolutionizing demand forecasting and inventory optimization

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  • Leveraging Artificial Intelligence for predictive supply chain management, focus on how AI- driven tools are revolutionizing demand forecasting and inventory optimization

Uche Nweje 1, * and Moyosore Taiwo 2

1 Department of Business Administration, University of New Haven, USA.

2 Supply Chain Manager, Pharma Technical division, Roche, Canada.

Review Article

International Journal of Science and Research Archive, 2025, 14(01), 230-250

Article DOI: 10.30574/ijsra.2025.14.1.0027

DOI url: https://doi.org/10.30574/ijsra.2025.14.1.0027

Received on 29 November 2024; revised on 06 January 2025; accepted on 08 January 2025

The dynamic landscape of global supply chains necessitates innovative solutions to tackle challenges in demand forecasting and inventory optimization. Traditional methods, often constrained by limited adaptability and scalability, struggle to manage the complexities of modern supply chains. Artificial Intelligence (AI) has emerged as a transformative force, enabling predictive supply chain management through advanced data analytics, machine learning algorithms, and real-time decision-making capabilities. By harnessing AI-driven tools, businesses can accurately forecast demand patterns, reduce stockouts, and minimize excess inventory, thereby improving operational efficiency and customer satisfaction. AI-powered systems leverage historical data, market trends, and external factors such as economic shifts and weather conditions to provide precise predictions. These tools enhance responsiveness by identifying potential disruptions and enabling proactive measures, ensuring supply chain resilience. Furthermore, AI facilitates seamless integration across supply chain nodes, fostering collaboration and enabling data-driven insights that were previously unattainable. From predictive analytics for demand forecasting to intelligent automation in inventory management, AI-driven tools are revolutionizing the traditional supply chain model. Case studies reveal substantial reductions in holding costs, improved lead times, and enhanced supply chain visibility. However, challenges such as data quality, system integration, and ethical considerations in AI deployment remain critical areas for exploration. This paper looks into the transformative impact of AI on predictive supply chain management, highlighting key advancements, practical applications, and challenges. The insights presented underscore the pivotal role of AI in driving efficiency and innovation in an increasingly complex and competitive global economy.

Artificial Intelligence; Predictive Supply Chain Management; Demand Forecasting; Inventory Optimization; Machine Learning; Supply Chain Resilience

https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0027.pdf

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Uche Nweje and Moyosore Taiwo. Leveraging Artificial Intelligence for predictive supply chain management, focus on how AI- driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 2025, 14(01), 230-250. Article DOI: https://doi.org/10.30574/ijsra.2025.14.1.0027.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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