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

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.

Authors

Publication Details

Journal
International Journal of Science and Research Archive
Published
2025-01-10
DOI
https://doi.org/10.30574/ijsra.2025.14.1.0027
Citations
66
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
136.93
Controls
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article

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

Moyosore Taiwo, Uche Nweje
66 citations
International Journal of Science and Research Archive
Forecasting Techniques and Applications
136.93
article

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

Moyosore Taiwo, Uche Nweje
article en
66 citations

Abstract

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.

International Journal of Science and Research ArchiveVol. 14(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Forecasting Techniques and Applications
136.93
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