AI-Powered Supply Chain Optimization: Enhancing Resilience through Predictive Analytics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P102Keywords:
AI in supply chain, predictive analytics, demand forecasting, inventory optimization, risk management, logistics automation, supply chain visibility, data-driven decision-making, AI integration, supply chain efficiencyAbstract
In the rapidly evolving global business environment, supply chain management (SCM) has become increasingly complex and susceptible to disruptions. Traditional supply chain strategies, which often rely on historical data and manual decision-making, are inadequate in addressing the dynamic challenges posed by market volatility, geopolitical tensions, and natural disasters. Artificial Intelligence (AI) and predictive analytics offer a transformative solution by enabling real-time insights, proactive risk management, and optimized decision-making. This paper explores the application of AI and predictive analytics in supply chain optimization, focusing on how these technologies enhance resilience and operational efficiency. Through a comprehensive review of existing literature, case studies, and empirical analysis, the paper highlights the key benefits, challenges, and future directions of AI-powered supply chain management. The findings underscore the critical role of AI in building resilient and agile supply chains capable of navigating uncertainties and achieving sustainable growth
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