Published Paper Details:

Abstract Accurate demand forecasting is the backbone of an efficient retail supply chain. Traditional methods, such as moving averages and linear regression, often fail to navigate the complexities of modern consumer behavior, characterized by rapid trend shifts and external disruptions. This study explores the transformative role of Artificial Intelligence (AI) in bridging this gap. By utilizing machine learning and deep learning, AI can process massive datasets including social media trends, weather patterns, and economic shifts to predict demand with unprecedented precision. The research highlights how AI-driven insights lead to optimized inventory levels, a significant reduction in stock-outs, and improved overall responsiveness. However, the study also notes that the transition to AI is not without hurdles; issues regarding data quality, high implementation costs, and the need for organizational cultural shifts remain prevalent. Ultimately, the findings suggest that while AI is a powerful tool for competitive advantage, its success depends on a balanced integration of technology and human expertise.