Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: This article examines the transformative impact of artificial intelligence and machine learning technologies on e-commerce demand forecasting and inventory management systems. The integration of advanced machine learning techniques, including ensemble methods like XGBoost and deep learning architectures such as LSTM networks, has revolutionized how businesses predict consumer demand and optimize inventory levels. The article explores how these sophisticated algorithms work in concert with innovative feature engineering approaches, including Fourier transforms for seasonality detection, natural language processing for sentiment analysis, and dimensionality reduction techniques for managing complex datasets. Furthermore, the article investigates the cutting-edge application of computer vision technologies in detecting emerging trends through visual analysis of social media content and user-generated images. A critical finding is that while AI systems excel at processing vast amounts of data and identifying complex patterns, the most effective demand forecasting systems emerge from synergistic human-AI collaboration, where human expertise provides contextual understanding, strategic direction, and ethical oversight while AI delivers computational power and pattern recognition capabilities. This comprehensive analysis demonstrates that the future of e-commerce inventory management lies not in replacing human judgment with artificial intelligence, but in creating sophisticated partnerships that leverage the unique strengths of both human intuition and machine intelligence to achieve superior forecasting accuracy and operational efficiency.