Machine Learning-Driven Demand Forecasting: A Comparative Analysis of Advanced Techniques and Real-Time Integration

Abstract

Recent advancements in machine learning techniques have revolutionized demand forecasting capabilities, offering unprecedented opportunities for supply chain optimization. This article presents a systematic analysis of cutting-edge machine learning approaches, including deep learning architectures, ensemble methods, and transfer learning techniques, examining their effectiveness in enhancing forecasting accuracy. Through rigorous evaluation of 127 implementation cases across retail and manufacturing sectors, the article demonstrates that hybrid models incorporating real-time IoT data achieve a 34.6% improvement in prediction accuracy compared to traditional forecasting methods. The article further identifies critical success factors for implementation, addressing key challenges such as data quality management, computational resource optimization, and system integration. The findings reveal that organizations implementing these advanced techniques reported a 28% reduction in inventory holding costs and a 42% decrease in stockout incidents. This article contributes to both theoretical understanding and practical application by providing a comprehensive framework for selecting and implementing appropriate machine learning techniques based on specific industry contexts and data characteristics. The results underscore the transformative potential of machine learning in modern supply chain management, while also highlighting the importance of systematic implementation approaches for maximizing business value.