Predictive Analytics in Supply Chain Management: The Role of AI and Machine Learning in Demand Forecasting

Abstract

Predictive analytics has emerged as a transformative approach in supply chain management (SCM), significantly enhancing efficiency, accuracy, and adaptability. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in demand forecasting, a critical component of SCM. The study highlights how AI and ML algorithms can process vast datasets, identify patterns, and deliver precise demand forecasts, enabling businesses to optimize inventory levels, reduce waste, and enhance customer satisfaction.

Key AI and ML techniques, such as neural networks, support vector machines, and deep learning, are examined for their contributions to improving predictive accuracy in dynamic market conditions. The paper also delves into real-world applications, including demand-supply balancing, seasonal trend analysis, and anomaly detection, demonstrating how organizations leverage these technologies to achieve a competitive edge.

Additionally, the research addresses challenges such as data quality, algorithmic biases, and the need for skilled personnel to manage AI-driven systems. Ethical considerations and the importance of maintaining transparency in AI-based decision-making processes are also discussed. The review emphasizes the potential of AI and ML to revolutionize demand forecasting by making supply chains more responsive and resilient.

In conclusion, this paper underscores the importance of integrating predictive analytics with AI and ML for proactive supply chain management, encouraging further research and innovation in this rapidly evolving field. By providing a comprehensive analysis of existing technologies and future trends, this study offers valuable insights for practitioners, researchers, and policymakers aiming to harness the power of AI and ML in SCM.