TOWARDS NEXT-GENERATION SMART SUPPLY CHAIN MANAGEMENT: AI-DRIVEN BIG DATA ANALYTICS FOR ROBUST DEMAND FORECASTING, PREDICTIVE METHODS, AND APPLICATIONS

Abstract

The growing complexity and volatility of global supply chains has amplified the need for accurate and timely demand forecasting to ensure resilience, efficiency, and competitiveness. Traditional statistical approaches, while effective in stable environments, often fail to capture the nonlinearities, uncertainties, and disruptions that characterize modern markets. Recent advances in artificial intelligence (AI) and big data analytics have opened new opportunities for developing predictive systems capable of learning from massive, heterogeneous, and dynamic datasets. This paper provides a comprehensive investigation into the role of AI-driven big data analytics in enabling robust demand forecasting for next-generation smart supply chain management. The study first reviews established forecasting methods, including classical time-series models such as ARIMA and exponential smoothing, before examining the transition to machine learning techniques (e.g., random forests, gradient boosting, support vector regression) and deep learning architectures (e.g., LSTM, GRU, and transformer-based models). Particular attention is given to hybrid and ensemble approaches that combine statistical foundations with AI techniques to enhance predictive accuracy and robustness. Beyond methodological advances, the paper highlights the enabling role of big data infrastructures, including distributed computing platforms, cloud-based analytics, and IoT-driven data pipelines, which facilitate real-time processing and integration of multi-source data streams such as ERP records, sensor data, social media signals, and external economic indicators. Applications across diverse industries including retail, e-commerce, manufacturing, healthcare, and logistics are discussed to illustrate how AI-enhanced forecasting improves inventory optimization, reduces lead times, and enhances service-level performance. In addition, the paper critically examines emerging challenges, such as data heterogeneity, model interpretability, ethical considerations, and privacy constraints, which must be addressed to enable sustainable and fair adoption of predictive analytics in supply chains. Finally, the paper identifies future research opportunities, emphasizing the integration of explainable AI, reinforcement learning, digital twins, and blockchain-enabled transparency to further strengthen predictive accuracy and trustworthiness. By synthesizing methods, applications, and open challenges, this study positions AI-driven big data analytics as a transformative enabler of resilient, adaptive, and smart supply chain ecosystems.