The rapid of evolution of Industry 4.0 has transformed supply chain management by integrating advanced technologies such as the Internet of Things (loT), Artificial Intelligence (AI), big data, and machine learning. However, traditional supply chains struggle to adapt to this data-driven era, creating a need for supply Chain Analytics (SCA) to optimize operations, enhance decision-making, and improve resilience. This study explores the application of SCA within the Industry 4.0 framework and provides a comprehensive between traditional and industry 4.0 supply chains. The research contributes by offering a practical step-by-step guide to conducting a supply chain analysis, this study reveals that implementing SCA can improve operational efficiency, reduce costs, and enhance supply chain resilience through real-time monitoring and predictive analytics. Furthermore, a machine learning model (LGBM Regressor, Random Forest, Neural Network) demonstrated high accuracy in forecasting supply chain performance with minimal overfitting. The finding highlights the managerial implications of adopting SCA, including optimizing profitability across sales channels, benchmarking warehouse performance, and addressing implementation, challenge such as data integration and skill gaps. By leveraging SCA, organizations can transition to agile, data-driven supply chains that thrive in the competitive Industry 4.0 landscape such as blockchain and sustainability metrics in SCA applications.