Digital technologies are transforming modern supply chain systems by enabling real-time decision-making, predictive analytics, and operational automation. Among these technologies, Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics (BDA) have emerged as critical drivers of supply chain optimization. This study investigates the individual and integrated effects of AI, ML, and BDA on supply chain optimization using empirical evidence from large-scale digital logistics operations in the United States, with a primary focus on Amazon and its logistics ecosystem. A quantitative research design was employed, collecting survey data from 300 supply chain professionals working in logistics, operations management, and analytics roles. Structural Equation Modeling (SEM) using IBM AMOS was applied to examine the relationships among technology adoption and operational performance outcomes. Confirmatory Factor Analysis validated the measurement model with strong reliability and validity indicators. The findings indicate that AI, ML, and BDA each have significant positive effects on supply chain optimization, jointly explaining a substantial proportion of operational performance variance. Additionally, AI adoption strengthens machine learning capability, while ML capability enhances big data analytics integration, highlighting a cascading technology synergy within digital supply chains.

Keywords

Artificial Intelligence, Machine Learning, Big Data Analytics, Supply Chain Optimization, Digital Logistics