Smart supply chain optimization has become a critical area of research in modern logistics and industrial engineering, driven by the increasing complexity of global trade networks and the need for real-time decision-making. Traditional supply chain systems often suffer from inefficiencies such as demand-supply mismatches, inventory overstocking, transportation delays, and lack of real-time visibility. The integration of Machine Learning (ML) and cloud-based predictive analytics platforms offers a transformative solution by enabling data-driven forecasting, intelligent automation, and adaptive decision-making across supply chain networks
Smart supply chain optimization has become a critical area of research in modern logistics and industrial engineering, driven by the increasing complexity of global trade networks and the need for real-time decision-making. Traditional supply chain systems often suffer from inefficiencies such as demand-supply mismatches, inventory overstocking, transportation delays, and lack of real-time visibility. The integration of Machine Learning (ML) and cloud-based predictive analytics platforms offers a transformative solution by enabling data-driven forecasting, intelligent automation, and adaptive decision-making across supply chain networks
This study explores smart supply chain optimization using machine learning and cloud-based predictive analytics. It reviews existing literature, evaluates system architectures, and proposes a structured research methodology. The research highlights the role of AI-driven forecasting models, real-time analytics, and cloud integration in improving supply chain resilience, efficiency, and sustainability. Challenges such as data quality, system interoperability, cybersecurity, and scalability are also discussed. The study concludes that integrating ML with cloud analytics significantly enhances supply chain performance and enables intelligent, adaptive, and resilient logistics ecosystems
