Advancing Supply Chains with Cloud-Integrated Digital Twin Vehicle Pipelines Using Neural Networks, Microservices, and Oracle Integration
DOI:
https://doi.org/10.15662/IJARCST.2023.0606004Keywords:
Digital Twin Vehicles, Supply Chain Optimization, Cloud-Integrated Pipelines, Neural Networks, Microservices, Containerization, Oracle Integration, Predictive Analytics, Generative AIAbstract
The integration of digital twin technology with cloud computing has emerged as a transformative approach for optimizing supply chain operations. This paper presents a comprehensive framework for cloud-integrated generative pipelines for digital twin vehicles, leveraging neural networks, microservices, containerization, and Oracle integration. The framework enables real-time simulation, predictive analytics, and intelligent decision-making across supply chain networks, enhancing operational efficiency, resilience, and scalability. Neural networks are employed for predictive modeling and anomaly detection, while microservices and containerization ensure modularity, interoperability, and rapid deployment of services. Oracle integration facilitates secure, reliable, and consistent data management between cloud and enterprise systems. Experimental results demonstrate improvements in resource utilization, latency reduction, and predictive accuracy, highlighting the potential of combining advanced AI, cloud infrastructure, and digital twin technologies to revolutionize supply chain management. This study provides actionable insights for designing next-generation intelligent supply chain ecosystems.
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