CONTEXT-AWARE DIGITAL TWIN ARCHITECTURES FOR REAL-TIME TRANSPORTATION AND WAREHOUSE OPTIMIZATION IN CLOUD-NATIVE SUPPLY CHAIN PLATFORMS

Context-aware digital twins are emerging as enabling architectures for coordinating responses to disruptions across inventory, warehouse, and transportation operations in cloud-based supply-chain platforms. They are important because they can be used to link continually evolving operational representations with optimization services that are able to react to traffic conditions, order arrivals, equipment status, changes in inventory, and facility constraints. This article reviews peer-reviewed journal literature on digital supply-chain twins, warehouse intelligence, dynamic vehicle routing, Internet of Things-enabled logistics, microservice architectures, and computational modelling. Some studies indicate that digital-twin approaches can improve visibility, scenario evaluation, and decision coordination in specific settings, but there is very little evidence of fully integrated real-time optimization of transportation and warehouse operations. Closed-loop orchestration has not been a primary focus of existing implementations, which tend to focus on warehouse simulation or monitoring, route adjustment, or resilience analysis across logistics domains. Other limitations include insufficient semantic interoperability, inadequate model fidelity, inconsistent latency assessment, deployment evidence, and consideration of cybersecurity and human intervention. The field therefore needs architectures that integrates event-driven context management, federated twin models, scalable optimization, auditable decision services, and explicit uncertainty handling.