A Low-Cost Digital Twin Framework for Sustainable Manufacturing Education Integrating SAP, Node-RED, and AI-Based Decision Support

The excessive cost and complexity of Industry 4.0 laboratory infrastructure limit the adoption of Digital Twin concepts in engineering education. This paper proposes a low-cost Digital Twin framework for sustainable manufacturing education integrating SAP NetWeaver, Node-RED, and AI-based decision support. The framework adopts a layered architecture that connects PLC-based simulation, IoT middleware, enterprise resource planning systems, and intelligent decision-making components. Node-RED enables real-time data exchange, while SAP NetWeaver provides enterprise-level integration through OData services. An AI module supports decision-making for production and inventory management. The framework has been validated through the implementation of a functional prototype and a series of end-to-end integration tests that evaluated communication reliability, system interoperability, API response performance, and AI-assisted decision-support capabilities. Competency-based mapping aligns the framework with Industry 4.0 engineering skills, supporting its use in academic environments. A sustainability assessment highlights reductions in infrastructure cost, energy consumption, and resource usage compared to traditional laboratory approaches. The results indicate that the framework has the potential to provide a scalable and accessible solution for teaching Digital Twin concepts, pending further classroom-based validation.