The AI-assisted DTs role is to simulate the expected behaviour of
manufacturing and supply chain operations before decisions are applied to the real operational environments. The aim of the AI-assisted DT is to capture the dynamic behaviour and operational characteristics of manufacturing and supply chain systems, enabling the evaluation of alternative scheduling policies and operational scenarios under varying conditions. Considering the different requirements of real-time operational decision-making and long-term optimization, two complementary operational loops involve the functionalities of the AI-assisted Digital Twin component. The first loop concerns short-term validation and adaptive scheduling, where the component evaluates candidate planning and scheduling policies. The second loop concerns long-term evaluation and infrastructure-level re-optimization, where the component analyses broader objectives related to sustainability, circularity, and infrastructure utilization, considering the re-optimization of currently running MSCs as well as the adaptation of configurable infrastructure elements where applicable, such as manufacturing routing policies, resource allocation strategies, or energy-management configurations.

• Emulation of underlying infrastructure
• Evaluation of planning and scheduling algorithms towards resilient manufacturing
• Long-term infrastructure optimization based on circularity and
• sustainability