Cyber-Physical Materials and Supply Network Intelligence with Artificial Intelligence and Digital Twins

Dear Colleagues,

The digital transformation in industries and manufacturing networks has brought about fundamental changes in the way materials are managed and resource flows are coordinated in supply chains. In modern industrial environments, materials management is no longer limited to inventory planning or goods flow control but has become an intelligent and data-driven system that requires real-time data analysis, forecasting of future conditions, and adaptive decision-making in complex production and distribution networks.

In the meantime, cyber–physical systems, artificial intelligence, and digital twin technology are recognized as key infrastructures for creating intelligent materials management systems. Digital twins enable the creation of digital versions of physical systems and simulate and analyze the operating conditions of supply chains and production systems using real-time data. In addition, artificial intelligence algorithms are able to identify complex patterns in operational data and provide optimal decisions for material flow management, resource allocation, and coordination of activities in supply chains.

The aim of this Special Issue is to collect innovative research that explores conceptual frameworks, analytical models, computational methods, and industrial applications related to the intelligentization of materials management and supply networks in the context of cyber–physical systems, artificial intelligence, and digital twins.

The increasing complexity of global production and supply networks, the increasing uncertainty in markets, and the need to respond quickly to environmental changes have exposed the limitations of traditional approaches to materials management and supply chain planning. Traditional methods often rely on historical data and static models and have limited ability to deal with sudden disruptions, demand changes, and the dynamics of production networks.

Recent advances in the industrial Internet of Things, cyber–physical systems, artificial intelligence, and digital twin technology have created new opportunities for the transformation of materials management and supply networks. These technologies enable the creation of intelligent systems that can monitor the status of physical systems in real time, predict future system behavior, and evaluate different operational scenarios for better decision-making.

Despite the rapid growth of research in each of these areas, the integration of AI, digital twins, and cyber–physical systems in materials management and supply chains is still sparsely explored in the scientific literature. Therefore, establishing a focused research platform to explore this technological convergence could help develop new theoretical and applied frameworks in this area.

Topics of interest include, but are not limited to, the following:

• Digital twin architectures for materials and supply network management;
• AI-driven materials planning and intelligent inventory systems;
• Cyber–physical infrastructures for real-time supply network monitoring;
• Intelligent resource orchestration in smart manufacturing systems;
• Autonomous materials management and adaptive supply networks;
• Predictive analytics for material flow optimization;
• Simulation and digital twin models for industrial supply systems;
• AI-enabled risk management and resilience in supply networks;
• Integration of IoT sensing and digital twins in logistics systems;
• Sustainable materials management using AI and cyber–physical technologies;
• Decision intelligence platforms for supply network optimization.

Dr. Hamed Nozari
Dr. Zornitsa Yordanova
Guest Editors

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