Systems Engineering Tools and Digital Twin Technologies for Industry 6.0

Dear Colleagues,

Industry 6.0 marks the next evolutionary step in global industrial transformation, extending beyond the human-centric and sustainable principles of Industry 5.0. It envisions sentient, autonomous, and symbiotically networked industrial ecosystems in which humans, intelligent machines, and adaptive environments interact seamlessly. Driven by hyper-connectivity, collective intelligence, and biological–digital convergence, Industry 6.0 emphasizes self-evolving systems, zero-latency decision-making, planet-centric sustainability, and trusted autonomy across all layers of production and organizational networks.

Systems engineering plays a foundational role in shaping these ultra-complex, multi-layered ecosystems. Advanced systems engineering methodologies enable the specification, modelling, orchestration, and continuous evolution of interconnected cyber–physical–human–biological systems. As Industry 6.0 introduces new dimensions—such as autonomous swarms, cognitive manufacturing agents, bio-integrated sensors, and self-adaptive supply webs—systems engineering ensures that these technologies remain interoperable, ethical, resilient, and aligned with societal and planetary goals.

Digital twins in Industry 6.0 evolve into Symbiotic Cognitive Twins–persistent, intelligent, multi-domain representations capable of self-learning, self-updating, and collaborative reasoning. These next-generation digital twins not only mirror physical systems but also anticipate emerging states, negotiate system behavior, co-design operational strategies, and autonomously coordinate with other digital and physical entities. Combined with advanced systems engineering tools, cognitive digital twins will enable real-time optimization, proactive adaptation, and decentralized decision-making across large-scale, distributed industrial ecosystems.

This Special Issue invites original research, case studies, and comprehensive reviews addressing how systems engineering tools, cognitive digital twins, and advanced AI technologies shape the realization of Industry 6.0. Topics of interest include modelling methodologies, autonomous system integration, collective intelligence, ethical autonomous behavior, bio-cyber interfaces, ultra-resilient supply networks, and new theoretical frameworks for symbiotic socio-technical systems. We aim to advance interdisciplinary insights and guide the engineering of next-generation industrial ecosystems that are autonomous, mutually beneficial, sustainable, and aligned with planetary well-being.

Papers are being sought in the following areas:

• Systems engineering tools for autonomous, symbiotic industrial and business ecosystems;
• Cognitive and symbiotic digital twin technologies for predictive, prescriptive, and cooperative decision-making;
• High-fidelity, real-time simulation and virtual universes (e.g., NVIDIA Isaac Sim, Omniverse, Modelica, MATLAB/Simulink);
• Evolving and adaptive Model-Based Systems Engineering (MBSE), including SysML v2, knowledge graphs, and semantic engineering;
• Next-generation Cyber–Physical–Human–Biological Systems (CPHBS);
• Autonomous human–robot–AI collaboration (cobots, humanoids, swarm robotics, autonomous drones);
• Integration of trustworthy AI, machine learning, generative AI, and collective intelligence into engineering workflows;
• Virtual commissioning, formal verification, and self-validation of autonomous systems;
• Digital twins for planet-centric sustainability, circularity, climate resilience, and regenerative industry;
• Hybrid modelling approaches (system dynamics, agent-based, discrete event, AI-driven modelling, and hybrid intelligence modelling);
• Real-time AIoT, bio-IoT, and edge-intelligent sensing for zero-latency data ecosystems;
• Ethical, transparent, and accountable autonomous systems aligned with Industry 6.0 values.

Prof. Dr. Andrej Škraba
Guest Editor

Manuscript Submission Information

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• Industry 6.0
• symbiotic digital twin technology
• systems engineering tools
• cyber-physical-human-biological systems (CPHBS)
• autonomous human–AI–robot collaboration
• model-based systems engineering (MBSE)
• trustworthy AI and collective intelligence
• real-time hyper-connected simulation
• autonomous robotics and swarm systems
• AIoT/bio-IoT sensor integration