Digital and Data-Driven Systems Engineering: Bridging Theory and Practice

Dear Colleagues,

The proliferation of digital and data-driven technologies is fundamentally transforming the field of Systems Engineering (SE). As organizations increasingly adopt digital twins, big data analytics, artificial intelligence, and cyber–physical systems, there is an urgent need for innovative approaches that seamlessly integrate these advancements into SE practices. Systems engineers must acquire expertise in data management and demonstrate advanced digital proficiency to effectively navigate emerging technologies and workflows. Additionally, they must develop the capability to adapt to the increasing complexity of modern engineering challenges, ensuring effective integration across the lifecycle.

This Special Issue aims to explore the intersection of digital and data engineering with systems engineering, emphasizing the transition from theoretical frameworks to practical applications and the digital transformation of the engineering enterprise. It provides a platform for researchers, practitioners, and policymakers to share insights into how digital and data-driven innovations are reshaping the role of systems engineers, as well as SE processes, tools, and outcomes. By fostering dialogue between theory and practice, this Special Issue seeks to address the challenges of designing, developing, and managing systems in an increasingly interconnected world while exploring strategies for enterprise-wide digital transformation.

Topics of Interest:

The scope of this Special Issue includes, but is not limited to, innovative and emerging topics at the intersection of digital technologies and systems engineering, such as the following:

• Next-Generation Model-Based Systems Engineering (MBSE): Evolving MBSE tools and methodologies to support a digital-first approach in the development and operation of systems.
• Digital twins and integrated workflows: Advances in digital twin technologies and their integration into system design, simulation, and lifecycle management.
• Agile and novel development methodologies: The exploration of agile practices and innovative methodologies tailored to digital and data engineering within systems engineering to enhance adaptability, collaboration, and efficiency.
• Simulation and virtual testing environments: The development and application of advanced simulation tools and virtual environments for the robust testing, evaluation, and optimization of systems throughout their lifecycle.
• Big data and predictive analytics in SE: Leveraging big data and predictive analytics to enhance decision-making, optimize performance, and manage complex systems.
• AI-augmented Systems Engineering: Integration of artificial intelligence and machine learning across the Systems Engineering lifecycle to drive innovation and efficiency.
• Cyber-Physical systems and interoperability: Development and management of cyber-physical systems with a focus on interoperability, resilience, and performance optimisation.
• Systems thinking and critical thinking in practice: Leveraging systems thinking and critical analysis to address the complexity of modern engineering challenges and foster innovative problem-solving approaches in digital and data-driven workflows.
• Data-driven risk and resilience management: Employing advanced data analytics to identify, assess, and mitigate risks while enhancing system resilience.
• Enterprise-wide digital transformation: Strategies, frameworks, and case studies highlighting the digital transformation of engineering enterprises and their workflows.
• Sustainability and Circular Economy in SE: Digital solutions for achieving sustainability and fostering circular economy practices within engineering systems.
• Digital skills and education in SE: Developing frameworks and tools for upskilling systems engineers in digital tools, data management, and emerging technologies.

Dr. Melanie King
Guest Editor

Manuscript Submission Information

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