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

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 9899

Special Issue Editor

Dr. Melanie King

E-Mail Website
Guest Editor
The Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: digital and data engineering; systems of systems; enterprise engineering; digital innovation; software development

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systems engineering
  • digital engineering
  • data engineering
  • digital twins
  • big data analytics
  • artificial intelligence
  • cyber-physical systems
  • model-based systems engineering (MBSE)
  • digital transformation
  • data-driven risk management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

46 pages, 1895 KB  
Article
Aero-Engine Quality Assessment Under the RAMS Framework: Coupling Interval Type-2 Fuzzy Group Decision-Making with PLS-SEM for Dimensional Correlation Modelling
by Yuhui Wang, Sining Xu, Xiangjun Cheng and Borui Xie
Systems 2026, 14(5), 464; https://doi.org/10.3390/systems14050464 (registering DOI) - 24 Apr 2026
Viewed by 110
Abstract
Aero-engine quality assessment under the RAMS framework faces two persistent challenges: the inherent epistemic and linguistic uncertainty in expert evaluation, and the systematic neglect of inter-dimensional coupling. This paper proposes an integrated assessment method that combines Interval Type-2 Fuzzy Sets (IT2FS)-based group decision-making [...] Read more.
Aero-engine quality assessment under the RAMS framework faces two persistent challenges: the inherent epistemic and linguistic uncertainty in expert evaluation, and the systematic neglect of inter-dimensional coupling. This paper proposes an integrated assessment method that combines Interval Type-2 Fuzzy Sets (IT2FS)-based group decision-making with Partial Least Squares Structural Equation Modeling (PLS-SEM). At the measurement level, IT2FS encodes dual-layered uncertainty through the Footprint of Uncertainty (FOU); multi-expert judgments are aggregated via the fuzzy weighted geometric average operator and defuzzified using the Karnik–Mendel algorithm. At the structural level, a reflective second-order PLS-SEM model built on the RAMS framework enables parametric estimation and significance testing of inter-dimensional coupling. Validation on a 63-engine turbofan dataset confirms that all measurement model criteria are satisfied, the second-order model explains 82.4% of the variance in overall quality (R2 = 0.824), and predictive relevance is strong (Q2 = 0.567). Comparative experiments against three benchmark methods demonstrate consistent advantages in quality grade discrimination, information richness, sensitivity to technical improvements, and ranking robustness. These properties position the framework as a statistically rigorous, model-based complement to existing condition-monitoring and digital health management systems for complex propulsion systems, supporting quantitative decision-making within digital engineering programmes. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
26 pages, 3163 KB  
Article
Identification of Physical Boundary Conditions for Mechatronic Test-Case Generation Using Large Language Models and MBSE System Models
by Matthias May, Georg Jacobs, Simon Dehn, Gregor Höpfner, Thilo Zerwas, Kathrin Boelsen and Sebastian Hacker
Systems 2026, 14(3), 302; https://doi.org/10.3390/systems14030302 - 12 Mar 2026
Viewed by 529
Abstract
Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With [...] Read more.
Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
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20 pages, 614 KB  
Article
A Systemic Approach to Decision Support and Automation: The Role of Big Data Analytics and Real-Time Processing in Management Information Systems
by Abdullah Önden
Systems 2026, 14(2), 216; https://doi.org/10.3390/systems14020216 - 19 Feb 2026
Cited by 2 | Viewed by 1034
Abstract
Management Information Systems (MIS) are increasingly expected to support real-time, evidence-based decision-making and to automate routine workflows. Nevertheless, many organizations still struggle to transform heterogeneous, high-velocity data into trustworthy decision support and process execution at scale. Adopting a socio-technical systems perspective, this study [...] Read more.
Management Information Systems (MIS) are increasingly expected to support real-time, evidence-based decision-making and to automate routine workflows. Nevertheless, many organizations still struggle to transform heterogeneous, high-velocity data into trustworthy decision support and process execution at scale. Adopting a socio-technical systems perspective, this study explores the interplay between data infrastructure, analytics capabilities, and decision-making processes. We adopted a mixed-methods design, which incorporated (i) a cross-sectional survey of MIS professionals (n = 150) from organizations across three industries (retail, healthcare, and financial services) and (ii) 12 semi-structured stakeholder interviews. The survey data show that the performance outcomes of the organizations reporting a higher level of BDA and maturity in real-time processing are stronger, characterized by self-reported average revenue growth of 12% among retailers, a material decrease in operational costs, and improvements in overall system efficiency. These figures reflect respondents’ estimates rather than audited financial statements. BDA, real-time processing, and data infrastructure readiness were statistically significant predictors in an OLS regression model of perceived organizational performance, explaining a substantial percentage of variance (R2 = 0.72). The insights provided by the interviews explain how these effects were achieved: performance improvements materialized through real-time feedback loops where streaming and batch pipelines were integrated, data-quality controls were embedded in ingestion, and decision outputs were linked to workflow automation. The research contributes a holistic view to the MIS capability framework, linking data infrastructure decisions to the timeliness of decisions and automation preparedness, while contributing to the theoretical refinement of MIS capability frameworks and offering practical guidance for governance and technology selection. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
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23 pages, 3985 KB  
Article
Enabling Humans and AI Systems to Retrieve Information from System Architectures in Model-Based Systems Engineering
by Vincent Quast, Georg Jacobs, Simon Dehn and Gregor Höpfner
Systems 2026, 14(1), 83; https://doi.org/10.3390/systems14010083 - 12 Jan 2026
Cited by 1 | Viewed by 1472
Abstract
The complexity of modern cyber–physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as model-based systems engineering (MBSE). By creating system models, MBSE promises significant advantages [...] Read more.
The complexity of modern cyber–physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as model-based systems engineering (MBSE). By creating system models, MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through artificial intelligence (AI), especially generative AI, has so far focused mainly on easing the introduction phase, for example by using large language models (LLMs) to assist in creating system models. However, generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph Retrieval-Augmented Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
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25 pages, 5491 KB  
Article
When BIM Meets MBSE: Building a Semantic Bridge for Infrastructure Data Integration
by Joseph Murphy, Siyuan Ji, Charles Dickerson, Chris Goodier, Sonia Zahiroddiny and Tony Thorpe
Systems 2025, 13(9), 770; https://doi.org/10.3390/systems13090770 - 2 Sep 2025
Viewed by 1957
Abstract
The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient [...] Read more.
The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
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45 pages, 9147 KB  
Article
Decision Analysis Data Model for Digital Engineering Decision Management
by Gregory S. Parnell, C. Robert Kenley, Devon Clark, Jared Smith, Frank Salvatore, Chiemeke Nwobodo and Sheena Davis
Systems 2025, 13(7), 596; https://doi.org/10.3390/systems13070596 - 17 Jul 2025
Cited by 2 | Viewed by 3650
Abstract
Decision management is the systems engineering life cycle process for making program/system decisions. The purpose of the decision management process is: “…to provide a structured, analytical framework for objectively identifying, characterizing and evaluating a set of alternatives for a decision at any point [...] Read more.
Decision management is the systems engineering life cycle process for making program/system decisions. The purpose of the decision management process is: “…to provide a structured, analytical framework for objectively identifying, characterizing and evaluating a set of alternatives for a decision at any point in the life cycle and select the most beneficial course of action”. Systems engineers and systems analysts need to inform decisions in a digital engineering environment. This paper describes a Decision Analysis Data Model (DADM) developed in model-based systems engineering software to provide the process, methods, models, and data to support decision management. DADM can support digital engineering for waterfall, spiral, and agile development processes. This paper describes the decision management processes and provides the definition of the data elements. DADM is based on ISO/IEC/IEEE 15288, the INCOSE SE Handbook, the SE Body of Knowledge, the Data Management Body of Knowledge, systems engineering textbooks, and journal articles. The DADM was developed to establish a decision management process and data definitions that organizations and programs can tailor for their system life cycles and processes. The DADM can also be used to assess organizational processes and decision quality. Full article
(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)
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