Model-based Systems Engineering (MBSE) supports managing complex engineering projects. A pivotal element of MBSE is the concept of views which provide tailored representations of a system model to address stakeholder concerns. Despite standards describing the use and generation of views, the adoption and practical implementation of MBSE views and viewpoints in industrial practice remain insufficiently explored. Interviews with German practitioners reveal a disconnect between theory and practice: views and viewpoints and the involvement in MBSE are often limited to technical experts, excluding non-technical stakeholders. High complexity, abstract representations, and tool-related barriers impede broader engagement. The findings suggest stakeholder-specific, accessible visualizations integrated into easy-to-use tools to improve understanding, collaboration, and decision-making.

The integration of Model-Based Systems Engineering (MBSE) and data analytics (DA) has introduced a novel approach, Data-Driven Model-Based Systems Engineering (DDMBSE), which combines structured system modelling with data-driven insights. DDMBSE offers the potential for improvements in model optimisation, economic efficiency and the implementation of dynamic system updates based on real-time data. However, the diverse applications of DDMBSE lack a structured overview of its use cases. This paper addresses this gap by proposing a comprehensive framework for the categorisation and description of DDMBSE use cases. It provides users with a structure to navigate within DDMBSE landscape, consolidate knowledge, and identify underexplored areas for future research. This contribution establishes a foundation for advancing the implementation of DDMBSE across industries and fostering its adoption.

This paper presents a systematic method and coding scheme to convert concept maps into bi-partite graphs that can be computationally evaluated for topological complexity measurements. The coding scheme is focused on splitting concepts with multiple elements embedded and linking these objectively. The guidance for this is established and the method presented with examples. The motivation for this work is to establish a means to objectively compare concept maps generated by individuals at the beginning and the end of an intervention to measure the impact of the intervention. The reliability of the coding scheme is presented in separate work.

Digital Twins are digital representations of products or product-service systems comprising a Digital Master, which consists of product description models, and a Digital Shadow, which encompasses data collected throughout the product’s life cycle. To create a Digital Twin, the Digital Master and Digital Shadow must be interlinked. The Digital Master, Digital Shadow, and thus their twinning can vary in complexity and analytical capabilities. This paper introduces a systematic description of six twinning levels ranging from simple data exchange based on generic models to more complex forms targeting model parameter and Digital Twin goal optimization. The example of a valve is used for illustration. The presented description aids in understanding the potential of Digital Twins and serves as a guide to select appropriate twinning levels based on specific product requirements and use cases.

Today, Manufacturing companies are adopting a servitization strategy and Product-Service System model to enhance value and remain competitive. Often, this transition also means to embrace a System-of-Systems (SoS) perspective. Concurrently, companies face challenges with volatile, uncertain, complex, and ambiguous (VUCA) environments. One way to tackle VUCA is to utilize simulation modeling. However, developing SoS simulations can be complex and cumbersome. This paper extracts lessons learned from six case studies to identify effective and ineffective practices in developing simulation models. The analysis has led to nine design principles for more effective simulation modeling. Furthermore, the paper explores simulation techniques for modeling SoS and discusses effective VUCA management. Finally, the paper proposes four future research directions to advance SoS simulation research.

CAD tasks require engineering designers to manage cognitive, perceptual, and motor demands while solving complex design problems. Understanding the relationship between workload (WL) and CAD performance is essential for improving design outcomes and processes. However, this relationship, particularly under varying task complexities, remains insufficiently explored. This study investigates WL-performance relationships in two CAD modelling tasks of differing complexity. WL was measured with NASA TLX, including its individual components. CAD performance was evaluated and described through outcomes and processes using multiple metrics. The results revealed significant monotonic relationships between WL and performance, with stronger correlations in the high-complexity task.

How well a team can design something depends on how well their collective understanding comes together. In the design of modern complex systems this involves multiple conceptualisations of the system undergoing design. These perspectives become instantiated in a large volume of design description that is deep, wide and diverse. This must carry shared meaning reliably, which is impossible to assure if the ontology in which every statement is nested is left implicit and unmanaged. This paper outlines a technical approach to assure ontological harmony without necessarily or only employing formal semantically rigorous knowledge representations. It empowers an incremental investment in description coverage and ontological coherence, better supporting the spectrum of thinking styles and description needs that design teams encounter when taking on complex systems development today.

Climate change and rapid urbanisation constitute wicked problems to which the design community must respond. This paper focuses on hybrid smart Nature Based Solutions (NBS) which combine digital, engineered and natural components. Based on case studies and interviews, this paper presents a model to enable manufacturing organisations to navigate the complexities of designing and commercialising such complex systems, focusing on the inter-organisational partnerships required and mitigation techniques to address complexities throughout the project lifecycle. This work challenges existing concepts of hybrid, complex systems to account for NBS and their unique complexities. We argue that smart Nature Based System is a more apt way to conceptualise these solutions which incorporate digital twin, A.I and weather data to deliver urban resilience and sustainability.

Products are often optimized for “most likely” conditions, but unexpected variations can render designs ineffective. Using examples from engineering systems, this paper explores the benefits of leveraging non-linear “payoff functions,” where small changes in conditions lead to disproportionate outcomes. By analyzing the direction and curvature of these functions near observed boundaries, designers could gain an understanding of behavior beyond expected ranges. Non-linear modeling can aid in assessing design margins, especially in long-lived systems. Integrating this approach into design processes can be helpful and effective in considering the “preparedness” of a system in the face of unexpected events of different natures.

This paper examines the impact of complexity on New Product Development (NPD) within the context of an Engineer-to-Order (ETO) organisation. A descriptive literature review identified three categories of complexity: organisational, process and product complexity. The influence on NPD performance due to the dimensions contained in these categories are investigated in terms of the Law of Requisite Variety. A case study of NPD at Héroux-Devtek Inc., a landing gear supplier, evaluates these dimensions in practice. The findings reveal that increased organisational complexity often improves NPD performance, while increased process complexity reduces NPD performance. Product complexity evolves from being ‘complex’ initially to ‘complicated’ or ‘simple’ at delivery. Insights into managing these complexities contribute to understanding their role in achieving project success in the ETO context.

This work develops a method to integrate operational data into system models following MBSE principles. Empirical analysis reveals significant obstacles to data-driven development, including heterogeneous and non-transparent data structures, poor metadata documentation, insufficient data quality, lack of references, and limited data-driven mindset. A method based on the RFLP chain links operating data structures to logical-level elements. Data analyses are aligned with specific requirements or functional/physical elements, enabling systematic data-driven modeling. This method improves efficiency, fosters system knowledge development, and connects technical systems with operational data.

Topology optimization combined with additive manufacturing enables the creation of complex, high-performance products. However, industrial applications often involve numerous and complex requirements, making it challenging to align the design and manufacturing process to meet all demands. A particular challenge is to determine which requirements should be included in the optimization problem statement. This paper presents a procedure model to integrate requirements and feasibility constraints into the design and manufacturing process. It includes two major steps: organizing requirements and constraints in the process and identifying the problem statement. The procedure is applied to the requirements of an engine bracket of AUDI AG, demonstrating its ability to handle numerous requirements and to specify the problem statement.

Advances in information and communication technology (ICT) foster smart systems. Seamless data flows between stakeholders are crucial for their functioning. Designing communication systems to manage data exchange in distributed multi-stakeholder networks is challenged by the complexity of diverse stakeholders with varying interests and data needs. This requires a comprehensive understanding of data flows and communication dynamics. This paper investigates methods for modeling and analyzing data-related links between stakeholders in complex systems. After defining requirements and reviewing available methods, an approach combining dependency and structure modeling (DSM) and systems modeling language (SysML) is identified as most suitable. This is applied to a case study of autonomous buses in public transport, demonstrating its applicability and providing a foundation for further work.

This paper explores the influence of layer variations within Artificial Neural Network (ANN) crowds on their collective behavior and prediction accuracy. While prior research has demonstrated the effectiveness of ANN crowds, understanding how architectural variations impact performance is limited. A coding scheme is used to categorize architectures into distinct behavioral profiles (Normality, Centrality, Width). These profiles provide insights into how individual architecture contributes to the overall behavior and performance of the crowd. The research uses two prediction models. Analysis of behavior distributions across layers reveals minimal fluctuations in both models, suggesting consistent behavior across varying layer configurations. Future work will explore the relationship between layer variations and error metrics to understand their impact on performance.

A major cause of diagnostic errors is the underlying complexity caused by patient presentations and the context in which diagnosis is being undertaken. This is especially true for settings like emergency medicine and disease spectrums like infectious diseases. To design artefacts that counter such errors, it is essential to map the factors contributing to diagnostic complexity. However, existing complexity assessment methods in healthcare are limited in scope. Addressing this gap, our work operationalises a complexity estimation tool to identify factors contributing to the diagnostic complexity of 10 infectious disease cases in an emergency medicine setting. Our objective findings are further validated by a strong correlation with the difficulty perceived by attending doctors. The work provides a basis for the design of targeted interventions aiming to mitigate complexity in diagnosis.

Design has shifted from product manufacturing to tackling systems’ complexities in social innovation, focusing on participatory and human-centered design. Despite tools developed to enhance participation, differing perspectives complicate co-creation, necessitating better ways for interdependent thinking and communication. Designers must be embedded within the same social and cultural contexts as others, engaging in long-term participation. Establishing a design context that transcends temporary action but with a joint vision and tasks achievement is crucial. This study identifies varying levels of designers’ involvement and the differences of design context construction. Three modes are illustrated: (1) patching-based, (2) intertwining-based, and (3) expanding-based design context construction. This study advances design theory, encouraging designers to engage in multi-level collaboration.

Challenges of increasing system complexity and the need for interdisciplinary collaboration are prompting companies to reorganize towards Systems Engineering (SE). As part of the implementation of large-scale transformation programs, transformation progress is of great interest to management and employees involved. Existing maturity models lack measurable variables and reliable forecast. For this reason, a maturity model for evaluating SE Transformation is developed, that builds on quantitative metrics and enables an overarching view on transformation considering cultural aspects. Literature-based criteria for evaluating SE Transformation lay the foundation for measures and referenced metrics and indicators. Due to its data-centricity, the model presented enables a more comprehensive, fact-based decision-making basis for the design and steering of SE Transformation programs.

In this paper, we present a flexible approach to estimating parametric cumulative Prospect Theory using Hierarchical Bayesian methods. Bayesian methods allow us to include prior knowledge in estimation and heterogeneity in individual responses. The model employs a generalised parametric specification of the value function allowing each individual to be risk-seeking in low-stakes mixed prospects. In addition, it includes parameters accounting for varying levels of model noise across domains (gain, loss, and mixed) and several aspects of lottery design that can influence respondent behaviour. Our results indicate that enhancing value function flexibility leads to improved model performance. Our analysis reveals that choices within the gain domain tend to be more predictable. This implies that respondents find tasks in the gain domain cognitively less challenging in comparison to making choices within the loss and mixed domains.