Mixed reality assistance guide posture and hand positioning, and familiarize material behaviour within craft prototyping. The development of the resulting framework focuses on non-intrusive assistance. Barriers include reduced immersion, observing precise hand movements, limited spatial interpretation, and understanding material behaviour. A 3D animation prototype, with roots in embodied knowledge, intends to improve spatial comprehension and enhance process and material understanding. The eventual framework should aid virtual assistance for design skill transfer.

While top-down System Engineering supports the definition of system boundaries and interfaces, practical implementation often proceeds in a bottom-up manner, resulting in the need for model integration of SysML models. This is currently hindered by inconsistent naming, different abstraction levels, and unclear interfaces. In this work, a novel approach to integration is proposed, utilizing RDF knowledge graphs and LLM-driven entity alignment with similarity thresholds to perform semantic fusion. A practical use case shows correct consolidation based on cosine similarity thresholds.

Knowledge management (KM) is crucial for efficient cross-sectoral R&D. Our study, performed with academic and industry experts (n=17), reveals a deep distrust in formal KM platforms and a high reliance on personal networks. Based on the findings of how the personal networks serve for knowledge management and exchange, we propose a concept and basic design requirements for an AI-powered ‘knowledge orchestrator’. Accounting the promise and the capabilities of the modern AI, this AI-powered ‘knowledge orchestrator’ may serve as a new generation of KM system for modern cross-sectoral R&D.

Digital design tools are omnipresent today, but which is right for the job? This study reviews previous approaches to categorise design tools revealing a lack of comprehensive catalogues. Given this gap, a set of requirements, classification schema and prototype catalogue (IDEATE) were developed. A survey explored selection factors, format preferences and evaluated the prototype with IDEATE scoring 6.44/10 compared to 5.28/10 for a table format. This evidenced interest in mapping the ecosystem though future iterations should prioritise refined navigation and enhanced searchability of tools.

Development of complex interdisciplinary products increases engineering challenges, that AI supported engineering approaches attempt to reduce by increasing automation. The resulting AI generated engineering artifacts, however, need to be classified, verified and managed to enable traceability and auditability of engineering decisions. This paper presents a classification and management approach for these artifacts, allowing verification of AI generated engineering artifacts. A use case on the iterative development of an e-bike demonstrates the approach.

The aim of this editorial is to provide an update about the Journal of Management & Organization in terms of its progress during the year 2025. This will help to understand how the journal has progressed over time and the main changes occurring in 2025 in term of analysis of articles, subject topic, author, reviewer, and other relevant information. To do this, a historical perspective is provided that highlights the main contributions in 2025 that are especially relevant given the journal’s 30th birthday celebrations. The core management topics and areas of interest published in 2025 are discussed in terms of final decisions about total number of articles accepted and acceptances based on country of main authors. A list of best reviewers for the journal is included as well as a published list of reviewers. The journal metrics are discussed as well as future objectives and goals.

End-user engagement is a critical success factor for knowledge management (KM) initiatives in legal practice, yet it remains one of the most persistent challenges faced by knowledge professionals. Drawing on interviews with legal industry practitioners and observations from recent BIALL sessions, this article by Clare Bilobrk examines the barriers to effective engagement with KM tools and proposes practical strategies for overcoming them. The analysis combines theoretical frameworks, including organisational silos, with actionable recommendations. A concluding LEXICON distils key principles into a mnemonic model, offering legal information managers a framework for embedding KM tools into daily workflows, building trust and sustaining a culture of knowledge-sharing.

Technology platforms spanning several scientific and technological fields hold great promise, both as future innovative tools for industry and as future experimental tools for academia. However, some of their characteristics are also still unknown and need to be designed. A classical approach to initiate their evolution dynamics is to seek funding for a subsequent design project. Using a single case study, we show that a much less costly approach is possible: adding training to the platform can play a central role in increasing the intensity of its use, with both scientific and industrial impacts. Yet, this approach requires that the training knowledge enables the exchange of ‘independent knowledge’ between platform designers and users: this demanding condition requires further research to characterise this promising training model which we propose to call “double impact training”.

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 demonstrates how the Portfolio of Capability Constraint Network (PCCN) facilitates modeling and analyzing complex manufacturing networks by framing them as constraint satisfaction problems (CSPs). These models face high complexity due to numerous n-ary constraints and large solution spaces, posing challenges for standard solution algorithms. Existing CSP remodeling approaches were reviewed but found unsuitable for the specific needs of PCCNs. As a result, tailored design guidelines and heuristics were developed to reduce problem complexity effectively. The applicability of these guidelines was validated using a use case involving the production of a multi-material shaft with tailored forming technology. Results showed significant efficiency gains in solution searches, emphasizing the practical value of the proposed methods in simplifying and optimizing PCCN-based models.

This paper investigates the integration of player profiles and gamification elements into knowledge management practices within communities of practice engaged in engineering design. The study proposes a framework combining the MEREX method with gamification, tailored to Marczewski’s player types. The research aims to personalize knowledge sharing, promote user engagement, and structure engineering design knowledge effectively. The framework leverages MEREX sheets with a narrative format structured around phases of the engineering design process. Additionally, it features personalized knowledge maps and contributor profiles to foster collaboration, facilitate knowledge formalization, and encourage knowledge reuse. This integrated approach seeks to improve both community animation and overall knowledge management within engineering design contexts.

It is necessary to pass on design knowledge through links between product models to efficiently utilise the design knowledge built up throughout a design process. Yet, researchers lack support for deriving new links between product models. Based on the findings from analysing publications that present links, a systematic approach to deriving links between product models in engineering design research is developed and subsequently demonstrated in an illustrative case linking two product models. The approach enables researchers to derive new links between different product models in a systematic and traceable way. This offers the potential to increase the density of known links within the body of product models. Further, this facilitates the integration of previously unlinked product models into design processes and their efficient combination through the passing on of design knowledge.

The development of interdisciplinary Smart Products involves complex architectures and processes, which results in new challenges like managing heterogeneous and unstructured data causing inefficiencies. Model-Based Systems Engineering (MBSE) addresses these issues through precise system modeling but encounters obstacles like a lack of model reuse and complexity. This paper introduces a novel framework integrating Artificial Intelligence into MBSE to enhance sustainability and circularity by automating model generation and reusing existing system models. Using ontology-based knowledge management and large language models, model creation, interoperability, and decision-making can be enhanced and automated and visualized in real-time. The framework's capabilities and benefits are demonstrated through the instantiation of a wireless charger system example.

The diverse knowledge levels among first-year mechanical engineering students lead to significant disparities in individual learning. Intelligent tutoring systems (ITS) offer a solution by providing tailored digital one-to-one instruction, bridging knowledge gaps, and equalizing learning outcomes. This thesis develops an ITS for design theory based on a knowledge-based engineering system, presenting an innovative model that integrates key features of ITS and knowledge-based systems. Implemented in a specialized environment, the system’s application and validation demonstrate its ability to meet context-sensitive design teaching requirements and provide adaptive tutoring.

A design catalog is a repository of design problems and their solutions, enabling designers to explore and discover applicable solutions for their specific design challenges. Creating such catalogs has depended on human knowledge and implicit judgment, with no systematic approach established. This study aims to develop a systematic method to create a design catalog from patent documents. We utilize a large language model (LLM) to extract problem-solution pairs described in the documents, presenting them as general purpose-means pairs. Subsequently, we create a design catalog by classifying the problems using similarity-based clustering, enhanced by the LLM’s semantic text similarity capabilities. We demonstrate a case study of creating a design catalog for martial arts devices and generating new design concepts based on the catalog to verify the effectiveness of the proposed method.

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.

Large Language Models offer a novel approach with low barriers to entry to potentially improve knowledge transfer in product development. After identifying knowledge barriers from literature that are potentially addressable through LLM-based applications, we analyze two GDPR-compliant LLM applications - ChatGPT Enterprise and Langdock - examining their key features: assistants and chatbots for both, and prompt libraries and LLM-based file search for Langdock. Then, we evaluate each feature’s potential to mitigate each barrier. Our findings show that assistants and chatbots provide wide-ranging support across many barriers, whereas prompt libraries and file search deliver targeted solutions for a narrower set of specific challenges. Given the numerous influencing factors and the rapidly evolving field of LLMs, the study concludes with a research agenda to validate the theoretical findings.

This study proposed a framework to visualize research trends and create methods to forecast future directions in the design research methodology field from 2018 to 2022. A case study is conducted using a dataset of abstracts from conference proceedings included in the American Society of Mechanical Engineers (ASME) International Design Theory and Methodology Conference track from 2018 to 2022. The proposed method involves extracting keywords from research articles, transforming them into vectors, determining the similarity between keyword pairs to form a keyword network, and constructing a Sankey diagram to show the topic evolution pathways. The resulting Sankey diagrams provide insight into relationships between research topics.

Developing new factories is effectively a design task. In this paper a case study on barriers to efficient project communication is presented. Preceding research has shown that production systems design projects can be more efficiently executed and that as many as 95% of all problems in collaborations are due to a lack of communication. The study was designed to grasp project communication barriers from three projects and developed a visual planning tool. The findings show that digital planning software supports mainly in the categories of Egocentrism and Mistrust, Equivocality and Ambiguity and less in Interaction Capability, Asynchronisity and Noise and Information-sharing Behaviour. Recommendations for future research is to connect the project communication support to quantitative project performance aswell as the acceptance of technology in production systems design.