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.

Need analysis is essential for organisations to design efficient knowledge management (KM) practices, especially in contexts where knowledge is a critical asset and evolving fast. The research explores the application of large language model (LLM)-based agents in automating need analysis for KM practices. A two-layered model using Retrieval-Augmented Generation (RAG) architecture was developed and tested on datasets, including interviews with managers and consultants. The system automates NLP analysis, identifies stakeholder needs, and generates insights comparable to manual methods. Results demonstrate high efficiency and accuracy, with the model aligning with expert conclusions and offering actionable recommendations. This study highlights the potential of LLM-based systems to enhance KM processes, addressing challenges faced by non-technical professionals and optimising workflows.

Design by Analogy (DbA) is a powerful method for fostering innovation by transferring knowledge from a source domain to solve problems in a target domain. However, traditional DbA approaches face significant challenges, including resource-intensive database management, linguistic and representational differences across domains, and the complexity of access and mapping processes. These limitations hinder scalability and efficiency, particularly for cross-domain analogies. Recent advancements in Artificial Intelligence (AI), especially Large Language Models (LLMs), offer promising solutions by facilitating efficient knowledge retrieval, bridging linguistic gaps, and enhancing semantic reasoning. This paper explores the potential of AI technologies to address these challenges, proposing a framework for analogical reasoning.

Decision-making in product development is a complex process that benefits from leveraging past experiences. This paper presents an ontology-based framework to facilitate decision-reuse in product development by classifying current decisions within a structured scheme. The proposed decision-reuse ontology provides similar past decisions, linking them to their classifications and offering SPARQL queries, conditions, and decision outcomes. By integrating the ontology with other domain-specific ontologies, it supports product developers in making informed decisions based on historical knowledge. The resulting decision outcomes, classifications, and metadata are fed back into the decision-reuse ontology, ensuring a continuous cycle of knowledge enrichment. This approach not only enhances decision-making but also fosters knowledge transfer throughout the development process.

Failure mode and effects analysis (FMEA) is a critical but labor-intensive process in product development that aims to identify and mitigate potential failure modes to ensure product quality and reliability. In this paper, a novel framework to improve the FMEA process by integrating generative artificial intelligence (AI), in particular large language models (LLMs), is presented. By using these advanced AI tools, we aim to streamline collaborative work in FMEA, reduce manual effort and improve the accuracy of risk assessments. The proposed framework includes LLMs to support data collection, pre-processing, risk identification, and decision-making in FMEA. This integration enables a more efficient and reliable analysis process and leverages the strengths of human expertise and AI capabilities. To validate the framework, we conducted a case study where we first used GPT-3.5 as a proof of concept, followed by a comparison of the performance of three well-known LLMs: GPT-4, GPT-4o and Gemini. These comparisons show significant improvements in terms of speed, accuracy, and reliability of FMEA results compared to traditional methods. Our results emphasize the transformative potential of LLMs in FMEA processes and contribute to more robust design and quality assurance practices. The paper concludes with recommendations for future research focusing on data security and the development of domain-specific LLM training protocols.

The Japanese art of Kintsugi teaches that imperfections and failures are not flaws to hide but opportunities for growth and enrichment; it says that true strength and beauty come from embracing imperfection and learning from the fractures along the way. In this article Hélène Russell draws on insights from three conference experiences to show how KM professionals can make use of Kintsugi and act as the ‘golden joiners’ within their firms when it comes to AI projects, making use of their blend of resilience, organisational cultural awareness, communication skills, and adaptive knowledge-sharing practices.

Learning orientation emphasizes the importance of learning from any experience. It is grounded on commitment to learn, shared vision, open‐mindedness, and knowledge sharing. Organizational knowledge management literature based on social complexity theory posits that learning orientation makes companies generate new knowledge through spontaneous multi-level iterations and self-organization. Challenges related to the current business environment requires companies to constantly adjust to remain competitive. Still, the mechanisms making learning-oriented companies more capable to develop innovative product have been scantly explored. Pertinent literature actually conjectures this relationship as spontaneous, directed, and unmediated. Moreover, Small and Medium Enterprises (SMEs)rarely represent the context of analysis of research on this topic. Frequently lacking resources to systematically pursue product innovation, SMEs rely on solutions deriving from the combination of internal knowledge and external sources; thus, these companies depend on learning orientation principles to remain innovative. In this vein, the research aims to understand how learning orientation allows product innovation in SMEs through the achievement of strategic flexibility. Structural equation modelling was used to analyse data from 300 British SMEs. The results demonstrate the mediating role of strategic flexibility in the relationships between learning orientation and product innovation. The importance of innovation culture also emerged.

This study explores the application of competency mapping models, incorporating in knowledge management for consulting firms. It evaluates 15 different models, focusing on their suitability for consulting contexts based on data collection, advantages, risks, and limitations. The findings indicate that AI and ML-enhanced competency mapping models are particularly more effective in consulting firms. Finally, the article proposes three key applications of these models for improving knowledge management in consulting firms via empowering communities and collaboration.