Companies in the development of cyber-physical systems are responding to the ever faster changing requirements of their own products by implementing agile methods. Until now, however, there has been a lack of ways to determine the true effects of agile transformation on their own processes to operate them in a targeted manner. This paper presents an impact model that defines factors that can be used to describe process changes and outlines the interdependencies between the individual factors and describes the influence of known agile methods. This allows the benefits of agile methods to be presented transparently and objectively.

The aim of this research is to analyze the potential of Generative Artificial Intelligence (GenAI) to support the design process and overcome creative fixation in teams during the initial problem framing, ideation and concept exploration stage. Fixation is a common problem in design, and can be exacerbated during collaborative work due to diverse issues such as team dynamics or perceived hierarchy. Current research is exploring whether AI can help teams overcome this problem or on the contrary, might actually contribute to it. Through a creative ideation workshop with design students, we investigate how AI influences team dynamics as well as the creative results. We propose a conceptual model to work with AI in a team setting.

Artificial intelligence is a transforming design practice. This research explores human-AI interaction in relation to human centred design principles in early stage design projects. Using a qualitative workshop methodology, this empirical study took a multidisciplinary team of participants from a yacht manufacturer through a series of divergent, discover phase activities that were augmented by AI tools. The results demonstrated how the advanced capabilities of AI to rapidly analyse vast quantities of data could be purposefully implemented to enhance engagement. the role off facilitator as an intermediary between the AI and participants allowed the interface between human and AI to be moderated and provided insights into effective effective use of AI during the fuzzy front end.

This paper investigates the role of generative Artificial Intelligence (AI) in academic settings, focusing on its effectiveness in providing feedback during the brainstorming phase of the design process. A controlled study with 25 students (n=25) compared feedback from Generative AI (GPT-4) to that from six human educators. Findings reveal that AI-generated feedback enhances student motivation during ideation and facilitates iterative idea refinement. Generative AI’s ability to deliver rapid, scalable feedback proves advantageous in resource-constrained contexts, supporting more effective design processes. This research highlights the potential for AI-driven feedback mechanisms to transform human-AI collaboration in design education, addressing key challenges in personalized and scalable feedback delivery.

This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.

As modern technical systems grow in complexity, ensuring the quality of these systems during early development phases becomes more challenging. This is particularly evident in the development of modern passenger vehicles, where non-functional requirements (NFRs) play a critical role in ensuring that a vehicle operates according to specified standards and expectations, especially across different vehicle configurations and environmental conditions. The introduction of Artificial Intelligence (AI) in automotive engineering has transformed the approach to vehicle system design and development. This paper presents a pipeline for analyzing and generating NFRs for vehicle systems using generative AI-based methods. The pipeline categorizes NFRs, explores their interdependencies with vehicle configurations and environmental conditions, and addresses the completeness of NFRs in relation to specific vehicle use cases. The paper focuses on selecting appropriate NFR types for various use cases, taking into account diverse configurations and environmental factors. Examples of NFRs with varying parameters are provided for an electric vehicle under development at a leading car manufacturer, illustrating the benefit as well as the challenges of applying generative AI to automotive engineering.

Engineering design is inherently a collaborative process that requires active engagement and effective communication. Project-based Learning (PBL) is increasingly recognized for fostering these essential skills. However, instructors face challenges in objectively monitoring interactions and providing process-oriented feedback, particularly in large-scale settings where free-riders and disengaged participants affect team dynamics. This study introduces a generative AI approach to deliver real-time, scalable, and empathetic feedback that enhances team collaboration. Findings highlight the potential of AI-driven systems to improve student engagement and learning outcomes, though limitations remain in providing context-specific advice. A secure framework for AI integration in collaborative learning environments is also proposed.

With recent advancements in data-driven methods, there has been a growing interest in implementing AI in design. Despite this, a comprehensive understanding of the critical AI methods in design and how they support design practices remains lacking. To deepen our understanding, we conduct a comprehensive literature review and propose a novel, design-centric AI typology, associated with six AI assistance types for product service development. Our typology differs from traditional ones by shifting the focus from an algorithmic perspective to how models support design practice. Key findings highlight how these six design-centric AI methods support design practices in different ways, each with its own application challenges. Establishing a shared design-centric AI typology and assistance framework will enhance the understanding of how AI works differently and supports practitioners.

Generative AI, guided by inventive heuristics, can systematically and rapidly generate hundreds of ideas for engineering inventive design problems. This paper examines the reliability and effectiveness of AI-powered “idea funnelling,” a process that generates, evaluates, filters, and synthesizes raw ideas into feasible solution concepts. Key challenges include the consistency and objectivity of AI-driven evaluations, the robustness of concept generation, and the collaboration of multiple AI chatbots such as ChatGPT and Gemini. The study explores the integration of human expertise in hybrid problem-solving teams to improve feasibility, contextual relevance, and innovation quality. Through comparative experiments, it provides insights to improve the reliability of AI-driven concept creation and the performance of hybrid AI-human teams in solving complex engineering design problems.

Rapid pace of change and increasing complexity in today’s world demand innovative approaches to product development. Foresight methods enable the anticipation of future scenarios and the derivation of product properties. However, current approaches lack mechanisms to continuously align product development with evolving environment and customer requirements, often resulting in late changes and high costs. Early detection of deviations is needed. This paper presents an approach for continuous monitoring, bridging strategic foresight and the product engineering process (PEP). By analyzing prior work and literature, a process model was developed to identify tipping points where product adaptations are necessary using indications and indicators. Initial evaluation through a case study using coffee machines showed the approach’s usability but improvement potential was also identified.

Resonance, where individual creative moments resonate with each other, has been qualitatively recognized as an important phenomenon during co-creation. In a previous study, the authors conducted a concept generation pair work experiment using biosignal indicators and quantitatively grasped the difference between creative states that are simply creative and those that are resonant. This study explores whether it is possible to estimate these creative states using biosignal indicators with the Hidden Markov Model. The parameters for the Hidden Markov Model were based on multimodal biosignal indicators and subjective self-reflection reports regarding the creative states during co-creation. The results suggested that creative states can be estimated during co-creation using a Hidden Markov Model, and resonance can be understood as a shared form of self-resonance driven by concept generation.

Engineering systems are represented in a variety of physical, graphical, and virtual ways, supporting decision making about the systems and their operation. As part of a larger research endeavor exploring influences of representation modality, the presented work examines how product identification impacts subsystem clustering behavior. This is achieved through a study using pictorial and functional representations of common household products. Participants were tasked with grouping elements into non-overlapping clusters. Results suggest that correctly identifying a product does not affect clustering behavior regardless of representation modality. This implies that other aspects of the representations are impacting partition convergence. These factors, along with connections to prior work are explored as discussion points and areas of future research.

Assumption-making is a critical cognitive process in design, where incomplete information is ever-present. Understanding how assumptions are formed, maintained, and adapted can offer key insights into decision-making. While theoretical explorations of assumptions exist, empirical research remains limited. This pilot study investigates how varying temporal constraints influence assumption-making while solving ill-structured problems. The challenge lies in isolating the temporal and cognitive factors at play. The early insights reveal that task ambiguity, contextual framing, and time constraints play significant roles in shaping responses, highlighting the dual nature of assumption-making as both adaptable and resistant to change. The insights highlight the importance of strategic task design that balances ambiguity and structure to deepen our understanding of assumption-making.

Developing products with diverse features presents challenges, especially when involving multidisciplinary teams and managing extensive Compliance Requirements (CRs). Ineffective handling of CRs can lead to inconsistencies in subsystem designs or failures. This study introduces an application of Quality Function Deployment to integrate CRs systematically in design lifecycle. The proposed approach utilizes a multi-layered matrix to translate CRs to specific design parameters, cascading requirements to subsystems and engineering directives. A case study on Sunswift Racing, UNSW solar car team, demonstrates the method’s efficacy in embedding compliance in iterative design, enhancing cross-disciplinary collaboration, ensuring adherence to CRs. Findings present a robust traceability model linking CRs to design parameters, offering a replicable solution for multidisciplinary design challenges.

Digital transformation has reshaped the manufacturing sector, driving innovation and new business models. Simultaneously, sustainability pressures and stricter regulations push companies to adopt circular economy (CE) principles, focusing on reducing, reusing, and recycling materials. This transition requires adapting business models, product design, and management while integrating processes such as reverse logistics. Digital technologies play a crucial role by enabling data generation, processing, and analysis, optimizing production, and reducing resource use. However, many companies face knowledge gaps regarding how to implement these technologies effectively for CE. This study addresses these challenges through a systematic literature review, offering a framework that links digital technologies to CE principles, focusing on slowing, narrowing, and closing material loops.

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.

Text-to-Image Generative AI (GenAI) platforms offer designers new opportunities for inspiration-seeking and concept generation, marking a significant shift from traditional visualisation approaches like sketching. This study investigates how designers work with text-to-image GenAI during inspiration-seeking and ideation, aiming to characterise designers’ behaviours through designer-GenAI interaction data. Analysis of 503 prompts by four designers engaging in a GenAI supported design task identifies two distinct behaviours: exploratory, characterised by short, diverse prompts with low similarity; and narrowing, characterised by longer, high-similarity prompts used with detail focused variation functions. The findings highlight the value of GenAI interaction data to reveal patterns in designers’ behaviours, offering insights into how these tools support designers and inform best practices.

This paper examines the effects of prototyping on design space exploration (DSE). Based on a literature review, a study design is proposed that attempts to integrate a longitudinal view from downstream development steps in the point-in-time investigation of design fixation. This study design is tested in a pilot study, the results are presented and discussed. The observation of participants’ design fixation (DF) in downstream activities shows that the need to create prototypes limited DSE behaviour, and suggestions for further adaptation of the study design are made. Challenges related to group dynamics, bias and logistical issues highlighted the need for a more refined study design. The findings highlight the role of prototyping in limiting DSE behaviour and suggest improving metrics, refining interventions and using structured moderation to improve future DF and DSE research.

Organizational capability is key to achieving strategic goals and adaptability. This study applies the TASKS framework to evaluate taskload, affect, skills, knowledge, and stress using a questionnaire developed through the Environment-Based Design (EBD) methodology. A structured perception-centered evaluation was conducted to assess employees’ perceptions of organizational alignment, with middle managers’ responses serving as a reference. Findings emphasize the need for better communication, leadership engagement, and goal clarity to enhance transformation readiness. The TASKS framework’s perception-centered evaluation assesses organizational capability and identifies role-based misalignments. Future research will expand the framework’s application to validate its effectiveness and refine strategies for enhancing organizational capability.

This paper explores the employment implications of integrating service robots in waste management. Using the scenario technique method, 14 critical influencing factors were identified and analyzed to develop a Best-Case, Worst-Case, and Trend scenario. A SWOT analysis was used to identify implications and develop measures. The findings indicate that service robots can enhance working conditions and enable service expansion but pose risks like job displacement without proper education and reskilling. The study underscores the need for regulatory frameworks, workforce adaptation, and education to ensure socially sustainable robotic integration.