This study synthesises interdisciplinary research on design strategies and attributes for extended product life of furniture. Through an integrative literature review, it develops the heuristic Longevity Trinity framework, comprising technical, functional, and emotional orientations. The framework consolidates dispersed design principles and highlight how the physical and psychological properties of furniture interact with the component of time, positioning product longevity as a design problem of continuity; continuity of materials, usefulness, and meaning across multiple lifecycles.

As the field of biodesign has grown, so has the number of spaces dedicated to biodesign practice. However, little attention has been paid to the ongoing efforts of those who keep these spaces functioning on a day-to-day basis. Based on tour-and-interviews with 19 biodesign lab managers (LMs) across European biodesign laboratories (BioLabs), this paper aims to develop an initial understanding of what biodesign LMs’ everyday work entails. The findings highlight three key dimensions of biodesign LMs’ work and surface how they hold together the interdisciplinary and emergent nature of the biodesign field. In this respect, keeping BioLabs ‘alive’ also entails maintaining conditions under which biodesign LMs themselves can effectively perform their roles. This study contributes to better supporting, communicating, acknowledging, and making resilient the current, emerging and future BioLabs and professionals in similar roles, as well as to open up new opportunities for biodesign research.

We reviewed 36 web-based toolkits supporting health and care design and improvement and identified five classification dimensions: novelty (novel or established tools), scope (specialised or generic), origin (research- or practice-based), motivation for use (risk reduction or benefit enhancement), and application level (individual or group use). We also identified five types of toolkit developers and seven end-user roles. Most toolkits were generic and practice-based, developed by commercial or academic actors, targeted at practitioners and leaders, and supported both individual and group use.

This study examines how product development activities influence the environmental sustainability of complex mechatronic systems using a 2D-flatbed laser cutting system as a case study. Three levels are identified, the machine, operation and part level, at which design changes can affect environmental sustainability during machine operation. Utilizing operational machine data, nine design changes are derived indicating that ∼36% of the environmental impact in the use phase can be reduced through technical design solutions, enabling EcoDesign principles supported by data-driven approaches.

Functional decomposition shapes early design decisions but is largely qualitative, leaving units and measures implicit. This work introduces the Quantitative Functional Decomposition Problem, which formalizes functions and interfaces with measurable quantities, making decomposition solvable as a quantified planning problem. Two case studies show that the approach gives immediate feedback on the admissibility of functions and their connections. Design engineers get consistent quantified structures, which speed up iteration, reduce work and set targets for subsequent steps in the design process.

The pilot project initiatives using Knowledge-Based Engineering (KBE), Design Automation (DA), and visual modelling techniques, at Saab Aeronautics, are presented in this work. The aim is to evaluate their practical applicability and outline how organisations can implement accelerated product development methodologies. By integrating organisational knowledge, parametric models, standardized workflows and automation tools, design lead times are significantly reduced, allowing design expertise to focus on innovation, quality, and strategic problem-solving.

Systems engineering continues to face challenges such as high manual modelling costs and insufficient tool support. With the rising importance of AI methods, SE assistants, software systems that support engineers in typical SE tasks, are gaining attention. However, there is currently no systematic classification of such assistants. At the same time, their usefulness depends heavily on the quality of the human AI interaction. This paper addresses these gaps by systematically categorising SE assistants and analysing the role of interaction design in their development and application.

This exploratory study examines what forms of expert support DfAM novices need and how they perceive AI-based expert systems. The results show that cognitive orientation, transparent communication and reliable information are most valued, while social or emotional expert attributes play a minor role. The study derives requirements for explainable, trustworthy AI support tailored to the early needs of DfAM.

This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set.

This paper presents a design support framework that focuses on linking product design with risk management within the pharmaceutical packaging industry. The framework is intended for use by packaging designers and adopts a multi-user perspective to identify design requirements and integrate them into risk mitigation activities. It promotes safe and effective packaging through proactive design, reducing costly redesign measures. A preliminary version is presented which has been developed through studies with key industry stakeholders, including pharmaceutical packaging designers.

This study compares solid and shell generatively designed PLA components for material extrusion (MEX) at matched mass targets (100, 150, 200 g). Geometries were generated Generative Design (GD) and manufactured by MEX, then tested in a 30° quasi-static compression rig representing prosthetic heel strike. Solid designs achieved up to 92% higher peak load, but failed abruptly, whereas shells exhibited lower strength but progressive, energy-dissipating failure. Results show that simple shelling of GD outcomes cannot replace iterative GD refinement for weight-critical, load-bearing parts.

The product life cycle (PLC) is the basis for every development task. Its modeling is especially important in the context of the circular economy, as recirculation within the PLC forms its basic concept. To derive requirements for circular products, it must be known which phases are to be passed through within which circular strategy. This paper links R-strategies and life cycle phases by analyzing 56 life cycle models in regard to the number of phases, sequence, and other characteristics. A dependency matrix consolidated from the life cycles allows the findings to be utilized further.

This paper conceptualizes data-driven product management (DDPM) as an organisational capability requiring clearly defined roles for effective coordination across technical, analytical, and managerial domains. Based on a systematic literature review and industry workshops, twelve interdependent roles were identified and specified through standardized role profiles. The resulting role architecture provides a structured foundation for organisational design, enabling companies to assign responsibilities, align competences, and operationalise DDPM in practice.

The rapid diffusion of generative AI is pushing creative work toward human–AI co-creation (HAIC). This paper designs a conceptual HAIC model that specifies several indispensable elements of effective co-creation: Human, AI, Artifact, Instruction, and Interaction. We demonstrated through a case study of a large-scale management information system development project how the HAIC model helps organizations implement HAIC. The proposed framework offers both an analytical lens for researchers and prescriptive guidance for practitioners seeking to engineer reliable human–AI collaboration.