As we enter the era of longevity economics, the desire for longer life spans and health spans is increasingly prevalent. Recognizing the importance of longevity planning (LP) has become particularly significant, as individuals seek to enhance lifespan quality starting at younger ages. This article explores how 12 LP blocks (LPBs) can serve as boundary objects (BOs) to facilitate conversations and identify user needs in LP services. Using constructivist grounded theory, this research analyzes data from 69 in-person experiments at MIT AgeLab, across adulthood (25–54 years), preretirement (55–64 years) and postretirement (65–74 years). Through a qualitative data analysis supported by a comparison of surveys, the authors identified and clustered 51 initial codes, 15 focused codes, 5 axial codes and 1 thematic code. This led to the development of four personas, each corresponding to one of the four types of BOs defined by Star in 1989: repositories, ideal types, terrains with coincident boundaries and forms and labels. The findings highlight the value and challenges of using LPBs as BOs to enhance LP service, ultimately contributing to design for longevity (D4L). This qualitative research aims to facilitate sensitive conversations and foster comprehension of D4L, positioning LPBs as components in creating LP services.

The increasing prevalence of embedded software in today’s vehicles is leading to growing complexity, which can only be managed effectively through the use of reliable interdisciplinary engineering processes. With this in mind, systems engineering (SE) is currently being introduced on a large scale into the automotive industry. Pilot projects have demonstrated the potential for implementing changes, but these have not yet been accompanied by viable implementation concepts for SE. In the context of the proposed application-based research, the SETup automotive method (Systems Engineering Transformation under piloting in the automotive industry) is presented, which comprises a step-by-step procedure of introducing SE into large automotive companies. By introducing SE by pilot projects first, both an in-process tailoring of all processes, methods, tools and structures (PMTS) required for the introduction and an in-process validation of the pilot scheme elaborated by the pilot projects are achieved. The presented method builds upon fundamental approaches to change management, which have been developed over many years in both research and practice. It has been validated by the industrial practice of SE transformation at German car manufacturers and suppliers. As a result, decision-makers, transformation managers and systems engineers are provided with a scientifically based and field-tested set of steps for the introduction of SE in their own company.

Increasing sustainability expectations requires support for the design of systems that are reactive in minimizing potential negative impact and proactive in guiding engineering decision-making toward more value-robust long-term decisions. This article identifies a gap in the methodological support for the design of circular systems, building on the hypothesis that computer-based simulation models will drive the development of more value-robust systems designed to behave positively in a changeable operational environment during the whole lifecycle. The article presents a framework for value-robust circular systems design, complementing the current approaches for circular design aimed at increasing decision-makers’ awareness about the complexity of circular systems to be designed. The framework is theoretically described and demonstrated through its applications in four case studies in the field of construction machinery investigating new circular solutions for the future of mining, quarrying and road construction. The framework supports the development of more resilient and sustainable systems, strengthening the feedback loop between exploring new technologies, proposing innovative concepts and evaluating system performance.

The ARLE GPS tool provides computer-aided design support for solving problems with the spatial planning and design of houses, using a robust design model with physical-biological and cost strategies. This enables architects to eliminate uncertainties and to make robust decisions by applying computational thinking to decision making and action implementation. This support enables the architect to deal with the complexity arising from the interrelationships between the design variables and transforms the spatial planning problem, which is conceptualized as illdefined, into a well-defined problem. A scientific method is used, based on mathematical modeling of the action-decision field of design geometric variables, rather than a drawn method involving sketches. This tool acts as an aid mechanism, an assembler, a simulator, and an evaluator of geometric prototypes (virtual or graphical) and can be used to systematize the assembly or modeling of the FPL structure, particularly with respect to the performance required of a house. This candidate solution, provided by the tool, defines the spatial dimensions of the rooms in the house, the topological data of the assembly sequence, and the connections between rooms. The architect converts this virtual prototype into a graphical FPL prototype, which is then modeled, refined and evaluated continuously and objectively with the aid of ARLE GPS until a solution is obtained that satisfies the requirements, constraints and objectives of the problem. In this way, a solution to the problem (i.e., the project) can be captured and generated.

A structurally transforming multi-mode product can realize a changing set of functions across its modes, replacing multiple related products while offering increased cost, space, and time efficiency. However, there is a lack of connected methods that address the additional design complexities due to the product’s physical transformations and the resulting structural component-sharing between modes. A framework, grounded in standard design practice and built upon existing methods, is proposed to help navigate the two most impacted design stages: 1. Problem Definition and 2. Conceptual Design. The Problem Definition stage in this new framework involves identifying the external factor that determines the product’s modes and defining the functional requirements for the modes and transformation methods. The Conceptual Design stage involves iteratively linking conceptualized forms of each mode to adjacent modes through conceptualized transformation methods. The framework is demonstrated in a case study involving the design of a structurally transforming multi-mode piece of children’s furniture that transforms between a cradle, floor seat and a multipurpose toddler step stool. The proposed framework is a promising step toward systematically, cohesively, and comprehensively addressing design challenges during the development of a wide variety of structurally transforming multi-mode products, therefore facilitating better, more effective product design.

Methods comprise a significant part of the knowledge engineers are taught and that they use in professional practice. However, methods have been largely neglected in discussions of the nature of engineering knowledge. In particular, methods prove to be hard to track down in the best-known and most influential typology of engineering knowledge, put forward by Walter G. Vincenti in his book What Engineers Know and How They Know It. This article discusses contemporary views of what engineering methods are and what they contain, how methods (fail to) fit into Vincenti’s analysis, and some characteristics of method knowledge. It argues that methods should be seen as a distinct type of engineering knowledge. While characterizing the knowledge that methods include can be done in different ways for different purposes, the core of method knowledge that does not fit into other categories is explicit ‘how-to’ knowledge of procedures, that draw on other types of knowledge.

Along with increasing product complexity and quality requirements, the consistent consideration of inevitable production-induced variations within the product development process becomes a decisive factor for the market success of products. Consequently, various tolerancing approaches have emerged over the last few decades. However, tolerancing is considered complex in education, research, and industry, as it is a highly interdisciplinary task that takes place at different levels of detail, ranging from Robust Design to tolerance specification to manufacturing process design. This contribution proposes a novel approach that allows a holistic and structured description of tolerancing and fosters a common understanding among all involved stakeholders from design, manufacturing, and inspection. This is achieved by categorizing it into several distinct elements: activities, methods, tools, models and data, information, and knowledge. This ensures clarity and supports the utilization of existing approaches. While this contribution focuses on tolerancing in product design, the linkage to subsequent product realization stages and further engineering domains is also addressed in the proposed description scheme. An exemplary classification of research papers and a description of a practical development process of a technical system with its different tolerancing activities illustrate the benefits of the proposed description scheme.

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.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

The literature suggests that product and product-service system (PSS) design problems are characteristically different. However, there is limited empirical evidence to suggest that the design cognition specific to the respective design activities is different. This article reports the findings of a comparative study of protocols of conceptual product and PSS designing carried out in a laboratory environment by 28 pairs of experienced product designers from the manufacturing industry. First, differences between product and PSS design problems were theoretically characterized in terms of their respective sources of complexity. Based on these differences, hypotheses concerning differences in the cognitive processes of conceptual product and PSS designing were developed and empirically tested. Results indicate that PSS designing by experienced product designers is more problem-focused while product designing is more solution-focused. PSS designing was found to focus more on the design issue function and the design process formulation. Further, PSS designing was more likely to apply a depth-first search strategy, while product designing was more apt to apply a breadth-first search strategy. Results point towards the need to support the analysis of derived behavior of structure and the application of a breadth-first strategy during PSS designing by product designers.

Resource management skills are critical to success during new product development processes. Design processes are ambiguous and complex, and designers often face a scarcity of resources that limits their ability to move the new product development process forward, such as limited financial capital, time or human resources. A team’s ability to use resources effectively may determine their likelihood of success during new product development processes. Technology-based startup teams represent an authentic, unique subset of new product development teams that are trying to bring innovative technologies to market. While prior work has identified salient traits of team members that affect a team’s trajectory, little work has investigated how these traits may interact with each other and how they affect an individual’s ability to manage resources. Using a mixed-methods approach, we leveraged data from 241 startup team members to study the relationship between individual traits, team characteristics and resource management skills. A k-means cluster analysis unveiled two distinct archetypes of startup team members, differentiated by (1) self-efficacy, (2) bricolage, (3) risk propensity and (4) perceptions of psychological safety. Team members with higher levels of these traits exhibited greater resource management skills.

The ideating phase of product design is critical, as decisions made here influence the rest of the product’s lifecycle. Usually, early preliminary designs in engineering are created with pen and paper, which are incompatible with the subsequent digital design process. In an effort to find a modeling tool for early designs that provides the creative flexibility of freehand sketching but also the further processability of digital models, this research investigates natural modeling in virtual reality (VR). To do so, a VR modeling method allowing the intuitive creation of preliminary designs as simplified computer-aided design (CAD) models is presented. The main contribution is the evaluation of this natural VR modeling method against freehand sketching in an extensive user study.

This research proposes a novel conceptual framework that combines the concepts of Human-Computer Interaction (HCI) and Ambient Intelligence (AmI). The proposed framework aims to shed light on the importance of considering the needs and the social interactions of various building occupants in different types of buildings and designing HBI strategies accordingly. Specifically, we take educational buildings as a case that is less explored in the HBI research and apply the proposed framework, investigating how HBI strategies and interactions should be designed to address the needs of students, as primary occupants. Focus groups and semi-structured interviews were conducted among students in a flagship smart engineering building at Virginia Tech. Qualitative coding and concept mapping were used to analyze the qualitative data and determine the impact of occupant-specific needs on the learning experience of students. “Finding study space” was found to have the highest direct impact on the learning experience of students, and “Indoor Environment Quality (IEQ)” was found to have the highest indirect impact. The results show a clear need to integrate occupant needs in designing HBI strategies in different types of buildings. Finally, we discuss new ideas for designing potential Intelligent User Interfaces (IUI) to address the identified needs.

Analysing hierarchical design processes is difficult due to the technical and organizational dependencies spanning over multiple levels. The V-Model of Systems Engineering considers multiple levels. It is, however, not quantitative. We propose a model for simulating hierarchical product design processes based on the V-Model. It includes, first, a product model which structures physical product properties in a hierarchical dependency graph; second, an organizational model which formalizes the assignment of stakeholder responsibility; third, a process model which describes the top-down and bottom-up flow of design information; fourth, an actor model which simulates the combination of product, organization and process by using computational agents. The quantitative model is applied to a simple design problem with three stakeholders and three separate areas of responsibility. The results show the following phenomena observed in real-world product design: design iterations occur naturally as a consequence of the designers’ individual behaviour; inconsistencies in designs emerge and are resolved. The simple design problem is used to compare point-based and interval-based requirement decomposition quantitatively. It is shown that development time can be reduced significantly by using interval-based requirements if requirements are always broken down immediately.

The design provides innovative solutions to problems in the medical field. Collaboration between design and medicine can be fostered in several ways; however, educational programs linking these two academic fields are limited, and their frameworks and effectiveness are unknown. Hence, we launched an educational project to address medical problems through design. The framework and creative outcomes are based on the results of two consecutive one-year programs. The research subjects were 35 participants from three departments. The majority (22/35, 63%) were master’s and doctoral students in design. Eight participants were doctoral students and researchers who volunteered from the surgery, oral surgery, neurology and nursing departments at the Graduate School of Medicine and Hospital. The impact of the program on creativity was evaluated by the quality of ideas and the participants’ assessments. In total, 424 problems were identified and 387 ideas were created. Nine prototypes with mock-ups and functional models of products, games or service designs were created and positively evaluated for novelty, workability and relevance. Participants benefitted from the collaboration and gained new perspectives. Career expectations increased after the class, whereas motivation and skills remained high. A framework for a continuing educational program was suggested.

Text-to-image models are enabling efficient design space exploration, rapidly generating images from text prompts. However, many generative AI tools are imperfect for product design applications as they are not built for the goals and requirements of product design. The unclear link between text input and image output further complicates their application. This work empirically investigates design space exploration strategies that can successfully yield product images that are feasible, novel and aesthetic – three common goals in product design. Specifically, users’ actions within the global and local editing modes, including their time spent, prompt length, mono versus multi-criteria prompts, and goal orientation of prompts, are analyzed. Key findings reveal the pivotal role of mono versus multi-criteria and goal orientation of prompts in achieving specific design goals over time and prompt length. The study recommends prioritizing the use of multi-criteria prompts for feasibility and novelty during global editing while favoring mono-criteria prompts for aesthetics during local editing. Overall, this article underscores the nuanced relationship between the AI-driven text-to-image models and their effectiveness in product design, urging designers to carefully structure prompts during different editing modes to better meet the unique demands of product design.