Due to the ever-increasing complexity of technical products, the quantity of system requirements, which are typically expressed in natural language, is inevitably rising. Model-based formalization through the application of Model-based Systems Engineering is a common solution to cope with rising complexity. Thereby, grouping requirements to use cases forms the first step towards model-based requirements and allows to improve the understanding of the system. To support this manual and subjective task, automation by artificial intelligence and methods of natural language processing are needed. This contribution proposes a novel pipeline to derive use cases from natural language requirements by considering incomplete manual mappings and the possibility that one requirement contributes to multiple use cases. The approach utilizes semi-supervised requirements graph generation with subsequent overlapping graph clustering. Each identified use case is described by keyphrases to increase accessibility for the user. Industrial requirement sets from the automotive industry are used to evaluate the pipeline in two application scenarios. The proposed pipeline overcomes limitations of prior work in the practical application, which is emphasized by critical discussions with experts from the industry. The proposed pipeline automatically generates proposals for use cases defined in the requirement set, forming the basis for use case diagrams.

Designers often rely on their self-evaluations – either independently or using design tools – to make concept selection decisions. When evaluating designs for sustainability, novice designers, given their lack of experience, could demonstrate psychological distance from sustainability-related issues, leading to faulty concept evaluations. We aim to investigate the accuracy of novice designers’ self-evaluations of the sustainability of their solutions and the moderating role of their (1) trait empathy and (2) their beliefs, attitudes and intentions toward sustainability on this accuracy. We conducted an experiment with first-year engineering students comprising a sustainable design activity. In the activity, participants evaluated the sustainability of their own designs, and these self-evaluations were compared against expert evaluations. We see that participants’ self-evaluations were consistent with the expert evaluations on the following sustainable design heuristics: (1) longevity and (2) finding wholesome alternatives. Second, trait empathy moderated the accuracy of self-evaluations, with lower levels of fantasy and perspective-taking relating to more accurate self-evaluations. Finally, beliefs, attitudes and intentions toward sustainability also moderated the accuracy of self-evaluations, and these effects vary based on the sustainable design heuristic. Taken together, these findings suggest that novice designers’ individual differences (e.g., trait empathy) could moderate the accuracy of the evaluation of their designs in the context of sustainability.

Unintended technical interactions across system interfaces can lead to costly failures and rework, particularly in the early design stages of complex products. This study examines how structured risk assessment tools influence teams’ ability to identify, evaluate and mitigate risks from such indirect interactions. In a controlled experiment, 14 engineering teams (comprising professionals and graduate students) engaged in simulated design decisions across three system configurations. Tool usage – including models of direct and indirect risk propagation and value-based trade-offs – was continuously logged and linked to outcomes. Teams that engaged earlier and more deliberately with the tools identified risks sooner and selected mitigation actions with more favourable cost–benefit profiles. Results show that strategic, not merely frequent, tool use improves risk management performance, particularly when addressing cascading effects from indirect physical interactions. These findings support the use of structured supports to enhance both the efficiency of early-stage risk evaluation and the efficacy of risk treatment.

Life cycle assessment (LCA) reports are commonly used for sustainability documentation, but extracting useful information from them is challenging and requires expert oversight. Designers frequently face technical obstacles and time constraints when interpreting LCA documents. As AI-driven tools become increasingly integrated into design workflows, there is an opportunity to improve access to sustainability data. This study used a mixed-methods approach to develop life cycle design heuristics to help non-LCA experts acquire relevant design knowledge from LCA reports. Developed through in-depth interviews with LCA experts (n = 9), these heuristics revealed five prominent categories of information: (1) scope of analysis, (2) priority components, (3) eco hotspots, (4) key metrics, and (5) design strategies. The utility of these heuristics was tested in a need-finding study with designers (n = 17), who annotated an LCA report using the heuristics. Findings suggest a need for additional support to help designers contextualize quantitative metrics (e.g., carbon footprints) and suggest relevant design strategies. A follow-up reflective interview study with LCA experts gathered feedback on the heuristics. These heuristics offer designers a framework for engaging with sustainability data, supporting product redesign, and a foundation for AI-assisted knowledge extraction to integrate life cycle information into design workflows efficiently.

The value-creation opportunities enabled by the ubiquitous availability of data indisputably lead to the necessity of restructuring innovation processes. Moreover, the variety of stakeholders potentially involved in innovation processes and the apparent heterogeneity of scenarios and contexts imply much less established practices and routines and not yet constituted reference frameworks to lead the transition to data-driven product innovation. In this context, the paper attempts, from the analysis of the data-driven innovation processes of 36 Italian companies, to recognise the emerging innovation opportunities offered by the rich network of the resulting data flows. However, these opportunities also imply new tasks, which in turn raise further concerns. Building on data-driven design literature and on industrial practices in the field of innovation management, the authors discuss the role that research achievements in the field of engineering design can play in addressing such concerns.

Design computing refers to the usage of computer frameworks, models or systems in design-related activities. Design computing research, in turn, refers to the development of these frameworks/models/systems, and so forth. As design practice increasingly relies on computer tools, the demand for research in design computing grows. While this opens innumerable venues for research, the profusion of information in the field poses significant challenges for researchers. Therefore, meta-level surveys of the field are called for. To provide researchers with a useful overview of design computing research, we set out to identify some of the main clusters of activity in the field. By “clusters of activity”, we refer to groups of researchers pursuing similar or identical research questions. Our PRISMA-style review focuses on the identification of such clusters, based on the complete proceedings (N = 404) of a long-standing conference (Design Computing and Cognition, DCC, 2004–2024), which captures the richness and diversity of the field. The primary contribution of this work is a map that organizes the main questions explored and the approaches taken in exploring them, which are informative for researchers and educators alike. This map may also help to execute large-scale surveys via automation, toward obtaining a comprehensive view of the field.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

In this study, professional engineers and designers (n = 30) participated in a 1-hour-long design activity in which they brainstormed a list of ideas for two design problems (a smart grill and a smart laundry machine), created a sketched concept for each design problem, filled out a survey about their perceptions of the market for the concept they developed, participated in a bias mitigation intervention and then repeated the pre-intervention steps. The design problems were intended to trigger availability bias based on the participants’ occupations (engineers and designers at a kitchen appliance company) as well as conflict between the gender of the participants and the gender-stereotyping of the household tasks fulfilled by the smart machines. Based on correlations in the market survey, the participants, who were mostly men, displayed availability bias toward the smart laundry machine design problem. A key marker of availability bias – an association between participants’ personal enjoyment of the product and the belief that the product would be commercially successful – was eliminated after the bias mitigation intervention. Qualitative analysis of participants’ reflections indicated that the intervention primarily assisted designers in making additional considerations for users, such as increasing accessibility and building awareness of excluded user groups.

The central role of creativity in product engineering is evident in the generation of solutions with high innovative potential. Even in times of artificial intelligence being creative is still a skill in which the human outperforms the machine. Product engineering activities often take place in distributed environments, which elevates the importance of creative tasks due to the unique challenges these settings present. Furthermore, these distributed environments frequently involve intercultural teams. With intercultural team settings come additional benefits but also challenges. To support the creative processes of intercultural, distributed product engineering teams, the cultural synergy spectrum (CSS) method has been developed. The CSS method is designed to assist distributed product engineering teams with being creative while being culturally sensitive. To achieve this goal, mutual understanding is enhanced, and learning within the team is promoted. Using five phases to lead the participants through a creative process, the CSS starts with a warm-up, followed by building a knowledge baseline. The third phase is targeted at cultural learning, after which the creativity phase starts. Here, the actual problem-solving takes place. The final phase is for reflection and feedback. This study seeks to validate the CSS method’s effectiveness through application in a partially distributed team. Two teams, consisting of mechanical engineers in a research group at Shanghai Jiao Tong University, collaborated to address a practical problem using this method. The team is primarily Chinese as a follow-up to previous validation iterations that were done with teams with more diverse backgrounds, but who lived in Germany. To ensure that this bias due to the intercultural experience of living in another country is overcome, this study is performed with researchers in China with little intercultural experience. The CSS was applied successfully, proving that the CSS is suitable for the partially distributed or hybrid setting in which it was applied and for the team that applied it. The participants made use of the option to include additional tools and improvements to the method, like a more comprehensive warm-up.

Participatory Design – an iterative, flexible design process that closely involves stakeholders, often end users – is growing in use across design disciplines. As more practitioners use Participatory Design (PD), it has become less rigidly defined, with stakeholders engaged to varying degrees through disjointed techniques. This ambiguity can be counterproductive when discussing PD processes. We performed a systematic literature review that builds shared, foundational knowledge of PD processes and techniques while also summarizing the state of PD research in the field, as a first step in supporting richer understandings of how best to equitably engage with stakeholders. We found that a majority of PD literature examined specific case studies of PD, with the design of intangible systems representing the most common design context. Stakeholders most often participated throughout multiple stages of a design process, recruited in a variety of ways, and engaged in several of the 14 specific participatory techniques identified. Our findings also identify leverage points for creators of PD processes and how the leverage points impact design equity, including: (1) emergent versus predetermined processes; (2) direct versus indirect participation; (3) early versus late participation; (4) one time versus iterative participation; and (5) singular versus multiple PD techniques.

Collaborative engineering design is increasingly important for modern engineering practices as projects routinely require collaboration across multiple domains. Reaching shared understanding within the team is a critical factor in constructing a successful and enjoyable collaboration. One way to promote shared understanding is through the use of design artifacts and design representations as boundary objects. Different design representations have unique characteristics that benefit the engineering design process but could also hinder the development of shared understanding. It is important to identify the limitations of the design artifacts to select the suitable design artifact for the situation and mitigate potential adverse effects, including design fixation and miscommunication. Despite previous studies’ findings, there are still unsolved questions regarding the exact effect of the modality of the design representations on the development of team-shared understanding. This work examines three types of commonly used design representations in the engineering design community, namely, textual description, hand sketch and engineering CAD model. Their unique effect on the development of shared understanding is investigated in a collaborative engineering design setting. The results indicate that the modality of the design artifact would affect the development of shared understanding, and using visual representations can yield better team outcomes regardless of the modality complexity, mainly for design structures. This work shows the importance of using the proper design representation in collaborative engineering design tasks, and such a finding is a critical and timely reminder in the current age when team interactions constantly involve text-dominant online communications.

The manufacturing sector is witnessing a paradigm shift toward servitization, where companies are transitioning from selling products to offering product–service systems. This shift creates additional challenges, where the providers must ensure the expected value throughout the operational phase of the solution. Especially when dealing with a system-of-systems (SoS), evaluating performance across diverse contexts and business models while understanding the interconnectedness between systems becomes critical. To address these challenges during the design phase, this article presents a novel integrated simulation framework that supports the development team in exploring value from a SoS perspective. This framework utilizes agent-based simulation and offers three key features: multifidelity, modular and multidisciplinary. The applicability of the proposed framework is further demonstrated in a quarry industry case.

Human-centered design involves designing for users who may have social identities that are dissimilar from designers’ social identities. These differences could impact designers’ ability to understand users’ needs and integrate considerations of social identity into design decisions. Reflective interventions could encourage designers to actively consider social identity in design and our aim in this research is to explore this hypothesis through an experimental study. We tested the effects of completing a social identity-based reflection exercise on novice designers’ task clarification behavior. We also qualitatively examined the quality and content of the reflection responses. We find that participants who completed the intervention generated more social identity-focused design requirements, irrespective of the persona provided to them. Additionally, the content analysis revealed that designers who occupy minority identities (e.g., women and students of color) were more likely to provide deeper and higher-quality reflection responses. These findings suggest that reflective interventions could be an effective mechanism to promote inclusive design, leading to the design of products that users across social identities can use equitably. Furthermore, designers with different social identities may require different reflection cues (e.g., ones more focused on their personal experiences), to encourage deeper reflection on the effects of social identity in design.

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.