Product–service systems (PSSs) have drawn significant attention as a driver for business innovation and manufacturing servitization. A PSS is a system of products and services, supporting networks and infrastructure that are designed to satisfy customer needs and to generate values. In this paper, a representation framework for PSSs with eight spaces of product, customer, value, actor, service, business model, interaction context and time space has been proposed to help compare different PSSs and to support the design process. A PSS repository using the representation framework has been developed. To illustrate the representation method and its utility, 15 industry PSS cases are briefly introduced, and comparisons of these cases using the PSS representation framework and similarity assessments are explained. The utilities of the framework in designing PSSs and in analyzing and planning manufacturing servitization are discussed as well.

Serious gaming is the use of games for purposes beyond entertainment. In this paper, we investigate the use of serious gaming as a tool for research into decision-making in engineering systems design. Serious gaming provides a fully controllable environment in which to study the decision-making behavior of engineers in simulated design scenarios. However, given the nature of games and their inherent association with entertainment, it is possible that gaming environments themselves induce unexpected, or unrepresentative behavior. We present two experiments in which we investigate serious gaming as a research tool. Both experiments deal with design decisions in the presence of sunk costs and compare two approaches for communicating the decision-making scenario: (1) an interactive game and (2) a written narrative. The written narrative approach for communicating decision-making scenarios is a widely used and accepted technique for decision-making research. We find that behavior observed in the game variants did not significantly differ from behavior observed in their written narrative equivalents. This result builds confidence for the use of game-based research approaches. However, the results in this paper suggest that response distributions collected from a game have more noise than those from an equivalent written narrative.

During the early stages of any system design, a thorough exploration of the design space can prove to be challenging and computationally expensive. The challenges are further exacerbated when dealing with complex systems, such as an aircraft, due to the high dimensionality of their design space. Arising from the Toyota Product Development System, set-based design allows parallel evaluation of multiple alternative configurations in the early design stages. At the same time, optimisation methods can be employed at later stages to fine-tune the engineering characteristics of design variants. Presented in this paper, is the Augmented set-based Design and OPTimisation (ADOPT) Framework that introduces a novel methodology for integrating the two areas. This allows for a thorough design-space exploration while ensuring the optimality of the selected designs. The framework has been developed using a process-independent and tool-agnostic approach so that it can be applied to the design process of varying kinds of systems. To demonstrate the implementation and potential benefits, the framework has been applied to the design of a generic aircraft fuel system. The results from the case study and the framework itself are discussed, with a number of areas for further development and future work being identified and presented.

Increasingly, products are designed for global markets, yet studies of design practices primarily investigate designers from high-income countries. Specifically, the use of prototypes during design is likely affected by the background of the designer and the environment in which they are designing. To broaden our understanding of the extent to which prototyping best practices are used beyond Western designers, in this study, we conducted interviews with novice designers from Ghana, a middle-income country (MIC), to examine how Ghanaian novice designers (upper-level undergraduate students) used prototypes throughout their design courses. We compared the reported use of prototypes to best practice behaviors and analyzed the types of prototypes used. We found evidence that these Ghanaian novice designers used some critical prototyping best practice behaviors, while other behaviors were underutilized, specifically during the front-end phases of design and for the purpose of engaging with stakeholders. Additionally, virtual models dominated their prototyping choices. We discuss likely reasons for these trends based on participants’ design experiences and design contexts.

Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.

Value models are increasingly discussed today as a means to frontload conceptual design activities in engineering design, with the final goal of reducing cost and rework associated with sub-optimal decisions made from a system perspective. However, there is no shared agreement in the research community about what a value model exactly is, how many types of value models are there, their input–output relationships and their usage along the engineering design process timeline. Emerging from five case studies conducted in the aerospace and in the construction equipment industry, this paper describes how to tailor the development of value models in the engineering design process. The initial descriptive study findings are summarized in the form of seven lessons learned that shall be taken into account when designing value models for design decision support. From these lessons, the paper proposes a six-step framework that considers the need to update the nature and definition of value models as far as new information becomes available, moving from initial estimations based on expert judgment to detailed quantitative analysis.

This paper provides an empirical review of the reward-based crowdfunding platform Kickstarter.com, with the aim to explore and identify challenges in crowdfunded product development, which consequently can lead to failure of the crowdfunding campaign. The review was based on the analysis of a total of 144 successfully funded ‘technology’ campaigns, which all concerned the creation of physical consumer hardware preordered by campaign backers. The analysis was built around a failure mode model, which was established through a pre-study. The study reveals that (i) no more than 32% of the campaigns managed to deliver the crowdfunded products on time, and, if campaigns are delayed, (ii) there is a significantly higher probability that the delivered products might lack expected attributes. The causes for delay have many reasons, but (iii) a set of particular product development issues were identified as the main challenges. A better understanding of crowdfunded product development can help researchers and practitioners to better understand and utilize the opportunities of this new product development paradigm.

Although TRIZ is widely acknowledged as a powerful aid to improve efficacy and efficiency of the creative design process, practitioners diffusedly experience difficulties in the selection of the most suitable tool. Such an issue represents a severe limitation in consideration of the large number of tools TRIZ offers. Here, Inventive Principles (IPs) are acknowledged as the most popular TRIZ technique, and their conjointly use with the Contradiction Matrix makes the selection of the appropriate IP a sufficiently supported task. However, the reliability of the Contradiction Matrix is often questioned and an agreement on a solid and reliable procedure for the selection of IPs is far from being reached. In such a context, the paper investigates the recurrence of IPs to solve contradictions, with reference to a classification framework that takes into consideration the nature of the problem to be solved and the technical-scientific domain it belongs to. The outcomes of the analysis reveal that leveraged IPs are considerably related with the technical-scientific domain and the nature of the problem to be solved. The found relationships are worth delving into and translating into selection guidelines.

The engineering design process can produce stress that endures even after it has been completed. This may be particularly true for students who engage with the process as novices. However, it is not known how individual components of the design process induce stress in designers. This study explored the cognitive experience of introductory engineering design students during concept generation, concept selection and physical modelling to identify stress signatures for these three design activities. Data were collected for the design activities using pre- and post-task surveys. Each design activity produced distinct markers of cognitive experience and a unique stress signature that was stable across design activity themes. Rankings of perceived sources of stress also differed for each design activity. Students, however, did not perceive any physiological changes due to the stress of design for any of the design activities. Findings indicate that physical modelling was the most stressful for students, followed by concept generation and then concept selection. Additionally, recommendations for instructors of introductory engineering design courses were provided to help them apply the results of this study. Better understanding of the cognitive experience of students during design can support instructors as they learn to better teach design.

Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organization. In this paper, we investigate KM practices and needs in Open Source Hardware (OSH) communities. The aim is to provide insights into the KM issues and challenges facing these communities. Our analysis is based on interviews carried out with the participants of 22 projects. Interview transcriptions were coded and tagged with concepts extracted from the literature. Text mining was used to uncover the main concepts embedded in the corpus. From this analysis, knowledge sharing emerged as one of the top-rated concepts. Codification and personalization also proved to be important KM approaches, both requiring support in the OSH design process. Using a dendrogram, we highlighted the benefits and challenges of codification together with some interesting concept associations. High contributor turnover, little or no standardization and weak project structuring are still key challenges for OSH communities when it comes to ensuring awareness, making decisions and sharing knowledge.

Human-centered design (HCD) offers a systematic approach to innovation practice, driven by customer research and feedback throughout the design process. Within the community of engineers and researchers who engage in design for global development, interest in HCD has grown in the past decade. In this paper, we examine the human-centered design for development (HCD+D) academic community to better understand the interactions between researchers. By building and evaluating a co-authorship network from a dataset of HCD+D papers, in which the nodes are researchers and the connecting links are co-authorship relationships, we provide a decade-long benchmark to answer a variety of questions about collaboration patterns within this emerging field. Our analysis shows that most HCD+D authors publish few papers and are part of small, well-connected sub-communities. Influential authors that bridge separate communities are few. HCD+D is emerging from disparate disciplines and widely shared scholarship across disciplines continues to be developed. Influential authors in HCD+D play a large role in shaping HCD+D, yet there are few authors that are in a position to connect and influence collaborative research. Our analysis gives rise to several implications including an increased need for cross-disciplinary collaboration and the need for a stronger core of HCD+D practitioners.

Combining additive manufacturing (AM) with carbon fiber reinforced polymer patched composites unlocks potentials in the design of individualized, lightweight biomedical structures. Arising design opportunities are geometrical individualization of structures using the design freedom of AM and the patient-individual design of the load-bearing components employing carbon fiber patch placement. To date, however, full exploitation of these opportunities is a complex recurring task, which requires a high amount of knowledge and engineering effort for design, optimization, and manufacturing. The goal of this study is to make this complexity manageable by introducing a suitable manufacturing strategy for individualized lightweight structures and by developing a digitized end-to-end design process chain, which provides a high degree of task automation. The approach to achieve full individualization uses a parametric model of the structure which is adapted to patients’ 3D scans. Moreover, patient data is used to define individual load cases and perform structural optimization. The potentials of the approach are demonstrated on an exoskeleton hip structure. A significant reduction of weight compared to a standard design suggests that the design and manufacturing chain is promising for the realization of individualized high-performance structures.

In recent years, ‘living lab (LL)’, a design approach that actively involves users as partners from the early stage of the design process, has been attracting much attention. Compared with the traditional participatory design or co-design approaches, one of the distinctive features of the LL approach is that the process of and opportunity for user participation tends to be long-term and complex. Thus, LL practitioners must appropriately plan and design effective integration of user participation into the design process to promote co-creation with users. In other words, LL practitioners are required to ‘configure user participation’ for the effective promotion of co-creation. However, to date, the knowledge on how to properly configure long-term and complex user participation in LLs has not been systematically clarified, nor have its methodologies been developed. This study develops a novel framework for configuring user participation in LLs. Through a literature review and analysis on LL case studies, we identified the 11 key elements in five categories that should be considered while configuring user participation in LLs. Furthermore, on the basis of the identified elements, we developed a novel framework for configuring user participation in LLs, which is called the participation blueprint. We have demonstrated its use and have also discussed its theoretical and practical contributions to the LL and co-design research community.

Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori through a sensitivity analysis, which does not guarantee optimality in terms of robustness. This article introduces an automated design framework based on multiobjective optimisation to evaluate robustness as an additional competing objective. Robustness is computed as a sampled hypervolume of imposed geometrical and operational deviations from the nominal point. In order to address the high number of additional evaluations needed to compute robustness, artificial neutral networks are used to generate fast and accurate surrogates of high-fidelity models. The identification of their hyperparameters is formulated as an optimisation problem. In the frame of a case study, the developed methodology was applied to the design of a small-scale turbocompressor. Robustness was included as an objective to be maximised alongside nominal efficiency and mass-flow range between surge and choke. An experimentally validated 1D radial turbocompressor meanline model was used to generate the training data. The optimisation results suggest a clear competition between efficiency, range and robustness, while the use of neural networks led to a speed-up by four orders of magnitude compared to the 1D code.

Recommendations of sustainable design methods are usually based on theory, not empirical industry tests. Furthermore, since professionals often mix components of different design methods, recommending whole methods may not be relevant. It may be better to recommend component activities or mindsets. To provide empirical grounding for recommendations, this study performed 23 workshops on three sustainable design methods involving over 172 professionals from 27 companies, including consultancies and manufacturers in three industries (consumer electronics, furniture and clothing). The design methods tested were The Natural Step, Whole System Mapping and Biomimicry. Participants were surveyed about what components in each design method drove perceived innovation, sustainability or other value, and why. The most valued components only partially supported theoretical predictions. Thus, recommendations should be more empirically based. Results also found unique and complementary value in components of each method, which suggests recommending mixed methods for sustainable design. This may help design professionals find more value in green design practices, and thus integrate sustainability more into their practice.

This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

The design process often requires work by teams, rather than individuals. During team based design it is likely that situations will arise in which individual members of the team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s impossibility theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of ‘fair’ conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions, enabling decisions that are fairer in practice. First, experiential conjoint analysis was used to obtain individual empirical utility functions for drinking mug designs. Each empirical utility function represented individual members who were part of a design team. Then, a number of functions for constructing group preference were analysed using both randomly generated preferences and empirical preferences derived from the experiential conjoint survey. The analysis involved checking each of Arrow’s conditions, as well as assessing the potential impact of strategic voting. Based on the results, methods that should be used to aggregate group preference within a design team in practice were identified and recommended.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

Empathy is argued to be a key factor for a successful design discussion. However, such causality cannot be empirically proven based on how empathy is currently defined in design community. Empathy is used as an umbrella construct, broad and encompassing of diverse phenomena, making it difficult to quantify. We suggest improving such a situation by introducing a definition of empathy based on psychology literature, which provides structure and guidance for studying the role of empathy in design. We first break empathy to components. Then, we review empathy as used in design. Finally, we synthetize the reviewed material. From this synthesis, we conclude that empathy in design shares several key components of empathy in psychology; particularly with state influences, top-down control process and emotional stimuli. These are present in design methods although they have not been studied using such terms. Incorporating psychological components of empathy into design can help conceptualising empathy from a different angle, thus opening interesting new avenues for future research. We hope that our treatment provides present and future designers with some useful guidance.