In the last few years, numerous approaches have been introduced for supporting design engineers in developing more sustainable products. However, so far, these efforts have not led to the establishment of a commonly acknowledged standard methodology for Sustainable Product Development (SPD). This brings into question the relevance of developing new methods and calls for more efforts in testing the available ones. This article provides a reflection about the benefits and obstacles of applying existing SPD approaches to a real product development project. It reports the results of a project aimed at developing a new mobility solution under the constraints of sustainability-related targets. This project has led to the development of a new pedelec concept, focusing on the substitution of small passenger cars with the help of three SPD methods – Design for Sustainability Guidelines, Product Sustainability Index, and Life Cycle Sustainability Assessment. These methods have proved to be generally beneficial, thanks to a combination of qualitative and quantitative perspectives. However, the multitude of criteria offered by the methods put forth difficulties in evaluating which sustainability aspects are relevant and therefore lead to higher effort for information retrieval analysis and decision processes. Furthermore, the methods still lack an integrated perspective on the product, the corresponding services and the overarching system.

As products are being developed over time and across organisations, the risk for unintended accumulation and mis-conception of margins allocated may occur. Accumulation of margins can result in over design, but also add risk due to under allocation. This paper describes the different terminology used in one organisation and shows the different roles margins play across the design process and in particular the how margins are a critical but often overlooked aspect of product platform design. The research was conducted in close collaboration with a truck manufacturer between 2013 and 2018. The objective was to gain understanding of the current use of margins, and associated concepts evolve along the product life cycle, across organisation and product platform representations. It was found that margins already play an important role throughout the entire design process; however, it is not recognised as a unified concept which is clearly communicated and tracked throughout the design process. Rather different stakeholders have different notions of margins and do not disclose the rationale behind adding margins or the amount that they have added. Margins also enabled designers to avoid design changes as existing components and systems can accommodate new requirements and thereby saving significant design time.

This paper gives a general and logical analysis of the expert position in design research by which methods for innovative design can be derived from expert design practices. It first gives a framework for characterising accounts of design by the way in which they define and relate general, descriptive and prescribed types of design practices. Second, it analyses with this framework the expert position’s conservatism of prescribing existing expert design practices to non-expert designers. Third, it argues that the expert status of expert designers does not provide sufficient justification for prescribing expert design practices to non-expert designers; it is shown that this justification needs support by empirical testing. Fourth, it discusses validation of design methods for presenting an approach to this testing. One consequence of the need to empirically test the expert position is that its prescription has to be formulated in more detail. Another consequence is that it undermines the expert position since expert design practices are not anymore certain sources for deriving design methods with. Yet it also opens the expert position to other sources for developing design methods for innovation, such as the practices of contemporary designers and the insights of design researchers.

Mass collaboration efforts can increase innovation and design possibilities. Incorporation of open innovation into the product development process allows for a vast array of unique perspectives and ideas. However, with the broad expansion of design possibilities, coordination of these development processes is paramount. To best make use of open innovation in product development, increased organizational efforts must be considered. The mass collaboration of individuals must account for individual intellectual abilities (competencies), working experience and even personality traits or idiosyncrasies. Approaches to this problem require the fusion of social network analysis with quantifiable design impacts. This work proposes a simulation framework that evaluates the design potential of a project team based on individual attributes and the team network structure. The overall contribution of this work comes from the exploration of team structure, focusing on network composition metrics such as centrality and network density, while attempting to understand the role of individual ability and positioning on the success of the design process. This work aims to garner a more thorough understanding of how the network structure of design teams correlates with their potential performance through a generalized simulation framework, applicable to future crowd and design initiatives.

This paper proposes a task-related electroencephalogram research framework (tEEG framework) to guide scholars’ research on EEG-based cognitive and affective studies in the context of design. The proposed tEEG framework aims to investigate design activities with loosely controlled experiments and decompose a complex design process into multiple primitive cognitive activities, corresponding to which different research hypotheses on basic design activities can be effectively formulated and tested. Thereafter, existing EEG techniques and methods can be applied to analyse EEG signals related to design. Three application examples are presented at the end of this paper to demonstrate how the proposed framework can be applied to analyse design activities. The tEEG framework is presented to guide EEG-based cognitive and affective studies in the context of design. Existing methods and models are summarized, for the effective application of the tEEG framework, from the current literature spread in a wide spectrum of resources and fields.

Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers’ cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role.

The use of metal Additive Manufacturing (AM) has increased in recent years with potential benefits for novel design solutions and efficient manufacturing. In order to utilise these potentials, engineers need to address uncertainties related to product design and the AM process. This paper presents a design process utilising product-specific AM Design Artefacts (AMDAs) to assess uncertainties identified during design. The process emphasises the importance of concurrently developing the product and AM knowledge. Based on a research collaboration with industry, three case studies describe the use of this process in the development of products for AM. In total, six different types of AMDAs show how AM-related uncertainties are resolved to provide confidence in design solutions and manufacturability. The contributions of this paper are: (i) a design process where AMDAs are used as support in evolving and defining an AM design specification, (ii) an example of how Design for AM (DfAM) is practiced in industry and of typical AM uncertainties that are encountered and addressed, and (iii) an example of how collaborative research can facilitate new knowledge for both industry and academia. The practical implication is a DfAM process for engineers to use and adapt according to existing AM knowledge.

Design fixation refers to blind adherence to a set of ideas, which can limit the output of conceptual design. Engineering designers tend to fixate on features of pre-existing solutions and consequently generate designs with similar features. The objective of this study is to leverage functional magnetic resonance imaging (fMRI) to study the brain activity of engineering designers during conceptual design in order to understand whether/where design fixation can be detected in a person’s brain when solving design problems. Design solutions indicated that fixation effects were detectable at a statistically significant level. fMRI results show increased activation in areas associated with visuospatial processing when comparing ideation activities using an Example solution to No Example solution. Activation was found in the right inferior temporal gyrus, left middle occipital gyrus, and right superior parietal lobule regions. The left lingual and superior frontal gyri were found to be less active in the example condition; these gyri are close in proximity to the prefrontal cortex, associated with creative output. The spatial patterns of activation provide evidence that a shift in mental resources can occur when a designer becomes fixated. For designers, the timing of ideation relative to the timing of benchmarking existing solutions should be considered.

Pahl and Beitz’ ‘Systematic Approach’ is generally seen as a prescriptive model of designing based on observations of professional design practice. In this paper, we examine whether this model can be used as a predictive model. This is done by testing its predictive capacity for the design behaviour of students that are formally taught design and design methods. The behavioural observations used in this study are based on protocols of 15 design sessions involving mechanical engineering students after their first year of design education and 31 design sessions of the students using various concept generation methods. The design protocols and the Systematic Approach are coded uniformly using the Function–Behaviour–Structure (FBS) design issue schema. Cumulative occurrence analysis is used to derive qualitative and quantitative measures as a basis for comparison between the Systematic Approach’s prediction and the students’ design behaviour. The results indicate that the Systematic Approach can predict some but not all of students’ design issue behaviour.

This paper builds on the author’s keynote address to the Design Society’s 21st International Conference on Engineering Design in 2017 and in doing so provides a personal perspective to the question of the title. It begins by describing the engineering experience of the author which led to an understanding of the importance of taking a systems approach to the development of engineering products and services. This is followed by reflections on the development of a research portfolio focused on the design of complex engineering systems, inclusive design and healthcare improvement. The paper then reports on the recent work of engineers, clinicians and healthcare leaders, who came together under the guidance of the author, to explore how an engineering systems approach could be described that might simultaneously meet the needs of patients, carers and healthcare staff. It discusses the challenges associated with the translation of this narrative description of a systems approach (What?) into a practical implementation guide or toolkit (How?), supported by evidence of its effective use in health and care improvement practice. Finally, the paper reflects on the lessons to be learned from this process and their possible repercussions for design research and the practice of design.

The construction of future technological systems in work domains that do not yet exist, known as the envisioned world problem, is an increasingly important topic for designers, particularly given the rapid rate of technological advancement in the modern era. This paper first discusses the theoretical underpinnings of using cognitive work analysis (CWA) for developing a decision support system (DSS) situated within the envisioned world problem and recasts the problem as pathway-dependent processes. Using this pathway-dependent framework, each stage of the envisioning process is described to reveal how human factors experts can link existing work domains to envisioned instances. Finally, a case study example of the envisioning process that incorporates CWA modelling is demonstrated as it pertains to the advancement of the human spaceflight domain. As a result, this paper provides a unified treatment of the envisioned world problem with an end-to-end example of one approach to designing future technologies for future work domains.

Within the last few years, a need for renewed product personalisation has been observed in some markets such as consumer electronics, fashion to meet the exact demands of the customers. Product customisation emphasises the fulfilment of explicit requirements of a defined market segment, but product individualisation targets at satisfying the particular needs of a customer. Mass Individualisation (MI) is a new product design approach comprising of an open hardware platform and multiple modules to be integrated with the platform. It gives freedom to end-users to integrate different modules into the platform as per their choice. Technological and strategic integration of all actors involved in the design process is the primary focus of this research. This paper identifies key areas which need to be focussed on to realise this approach and convert it into an industrial practice by an explorative study of existing product design and customisation approaches. An industrial survey was conducted, and results for the industrial implication and insights on this approach are presented. The findings show that the end product from product design for MI will be more creative and innovative by the networking of all actors, and offers more individualised and technologically advanced products.

This study deals with the role of mental models in the coordination of team activities during design problem-solving. The work centers on the sharedness of mental models in a design team setting, mainly on the interaction between an architect and two clients. A major goal is to gain insight into how modifications in mental models affect coordination, and how sharedness develops through the process. Our focus is to explore, through a case study, the individual contributions of the architect and the clients to coordination of the work process, and how sharedness of the development of the team mental model evolves in the early stage of concept generation. Our claim is that work teams develop a certain degree of sharedness of the mental models of individual team members during information exchange. This team mental model can be insufficient or even wrong, but as long as the team members feel agreement in the team, they coordinate their work on that basis. Thus, sharedness of mental models is believed to be a powerful team asset, especially when it is reached in the earlier phases of the design process. Our findings suggest that in order to attain sharedness among design team members, design activities related to the task mental model should be encouraged, specifically the generation of new ideas and the analysis of solutions. Implications for practice and education are suggested.

Prior studies on design ideation have demonstrated the efficacy of using patents as stimuli for concept generation. However, the following questions remain: (a) From which part of the large patent database can designers identify stimuli? (b) What are their implications on ideation outcomes? This research aims to answer these questions through a design experiment of searching and identifying patent stimuli to generate new concepts of spherical rolling robots. We position the identified patent stimuli in the home, near and far fields defined in the network of patent technology classes, according to the network’s community structure and the knowledge proximity of the stimuli to the spherical rolling robot design. Significant findings are: designers are most likely to find patent stimuli in the home field, whereas most patent stimuli are identified in the near field; near-field patents stimulate the most concepts, which exhibit a higher average novelty; combined home- and far-field stimuli are most beneficial for high concept quality. These findings offer insights on designers’ preferences in search for patent stimuli and the influence of stimulation distance on ideation outcomes. The findings will also help guide the development of a computational tool for the search of patents for design inspiration.

Significant challenge exists in the effective monitoring and management of engineering design and development projects. Due to traits such as contextual variation and scale, detailed understanding of engineering projects and activity are difficult to form, with monitoring hence reliant on interpretation of managerial personnel and adherence to defined performance indicators. This paper presents a novel approach to the quantitative monitoring and analysis of engineering activity through computational topic identification and analysis of low-level communication data. Through three metrics of communication activity, this approach enables detailed detection and tracking of activity associated with specific project work areas. By application to 11,832 emails within two industry email corpora, this work identifies four distinct patterns in activity, and derives seven characteristics of communication activity within engineering design and development. Patterns identified are associated with background discussion, focused working, and the appearance of issues, supporting detailed managerial understanding. Characteristics identified relate to through-process norms against which a manager may compare and assess. Such project-specific information extends the ability of managers to understand the activity within their specific project scenario. Through detailed description of activity and its characteristics, in tandem with existing toolsets, a manager may be supported in their interpretation and decision-making processes.

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.

To better understand the impact of early versus late design decisions, a study was undertaken on the root causes of missed requirements in new product development and their impact on development cost through rework activities. The context is the industrial development of unmanned aerial vehicles. The aim is to understand the occurrence rate of missed requirements, their root causes, and their relative impact. A quantitative approach of counting requirements changes and using engineering documentation enabled traceability from observation back to root cause origin. The development process was partitioned into sequential program segments, to categorize activities to before and after concept and design freeze. We found that there was a significant difference in the rate of design defects arising before and after concept freeze; and found there was a significantly higher number of corrective activities required for design defects arising earlier before concept freeze. The revision rate of concept phase decisions was over 50%, and the rework multiplier if detected late was over 10X. In combination, design decisions made before design freeze accounted for 86% of the total expected program cost, and 34% was determined before concept freeze. These results quantify and support the anecdotal 80–20 impact rule for design decisions.

Travelers interact with a large number and variety of products and services during their journeys. The quality of a travel experience depends on a whole urban mobility system considered in space and time. This paper outlines the relevant concepts to be considered in designing urban mobility. The goal is to provide a language and insights for the early stages of a design process. A literature review sheds light on the complexity of urban mobility from technical, sociotechnical, and user experience perspectives. Observations of experiences in urban areas provide data for describing and understanding travel experience patterns and issues. The paper proposes a conceptual model to describe and analyze different facets of traveler experience and categorizes problems that travelers face when they interact with an urban mobility system. A case study is reported illustrating the use of the conceptual model in identifying travel problems for a demand-responsive transport service.

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.

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.