It is an agreed fact among scholars that services are more sustainable compared to the products. By offering services, traditional companies can lock their customers into a long-term and sustainable revenue generation settlement. Available academic literature is abundant with methodologies related to service development. However, this study investigates various value adding service options related to financing and ownership of a product that can be offered on top of the existing products. It is important to understand these options from the consumer as well as supplier perspective. The most well established options available for financing/ownership are compiled, and sorted with respect to intangibility, ownership, financing and value addition scale. The study argues that the identified options incrementally add value to the existing offering by increasing the purchasing power and reducing the ownership obstacles for the customers. However, for the supplier, the decision depends on the trade-off between value addition for the customers and the business model changes required.

This research was motivated by the need to design for self-organized and sustained collaborative communities. A collaborative community is defined as a group of people who are bound by a sense of community and fulfil their unmet needs through collaboration (Baek, Meroni, & Manzini, 2015). A community with limited resources and premature organisational structure and therefore experience an unbalanced workload is fragile. If the community fails to distribute workloads fairly within and the commitment of the sacrificing members is exhausted, it is likely to fall apart. Inspired by the self- organization phenomena in nature, we designed a tool that these communities can use to conceive strategies that contribute to autonomy and collaboration. For validation, we applied the tool to an industrial design student club. The results demonstrate that despite the differences between social and ecological systems, there is a potential to learn from nature to design for self-organized collaborative communities with the condition that one has sufficient knowledge about both the references and the design target. We also discuss the problem-solving and learning effects of the tool.

More and more attention is paid nowadays to the human health concerns, associated with environmental problems arising from the fossil fuel use. The world scientific community offers new alternatives that, despite being more environmentally friendly, require an analysis of their potential environmental impacts. For example, biorefineries are becoming increasingly widespread nowadays offering a large gamma of bio-based products. However, it is necessary to take into account what potential effects such facility may have on the human health, depending on its geographical location since different territories may be more or less sensitive to its installation. Our study describes a new bioprocess implementation in terms of impact on human health through three countries: France, Belgium or China. In order to understand what territory can be more appropriate to the industrial process installation in terms of its effects on the human health, we propose to use the spatialized life-cycle analysis methodology.

Contrary to all agreement that methods can efficiently support design activities in the product development process, it can still be heard that notably complex methods often find little acceptance in industrial practice in terms of consistent use. Therefore, this concept paper aims to identify factors influencing the formation of acceptance to support a successful design and implementation of methods in product development. For this purpose, the first necessary step is to clarify the conceptualization of acceptance within this domain. Furthermore, the influencing variables, which are described in the acceptance models having been identified as development-relevant in a literature review, are first extracted and second newly structured using a behavioural model reflecting the mental stages from stimulus to behaviour. Third, the insights gained towards the factors' relevance throughout these stages are applied to the conditions of method application in engineering and design. In a subsequent step, basic assumptions towards central fields of action for concrete measures to increase acceptance concerning the use of methods in product development are derived.

Work-integrated learning (WIL) – a pedagogy that integrates academic studies with workplace experiences – presents an excellent opportunity for students to “deliberately practice” their design skills. To date there has been little investigation into the effect(s) of WIL experiences on developing novice designers’ design skills.

We performed a series of longitudinal interview case studies following three engineering students through the course of a 4-month work term. Interviews were semi-structured to gather rich contextual descriptions of participant experiences designing in WIL settings. Transcripts were analysed using an iterative thematic analysis approach.

Results indicate specific areas where WIL helps develop novice designers’ engineering design skills and mindsets beyond their early experiences in the engineering classroom. These include their experiences interacting with clients/users, the importance of project transition considerations, resource coordination, teamwork/collaboration, and the design process. We discuss how the structure of design tasks and their environment differ from the classroom experience, highlighting how WIL can supplement traditional design education.

Students mental health is declining. StudyWell is a project aiming at positive impact on student mental health in student cities in Norway; by integrating relational welfare with service design, and the study environment as a starting point. We discussfour implementation challenges: First, co-design depends on a shared mindset across disciplinary boundaries. Secondly, balancing the lenses of individuals, community, system and future require facilitation. Thirdly, societal impact requires continuous partner anchoring. Finally, approaches must not further pathologize university student.

Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

As organisations grow, consequences of poor knowledge management are evident for new employees in product developing companies. The problem of leveraging existing knowledge between development projects and departments is still relevant. This paper presents an industrial case study of a traditional manufacturing company and extends prior research addressing the reuse of organisational knowledge in new product development. The paper outlines barriers that hinder effective reuse of codified engineering knowledge and suggest means to overcome those barriers by using A3-reports in the PLM-system.

It is of vital importance to explore and understand future users' needs and requirements in the early phases of the product development process. However, in times of social distancing meeting users might not be possible. The project reported on in this paper has investigated the possibilities of conducting advanced user studies online. In total 30 small experimental studies have been conducted. Common digital tools that were used were e.g. Zoom, Teams, Mural, Miro, Snapchat, and Instagram.

The data was analyzed in a thematic content analysis by the authors on Mural. Identified challenges were excluding not tech-savvy user groups, missing out on interpersonal interaction and observations, as well as difficulties creating participant commitment and trust. On the positive side were perceived efficiency, a more levelled power distribution between participants, and ease of engagement and data retrieval for tech-savvy users.

Identified best practices included lowering social barriers through warm up activities and techniques to support open discussion during workshops. Furthermore, engagement could be supported through private social media groups, regular reminders, as well as clear communication of purpose and goal of the activities.

Atrial Fibrillation (AF) is one of the most prevalent cardiac diseases in the world. How might we design patient journeys improving quality of life using wearable cardiac devices for continuous out of hospital monitoring and support? Most of the studies to date have emphasised the technical aspects of implementing such devices with less focus on human factors. As such, remote cardiac monitoring appears to be burdened by poor patient adherence. This research study proposed a journey map based on Roger's technology adoption model to understand the challenges faced by AF patients and non/asymptomatic patients in using wearable devices to monitor their health. Data from semi-structured interviews conducted in Denmark with 12 participants aged 24 to 65 years was used. Interview results show that citizens prefer tracking heart activity only in conjunction with other measures such as steps or sleep and do not feel motivated to track their heart activity on a daily basis. Patients view wearables as a valuable tool to check if their health is all right, although apprehension that devices can cause unnecessary worry can lead to their rejection. Finally, recommendations for the design of patient journeys when using wearables were made.

This paper explores the suitability of Artificial Neural Networks (ANNs) as an enabler of Design Automation in the turbomachinery industry. Specifically, the paper provides 1) a preliminary estimation of the effectiveness of ANNs to define values for design variables of reciprocating compressors (RC) and 2) a comparison of ANNs performance with traditional and more computationally demanding methods like CFD. A tailored ANN trained on a dataset composed by 350+ Baker Hughes’ RC automatically assigns values to 8 geometrical variables belonging to multiple parts of the RC in order to satisfy two target conditions linked to their thermodynamic performance. The results highlight that the ANN-assigned parameters return an optimal solution for RC also when the target values do not belong to the training dataset. Their predictive capacity for RC thermodynamic performance, with respect to CFD, are comparable (i.e. less than 2% in terms of calculated absorbed power) and the approach enables a significant gain in terms of computational time (i.e. 2 minutes vs 10 hours). Future perspectives of this work may involve the integration of this tool in an advanced DA method to lead Design Engineers (DEs) during the whole design process.

Although it's human centered focus, design thinking has proven to be effective also in technology-driven projects, both in education and business. Yet, scant research has investigated whether and how design thinking might be leveraged to find new opportunities based on emerging technologies and design new innovation concepts accordingly. To address this gap, we employed an Action Innovation Management Research framework and co-designed a program called Tech to Market with Oper.Space, the design factory for Open Innovation of the University of Bologna. We ran 5 iterations of the program from October 2018 to December 2022, in which we conducted 52 interviews, observed 10 presentations, and held 10 meetings with the main stakeholders involved. Our results show how to apply design thinking to find and design a suitable application for a given technology, contributing to the ongoing conversation about the implementation of design thinking in technology-driven projects.

Drive units of eBikes are used in every type of bicycle and for different riding scenarios and riders. Due to the different riders and bike types, an enormous variety of influencing parameters and load spectra must be considered during the design process. Therefore, in this paper, a systematic approach for the optimization of the drive unit is presented, which adopts and combines several approaches from design theory. The focus is on efficient modeling and simulation of the relevant parameters and load spectra to minimize uncertainties in the design process.

Based on a system analysis, dimension-reduced parameter spaces are formed for the simulation of the system, meta-models are integrated into the simulation model and the results of the simulation are transferred into a data-based surrogate model to cover the parameter space in an efficient way with a minimum number of time consuming FE simulations. Furthermore, a coordinate-based evaluation method is presented for the FE model in order to form the input for the surrogate model, reduces the amount of data, and to allows a geometry- and mesh-independent evaluation to compare different models.

The importance of forming concepts in one's mind has been argued from various perspectives in design studies. This experimental study examines how the co-creative process affects concept formation considering its depth.

The authors conducted a learning experiment applying three processes; non-interactive (NI), interactive but non-co-creative (NC), and interactive and co-creative processes (C). To evaluate whether and how deep the concept is formed in the examinee's mind, mimetic Japanese words, which contain several different explicit concepts underlying a certain integrated implicit concept, were chosen as learning materials. The examinees without any knowledge about mimetic Japanese words were gathered globally and the experiment was conducted fully online using English. Examinees were tested several times to measure how they had formed these concepts for comparing the processes.

The findings suggest that the co-creative process enhances the depth of concept formation: involvement load and willingness to participate in the co-creative process lead to deeper concept formation.

The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the supply chain. This work aims to develop a graph data model of the Digital Thread, in the context of the vision of polyglot persistence, that interconnects the different phases of the lifecycle and their corresponding data models, processes, and IT systems. This work proposes a Digital Thread Graph that integrates a Digital Model and a derived Digital Twin, using object and relation attributes for view creation and filtering while minimizing redundancy.

Industry 4.0 is expected to change competitiveness of manufacturing firms. However, to completely achieve this goal, firms should manage barriers and complexity issues that my hinder its adoption or its effects. For this reason, the study explores, through a literature review, whether and how design theory may be a supporting theory to manage Industry 4.0 adoption and implementation to maximise the opportunities and minimise the risks. The results shows that these research questions require a design approach to innovate not only adopting technologies but reinventing the business practices.

Additive manufacturing (AM) technologies enable the design of new products due to their potentials. The potential of function integration can be extended through a combination of AM with a component integrating technology forming a hybrid integrating additive manufacturing technology (hiAM). With a created development method optimization areas within a product are identified on a functional level using characteristics, structural configurations and integrated functional areas. These are derived analysing examples in literature. The method is applied to a mechanical arm and hand prosthesis.

Additively manufactured final products and components are not always tailored to the additive manufacturing (AM) process, but they need to be in order to exploit the many advantages and potentials that AM provides. Therefore, an appropriate AM design should be targeted, which reduces the necessary iterations in the developing process of AM products. Although there is a large number of existing literature on the Design for Additive Manufacturing (DfAM), designers usually lack criteria in order to assess AM-conformity in conceptual design. In this paper, we provide a basis for the assessment of solution principles regarding their conformity for additive manufacturing.

First, existing literature on DfAM and AM products is reviewed comprehensively to derive criteria for the AM-conformity of solution principles. Subsequently, the correlations between these criteria are identified including the interdependencies to be considered when assessing AM-conformity. A basis for assessment is created, which offers designers early support in the development of AM-conformal designs.

This paper seeks to address the gap between qualitative Robust Design principles and parameter optimization. The former often fails to consider the challenging amount of details in embodiment and configuration design, while the latter is the widely accepted main thrust in traditional Robust Design. The gap is addressed by exploring the value of five quantitative robustness indicators for Design Space Exploration based on variables, objectives and constraints: The set level indicators, Design Space Size and Pareto Set Dispersion, and the point level indicators, Neighbourhood Performance, Failure Rate and Distance to Failure. As a background for the discussion of the limitations of these indicators an industrial case is presented. The case is an incremental encoder and includes two configurations for comparison, five objectives, eight variables, and a range of constraints. The design spaces are sampled and they show conflicting objectives, dispersed spaces and variables dependencies. Based on this it is suggested that set level indicators are more suitable than point level indicators of early robustness evaluation, but the available indicators are limited in their considerations of design space discontinuity and conflicts.

Precise sustainability assessment becomes increasingly important in decision-making, marketing, and regulations. Therefore, reliable and comparable LCA becomes mandatory. Currently, primary data is rarely available due to vastly complex value chains. Secondary data from eco-databases provide a remedy to estimate the sustainability impacts of up- and downstream processes. While giving insights and estimations, this data is seldom fitting exactly to the own processes and lacks comparability. Therefore, this paper proposes a method to close the gap between unreliable secondary data and unavailable primary data. This gap is to be closed by the integration of simulated process data. CAM is a tool during the work preparation to assess the design's manufacturability, decrease set-up times and optimize the NC code. However, integrating DES into LCA is still subject to research and will be discussed in this paper. This paper answers the question of the necessary steps to integrate the simulated production process in an LCA to increase the quality and reliability of sustainability indicators. A method is presented, and the implementation of the steps with the help of a developed assistance system on an example is performed.