The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, leveraging variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development. 

The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data. 

The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development. 

Human-centred approaches to problem-solving have gained significant popularity in recent years. Among them, Design Thinking (DT) has received widespread attention, particularly through the work of design firms like IDEO and the Stanford d.school. Needfinding, idea generation, and prototyping techniques have often been applied to design Product-Service Systems (PSS) ‘experiences’, where tangible products and intangible services are integrated to design value-adding solutions for customers and users. Yet, the literature does not fully explain how the DT body of knowledge can effectively be applied to intentionally design PSS in different contexts. This chapter reviews existing literature on the usage of the DT method and tools in PSS design, presenting a list of recommendations that emerged from applying DT in co-production mode with companies in the Swedish manufacturing sector.

This research investigates globally dispersed innovation teams involved in explorative projects within an engineering graduate course employing problem-based learning. Utilizing insights from a longitudinal study, the objective is to identify how to enhance both individual learning and team performance, thereby increasing the likelihood of a successful outcome. Initial observations revealed common patterns in learning experiences among the top-performing teams, prompting further investigation into how supporting cohorts might positively influence both team performance and students’ learning experiences throughout the course. In addition to advancing comprehension of innovation team performance, the study introduces two lightweight tools designed as shared visual representations of the team’s exploration journey. These tools can assist supporting cohorts in guiding teams effectively. This research augments the existing body of knowledge surrounding the achievement of breakthrough innovations. It provides understanding about how to facilitate team performance and individual learning within globally dispersed innovation teams undertaking explorative projects. The proposed lightweight tools offer practical solutions to enhance the supporting cohort’s ability to guide and impact team performance and individual learning experiences. This study holds implications for academia and industry, particularly organizations reliant on radical innovation for competitiveness and future-proving. Lastly, the study’s findings could inform the design and delivery of future problem-oriented, project-organized learning-based courses in engineering education.

Product Service System solutions have proven to be a valuable innovation approach for industry organizations to differentiate themselves in a competitive market. Modern interpretations of PSS design have urged a move towards developing transformative innovations which are more than the sum of their parts. Achieving this transition in PSS design requires new tools to support designers in broader exploration of the design space to find a potentially transformative solution concept.

This research seeks to investigate the effects of a Group Model Building approach on the sustainability knowledge base prior to the weighting of design requirements. Current practice shows that the knowledge about sustainability impacts and implications on other design objectives often is limited and dispersed among decision-makers. Including sustainability criteria in needs and requirements, therefore, tend to rely on limited knowledge and decision-support, leading to decision-makers often prioritizing design objectives that they are more experienced with. The aim of this research was to capture and discuss effects on team alignment of contextual sustainability understanding from co-modeling impacts and implications of sustainability on other design objectives. The research question guiding the study is ‘what are the effects of a group model building approach on early sustainability design decisions?’. A mixed-methods research, structured according to four assumptions, was designed to analyze effects quantitatively and qualitatively. The assumptions were that the proposed approach helps design teams foster an improved contextual sustainability understanding, align the view of the relative importance of sustainability, identify suitable sustainability improvement, and increase the knowledge maturity of their design decisions. The results triangulation showed that the participants showed signs of improved sustainability understanding of their design projects and that improvement actions were identified from applying the approach. A statistically relevant converging effect could be found on the team alignment of the weighting of sustainability criteria but not on the knowledge maturity. These findings are discussed to conclude on main contributions, limitations, and areas for future work.

The design process aims to maximize the value in the end solution. For traditional manufacturing firms pursuing a servitization strategy, non-functional requirements become more important compared to before. Therefore, this paper investigates issues in capturing and utilizing non-functional requirements for value creation in a servitizing firm. A single case study was conducted, which resulted in six issues across the design process. It was evident from the research that the firm requires new support to enable a better inclusion of non-functional requirements in the design process.

Engineering educations deploy capstone courses as a way of supporting students’ transition into the engineering profession. This paper presents a global collaborative product development project affected by several constraints due to the coronavirus pandemic in 2020. The paper analyses the students' journey, as they navigated the final part of the project, drawing on learnings for the coaching cohort to support projects with similar levels of autonomy. The analysis uses a hunter-gatherer model to analyze the journey of iterations in search of a meaningful problem to solve. By utilizing this type of tool the supporting cohort can maintain a dialogue about the journey towards a vision and having a foundation of when to nudge, when to push back, and when to refrain from involving in their work.

Prototypes are an established tool for rapidly increasing learning, communication and decision making rationale for design projects. The proven success has spawned a litany of approaches and methods for building and planning the efficient planning and construction of prototypes. Translating these methods into simple usable tools to assist novice designers has generated broadly applicable canvases to support prototyping across the design process. Product Service System design has similarly introduced prototyping methods and tools into the process. Presently there is a lack of support for generating early phase tangible prototypes for functional PSS design aimed at more radically innovative solutions instead of currently dominant traditional products with traditional add-on services. This work explores the viability of utilizing existing prototyping support tools in the context of early PSS design through workshops with student designers and practitioners. The data from these workshops illuminates the alignments and misalignment gaps presented as guidelines to enable better support for early PSS designers. 

This is a qualitative single case study of a geographically distributed student team that experienced a quite different graduate course, compared to previous year's. This was due to the restrictions placed upon them following coronavirus lockdowns. With already ongoing research, and continuous development of the course, the authors had documented individual reflections and identified patterns and behaviours that seemingly determined the quality of the end result, as well as the students expectations and experiences. Semi-structured interviews, surveys and the author's individual reflection notes were already in place as part of the larger research scope and when the student team during the covid-19 year showed unexpected performance and results, the authors decided to pause the larger research scope and focus on this unique single case and capture those learnings. Not knowing how the Covid-19 situation evolves and leaning on insights from previous years, as well as this unique year, the aim with this paper is to describe the unique Covid-19 year amd share knowledge that can help improve and evolve the development of this longlived collaborative graduate student course, and other similar distributed team contexts.

The paper presents a Model-Driven approach for Product-Service System (PSS) Design promoting an increased digitalization of the PSS design process based on the combination of data-driven design (DDD) activities and value-driven design (VDD) methods. The approach is the results of an 8-year long research profile named (omitted for blind review) featuring the collaboration between (omitted for blind review) and nine industrial companies, in the field of PSS Design. It combines VDD models and the supporting data-driven activities in the frame of PSS design and aligns with the product value stream and the knowledge value stream in the product innovation process as described by Kennedy et al. (2008). The paper provides a high-level overview of the approach describing the different stages and activities, and provides references to external scientific contributions for more exhaustive descriptions of the research rationale and validity. The approach is meant to ultimately drive the development and implementation of a simulation environment for cross-functional and multi-disciplinary decision making in PSS, named Model-Driven Decision Arena, describe in the concluding part of the paper.