Product-Service System (PSS) development prioritizes the technical aspects of implementing Artificial Intelligence (AI), overlooking the strategic rationale behind its adoption. This paper investigates the impact of AI on value co-creation within industrial contexts by analyzing successful AI applications in PSS development across diverse domains like industrial systems engineering and healthcare technology. Through a multiple case study approach, this paper aims to explore the strategic motivations for using AI and its influence on the co-creation process. The analysis reveals several key benefits of AI adoption. Firstly, it fosters collaborative and iterative development, empowering both internal teams and external stakeholders to actively participate in value co-creation. Secondly, AI helps uncover novel value propositions that might remain hidden through traditional methods thus boosting the value co-creation. Finally, AI acts as a catalyst for building dynamic knowledge ecosystems. By facilitating data-driven insights and collaboration, AI enables continuous learning and adoption within the co-creation process. 

This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.

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. 

The ongoing servitization journey of the manufacturing industries instills a through-life perspective of value, where a combination of products and services is delivered to meet expectations. Often described as a product-service system (PSS), these systems are poised with many complexity aspects, introducing uncertainties during the design phase. Incorporating changeability is one of the known strategies to deal with such uncertainties, where the system changes in the face of uncertainty to sustain value, thereby achieving value robustness. While the theme of dealing with multiple uncertainties has been discussed since the inception of PSS, changeability is still poorly addressed. To bridge this gap, an integrative literature review is performed to outline various complexities aspects and their link to uncertainty from a PSS perspective. Also, the state-of-the-art approach to achieving value robustness is presented via changeability incorporation. Subsequently, a reference framework is proposed to guide decision-makers in changeability incorporation in PSS, especially during the early design stages.

The presented work springs from the hypothesis that the learning process can beaugmented by a course design that caters to peer learning in diversified groups. Thepresented work focuses on the assessment part of the course design as the type and designof assessments in a course greatly influence students' learning and can therefore be used tostimulate a desired behavior in students.

Two, timewise parallel, engineering courses on the same topic, given on campus and atdistance respectively, are studied. The current situation and potential changes areinvestigated with the ambition of being able to provide common learning activities to harvestthe potential interaction effects originating from peer learning in diversified groups,specifically aiming to mix life-long learners with “regular” students.

As a considerable part of the students within the anticipated diversified cohort are expectedto be gainfully employed (life-long learners), learning activities are preferred to begeographically and temporally unbound. In order to fulfill this design requirement anexamination process is proposed where the student initially independently chooses a topicto research and explore. The students then share their findings with fellow students via thelearning platform. An examination process based on peer review, asynchronously via forumson the learning platform, and self-assessment is proposed to motivate and support learning.The Universal Design for Learning guidelines are considered while proposing theseactivities aiming to provide good conditions for learning and potentially increase retention inthe diversified student group.

The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry.

Learning by reflection on doing

Experiential learning may be defined as “learning by reflection on doing”. The “doing” part in this case refers to work on open-ended engineering problems. Open-ended problems are problems that, in contrast to traditional closed exercises, have more than one possible solution. In engineering design these problems are also commonly ill-defined and wicked. Open-ended problem solving, besides reinforcing theoretical disciplinary knowledge, promotes skills such as critical thinking, problem solving, self-efficacy, confidence etc. Transitioning from closed to open-ended problems, learners develop their self-directed learning skills in accordance to Grow‘s stages in learning autonomy.

A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, a dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.

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.

Since its introduction in 2011, industry 4.0 has been coined the“4th industrial revolution” following mechanization, industrialization and IT/automation as the first three, and represents the current trend of automation technologies (cyber‐physical systems, internet of things, cloudcomputing, etc.,) in the manufacturing industry, with their potential for disruption of the manufacturing paradigm as we know it. However, the effect and role of industry 4.0 on the design and development of the new products to be manufactured in industry 4.0 facilities is not clear. This research presents a literature review to; 1) understand the concept of industry 4.0 from an implementation (state of practice) viewpoint, 2) learn about approaches and considerations currently deployed for developing products to be produced in manufacturing plants progressively transforming into industry 4.0 environments. Results reveal that the potential of industry 4.0 is underexploited within product design and development, especially in the conceptual stages lacking methods, tools, and approaches.While later stages of the product development (production planning,ramp‐up) have received some attention in regards with optimizing production operations, several publications acknowledge its potential to benefit earlier process stages.