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. 

When bus manufacturing companies move forward in their serviti- zation journey for providing service solutions for tourism industry, there is an increasing need to exploit the prototype way to support the realization of design solutions in the early stages of the Product Service System (PSS) design. Digital twin, a new emerging and fast growing technology which connects the physical and virtual world, has attracted much attention worldwide recently. This paper presents a new method for PSS design based on the digital twin approach. The development of product design is briefly introduced first. The framework of digital twin approach is then proposed and analysed. The main work in this research work is how to trade off the ‘realism’ of the design output of the virtual concept. A case is presented to illustrate the application of the proposed DT approach for prod- uct prototyping. Verification activities performed the case in a virtual simulation environment prototype the design concepts in the tourism industry.

Education is not only about preparing students for a life in industry (reality), but also about moving the existing reality into the new normal by the hands of these fresh students. For more than 25 years we have created capstone project courses where industry (and society) have met the minds of students, and seen amazing results happen. Automotive, aerospace, medicine, healthcare, municipality; many are the application areas that our students from engineering have embarked upon.  Trust the process!

The Product-Service Systems (PSS) methodology faces new challenges as digital servitisation drives product-oriented companies to integrate digital services into their offerings. A value co-creation strategy and global collaborative innovation are now essential for these companies to develop smart PSS models. This study introduces the Future Innovation Framework (FIF) which is proposed as a mechanism to facilitate value cocreation in smart PSS design, specifically tailored for global manufacturing contexts. Through qualitative analysis and literature review, the research investigates collaboration among key stakeholders, defines a structured smart PSS design process, and demonstrates how value co-creation can enhance design outcomes. The proposed FIF framework, applied to a Smart Electric Vehicle (SEV) case with Volkswagen, supports early-stage collaborative innovation and informed decision-making. This paper discusses the practical implications, challenges, and future opportunities of implementing FIF in industrial smart PSS design. Finally, the potential for adapting FIF across various industry sectors is explored.

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.

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.

Objectives: The study aimed to investigate how the objective use of a powered toothbrush in frequency and duration affects plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm in elderly individuals with MCI. A second aim was to compare the objective results with the participants’ self-estimated brush use.

Materials and methods: Objective brush usage data was extracted from the participants’ powered toothbrushes and related to the oral health variables plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm. Furthermore, the objective usage data was compared with the participants’ self-reported brush usage reported in a questionnaire at baseline and 6- and 12-month examination.

Results: Out of a screened sample of 213 individuals, 170 fulfilled the 12-month visit. The principal findings are that despite the objective values registered for frequency and duration being lower than the recommended and less than the instructed, using powered toothbrushes after instruction and information led to improved values for PI, BOP, and PPD ≥ 4 mm in the group of elderly with MIC.

Conclusions: Despite lower brush frequency and duration than the generally recommended, using a powered toothbrush improved oral health. The objective brush data recorded from the powered toothbrush correlates poorly with the self-estimated brush use. Clinical relevance: Using objective brush data can become one of the factors in the collaboration to preserve and improve oral health in older people with mild cognitive impairment.

Trial registration: ClinicalTrials.gov Identifier: NCT05941611, retrospectively registered 11/07/2023. © 2023, The Author(s).

Manufacturing companies are increasingly transitioning from a product-centric to a smart Product Service System (smart PSS) approach to enhance customer satisfaction, service offerings, and product competitiveness through a combination of usage scenarios and digital components. In the context of Industry 5.0 transformation such as developing the Smart Electric Vehicle (SEV), the automotive industry faces the challenge of understanding customers’ descriptions of usage scenarios and translating the qualitative aspects of these scenarios into quantitatively assessed product features for collaborative value co-creation in smart PSS design. This paper addresses this challenge through utilizing Natural Language Processing (NLP) joint with Value-Driven Design (VDD) method for successfully supported a collaborative value exploration of in the smart PSS design stage. A case study was collaborated with a global automotive Original Equipment Manufacturer (OEM), Volkswagen, through proposing a NLP BERT model for VDD of Smart Electric Vehicle (SEV) design. Validation activities were performed by deploying the developed BERT model to the case company based on the scenario design of new car models.

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.