Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.

The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.

The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.

BackgroundApplied health technology is a research field that ties together several disciplines to improve and preserve the health and quality of life of individuals and society. Helping especially elderly to meet the above goals is an important and necessary task and assistive technology and collection of health data are part of this work.

AimsPaper I aims to investigate whether the use of a powered toothbrush could maintain oral health in a group of individuals with MCI and if changes in oral health affect various aspects of quality of life. Paper II and III aims to examine the capacity of a powered toothbrush as a carrier and mediator of health-related data.

MethodsFor papers I and II, the participants were recruited from the Swedish site of the multicenter project Support Monitoring And Reminder Technology for Mild Dementia and for paper III from the Department of Health at Blekinge Institute of Technology. In all three papers, a powered toothbrush has been used as a tool, sensor carrier and transmitter of data. For Quality-of-life assessment two instruments are used, The QoL-AD and OHIP 14.

ResultsBy introducing an intelligent powered toothbrush in the group of older individuals with mild cognitive impairment we have showed that they, regardless of cognitive level,improved their scores for plaque index, bleeding index and deepened periodontal pockets ≥ 4mm, over 12 months. The quality-of-life instrument related to oral health improved in parallel with the improvement in oral health. Furthermore, it is possible to use the intelligent powered toothbrush both as a carrier for healt related sensors and to transfer user data via Bluetooth technology to a single-core processor that stores or forwards the data via Wifi to an external computer for processing, analysis and storage. A fesibility study regarding temperature sensor for measuring body temperature during toothbrushing have been evaluated and found to be comparable to traditional oral temperature measurement.

ConclusionsAn intelligent powered toothbrush is a well-functioning tool for maintaining oral health in older people with mild cognitive impairment as well as for collecting and transferring brush and health data to external units for storage and analysis. 

Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.

This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.

The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.

Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.