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Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (PDRL - Product Development Research Laboratory)ORCID iD: 0000-0002-3876-5602
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024.
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2024:11
Keywords [en]
Product-Service System (PSS), Artificial Intelligence, Early-stage Decision Support, Data Challenges, Informed Decision-making
National Category
Mechanical Engineering Other Engineering and Technologies not elsewhere specified
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-26162ISBN: 978-91-7295-484-7 (print)OAI: oai:DiVA.org:bth-26162DiVA, id: diva2:1856487
Public defence
2024-06-14, J1630, Campus Gräsvik, Karlskrona, 09:30 (English)
Opponent
Supervisors
Available from: 2024-05-08 Created: 2024-05-07 Last updated: 2024-05-08Bibliographically approved
List of papers
1. A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
Open this publication in new window or tab >>A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
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2021 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2021, Vol. 1, p. 134-139Conference paper, Published paper (Refereed)
Abstract [en]

The advent of powered toothbrushes and associated mobile health applications provides an opportunity to collect and monitor the data, however collecting reliable and standardized data from large populations has been associated with efforts from the participants and researchers. Finding a way to collect data autonomously and without the need for cooperation imparts the potential to build large knowledge banks. A solution with Bluetooth low energy technology is designed to pair a powered toothbrush with a single-core processor to collect raw data in a real-time scenario, eliminating the manual transfer of powered toothbrush data with mobile health applications. Associating powered toothbrush with a single-core processor is believed to provide reliable and comprehensible data of toothbrush use and propensities can be a guide to improve individual exhortation and general plans on oral hygiene quantifies that can prompt improved oral wellbeing. The method makes a case for an expanded chance to plan assistant capacities to protect or improve factors that influence oral wellbeing in individuals with mild cognitive impairment. The proposed framework assists with determining various parameters, which makes it adaptable and conceivable to execute in various oral care contexts 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2021
Keywords
Dental Device, Oral health, Oral hygiene, Oral health information system
National Category
Dentistry Communication Systems
Identifiers
urn:nbn:se:bth-22249 (URN)10.1145/3472813.3473179 (DOI)2-s2.0-85118622969 (Scopus ID)978-1-4503-8984-6 (ISBN)
Conference
5th International Conference on Medical and Health Informatics, ICMHI, Kyoto, Japan, May 14 - 16, 2021
Funder
Knowledge Foundation, 20180159
Note

open access

Available from: 2021-10-29 Created: 2021-10-29 Last updated: 2024-05-07Bibliographically approved
2. Data analysis method supporting cause and effect studies in product-service system development
Open this publication in new window or tab >>Data analysis method supporting cause and effect studies in product-service system development
2020 (English)In: DESIGN 2020 - 16th International Design Conference, Cambridge University Press, 2020, Vol. 1, p. 461-470Conference paper, Published paper (Refereed)
Abstract [en]

A data analysis method aiming to support cause and effect analysis in design exploration studies is presented. The method clusters and aggregates effects of multiple design variables based on the structural hierarchy of the evaluated system. The resulting dataset is intended as input to a visualization construct based on colour-coding CAD models. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one

Place, publisher, year, edition, pages
Cambridge University Press, 2020
Series
Proceedings of the Design Society: DESIGN Conference, E-ISSN 2633-7762
Keywords
visualisation, product-service systems (PSS), product development, data analysis
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:bth-19758 (URN)10.1017/dsd.2020.123 (DOI)
Conference
DESIGN Conference, 16th International Design Conference, Cavtat, Kroatien, 26 okt. 2020 – tors 29 okt. 2020
Funder
Knowledge Foundation
Note

Open access

Available from: 2020-06-15 Created: 2020-06-15 Last updated: 2024-05-07Bibliographically approved
3. Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development
Open this publication in new window or tab >>Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development
2021 (English)In: Design for Tomorrow—Volume 2: Proceedings of ICoRD 2021 / [ed] Chakrabarti, A., Poovaiah, R., Bokil, P., Kant, V. (Eds.), Springer, 2021, p. 53-64Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2021
Series
Smart Innovation, Systems and Technologies, ISSN 2190-3018 ; 222
Keywords
Artificial neural networks; Data analysis; Design exploration; Product-Service System (PSS).
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Mechanical Engineering
Identifiers
urn:nbn:se:bth-21042 (URN)10.1007/978-981-16-0119-4_5 (DOI)2-s2.0-85105935034 (Scopus ID)9789811601187 (ISBN)
Conference
8th International Conference on Research Into Design (ICoRD' 21) 7-10 January 2021, IIT, Bombay, India
Funder
Knowledge Foundation, 20180159
Available from: 2021-02-09 Created: 2021-02-09 Last updated: 2024-05-08Bibliographically approved
4. AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development
Open this publication in new window or tab >>AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development
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2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Keywords
Predictive Maintenance; Autonomous Haulers; Product-Service Systems; Artificial Intelligence; Decision-Making.
National Category
Mechanical Engineering
Research subject
Mechanical Engineering; Mechanical Engineering
Identifiers
urn:nbn:se:bth-26158 (URN)
Conference
34th CIRP Design Conference, CIRP Design 2024,Cranfield University, UK, on 3-5 June 2024
Available from: 2024-05-03 Created: 2024-05-03 Last updated: 2024-05-13Bibliographically approved
5. AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
Open this publication in new window or tab >>AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
2024 (English)In: Proceedings of the 57th Annual Hawaii International Conference on System Sciences, University of Hawai'i at Manoa , 2024, p. 1017-1026Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
University of Hawai'i at Manoa, 2024
Series
Proceedings of the Hawaii International Conference on System Sciences, E-ISSN 2572-6862
Keywords
Product-Service System, Deep Learning, Autonomous Machine, Prototyping, Machine Behavior.
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Mechanical Engineering
Identifiers
urn:nbn:se:bth-25863 (URN)978-0-9981331-7-1 (ISBN)
Conference
57th Annual Hawaii International Conference on System Sciences, Hilton Hawaiian Village Waikiki Beach Resort, January 3-6, 2024
Funder
Vinnova, 2021-04347
Available from: 2024-01-06 Created: 2024-01-06 Last updated: 2024-05-07Bibliographically approved
6. AI-driven Ossification Assessment in Knee MRI: A Product-Service System Development for Informed Clinical Decision-Making
Open this publication in new window or tab >>AI-driven Ossification Assessment in Knee MRI: A Product-Service System Development for Informed Clinical Decision-Making
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Traditionally, assessing the degree of ossification in the epiphyseal plate for growth plate development relies on manual evaluation, which can be inefficient due to the complexities of the distal femoral epiphysis anatomy. Existing methods lack efficient detection techniques.

Method: This study proposes an AI-based decision support system, designed within a product-service system (PSS) framework, to automate ossification assessment and detection of the distal femoral epiphysis in knee magnetic resonance imaging (MRI) data. The system leverages advanced machine learning techniques, specifically two Convolutional Neural Networks (CNNs), combined with computer vision techniques. This intelligent system analyzes MRI slices to predict the optimal slice for analysis and identify variations in the degree of ossification within individual datasets.

Results: The proposed method's effectiveness is demonstrated using a set of T2-weighted gradient echo grayscale knee MRI data. The system successfully detects the complex anatomy of the distal femoral epiphysis, revealing variations in the degree of ossification ranging from completely closed/open to fully open/closed regions.

Conclusions: This study presents a robust and efficient AI-based method, integrated within a PSS framework, for measuring the degree of ossification in the distal femoral epiphysis. This approach automates ossification assessment, providing valuable insights for clinical decision-making by clinicians and forensic practitioners. The PSS framework ensures seamless integration of the AI technology into existing workflows.

Keywords
Degree of Ossification, Artificial Intelligence, Decision Support System, Product-Service Systems, Knee MRI
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:bth-26160 (URN)
Available from: 2024-05-03 Created: 2024-05-03 Last updated: 2024-05-08Bibliographically approved

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