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Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. ((PDRL - Product Development Research Lab))ORCID iD: 0000-0002-3876-5602
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. ((PDRL - Product Development Research Lab))ORCID iD: 0000-0002-7804-7306
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. ((PDRL - Product Development Research Lab))ORCID iD: 0000-0002-9662-4576
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. p. 53-64
Series
Smart Innovation, Systems and Technologies, ISSN 2190-3018 ; 222
Keywords [en]
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: urn:nbn:se:bth-21042DOI: 10.1007/978-981-16-0119-4_5Scopus ID: 2-s2.0-85105935034ISBN: 9789811601187 (print)OAI: oai:DiVA.org:bth-21042DiVA, id: diva2:1526914
Conference
8th International Conference on Research Into Design (ICoRD' 21) 7-10 January 2021, IIT, Bombay, India
Part of project
Model Driven Development and Decision Support – MD3S, Knowledge Foundation
Funder
Knowledge Foundation, 20180159Available from: 2021-02-09 Created: 2021-02-09 Last updated: 2024-05-08Bibliographically approved
In thesis
1. Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
Open this publication in new window or tab >>Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 2021:10
Keywords
Product Development, Data-driven DSS, Machine Learning, Conceptual Stage, Data Analytics
National Category
Mechanical Engineering
Research subject
Mechanical Engineering; Mechanical Engineering
Identifiers
urn:nbn:se:bth-22322 (URN)978-91-7295-433-5 (ISBN)
Presentation
2021-12-17, Karlskrona, 10:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2021-11-11 Created: 2021-11-08 Last updated: 2021-11-26Bibliographically approved
2. Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
Open this publication in new window or tab >>Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
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
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:nbn:se:bth-26162 (URN)978-91-7295-484-7 (ISBN)
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

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Aeddula, OmsriWall, JohanLarsson, Tobias

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