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Utilizing Natural Language Processing for Enhancing Collaborative Value-Driven Design of Smart Product Service System: Smart E-Vehicle Application
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. BIGMind Innovation, Shanghai, China. (Product Development Research Lab)ORCID iD: 0000-0003-3711-264X
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (Product Development Research Lab)ORCID iD: 0000-0003-2211-2436
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (Product Development Research Lab)ORCID iD: 0000-0002-9662-4576
City University, HongKong, China.
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2024 (English)In: Navigating Unpredictability: Collaborative Networks in Non-linear Worlds, Proceedings, Part I / [ed] Luis M. Camarinha-Matos, Angel Ortiz, Xavier Boucher, Anne-Marie Barthe-Delanoë, Springer, 2024, Vol. 726, p. 305-318Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2024. Vol. 726, p. 305-318
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X
Keywords [en]
Smart Product Service System, Value-Driven Design, Natural Language Processing, Scenario, Case
National Category
Design Production Engineering, Human Work Science and Ergonomics
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-26917DOI: 10.1007/978-3-031-71739-0_20ISI: 001336708500020Scopus ID: 2-s2.0-85205088866ISBN: 9783031717390 (electronic)OAI: oai:DiVA.org:bth-26917DiVA, id: diva2:1898188
Conference
25th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2024, Albi, France, October 28–30, 2024
Part of project
Model Driven Development and Decision Support – MD3S, Knowledge FoundationVirtual Production Studio Lab VPSL, Region Blekinge
Funder
Knowledge Foundation, 20180159European Regional Development Fund (ERDF), 20356967Available from: 2024-09-16 Created: 2024-09-16 Last updated: 2025-01-15Bibliographically approved
In thesis
1. Future innovation framework for smart product service system design: Exploring an innovative design approach for global manufacturing companies
Open this publication in new window or tab >>Future innovation framework for smart product service system design: Exploring an innovative design approach for global manufacturing companies
2024 (English)Doctoral thesis, comprehensive summary (Other academic) [Artistic work]
Abstract [en]

Today, the rise of digitalization is reshaping how products are designed, produced, and consumed, challenging conventional product development paradigms. In response, manufacturing companies are increasingly adopting service-oriented business models through digital servitization, fueling the emergence of smart Product Service System (sPSS). However, the inherent complexity and need for collaborative innovation in smart PSS design requires manufacturing companies to adopt innovative design approaches that enable value-adding solutions to customers. This research addresses critical gaps in early-stage of smart PSS design, particularly in leveraging Digital Twins (DT) technology to facilitate value co-creation and support design decision-making. Despite growing interest in Digital Twins and virtual simulations, their practical application in smart PSS design remains limited, highlighting the need for new design approaches that foster collaboration and innovation in the early design stages.  To address these challenges and opportunities, this research integrates literature reviews, case studies, and empirical analysis to propose the Future Innovation Framework (FIF) and the Super-System Digital Twins (SSDT) approach for smart PSS development. Through industrial case studies, the research provides practical insights and introduces a Digital Twins approach that supports the successful implementation of smart PSS design in the context of global manufacturing companies. The findings indicate that the proposed Digital Twins approach significantly enhances concept visualization, decision-making and design prototyping in smart PSS design. Future research should focus on refining the Future Innovation Framework (FIF) and Super-System Digital Twins (SSDT) approach, exploring their scalability across various industries, and incorporating advanced AI techniques to maximize their potential.  In summary, this research contributes to the theoretical and practical advancements in smart PSS design by demonstrating how FIF and SSDT can foster more effective and innovative approach in global manufacturing companies. The proposed approach provides a robust foundation for future research and industrial applications, promoting the development of sustainable and competitive smart PSS solutions.  

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2024. p. 400
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2024:16
Keywords
Smart Product Service System, smart PSS design, Future Innovation Framework, Digital Twins, Automotive manufacturing company
National Category
Mechanical Engineering
Research subject
Mechanical Engineering
Identifiers
urn:nbn:se:bth-27038 (URN)978-91-7295-490-8 (ISBN)
Public defence
2024-12-18, J1630, Karlskrona, 09:00 (English)
Opponent
Supervisors
Available from: 2024-10-31 Created: 2024-10-30 Last updated: 2025-01-17Bibliographically approved

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Zhang, YanLarsson, AndreasLarsson, Tobias

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