Utilizing Natural Language Processing for Enhancing Collaborative Value-Driven Design of Smart Product Service System: Smart E-Vehicle Application

Utilizing Natural Language Processing for Enhancing Collaborative Value-Driven Design of Smart Product Service System: Smart E-Vehicle Application

Abstract

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.

Keywords

Smart Product Service System, Value-Driven Design, Natural Language Processing, Scenario, Case

Reference

Zhang, Y., Larsson, A., Larsson, T., Tian, W., Zhang, L., Wang, W. (2024). Utilizing Natural Language Processing for Enhancing Collaborative Value-Driven Design of Smart Product Service System: Smart E-Vehicle Application. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Barthe-Delanoë, AM. (eds) Navigating Unpredictability: Collaborative Networks in Non-linear Worlds. PRO-VE 2024. IFIP Advances in Information and Communication Technology, vol 726. Springer, Cham. https://doi.org/10.1007/978-3-031-71739-0_20

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Categories: Publications, Research