New Paradigm of Data-Driven Smart Customisation through Digital Twin |
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| Affiliation: | 1. School of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia;2. School of Automation Science and Electrical Engineering, Behang University, Beijing, 100083, China;1. Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau, China;2. Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai 519070, China;3. School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070, China;4. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;5. Institute of the Belt and Road & Guangdong-Hong Kong-Macao Greater Bay Area, Jinan University, Guangzhou 510632, China;1. Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China;2. Hubei Digital Manufacturing Key Laboratory, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China;1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China;2. Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China;1. Institute of Internet of Things and Logistics Engineering, Jinan University, Zhuhai, 519070, China;2. School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, 519070, China;3. GREE Electric Appliances, INC. of Zhuhai, Zhuhai, 519070, China;4. Science and Technology on Reliability Physics and Application of Electronic Component Laboratory, The Fifth Electronics Research Institute of Ministry of Industry and Information Technology, Guangzhou, 510610, China;1. College of Mechanical Engineering, Donghua University, Shanghai, China;2. Department of Mechanical Engineering, The University of Auckland, New Zealand;1. Department of Mechanical Engineering, The University of Auckland, New Zealand;2. Department of Industrial and Manufacturing Systems, The University of Hong Kong, Hong Kong |
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| Abstract: | Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework. |
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| Keywords: | Digital twin Customisation Smart manufacturing Personalisation Product Service system |
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