New Paradigm of Data-Driven Smart Customisation through Digital Twin

Xingzhi Wang, Yuchen Wang, Fei Tao, Ang Liu

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)270-280
Number of pages11
JournalJournal of Manufacturing Systems
Volume58
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

  • Customisation
  • Digital twin
  • Personalisation
  • Product Service system
  • Smart manufacturing

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