Digital Twin-Based Mass Customization in Smart Fashion Manufacturing

Abstract

To meet demands in the industry and attain mass customization in fashion manufacturing-highly personalized products at near mass production efficiency-more data integration, real-time visibility, and coordination among manufacturing operations should be realized. In this paper, a Digital Twin-Based Mass Customization (DT-MC) framework is proposed to help to overcome these limitations, improve responsiveness, productivity, and sustainability within smart fashion manufacturing system. It consists of five integrated modules including physical system, data acquiring, digital twin modeling, analysis and optimization, user interface. Mixed methods approach based on simulation modeling and empirical study by industrial case study are adopted in this paper. The results indicate that, with the application of DT-MC framework, lead time, customization response time, defect rate and resource utilization decrease from 72 h to 46 h, from- to -, from 8.4% to 3.1% and from 68% to 87%, respectively. It prove ds to improve the performances by 36.1%, 41.3%, 63.1%, and 27.9% respectively. Sustainability wise, there was a reduction in the materials used by 22.5%, energy consumption by 18.7%, and the DT framework attains an accurate prediction rate of 94.6%.

The study also indicate that the DT-MC framework is capable of realize scalable and efficiency mass customization, and greatly optimize its performances in operational, environment and product aspects. This paper provides both a reliable quantitative and conceptual framework towards the Industry 4.0 driven transformation of the fashion manufacturing industry.

 Keywords: Digital Twin, Modern Fashion Industry, Mass Customization, Performances, Smart Fashion Production.