Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation
Artificial intelligence (AI) is revolutionizing industries by enhancing efficiency and optimizing labor. For apparel manufacturing, AI addresses critical challenges in productivity, personalization, and digital transformation. This study analyzes Youngor Group’s intelligent transformation (2015–2021...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02030.pdf |
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| Summary: | Artificial intelligence (AI) is revolutionizing industries by enhancing efficiency and optimizing labor. For apparel manufacturing, AI addresses critical challenges in productivity, personalization, and digital transformation. This study analyzes Youngor Group’s intelligent transformation (2015–2021) through case studies, financial data, and innovation assessments. Key findings show: Production: Smart factories with AI task allocation and MES systems reduced customization cycles by 67%, increased per-worker output by 27.8%, and achieved 100% mass customization capacity.Marketing: 3D body scanning and 5G+AR virtual fitting improved customer profiling accuracy by 40%, while integrated online-offline strategies drove 11% annual revenue growth. Innovations like digital twin workshops, smart logistics, and immersive retail spaces strengthened supply chain flexibility and consumer engagement. However, cross-departmental data silos lowered collaboration efficiency by 15%, and R&D investment remained below 3% of total expenditure, reflecting talent gaps. The study concludes that apparel firms must prioritize intelligent production as the cornerstone, adopt phased technology integration, and invest in data governance and cross-disciplinary talent. While offering a framework for traditional manufacturing transformation, the research highlights limitations in generalizing single-case results, advocating future multi-industry comparisons for broader validation. |
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| ISSN: | 2261-2424 |