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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| 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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| _version_ | 1849420903239122944 |
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| author | Shu Fanyuan |
| author_facet | Shu Fanyuan |
| author_sort | Shu Fanyuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fdab5cb5550147e1b30b515bf3c70eb9 |
| institution | Kabale University |
| issn | 2261-2424 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj-art-fdab5cb5550147e1b30b515bf3c70eb92025-08-20T03:31:37ZengEDP SciencesSHS Web of Conferences2261-24242025-01-012180203010.1051/shsconf/202521802030shsconf_icdde2025_02030Research on the Path and Effectiveness of Youngor Group’s Intelligent TransformationShu Fanyuan0Business School, Beijing Technology and Business UniversityArtificial 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.https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02030.pdf |
| spellingShingle | Shu Fanyuan Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation SHS Web of Conferences |
| title | Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation |
| title_full | Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation |
| title_fullStr | Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation |
| title_full_unstemmed | Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation |
| title_short | Research on the Path and Effectiveness of Youngor Group’s Intelligent Transformation |
| title_sort | research on the path and effectiveness of youngor group s intelligent transformation |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02030.pdf |
| work_keys_str_mv | AT shufanyuan researchonthepathandeffectivenessofyoungorgroupsintelligenttransformation |