The Critical Role of AI and Big Data in Revolutionizing Apparel Design
Article Sidebar
Main Article Content
Abstract
Background and Aim: While Artificial Intelligence (AI) and Big Data demonstrate significant potential in revolutionizing apparel design, existing research lacks empirical validation of AI’s predictive efficacy and fails to systematically deconstruct the structural barriers to technological integration. This study aims to evaluate the effectiveness of AI in trend and demand forecasting, analyze the optimization mechanisms of Big Data in design decision-making, and construct a framework to address integration barriers.
Materials and Methods: Grounded in a constructivist paradigm, this research employs a multi-method approach, including a systematic literature review, semi-structured interviews with 32 global fashion professionals, and a three-dimensional (technology-organization-creativity) case analysis of Zara, The Fabricant, and others. Thematic coding was conducted using NVivo.
Results: The findings confirm that AI improves prediction accuracy by over 30% through integrating social media, sales data, and other real-time sources, while Big Data-driven "just-in-time production" models reduce inventory waste by 25%. For the first time, barriers are deconstructed across three dimensions: (1) techno-ecological (fragmented infrastructure), (2) human-AI collaboration (digital literacy gaps), and (3) institutional governance (ethical compliance risks). A Multidimensional Barrier Co-evolution Framework for Digital Creative Transformation (MBCF-DCT) is proposed.
Conclusion: This study addresses critical empirical gaps and offers a closed-loop mechanism—data insight → creative translation → ethical calibration—to guide industry transformation. Future research should focus on blockchain-based copyright verification, algorithmic adaptation for non-Western aesthetics, and sustainable AI design models.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Ahiaveh, A. N., & Mawire, M. (2020). Exploring the mediating impact of identity on perceptions of racially diverse fast fashion advertisements: Evidence from millennial individuals.
Akhtar, W. H., Watanabe, C., Tou, Y., & Neittaanmäki, P. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633–656. https://doi.org/10.3390/textiles2040037
Arora, S., & Majumdar, A. (2022). Machine learning and soft computing applications in textile and clothing supply chain: Bibliometric and network analyses to delineate future research agenda. Expert Systems with Applications, 200, 117000. https://doi.org/10.1016/j.eswa.2022.117000
Berg, A., Chhaparia, H., Hedrich, S., & Magnus, K. H. (2021). What’s next for Bangladesh’s garment industry, after a decade of growth? McKinsey & Company, 7.
Bieńkowska, J. (2024). The effects of artificial intelligence on the fashion industry—Opportunities and challenges for sustainable transformation. Sustainable Development. https://doi.org/10.1002/sd.3312
Bočková, K., Procházka, D. A., & Bartoš, P. (2025). The role of artificial intelligence in managing scientific research projects funded by KEGA and VEGA grant schemes. Journal of Ecohumanism, 4(1), 1448–1476. https://doi.org/10.62754/joe.v4i1.5960
Evangelista, P. N. (2019). Artificial intelligence in fashion: How consumers and the fashion system are being impacted by AI-powered technologies. https://hdl.handle.net/10589/167521
Giri, C., Jain, S., Zeng, X., & Bruniaux, P. (2019). A detailed review of artificial intelligence applied in the fashion and apparel industry. IEEE Access, 7, 95376–95396. https://doi.org/10.1109/ACCESS.2019.2928979
Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López-de-Ipiña, D. (2023). Artificial intelligence in business-to-customer fashion retail: A literature review. Mathematics, 11(13), 2943. https://doi.org/10.3390/math11132943
Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology, 12(4), 1243–1257. https://doi.org/10.1007/s41870-020-00427-7
Kandi, N. A. (2023). How big data analytics will transform the future of fashion retailing. In Handbook of Big Data Research Methods (pp. 72–85). Edward Elgar Publishing. https://doi.org/10.4337/9781800888555.00009
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of big data analytics in supply chain management: Current trends and future perspectives. International Journal of Production Research, 59(6), 1875–1900. https://doi.org/10.1080/00207543.2020.1793011
Mohiuddin Babu, M., Akter, S., Rahman, M., Billah, M. M., & Hack-Polay, D. (2024). The role of artificial intelligence in shaping the future of the agile fashion industry. Production Planning & Control, 35(15), 2084–2098. https://doi.org/10.1080/09537287.2022.2060858
Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Symagulov, A., & Yelis, M. (2022). Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities, and challenges. Mathematics, 10(15), 2552. https://doi.org/10.3390/math10152552
Olatubosun, P., Charles, E., & Omoyele, T. (2021). Rethinking luxury brands and sustainable fashion business models in a risk society. Journal of Design, Business & Society, 7(1), 49–81. https://doi.org/10.1386/dbs_00020_1
