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The Role of Big Data Analysis in Fashion Design

Yıl 2024, Cilt: 2 Sayı: 34, 97 - 119, 01.12.2024
https://doi.org/10.18603/sanatvetasarim.1515756

Öz

Today, as the contemporary, economic and cultural landscape becomes increasingly volatile, the dynamics of the fashion industry are becoming less predictable. The fast-changing fashion industry is constantly generating data. Big data is a powerful tool that offers new and exciting opportunities for fashion designers. It is used for different purposes such as understanding consumer behaviour, predicting trends and creating more personalized products. Design processes that were previously driven by intuition and instinct have become more fluid, data and goal-oriented with the opportunities offered by big data. This article aims to reveal the purposes of using big data by investigating fashion design models based on big data in the fashion industry in recent years. For this purpose, the current models of big data in fashion design, its limitations and development direction are discussed. While many of the areas of use of big data in the fashion industry (trend analysis, demographic, geographical analysis, age and gender, etc.) have been intensively researched, the number of purely design-oriented research is only recently increasing. This study is unique in that it focuses on the design phenomenon and applications of big data in fashion. In this context, there is no academic study in the national literature. It is expected to contribute to the discussion on the effects of big data and big data-based digital technologies in fashion design.

Kaynakça

  • Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42, 90–101.
  • Ahsan, M., Hon, S. T., & Albarbar, A. (2020). Development of novel big data analytics framework for smart clothing. IEEE Access, 8, 146376–146394.
  • Bertola, P., & Teunissen, J. (2018). Fashion 4.0. Innovating fashion industry through digital transformation. Research Journal of Textile and Apparel, 22(4), 352–369.
  • Bhardwaj, V., & Fairhurst, A. (2010). Fast fashion: response to changes in the fashion industry. The International Review of Retail, Distribution and Consumer Research, 20(1), 165–173.
  • Black, S. (2019). Sustainability and Digitalization. In The End of Fashion. Bloomsbury Publishing Plc. https://doi.org/10.5040/9781350045071.ch-009
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Cassidy, T. D. (2019). Colour forecasting. Textile Progress, 51(1), 1–137.
  • Chen, K.-T., & Luo, J. (2017). When fashion meets big data: Discriminative mining of best selling clothing features. Proceedings of the 26th International Conference on World Wide Web Companion, 15–22.
  • Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2011). Body area networks: A survey. Mobile Networks and Applications, 16, 171–193.
  • Chen, M., Ma, Y., Song, J., Lai, C.-F., & Hu, B. (2016). Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring. Mobile Networks and Applications, 21(5), 825–845. https://doi.org/10.1007/s11036-016-0745-1
  • Chen, R.-Y. (2018). A traceability chain algorithm for artificial neural networks using T–S fuzzy cognitive maps in blockchain. Future Generation Computer Systems, 80, 198–210.
  • Cui, Y., Feng, X., & Yang, X. (2021). A matching degree management model of human body shape and fashion design based on big data analysis. Scientific Programming, 2021(1), 9384404.
  • De Chernatony, L., Harris, F., & Riley, F. D. (2000). Added value: its nature, roles and sustainability. European Journal of Marketing, 34(1/2), 39–56.
  • Doeringer, P., & Crean, S. (2006). Can fast fashion save the US apparel industry? Socio-Economic Review, 4(3), 353–377.
  • Dong, M., Zeng, X., Koehl, L., & Zhang, J. (2020). An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences, 540, 469–488.
  • DuBreuil, M., & Lu, S. (2020). Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED. International Journal of Fashion Design, Technology and Education, 13(1), 68–77.
  • Garcia, C. C. (2022). Fashion forecasting: an overview from material culture to industry. Journal of Fashion Marketing and Management: An International Journal, 26(3), 436–451.
  • Hirscher, A.-L., Niinimäki, K., & Joyner Armstrong, C. M. (2018). Social manufacturing in the fashion sector: New value creation through alternative design strategies? Journal of Cleaner Production, 172, 4544–4554.
  • İşmal, Ö. E., & Yüksel, E. (2016). Tekstil ve moda tasarımına teknolojik bir yaklaşım: akıllı ve renk değiştiren tekstiller. Yedi, 16, 87–98.
  • Jain, S., Bruniaux, J., Zeng, X., & Bruniaux, P. (2017). Big data in fashion industry. IOP Conference Series: Materials Science and Engineering, 254(15), 152005.
  • Jang, J., Ko, E., Chun, E., & Lee, E. (2012). A study of a social content model for sustainable development in the fast fashion industry. Journal of Global Fashion Marketing, 3(2), 61–70.
  • Ji, Y., & Jiang, G. (2020). Garment customization big data–processing and analysis in optimization design. Journal of Engineered Fibers and Fabrics, 15, 1558925020925405.
  • Kawamura, Y. (2018). Fashion-ology: An introduction to fashion studies. Bloomsbury Publishing.
  • Kim, R.-H. (2015). Cure performance and effectiveness of portable smart healthcare wear system using electro-conductive textiles. Procedia Manufacturing, 3, 542–549.
  • Lopes, M. V. (2019). The discourse of fashion change: Trend forecasting in the fashion industry. Fashion, Style & Popular Culture, 6(3), 333–349.
  • Ma, K., Wang, L., & Chen, Y. (2017). A collaborative cloud service platform for realizing sustainable make-to-order apparel supply chain. Sustainability, 10(1), 11.
  • McKelvey, K., & Munslow, J. (2011). Fashion design: process, innovation and practice. John Wiley & Sons.
  • Olaru, S., Popescu, G., Anastasiu, A., Mihăilă, G., & Săliştean, A. (2020). Innovative concept for personalized pattern design of safety equipment. Industria Textila, 71(1), 50–54.
  • Organization, W. H. (2015). World report on ageing and health. World Health Organization.
  • Ou, L., Luo, M. R., Woodcock, A., & Wright, A. (2004). A study of colour emotion and colour preference. Part I: Colour emotions for single colours. Color Research & Application, 29(3), 232–240.
  • Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2014). Recent advances in wearable sensors for health monitoring. IEEE Sensors Journal, 15(6), 3119–3126.
  • Särmäkari, N. (2023). Digital 3D fashion designers: Cases of atacac and the fabricant. Fashion Theory, 27(1), 85–114.
  • Särmäkari, N., & Vänskä, A. (2022). ‘Just hit a button!’ – fashion 4.0 designers as cyborgs, experimenting and designing with generative algorithms. International Journal of Fashion Design, Technology and Education, 15(2), 211–220.
  • Şen, C., Kılıç, A., & Öndoğan, Z. (2020). Endüstri 4.0 ve Moda Sektöründeki Uygulamaları. Turkish Journal of Fashion Design and Management, 2(2), 53–65.
  • Silva, E. S., Hassani, H., & Madsen, D. Ø. (2020). Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), 21–27. Silva, E. S., Hassani, H., Madsen, D. Ø., & Gee, L. (2019). Googling fashion: forecasting fashion consumer behaviour using google trends. Social Sciences, 8(4), 111.
  • Sun, L., & Zhao, L. (2018). Technology disruptions: Exploring the changing roles of designers, makers, and users in the fashion industry. International Journal of Fashion Design, Technology and Education, 11(3), 362–374.
  • Tamborrini, P., Remondino, C. L., & Marino, C. (2018). Fashion industry as a big data enterprise for sustainability. Curr Trends Fashion Technol Textile Eng, 3(4), 555616.
  • Tao, X. (2001). Smart technology for textiles and clothing-introduction and review. In Smart fibres, fabrics and clothing (pp. 1–6). Woodhead Pub.
  • Vinken, B., & Hewson, M. (2005). Fashion zeitgeist: Trends and cycles in the fashion system.
  • Westland, S., Laycock, K., Cheung, V., Henry, P., & Mahyar, F. (2007). Colour harmony. Colour: Design & Creativity, 1(1), 1–15.
  • Wong, M. Y., Zhou, Y., & Xu, H. (2016). Big data in fashion industry: Color cycle mining from runway data.
  • Yıldıran, M. (2022). Dördüncü Endüstri Devrimi ve Moda Endüstrisine Etkileri. Sanat ve Tasarım Dergisi, 12(2), 559–578.
  • Zhao, L., Liu, S., & Zhao, X. (2021). Big data and digital design models for fashion design. Journal of Engineered Fibers and Fabrics, 16, 15589250211019024.
  • Zhou, Z., Shangguan, L., Zheng, X., Yang, L., & Liu, Y. (2017). Design and implementation of an RFID-based customer shopping behavior mining system. IEEE/ACM Transactions on Networking, 25(4), 2405–2418.
  • Zhu, X., Lu, H., & Rätsch, M. (2018). An interactive clothing design and personalized virtual display system. Multimedia Tools and Applications, 77(20), 27163–27179.
  • INTERNET REFERENCES Boone, T. (2016). Fashion Trends. https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/fashion-trends-2016-google-data-consumer-insights/.Access date: 15.06.2024.
  • Bringe, A. (2023). The Future Of Marketing In The Fashion And Lifestyle Industries: AI, Personalization And Data-Driven Insights. https://www.forbes.com/sites/forbescommunicationscouncil/2023/11/30/the-future-of-marketing-in-the-fashion-and-lifestyle-industries-ai-personalization-and-data-driven-insights/.Access date: 1.06.2024.
  • Cgsinc. (2018). How Big Data is Impacting the Fashion Industry. https://www.cgsinc.com/blog/how-big-data-impacting-fashion-industry. Access date: 12.03.2024.
  • Clo 3D. (2024a). Our User Stories. https://www.clo3d.com/en/company/clo-users/stories. Access date: 15.01.2024.
  • Clo 3D. (2024b). Real materials. https://www.clo3d.com/en/clo. Access date: 15.04.2024.
  • Clo Virtual Fashion. (2024). Virtual Fashion. https://www.clovirtualfashion.com/. Access date: 15.05.2024.
  • Devillard, S., Harreis, H., Landry, N., & Altable, C. S. (2021). Jumpstarting value creation with data and analytics in fashion and luxury. https://www.mckinsey.com/industries/retail/our-insights/jumpstarting-value-creation-with-data-and-analytics-in-fashion-and-luxury. Access date: 16.05.2024.
  • Edited. (2024). Empowering retailers with AI-fueled retail intelligence. https://edited.com/. Access date: 15.06.2024.
  • Fashion, B. of. (2024). Artificial-Intelligence. https://www.businessoffashion.com/tags/tag/artificial-intelligence/. Access date: 07.03.2024.
  • McKinsey. (2024). The State of Fashion 2024: Finding pockets of growth as uncertainty reigns. https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion. Access date: 08.03.2024.
  • Wgsn. (2024). Fashion. http://www.wgsn.com/en/products/fashion. Access date: 09.03.2024.
  • FIGURES REFERENCES Figure 1. Zhu, X., Lu, H., &Rätsch, M. (2018). An interactive clothing design and personalized virtual display system. Multimedia Tools and Applications, 77(20), 27163–27179.
  • Figure 2. Boone, T. (2016). Fashion Trends. Access address:https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/fashion-trends-2016-google-data-consumer-insights/. Access date: 07.03.2024.
  • Figure 3. Jain, S., Bruniaux, J., Zeng, X., &Bruniaux, P. (2017). Big data in fashion industry. IOP Conference Series: Materials Science and Engineering, 254(15), 152005.
  • Figure 4. Clo 3D. (2023). 3D Software. Access address: https://pjgarment.com/ro/esantion-3d/. Access date: 09.03.2024.
  • Figure 5. Clo. (2024a). Clo 3D. Access address: https://www.clo3d.com/en/. Access date: 10.03.2024.
  • Figure 6. Clo-set. (2018). Virtual Fitting. Access address: https://style.clo-set.com/service/features#fitting.Access date: 07.05.2024.
  • Figure 7. Särmäkari, N. (2023). Digital 3D fashion designers: Cases of atacac and the fabricant. Fashion Theory, 27(1), 85–114.
  • Figure 8. Clo. (2024b). Clo Garment Fit Maps Guide. Access address: https://support.clo3d.com/hc/en-us/articles/360052622933-CLO-Garment-Fit-Maps-Guide. Access date: 03.03.2024.
  • Figure 9. Clo. (2022). Different color options with CLO. Access address: https://www.clo3d.com/en/clo/features. Access date: 02.03.2024.
  • Figure 10. Clo 3D. (2023). Esantion3D Software. Access address: https://pjgarment.com/ro/esantion-3d/. Access date: 01.03.2024.

MODA TASARIMINDA BÜYÜK VERİ ANALİZİNİN ROLÜ

Yıl 2024, Cilt: 2 Sayı: 34, 97 - 119, 01.12.2024
https://doi.org/10.18603/sanatvetasarim.1515756

Öz

Günümüzde çağdaş, ekonomik ve kültürel durum giderek daha değişken bir hal alırken moda endüstrisinin dinamikleri daha az öngörülebilir bir yapıya bürünmektedir. Hızlı değişen moda endüstrisi sürekli veri üretmektedir. Büyük veri, moda tasarımcıları için yeni ve heyecan verici fırsatlar sunan güçlü bir araçtır. Tüketici davranışlarını anlamak, trendleri tahmin etmek ve daha kişiselleştirilmiş ürünler oluşturmak gibi farklı amaçlar için kullanılmaktadır. Daha önceleri sezgiler ve içgüdüler ile ilerleyen tasarım süreçleri büyük verinin sunduğu fırsatlarla daha akıcı, veri ve hedef odaklı hale gelmiştir. Bu makale son yıllarda moda endüstrisinde büyük veriyi temel alan moda tasarım modellerini araştırarak büyük verinin kullanım amaçlarını ortaya koymayı hedeflemektedir. Bu amaçla büyük verinin moda tasarımında mevcut modelleri, sınırlılıkları, gelişim yönü tartışılmıştır. Büyük verinin moda endüstrisinde kullanım alanlarından birçoğu (trend analizi, demografik, coğrafi analizler, yaş ve cinsiyet vb.) yoğun olarak araştırılırken salt tasarım odaklı araştırmaların sayısı yeni yeni artmaktadır. Bu çalışma büyük verinin modadaki tasarım olgusuna ve uygulamalarına eğilmesi yönüyle özgündür. Bu kapsamda ulusal literatürde akademik bir çalışma bulunmamaktadır. Moda tasarımındaki büyük veri ve büyük veri temelli dijital teknolojilerin etkilerine ilişkin tartışmaya katkıda bulunması öngörülmektedir.

Kaynakça

  • Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42, 90–101.
  • Ahsan, M., Hon, S. T., & Albarbar, A. (2020). Development of novel big data analytics framework for smart clothing. IEEE Access, 8, 146376–146394.
  • Bertola, P., & Teunissen, J. (2018). Fashion 4.0. Innovating fashion industry through digital transformation. Research Journal of Textile and Apparel, 22(4), 352–369.
  • Bhardwaj, V., & Fairhurst, A. (2010). Fast fashion: response to changes in the fashion industry. The International Review of Retail, Distribution and Consumer Research, 20(1), 165–173.
  • Black, S. (2019). Sustainability and Digitalization. In The End of Fashion. Bloomsbury Publishing Plc. https://doi.org/10.5040/9781350045071.ch-009
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Cassidy, T. D. (2019). Colour forecasting. Textile Progress, 51(1), 1–137.
  • Chen, K.-T., & Luo, J. (2017). When fashion meets big data: Discriminative mining of best selling clothing features. Proceedings of the 26th International Conference on World Wide Web Companion, 15–22.
  • Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2011). Body area networks: A survey. Mobile Networks and Applications, 16, 171–193.
  • Chen, M., Ma, Y., Song, J., Lai, C.-F., & Hu, B. (2016). Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring. Mobile Networks and Applications, 21(5), 825–845. https://doi.org/10.1007/s11036-016-0745-1
  • Chen, R.-Y. (2018). A traceability chain algorithm for artificial neural networks using T–S fuzzy cognitive maps in blockchain. Future Generation Computer Systems, 80, 198–210.
  • Cui, Y., Feng, X., & Yang, X. (2021). A matching degree management model of human body shape and fashion design based on big data analysis. Scientific Programming, 2021(1), 9384404.
  • De Chernatony, L., Harris, F., & Riley, F. D. (2000). Added value: its nature, roles and sustainability. European Journal of Marketing, 34(1/2), 39–56.
  • Doeringer, P., & Crean, S. (2006). Can fast fashion save the US apparel industry? Socio-Economic Review, 4(3), 353–377.
  • Dong, M., Zeng, X., Koehl, L., & Zhang, J. (2020). An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences, 540, 469–488.
  • DuBreuil, M., & Lu, S. (2020). Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED. International Journal of Fashion Design, Technology and Education, 13(1), 68–77.
  • Garcia, C. C. (2022). Fashion forecasting: an overview from material culture to industry. Journal of Fashion Marketing and Management: An International Journal, 26(3), 436–451.
  • Hirscher, A.-L., Niinimäki, K., & Joyner Armstrong, C. M. (2018). Social manufacturing in the fashion sector: New value creation through alternative design strategies? Journal of Cleaner Production, 172, 4544–4554.
  • İşmal, Ö. E., & Yüksel, E. (2016). Tekstil ve moda tasarımına teknolojik bir yaklaşım: akıllı ve renk değiştiren tekstiller. Yedi, 16, 87–98.
  • Jain, S., Bruniaux, J., Zeng, X., & Bruniaux, P. (2017). Big data in fashion industry. IOP Conference Series: Materials Science and Engineering, 254(15), 152005.
  • Jang, J., Ko, E., Chun, E., & Lee, E. (2012). A study of a social content model for sustainable development in the fast fashion industry. Journal of Global Fashion Marketing, 3(2), 61–70.
  • Ji, Y., & Jiang, G. (2020). Garment customization big data–processing and analysis in optimization design. Journal of Engineered Fibers and Fabrics, 15, 1558925020925405.
  • Kawamura, Y. (2018). Fashion-ology: An introduction to fashion studies. Bloomsbury Publishing.
  • Kim, R.-H. (2015). Cure performance and effectiveness of portable smart healthcare wear system using electro-conductive textiles. Procedia Manufacturing, 3, 542–549.
  • Lopes, M. V. (2019). The discourse of fashion change: Trend forecasting in the fashion industry. Fashion, Style & Popular Culture, 6(3), 333–349.
  • Ma, K., Wang, L., & Chen, Y. (2017). A collaborative cloud service platform for realizing sustainable make-to-order apparel supply chain. Sustainability, 10(1), 11.
  • McKelvey, K., & Munslow, J. (2011). Fashion design: process, innovation and practice. John Wiley & Sons.
  • Olaru, S., Popescu, G., Anastasiu, A., Mihăilă, G., & Săliştean, A. (2020). Innovative concept for personalized pattern design of safety equipment. Industria Textila, 71(1), 50–54.
  • Organization, W. H. (2015). World report on ageing and health. World Health Organization.
  • Ou, L., Luo, M. R., Woodcock, A., & Wright, A. (2004). A study of colour emotion and colour preference. Part I: Colour emotions for single colours. Color Research & Application, 29(3), 232–240.
  • Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2014). Recent advances in wearable sensors for health monitoring. IEEE Sensors Journal, 15(6), 3119–3126.
  • Särmäkari, N. (2023). Digital 3D fashion designers: Cases of atacac and the fabricant. Fashion Theory, 27(1), 85–114.
  • Särmäkari, N., & Vänskä, A. (2022). ‘Just hit a button!’ – fashion 4.0 designers as cyborgs, experimenting and designing with generative algorithms. International Journal of Fashion Design, Technology and Education, 15(2), 211–220.
  • Şen, C., Kılıç, A., & Öndoğan, Z. (2020). Endüstri 4.0 ve Moda Sektöründeki Uygulamaları. Turkish Journal of Fashion Design and Management, 2(2), 53–65.
  • Silva, E. S., Hassani, H., & Madsen, D. Ø. (2020). Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), 21–27. Silva, E. S., Hassani, H., Madsen, D. Ø., & Gee, L. (2019). Googling fashion: forecasting fashion consumer behaviour using google trends. Social Sciences, 8(4), 111.
  • Sun, L., & Zhao, L. (2018). Technology disruptions: Exploring the changing roles of designers, makers, and users in the fashion industry. International Journal of Fashion Design, Technology and Education, 11(3), 362–374.
  • Tamborrini, P., Remondino, C. L., & Marino, C. (2018). Fashion industry as a big data enterprise for sustainability. Curr Trends Fashion Technol Textile Eng, 3(4), 555616.
  • Tao, X. (2001). Smart technology for textiles and clothing-introduction and review. In Smart fibres, fabrics and clothing (pp. 1–6). Woodhead Pub.
  • Vinken, B., & Hewson, M. (2005). Fashion zeitgeist: Trends and cycles in the fashion system.
  • Westland, S., Laycock, K., Cheung, V., Henry, P., & Mahyar, F. (2007). Colour harmony. Colour: Design & Creativity, 1(1), 1–15.
  • Wong, M. Y., Zhou, Y., & Xu, H. (2016). Big data in fashion industry: Color cycle mining from runway data.
  • Yıldıran, M. (2022). Dördüncü Endüstri Devrimi ve Moda Endüstrisine Etkileri. Sanat ve Tasarım Dergisi, 12(2), 559–578.
  • Zhao, L., Liu, S., & Zhao, X. (2021). Big data and digital design models for fashion design. Journal of Engineered Fibers and Fabrics, 16, 15589250211019024.
  • Zhou, Z., Shangguan, L., Zheng, X., Yang, L., & Liu, Y. (2017). Design and implementation of an RFID-based customer shopping behavior mining system. IEEE/ACM Transactions on Networking, 25(4), 2405–2418.
  • Zhu, X., Lu, H., & Rätsch, M. (2018). An interactive clothing design and personalized virtual display system. Multimedia Tools and Applications, 77(20), 27163–27179.
  • INTERNET REFERENCES Boone, T. (2016). Fashion Trends. https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/fashion-trends-2016-google-data-consumer-insights/.Access date: 15.06.2024.
  • Bringe, A. (2023). The Future Of Marketing In The Fashion And Lifestyle Industries: AI, Personalization And Data-Driven Insights. https://www.forbes.com/sites/forbescommunicationscouncil/2023/11/30/the-future-of-marketing-in-the-fashion-and-lifestyle-industries-ai-personalization-and-data-driven-insights/.Access date: 1.06.2024.
  • Cgsinc. (2018). How Big Data is Impacting the Fashion Industry. https://www.cgsinc.com/blog/how-big-data-impacting-fashion-industry. Access date: 12.03.2024.
  • Clo 3D. (2024a). Our User Stories. https://www.clo3d.com/en/company/clo-users/stories. Access date: 15.01.2024.
  • Clo 3D. (2024b). Real materials. https://www.clo3d.com/en/clo. Access date: 15.04.2024.
  • Clo Virtual Fashion. (2024). Virtual Fashion. https://www.clovirtualfashion.com/. Access date: 15.05.2024.
  • Devillard, S., Harreis, H., Landry, N., & Altable, C. S. (2021). Jumpstarting value creation with data and analytics in fashion and luxury. https://www.mckinsey.com/industries/retail/our-insights/jumpstarting-value-creation-with-data-and-analytics-in-fashion-and-luxury. Access date: 16.05.2024.
  • Edited. (2024). Empowering retailers with AI-fueled retail intelligence. https://edited.com/. Access date: 15.06.2024.
  • Fashion, B. of. (2024). Artificial-Intelligence. https://www.businessoffashion.com/tags/tag/artificial-intelligence/. Access date: 07.03.2024.
  • McKinsey. (2024). The State of Fashion 2024: Finding pockets of growth as uncertainty reigns. https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion. Access date: 08.03.2024.
  • Wgsn. (2024). Fashion. http://www.wgsn.com/en/products/fashion. Access date: 09.03.2024.
  • FIGURES REFERENCES Figure 1. Zhu, X., Lu, H., &Rätsch, M. (2018). An interactive clothing design and personalized virtual display system. Multimedia Tools and Applications, 77(20), 27163–27179.
  • Figure 2. Boone, T. (2016). Fashion Trends. Access address:https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/fashion-trends-2016-google-data-consumer-insights/. Access date: 07.03.2024.
  • Figure 3. Jain, S., Bruniaux, J., Zeng, X., &Bruniaux, P. (2017). Big data in fashion industry. IOP Conference Series: Materials Science and Engineering, 254(15), 152005.
  • Figure 4. Clo 3D. (2023). 3D Software. Access address: https://pjgarment.com/ro/esantion-3d/. Access date: 09.03.2024.
  • Figure 5. Clo. (2024a). Clo 3D. Access address: https://www.clo3d.com/en/. Access date: 10.03.2024.
  • Figure 6. Clo-set. (2018). Virtual Fitting. Access address: https://style.clo-set.com/service/features#fitting.Access date: 07.05.2024.
  • Figure 7. Särmäkari, N. (2023). Digital 3D fashion designers: Cases of atacac and the fabricant. Fashion Theory, 27(1), 85–114.
  • Figure 8. Clo. (2024b). Clo Garment Fit Maps Guide. Access address: https://support.clo3d.com/hc/en-us/articles/360052622933-CLO-Garment-Fit-Maps-Guide. Access date: 03.03.2024.
  • Figure 9. Clo. (2022). Different color options with CLO. Access address: https://www.clo3d.com/en/clo/features. Access date: 02.03.2024.
  • Figure 10. Clo 3D. (2023). Esantion3D Software. Access address: https://pjgarment.com/ro/esantion-3d/. Access date: 01.03.2024.
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Güzel Sanatlar
Bölüm Makaleler
Yazarlar

Muazzez Çetiner 0000-0002-7139-5121

Erken Görünüm Tarihi 27 Kasım 2024
Yayımlanma Tarihi 1 Aralık 2024
Gönderilme Tarihi 13 Temmuz 2024
Kabul Tarihi 6 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 2 Sayı: 34

Kaynak Göster

APA Çetiner, M. (2024). The Role of Big Data Analysis in Fashion Design. Sanat Ve Tasarım Dergisi, 2(34), 97-119. https://doi.org/10.18603/sanatvetasarim.1515756
AMA Çetiner M. The Role of Big Data Analysis in Fashion Design. Sanat ve Tasarım Dergisi. Aralık 2024;2(34):97-119. doi:10.18603/sanatvetasarim.1515756
Chicago Çetiner, Muazzez. “The Role of Big Data Analysis in Fashion Design”. Sanat Ve Tasarım Dergisi 2, sy. 34 (Aralık 2024): 97-119. https://doi.org/10.18603/sanatvetasarim.1515756.
EndNote Çetiner M (01 Aralık 2024) The Role of Big Data Analysis in Fashion Design. Sanat ve Tasarım Dergisi 2 34 97–119.
IEEE M. Çetiner, “The Role of Big Data Analysis in Fashion Design”, Sanat ve Tasarım Dergisi, c. 2, sy. 34, ss. 97–119, 2024, doi: 10.18603/sanatvetasarim.1515756.
ISNAD Çetiner, Muazzez. “The Role of Big Data Analysis in Fashion Design”. Sanat ve Tasarım Dergisi 2/34 (Aralık 2024), 97-119. https://doi.org/10.18603/sanatvetasarim.1515756.
JAMA Çetiner M. The Role of Big Data Analysis in Fashion Design. Sanat ve Tasarım Dergisi. 2024;2:97–119.
MLA Çetiner, Muazzez. “The Role of Big Data Analysis in Fashion Design”. Sanat Ve Tasarım Dergisi, c. 2, sy. 34, 2024, ss. 97-119, doi:10.18603/sanatvetasarim.1515756.
Vancouver Çetiner M. The Role of Big Data Analysis in Fashion Design. Sanat ve Tasarım Dergisi. 2024;2(34):97-119.