Architecting the eco-technological future of the fashion industry inspired by emerging AI technologies
Circular Economy; Artificial Intelligence; Machine Learning; Textile Industry; Sustainability
Fashion, a multi-trillion-dollar industry at the center of global trade, is under intense scrutiny for its unsustainable processes and operational challenges caused by trends that change faster than we can blink. The following paper unveils the promising prospects of Artificial Intelligence (AI) within a sustainable eco-technological future for the textile and apparel sectors. Using AI technologies, including machine learning, deep learning, and generative models, the industry can optimize processes and transparency in a more integrated way, aligning with the principles of the circular economy. The reported results offer a comprehensive assessment of AI use in the fashion value chain, from raw material research to the supply chain, with a focus on sustainability and productivity. The bibliometric assessment shows a 484% annual growth in the number of studies published on AI and fashion sustainability between 2018 and 2024, including information from a PRISMA-guided search conducted by Scopus that yielded a total of 2,548 articles, showing China, as well as the US/UK, with increasing peaks in contributions. This master's thesis provides a critical assessment of the future of predictive analytics, quality assurance, automated assembly, and traceability. Advances in AI in recent years have brought challenges such as technological disparities, ethical concerns, and data privacy, highlighting the importance of inclusion and sustainable adoption in our daily lives. Results like this illustrate and conclude that AI holds the promise of transforming the fashion industry into a circular, transparent, and innovative one, aligned with global sustainability goals, such as SDG 12.