Data Science, Artificial Intelligence and Machine Learning (DS, AI and ML)
Data science, artificial intelligence (AI), and machine learning (ML) increasingly play crucial roles in the fashion industry, revolutionizing various aspects of the business, from design and production to marketing and customer experience. Traditionally, every sound business utilizes historical sales data, market trends, and other relevant information to predict future demand and optimize inventory levels. Nowadays, Machine Learning algorithms allow for more accurate demand forecasting, further potentially reducing overstock and stockouts. Blockchain technologies also help by enhancing transparency and traceability in the supply chain, allowing for richer information, and using data analytics helps optimize production schedules, logistics, and supply chain efficiency. These technologies will largely aid the post-use sorting process. That information is also used in marketing, using data to understand customer preferences and behavior, enabling targeted marketing campaigns. Still, from a demand perspective, implementing AI-driven recommendation engines can help suggest personalized products based on individual customer preferences and browsing history. Natural language processing (NLP) and sentiment analysis connect that data to public opinion and trends on social media platforms, making these methods even more effective. Once a product of choice is determined, implementing AR and computer vision technologies for virtual try-on experiences allows customers to visualize how garments will look on them before making a purchase. Employing AI algorithms also aids in generating design ideas, helping designers explore a broader range of possibilities. By integrating computer vision to analyze trends and provide insights during the creative process, original designs can be made that have a higher chance to appeal to a larger public.
Case Studies
Sorabel (formerly Sale Stock)
Sorabel uses AI to predict fashion trends and minimize waste by producing only what consumers are likely to buy. Their predictive analytics help lower inventory risk while offering fashionable and affordable items. This lean production model contributes to sustainability by reducing unsold stock, aligning with consumer demands for ethical fashion. Read more here.
Project Cece
Project Cece, a Dutch tech startup, integrates visual AI to help consumers find sustainable versions of fashion items. Users can upload images, and the platform recommends eco-friendly alternatives, helping consumers make more sustainable choices and reducing the environmental impact of fast fashion. Learn more about Project Cece.
TextileGenesis and Blockchain for Supply Chain Traceability
TextileGenesis uses blockchain technology to track sustainable materials like TENCEL™ and LENZING™ ECOVERO™, ensuring each step of the production process is transparent. This system addresses consumer demand for traceable, ethically sourced products and boosts transparency in supply chains. Find out more about TextileGenesis.
Heuritech
Heuritech leverages AI for fashion trend detection, reducing forecasting errors by 50%. By accurately predicting trends and optimizing production, they help fashion brands avoid overproduction and reduce waste. Their AI-backed approach supports sustainable inventory management, creating a more eco-friendly fashion ecosystem. Read more about Heuritech.
References
- Chamodi, J., et al. (2021). The use of augmented reality to deliver enhanced user experiences in fashion industry. Lecture Notes in Computer Science, 12936.
- Harreis, H. et al. (2023). Generative AI: Unlocking the future of fashion. McKinsey & Company.
- Hsu, C.-H., et al. (2021). Deploying resilience enablers to mitigate risks in sustainable fashion supply chains. Sustainability, 13(5), 2943.
- Marlene, Z. (2020). Augmented reality try-on adoption in the Online Clothing Industry: Understanding key challenges and critical success factors. (Master’s thesis). University of Twente.
- Plotkina, D., & Saurel, H. (2019). Me or just like me? The role of virtual try-on and physical appearance in apparel M-retailing. Journal of Retailing and Consumer Services, 51, 362-377.
- Silvestri, B. (2020). The future of fashion: How the quest for digitisation and the use of artificial intelligence and extended reality will reshape the fashion industry after COVID-19. ZoneModa Journal, 10(2), 61-73.
- Reporter, G.S. (2023). ‘You’ve got to be data-driven’: the fashion forecasters using AI to predict the next trend. The Guardian, October 3.
- Jiang, E. (2021, November 18). Virtual reality: Growth engine for fashion?. The Business of Fashion.
- Stower, H. (2020, June 4). Transparency and resilience in fashion. Cleantech Group.