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.

Moncler Genius

Moncler Genius leverages data insights, AI-driven trend analysis, and machine learning to identify and anticipate customer preferences, informing their annual collaborations with diverse designers. This approach allows Moncler to maintain relevance and exclusivity through data-backed capsule collections, aligning luxury heritage with evolving consumer tastes. More details.

TextileGenesis

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.
Listen to a few insights as Blockchain & the Environmental Footprint of Garments.
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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.

Patagonia & DEPT
Product education

This experience needed to be personalised and always relevant to the consumer. Our goal was to ultimately guide users to the best product for their needs in one simple flow. By implementing an in-line Q&A and product comparison overlay, BASIC/DEPT® was able to help customers decide which product is right for them, saving them the hassle of toggling between product detail pages. As a result, users are directly routed through the sales funnel in one seamless UX flow. Learn more

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.

Stower, H. (2020, June 4). Transparency and resilience in fashion. Cleantech Group.

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