Dcard

Data Science, AI and ML

DS, AI and ML play a crucial role in Fashion ecosystem processes. From image generation for design, user profile creation, business systems, and production optimization; DS, AI and ML have been a . It can even support sustainability by analyzing images of what you wear understand what is in your wardrobe to helps companies understand what to make or connect you with existing items.

VET: How can DS, AI and ML help make fashion more eco-friendly and reduce waste?
HEI: How can DS, AI and ML predict demand for sustainable fashion, cut waste in production, and boost eco-friendly material use?


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. 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