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
Heuritech – AI trend forecasting for reduced overproduction
Heuritech provides AI-powered visual recognition and trend forecasting for fashion brands, analysing billions of social media images to predict demand at SKU level. By aligning design and buying decisions with data-driven forecasts, brands can reduce overproduction, optimize assortments, and lower the environmental impact of surplus stock.
Project link
Stitch Fix – Data-driven personalization and inventory optimization
Stitch Fix is an online personal styling service whose core business model is built around data science and machine learning. Algorithms match clients with stylists, optimize product recommendations, and inform buying decisions, enabling smaller, better-targeted inventories and reducing waste from unsold garments.
Project link
H&M Group & Google Cloud – Data intelligence for circular fashion
H&M Group has collaborated with Google Cloud to use big data and machine learning on weather, search, and store data to understand material performance and local demand patterns. These insights are used to inform design and sourcing decisions, supporting more efficient production planning and enabling circularity-oriented choices in materials and assortments.
Project link
TextileGenesis – Fiber-to-retail traceability using data and AI
TextileGenesis uses a data-driven platform and digital tokens to trace preferred fibers such as TENCEL™ and LENZING™ ECOVERO™ across the value chain. By combining unique identifiers, analytics, and verification workflows, the system gives brands granular visibility over material flows, enabling better demand planning, reduced material losses, and more credible sustainability claims.
Project link
Zalando – AI-enabled demand forecasting and size services
Zalando integrates machine-learning models in its merchandising and logistics systems to forecast demand, optimize replenishment, and improve size and fit recommendations. These tools help lower return rates and avoid overstocking by aligning purchasing and distribution more closely with real user behaviour, thus reducing associated emissions and waste.
Project link
Case studies
Heuritech – AI trend forecasting for reduced overproduction
Heuritech provides AI-powered visual recognition and trend forecasting for fashion brands, analysing large volumes of social media images to predict demand at SKU level. By aligning design and buying decisions with data-driven forecasts, brands can reduce overproduction, optimize assortments, and lower the environmental impact of surplus stock.
Project link
Stitch Fix – Data-driven personalization and inventory optimization
Stitch Fix is an online personal styling service whose core business model relies on data science and machine learning. Algorithms match clients with stylists, optimize product recommendations, and inform buying decisions, enabling smaller, better-targeted inventories and reducing waste from unsold garments.
Project link
H&M Group x Google Cloud – Data intelligence for circular fashion
H&M Group has collaborated with Google Cloud to use big data and machine learning on weather, search, and store data to understand material performance and local demand patterns. These insights inform design and sourcing decisions, supporting more efficient production planning and enabling circularity-oriented choices in materials and assortments.
Project link
TextileGenesis – Fiber-to-retail traceability using data and AI
TextileGenesis uses a data-driven platform and digital tokens to trace preferred fibers such as TENCEL™ and LENZING™ ECOVERO™ across the value chain. By combining unique identifiers, analytics, and verification workflows, the system gives brands granular visibility over material flows, enabling better demand planning, reduced material losses, and more credible sustainability claims.
Project link
Zalando – AI-enabled demand forecasting and size intelligence
Zalando integrates machine-learning models in its merchandising and logistics systems to forecast demand, optimize replenishment, and improve size and fit recommendations. These tools help lower return rates and avoid overstocking by aligning purchasing and distribution more closely with real user behaviour, thus reducing associated emissions and waste.
Project link
References
Akhtar, W. H., Liu, X., Han, H., & Qin, Y. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633–656.
Chan, H. H. Y., Henninger, C. E., Boardman, R., & Blazquez Cano, M. (2023). The adoption of digital fashion as an end product: A systematic literature review of research foci and future research agenda. Journal of Global Fashion Marketing, 15(1), 155–180. https://doi.org/10.1080/20932685.2023.2251033
Harreis, H., Kilcourse, A., & Ponce, D. (2023). Generative AI: Unlocking the future of fashion. McKinsey & Company. https://www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-the-future-of-fashion
Hsu, C.-H., Lee, Y.-C., Chiu, C.-F., & Wang, M.-C. (2021). Deploying resilience enablers to mitigate risks in sustainable fashion supply chains. Sustainability, 13(5), 2943.
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.