Artificial intelligence represents one of the most fascinating frontiers of innovation: through integrated processing of market and consumer data it can play a key role within all phases of a fashion product's life cycle, from design to sale. AI is helping to change design and production paradigms and make an entire industry more efficient, sustainable and people-oriented.
AI for trend forecasting and collection design
"Thanks to special forecasting algorithms and sophisticated machine learning and data mining techniques, AI weaves together and analyzes a huge amount of data from stores, social channels and fashion shows to identify patterns and emerging trends, helping designers predict consumer tastes and new trends," says Emanuele Frontoni, professor of UNIMC Computer Science and Co-Director of the VRAI Vision, Robotics & Artificial Intelligence Lab. A valuable support that allows planning new collections that are truly adherent with market demands. Not only that, using the insights gained, AI algorithms are also able to generate design proposals and suggest variations in color, fabrics, styles and cuts in accordance with identified preferences and trends.
NeRF technology and digital prototyping: more quality, less waste
Next, let's think about prototype development. Today, Neural Radiance Field (NeRF) technology leverages state-of-the-art generative artificial intelligence that can instantly transform a pair of photographic shots into a 3D object. This technology is able to digitally recreate textures, colors, and shades in 3D and allows for optimizing shapes, proportions, and enhancing details.It is therefore an important ally in increasing the quality and aesthetic performance of the object. Digital simulation of the prototype makes it possible to speed up production time and minimize the waste of materials and resources for more sustainable production.
Smart manufacturing and quality control through robots
The production process also benefits from AI. Thanks to automation, human-machine interaction and predictive analytics, it is possible to improve production planning, align it with sales expectations and also reduce energy and material consumption. Professor Frontoni tells how it is possible to manage quality control with a camera-equipped robot that takes hundreds of photos of the product, educating the machine to recognize defects in order to improve quality and increasingly reduce the marginality of error and defect.
Supply chain management with AI
Inventory management and logistics can also be optimized through AI. By leveraging consumer demand forecasting, companies can automate inventory management, optimize delivery routes and plan product movement, improving operational efficiency with leaner and faster operations.
3D avatars and virtual fitting rooms for online stores
"The NeRF technology, which we have already discussed," Professor Frontoni continues, "also makes it possible to create a realistic 3D avatar of the potential buyer from a photo of him or her. This system, integrated within an e-commerce platform, gives the possibility of having people virtually try on clothes and accessories. It is a technology with multiple advantages: on the one hand, it offers a personalized and immersive shopping experience; on the other hand, it reduces the problem of returns and the waste related to them, in terms of time, materials and CO2 immissions."
Virtual simulation of the in-store shopping experience
Finally, AI can improve the customer experience inside the physical store as well. By collecting data on consumer behaviors, AI can be used to recreate simulations of the movement of typical customers within the store, thus studying millions of trajectories, with the goal of understanding how the target audience moves and interacts in the space, what they buy, how they pay. With this data, it is possible to design a better in-store experience that is more adherent to the customer's interests and buying habits.
