AI Reshapes Fashion Design and Manufacturing

Artificial intelligence technologies and robotics are driving changes in the world of fashion. From market analysis to custom designs to waste reduction, AI opens the door for designers to be on trend, responsive, and conscientious global citizens.

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Image credit: Heuritech

With robots picking and packing online merchandise, it’s no surprise that they are also constructing clothing and shoes. Now artificial intelligence is driving fashion design and the rise of fast fashion.

Fashion companies currently use AI to predict style trends, textures, and colors. Even Google and Amazon are in that mix. Google launched Project Muze, which tapped AI to design clothing based on fashion trends and online merchant sales data. Amazon is working on a project that uses machine learning to identify “stylish” merchandise, which then could be replicated and produced under the Amazon Essentials line.

Paris-based Heuritech uses deep learning and image recognition to help designer brands like Dior figure out which trends will be on point in different seasons, markets, and countries. The designer selects representative items, and Heuritech’s algorithms scan thousands of public social media images for more than 3,000 details, including fabric, shape, and color. Heuritech claims its computer vision system will forecast popular trends with 90 percent accuracy as far as one year out. Designer brands use that data to guide their fashion lines.

Precision Clothing

AI-based design can lead to ready-to-wear custom clothing, an oxymoron in the fashion industry. AI works best with input from human designers—some initial Project Muze sketches were simple scribbles, but with a live designer guiding the project, AI can create bespoke garments.

Customization is the future of retail, as seen in everything from personalized M&Ms to Nike kicks. With AI and robotic sewing machines, custom designs are moving from big fashion houses to Etsy.

Fashion designers can take 3D images of customers and design one-of-a-kind, made-to-order clothes with their exact measurements. Once the item is created on the digital model, the designer sends an electronic file to a robotic sewing machine, such as the SoftWear Automation’s Sewbot. The Sewbot extracts the necessary construction data and starts cutting and stitching. The machine doesn’t care if it makes one item or 100 items; the process is the same.

Synflux's AI-powered Algorithmic Couture textile creation project

Image credit: Synflux Algorithmic Couture

The Sustainability Factor

Not to be overlooked is the global benefit of automating design and construction, especially in the fast fashion world. Automated robots use AI to stitch together clothing, which can drastically reduce the cost of production, not only in terms of labor but also by bringing manufacturing closer to the point of sale. It also makes for a more reliable supply chain—a benefit more valuable after the COVID-19 pandemic crippled procurement and fulfillment for retailers.

AI-based fashion design also improves sustainability. The software determines the most efficient use of the fabric to reduce waste. In addition, items can be produced on demand, even after purchase, which leads to less overhead and less waste.

Tokyo-based fashion lab Synflux is tackling sustainability through design. Synflux uses AI to develop what it dubs “Algorithmic Couture.” Using 3D scans of a person’s body, machine learning algorithms create clothing patterns that minimize textile waste. Designers use computer-aided design software to model and piece together the patterns, which are used to produce clothing designs with less scrap.

Artificial intelligence brings a new level of customization to the growing fashion industry. It has the capability of changing how items are sourced and manufactured, reducing the designer’s carbon footprint. AI also has the potential to level the playing field for designers and customers, enabling an online Etsy designer to compete with high-fashion brands to offer customized, ready-to-wear clothing. 

 

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