Sustainable fashion concept. Stylish young man supporting green fashion.

Algorithmic Retailing

Using AI for Sustainable Fast Fashion

 
October 14, 2020

In sharp contrast to the glitz and glamour of fashion retail, the Copenhagen Fashion Summit 2019 cited that fashion is responsible for 92 million tons of solid waste dumped in landfills every year.  The fashion industry also has a dubious reputation for being the second-largest consumer of the world's water supply, polluting the oceans with microplastics, and accounting for 10% of all humanity's carbon emissions.

COVID-19 has been a wake-up call to acknowledge that economic, environmental, and human health are all deeply interconnected. We are already witnessing a shift in mindset to produce less and demonstrate more consciousness towards the environment. With Paris, the world's fashion capital lobbying for climate change, the fashion industry is also looking at new ways to become more sustainable.

The shift from ‘loved one minute and cast in the bin next minute’ trend is slowing down to give way to green fashion—where sustainable fabrics can be recycled and reused. For fashionistas who consider being photographed in the same outfit again as social damnation, there are now options for renting, borrowing, reusing, fixing, and recycling. 

Further, with democratization of media in the form of social media and the body positive movement, fashion is increasingly perceived as an expression of oneself, and looking good in an outfit is more about feeling comfortable in your own skin and embracing who you are. 

While the body positive movement has addressed a latent market, it also presents new opportunities and challenges in sustainability for fashion merchants, buyers, designers, and stylists such as:

  • Sourcing sustainable fabrics (for instance, fabric made from plastic, discarded citrus peel, cast away fish nets; fabric infused with peppermint oils that needs less laundry; bacteria resistant fabrics for reuse, etc.
  • Creating various personalized looks and outfit choices complete with shoes, accessories, makeup, combining existing and new items for upcycling outfits
  • Experimenting with higher number of body fit points to suit various body shapes and types
  • Estimating the demand for reusing outfits
  • Estimating the demand at a style-color-size level at each store or DC to reduce wastage due to unsold garments and reduce carbon footprint in transportation 

While fashion merchandising has become increasingly complex, retailers can ensure that their customers look good while their operations remain profitable by investing in sustainable fashion technologies and applying newer technologies such as AI across the fashion value chain. 

Manufacturing:  Leveraging deep learning to identify manufacturing defects, color tolerance, and wrinkles. 3D modelling of yarn pattern without actually creating the yarn with new material can help speed up identification of sustainable alternatives to synthetic materials and simulate their flow and fall on the design. 

Sourcing: AI can help consolidate the required material compositions and identify similar previously sourced items. Reinforcement learning can also be leveraged for smarter vendor negotiation.   

Designing: AI in fashion design used in combination with other technologies can help retailers reduce time to market and create personalized tailored outfits. For designing new garments by leveraging AI and technologies like direct panel on loom, fashion retailers can do weaving, cutting, and patterning all at one go, minimizing or eliminating scraps, accelerating go to market. AI can also help in creating entirely new design combinations based on customer preferences and trending styles, and available material for upcycling (for example, converting elephant pants to skinny ones).

Ordering: AI can understand and predict trends based on social and other data and customize it to the retailers' customer preferences and sizes to ensure they buy the right quantities. AI can also estimate the success of the styles in conjunction with the marketing effort to reduce overstocking and adjust the next drop orders to protect from understocks. 

Trading: AI can help to match the fit and style for reused/recycled clothes, providing personalized recommendations to customers. For new apparel, based on the trending patterns, AI can adjust the buy/produce quantities for the next drop pre-emptively in alignment with the marketing budget and ROI. AI-based stylists can help put the look together based on personal style, body shape, and preferences.

As an intrapreneur, Shilpa is passionate about combining art and science to improve how businesses make decisions. Shilpa has helped leading Fortune 500 companies reimagine their merchandising processes with TCS Optumera™, AI powered retail optimization suite. She has been responsible for conceptualizing, developing, evangelizing, and executing the products and solutions strategy.