How AI is shaping the future of sustainable fibres

Supporting ecodesign

The environmental impact of any product or clothing is largely determined at the point of design – when someone chooses what it’s made of. AI can help designers create more sustainable products by considering the impact of their choice of fibre on everything from resources needed to manufacture it to end-of-life. 

 

For example, AI can analyse the material choices of the designer and suggest more sustainable options that meet the same performance criteria.

 

Fairly Made, a SaaS platform for fashion transparency, has developed an Ecodesign tool that helps fashion brands understand and improve the environmental impact of their designs.

 

This includes calculating the Life Cycle Assessment of a design in real time and simulating the impact of any modifications. The system can even make recommendations to reduce a product’s impact.

 

AI design tools, such as AiDA and Refabric, can also support designers with faster prototyping and testing for new concepts. This includes digital 3D simulations of garments. While these 3D models can’t fully replicate everything about the way a fabric falls and feels, the most advanced AI design tools can visualise drape and movement.

 

This helps reduce the number of physical prototypes required, reducing overall waste.

 

Developing new materials

AI doesn’t just help recommend more sustainable materials for production; it’s also helping create brand new ones.

 

Research and development into sustainable textile fibres is a slow process. AI is helping to speed this up by identifying and simulating viable options.

 

Materiom, an open access innovation platform that specialises in regenerative materials R&D, recently launched a new generative AI chatbot, Materiom AI, to accelerate the development of biobased materials.

 

Its aim is to help speed up the initial phase of creating new materials by providing information, identifying ingredients that combine well, and highlighting the possible performance improvements of the combinations.

 

TNO’s polyScout machine learning programme also helps speed up the development of biodegradable polymers. Researchers can highlight certain properties that they want from a material and polyScout will create the chemical structure for polymers that meets those criteria.

 

The model is already being used to assist Senbis, a biodegradable plastics and fibres producer, to develop a new biodegradable polyester that can be used in textiles. 

 

Reducing waste in manufacturing

One of the biggest ways AI is shaping the sustainable fashion future is by helping improve demand forecasting. 

 

Companies like Heuritech, an AI-based visual recognition technology and forecasting model, can help fashion brands to identify trends up to two years in advance. Heuritech analyses more than 3 million social media images each day, helping create a huge dataset for brands to draw from.

 

AI can also be used to support brands on an individual basis with models that are trained on a specific brand’s own design and sales data. This may give more tailored results that apply to the fashion brand’s customer base.

 

By creating designs that people want, brands have less unsold and waste product. Better demand forecasting also reduces overproduction because brands have a more accurate idea about what will sell. They can also monitor inventory and react to demand.

 

Another way that AI is reducing waste in manufacturing is by optimising raw material usage. This includes making sure that the most appropriate material is being used for each specific product to prevent recalls and waste. AI can also help more accurately predict the quantity of raw material needed, which reduces over-ordering.

 

AI can be used to optimise cutting patterns, which ensures that manufacturers get the most out of raw materials and reduces what they throw away. And it can identify and highlight product defects early, which could allow production to be altered and reduce the quantity of unusable product.

 

From sourcing to recycling

AI’s ability to analyse vast amounts of data quickly makes it a real asset when looking for sustainable sourcing partners.

 

Rather than trying to individually assess every option, brands can use AI to help them identify sustainable material options, based on factors like carbon footprint, water usage, chemical usage, and recyclability. This includes benchmarking any sustainability claims that a material might have or double-checking certifications.

 

AI can also analyse and compare different suppliers based on the materials they offer and their own sustainability performance. This might mean the difference between working with a supplier who engages in ethical labour and one that doesn’t.

 

When it comes to end-of-life, AI also has a part to play in helping to identify and sort different materials, especially more complex and hard-to-recycle blends. This can improve the percentage of products that are recycled or reused at the end of their life.

AI could also help in the development of new ways to break down and recycle mixed blends.

AI is a support, not a sustainability silver bullet

As we can see, AI has lots of potential applications in supporting the fashion industry’s mission to become more sustainable. And support is an important term here.

 

Because that is what AI is. It’s another tool – a potentially very powerful one – is helping make necessary changes. But it isn’t a sustainability silver bullet. 

 

Every use of AI takes its own environmental toll, so the industry must make sure that it is getting more from the tech than it requires to operate. Brands also need to make sure they have robust processes in place for checking, quantifying and confirming AI’s recommendations.

 

There’s another piece of the puzzle though. Brands need to be willing and able to implement any improvements. They need to be able to adjust their workflows to accommodate changes in processes. 

 

AI may be helping shape the future of sustainable fibres, but brands are the ones that will bring that future to life.