Boosting Innovation in Fashion with Generative AI


I recently had an interesting conversation with Mark Harrop. First, we discussed the range of solutions supporting the fashion supply chain, such as 2D CAD/CAM, 3D-DPC, Retail Planning, PLM, and ERP, amongst many others, and priorities based on business strategy. The discussion moved to the ‘evolution’ of the actions that fashion businesses could take to boost their innovation and speed to market.

In the ‘early days’ of PDM and PLM, it was about supporting streamlined processes with this new technology to remove admin tasks from the creative teams and allow greater focus on design. Over the last 15 years, we’ve seen the evolution of 3D-DPC to enable greater focus on design quality and reduce timelines and sampling costs. And now, we see AI’s rapid rise, specifically, the benefits of generative AI to increase productivity.

There are already many examples of generative AI in the fashion industry.

Generative design: G-Star used generative AI to design a denim collection inspired by natural phenomena such as lava flows and rock formations

Generative modelling: Levi’s used generative AI to create e-commerce models that matched the style and fit of their products

Generative content;

1) Marketing images and Chatbots: Revolve and Prada Beauty used generative AI to create marketing imagery tailored to different audiences and platforms. Kering and Zalando used generative AI to create customer-facing chatbots with product recommendations and styling advice2) Product descriptions: Adore Me used generative AI to write optimised product descriptions for SEO and conversion.

There are many use cases for generative AI in 3D modelling – it’s well established in engineering 3D CAD – but maybe a little longer before it becomes a standard feature in 3D-DPC applications. 

However, if we widen our thinking to the massive volume of variations of products and data throughout the supply chain, we see fantastic opportunities. What if, with the input of a Seasonal Budget and MFP guidelines, PLM could suggest styles, price points, volume, colourways, specs, sourcing options, costs, timelines, margin, and ecological impact for each product and collection, by way of Generative Design by Impact? It won’t generate the definitive collection but, via comparison, enables rapid decision-making to narrow options. It allows the creative team to focus on the most innovative, profitable, and sustainable line based on all variables in the current situation.

However, the opportunity and constraint to delivering these powerful tools are the same for all AI models – deep and accurate data. And this brings us full circle to the need for streamlined processes, science-based measurement of primary data, and carefully designed integration, to ensure generative AI models can access the comprehensive and accurate data required to provide a precise output.

It’s a fascinating time, and I can’t wait to see the evolution of generative AI across the fashion supply chain. However, plenty of old-fashioned manual work is still required to digitise the fashion supply chain to fully enable the great promise of generative AI.

Author Chris Jones 

Chris has helped global brands, retailers & manufacturers for more than three decades to align people, process & technology, driving transformation projects to provide maximum impact for the business.

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