From the development of the sewing machine to the rise of e-commerce, fashion has always been at the forefront of innovation. Fashion, like technology, is forward-thinking and cyclical.
According to CB Insights’ Industry Analyst Consensus, the fashion sector will be worth more than $3 trillion by the end of the decade, making it one of the world’s greatest sectors.
Fashion technology is evolving at a faster rate than ever before.
Sewing and cutting robots, AI algorithms that predict style trends, virtual reality mirrors in changing rooms, and a slew of other technologies demonstrate how technology is automating, customising, and speeding up the fashion industry.
We delve into the trends that are transforming the way our garments and accessories are designed, manufactured, distributed, and sold in this research.
Streamlining the Fashion Supply Chain
Some brands are “internalising” production to increase the speed with which they can meet consumer demand.
Gucci Art Lab, a 37,000-square-meter product development and lab testing centre featuring in-house prototyping and sample activity for leather goods, innovative materials, metal hardware, and packaging, opened its doors in April 2018. The goal of the project is to bring the Gucci supply chain closer to the brand’s headquarters, allowing it more control over product creation, sampling, and material development.
Vertical integration has aided the growth of companies ranging from Peloton and Apple to Netflix and Tesla. Peloton, for example, abandoned its intention to retrofit software onto existing bikes in favour of developing a custom bike, relying on company-owned manufacturers, and including other elements such as production studios and retail shops.
The Covid-19 dilemma has also brought to light the dangers of relying on a single supplier. As the pandemic raced across China, where many multinationals source their products, businesses saw manufacturing come to a full halt.
Al is becoming increasingly important for supply chain monitoring in circumstances where supplier diversification isn’t available.
Natural language processing (NLP) is being used by all AI businesses to examine news, government databases, trade journals, and other sources for supply chain interruptions, such as natural disasters or factory mishaps. Vendor risk scores are also generated using machine learning and their supply chain network.
Startups such as Interos and Resilinc, for example, manage vendor business relations databases and use machine learning to assess risk scores based on their supply chain networks. As part of our client-only Al in manufacturing series, we delve deeper into how manufacturers may utilise Al to reduce risk in sourcing and procurement in this analysis.
The Push for Sustainability in Fashion
In recent years, sustainability has emerged as a critical rising trend in a variety of industries. According to the NYU Stern Center for Sustainable Business, sustainability-marketed products contributed more than half of the growth in the consumer packaged goods (CPG) business between 2015 and 2019, despite accounting for only 16% of the market. CPG items branded as environmentally friendly increased their share of in-store purchases in the United States to 16.8% in 2021, valued over $130 billion.
Consumers are becoming more aware of the drawbacks of rapid fashion: Slow fashion, which emphasises sustainable materials and transparent, ethical labour and manufacturing, is gaining popularity among socially concerned customers.
In early 2020, Lyst, a fashion shopping app, saw a 37% rise in searches for sustainability-related keywords compared to the previous year, with average monthly searches surpassing 32,000, up from 27,000 in 2019.
The growing concern about sustainability is especially evident among younger generations: according to the Conference Board Global Consumer Confidence study, 83 percent of millennials in the United States respect companies that undertake programmes to enhance the environment. And 75% are willing to adjust their consumption habits in favour of more environmentally friendly options. According to First Insight, 73 percent of Generation Z buyers are willing to pay more for sustainable products.
In the fashion industry, young, up-and-coming firms are making attempts to fit with this shift in customer sensibilities. Girlfriend Collective, a sustainable sportswear business, stresses transparency and sells recycled polyester leggings. On, based in Switzerland, plans to release totally recyclable shoes in the fall of 2021, along with a subscription business to further seal the recycling loop.
Other brands, such as Everlane and Reformation, have risen to prominence by emphasising sustainability and ethics.
How is AI Influencing Brands
Tommy Hilfiger and IBM established a collaboration with the Fashion Institute of Technology in 2018. The project, dubbed “Reimagine Retail,” made use of IBM Al technologies to figure out:
• Customer perceptions of Tommy Hilfiger products and runway visuals in real time in the fashion business
• Themes reappearing in current patterns, shapes, hues, and styles
AI becomes the Designer
Project Muze, a 2016 project Google ran in collaboration with Germany-based fashion portal Zalando, tested the waters of user-driven Al fashion design.
The research used Google’s Fashion Trends Report as well as Zalando’s design and trend data to train a neural network to grasp colours, textures, style preferences, and other “aesthetic characteristics.”
Following that, Project Muze used an algorithm to produce designs based on users’ interests and in line with the network’s style preferences.
Amazon is also breaking new ground in this area. Machine learning would be used in one Amazon initiative led by Israeli experts to determine if an item is “stylish” or not.
Another, developed by Amazon’s California-based Lab126 R&D arm, would utilise photographs to learn about a specific fashion trend and then recreate it from scratch.