2022
COVER OPTION 2
The Future of Fashion:
From design to merchandising,
how tech is reshaping the industry
2021
COVER OPTION 2
The Future Of Fashion:
From Design To Merchandising,
How Tech Is Reshaping The Industry
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el futuro de la moda, Ejercicios de Fundamentos de Moda
Recientemente, Bloomberg reportaba que Rent the Runway había sufrido una perdida neta de 85 millones de dólares frente a 80 millones de beneficio a finales de julio, pero una de las fundadoras, Jennifer Hyman, defendía con fuerza su modelo porque «antes de Rent the Runway, llevar la maravillosa moda de un diseñador no era una opción realista para la mayoría de la gente. Nosotros hemos democratizado la moda de lujo para todas las mujeres». El tiempo dirá cómo evoluciona el mercado, pero hoy por h
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The Future of Fashion:
From design to merchandising,
how tech is reshaping the industry 2021
The Future Of Fashion:
From Design To Merchandising,
How Tech Is Reshaping The Industry
CB Insights helps the world’s leading companies make
smarter technology decisions with data, not opinion.
Our Technology Insights Platform provides companies
with comprehensive data, expert insights and work
management tools to drive growth and improve
operations with technology.
WHAT IS CB INSIGHTS?
SIGN UP FOR A FREE TRIAL
Fashion has always been at the forefront of innovation — from the invention of the sewing machine to the rise of e-commerce. Like tech, fashion is forward-looking and cyclical.
The fashion sector is also one of the largest industries in the world, estimated to be worth more than $3T by the end of the decade, according to CB Insights’ Industry Analyst Consensus.
And today, fashion technology is growing at a faster pace than ever.
Robots that sew and cut fabric, AI algorithms that predict style trends, clothes to be worn in virtual reality — an array of innovations show how technology is automating, personalizing, and speeding up the fashion space.
Seizing the opportunity to open more revenue streams and business models, fashion companies are partnering with technology providers, snapping up startups, and even building their own tech.
Meanwhile, as the industry faces a long-overdue reckoning with its environmental and social impact, it is reexamining processes across the value chain in an attempt to reinvent itself.
In this report, we dive into the trends reshaping how our clothes and accessories are designed, manufactured, distributed, and marketed.
A look at the evolution of the fashion
industry and where technology is taking
it next, from AR/VR dressing rooms to
temperature-changing smart fabrics to
virtual goods in the metaverse.
TECH IS AUTOMATING AND AUGMENTING THE FASHION
DESIGNER
Fashion brands of all sizes and specialties are using technology to understand and anticipate market demand and respond swiftly with trendy designs and customizable styles.
Artificial intelligence will reshape brands’ approach to product design and development, with a focus on predicting what customers will want to wear next. But algorithms aren’t taking the place of human designers any time soon. If there’s anything that fashion houses’ experiments have shown, it’s that human involvement is crucial to harnessing the insights provided by AI and translating them into appealing, wearable clothes.
Outside of fashion, manufacturers are already using AI to generate out-of-the-box prototypes for products ranging from aircraft parts to golf equipment. Generative design software is expected to be a $44.5B market by 2030, per CB Insights’ Industry Analyst Consensus.
AI BECOMES THE DESIGN PARTNER
Google has already tested the waters of user- driven AI fashion design with Project Muze, an experiment it deployed in partnership with Germany-based fashion platform Zalando in 2016.
The project trained a neural network to understand colors, textures, style preferences, and other “aesthetic parameters,” derived from Google’s Fashion Trends Report as well as design and trend data sourced by Zalando.
Product design
Source: Project Muze
The system, designed by China-based technology firm Shenlan Technology, uses deep learning to produce original designs drawn from images, themes, and keywords imported by human designers.
The Tokyo-based design consultancy firm Synflux has also been using AI to come up with innovative designs in a project called Algorithmic Couture. The team, consisting of designers and software engineers, built a tool that creates customized clothing in a series of steps.
First, the software 3D scans a body to capture its proportions. Then, machine learning algorithms analyze the collected data to come up with garment patterns intended to reduce fabric waste. In the last step, designers model these 2D patterns using computer- aided design (CAD) software and produce fashion patterns that can be used to sew clothing items.
An example of a dress design generated by Synflux’s software. Source: Dezeen
Synflux envisions delivering personalized designs that go beyond the typical division of small, medium, and large sizes — with minimized fabric waste, as the software optimizes the design for each customer.
More R&D is needed before brands can rely on AI-only designers. But today’s artificial intelligence is already helping brands create and iterate their designs more quickly.
HOW AI IS INFLUENCING BRANDS
Since purely AI-based design has at times missed the mark, fashion retailers have adopted a new mindset of viewing AI systems as creative partners rather than independent designers.
In 2018, Tommy Hilfiger announced a partnership with IBM and the Fashion Institute of Technology. The project, known as “Reimagine Retail,” used IBM AI tools to decipher:
- Real-time fashion industry trends
- Customer sentiment around Tommy Hilfiger products and runway images
- Resurfacing themes in trending patterns, silhouettes, colors, and styles
Knowledge from the AI system was then served back to human designers, who could use it to make informed design decisions for their next collection.
Such a use case is no longer novel today. Heuritech, for instance, offers an AI platform that analyzes millions of images to spot hues, cuts, shapes, and thousands of other fashion elements to predict how trendy they will become up to 1 year in advance. It might, for example, forecast the popularity of a certain color in the US next season. Brands like Dior use Heuritech to validate their intuitions about upcoming trends, while manufacturers like Wolverine Worldwide use it to gauge whether or not consumer demand is rising for specific products.
The company has said that the AI-designed pieces perform comparably in “keeper” sales to the garments from its fashion- brand suppliers. That’s likely because Stitch Fix has such vast troves of customer data informing its AI, thanks to its subscription-based, feedback-focused business model.
“We’re uniquely suited to do this,” says Eric
Colson, chief algorithms officer at Stitch Fix.
“This didn’t exist before because the necessary
data didn’t exist. A Nordstrom doesn’t have this
type of data because people try things on in
the fitting room, and you don’t know what they
didn’t buy or why. We have this access to great
data and we can do a lot with it.”
Design isn’t the only area where Stitch Fix is putting AI and machine learning (ML) initiatives to work. Along with 5,000 stylists, the company employs a team of almost 150 data scientists to oversee ML algorithms that are used to inform everything from client styling to logistics to inventory management.
According to Colson, the company is already seeing ROI from its AI investments, including increased revenue, decreased costs, and improved customer satisfaction. Stitch Fix reported net revenue of $581M in the first quarter of its fiscal 2022, up 19% year-over-year (YoY).
However, Stitch Fix’s success is not only attributable to its machine workforce.
For all the initial fanfare about AI, Stitch Fix has found that the more humans got involved in training ML models, the more customers they won over. While the startup mentioned the word “algorithm” 76 times in a 2017 listing document, its executives mentioned it only once during an investor call in summer 2021. Stitch Fix doubled its number of human stylists from 2017 to 2021.
With more rigorous, human-led training, AI “assistance” programs will continue to advance and become more accurate. The insights they generate will help brands make smarter strategic decisions around product development and new business lines.
3D design platforms like CLO also make it easy to tweak designs on the fly through real-time garment simulation. These allow brands to use real-time AI insights to modify fashions right up to the minute they hit production.
Below, we illustrate how tech is automating fashion design as styles become more personalized and influenced by digital signals.
FAST FASHION HAS CREATED AN INSTANT GRATIFICATION
MENTALITY
Since World War II, fashion has been broken up into seasons: Spring/summer lines debut on runways in early fall, while autumn/ winter lines debut in February.
The staggered timeline is designed to give brands enough time to gauge the interest of retail buyers and customers. In the time between when fashions are introduced and when they arrive on store shelves, brands assess demand so that they can manufacture the right number of garments for the season.
Fast fashion, in which designs move quickly from catwalk to store shelves, has upended that model.
Brands like Zara and H&M built their businesses on speed and agility. Once these retailers spot a new trend, they can deploy their hyper-rapid design and supply chain systems to bring the trend to market as quickly as possible.
This allows fast fashion brands to beat traditional labels to market. Garments and accessories that are strutted down runways in September and February get replicated by fast fashion brands before the originals even hit stores.
With a nearly real-time ability to get the newest styles on shelves, fast fashion brands can push out broader varieties of clothing styles to cater to the preferences of smaller, more targeted segments of customers. They can also push smaller runs to test the waters for customer demand, or sell collections for hyper- short lifespans.
Manufacturing
And cheap alternatives to high-fashion items remain hot consumer commodities. Even amid the retail slowdown — and the economic uncertainty of the Covid-19 pandemic — Zara’s owner, Inditex, reported over $23B in revenue in 2020, beating analyst estimates. H&M also posted a profit in 2020, thanks to a rapid rebound in business.
WHAT FAST FASHION MEANS FOR SEASONS
The rise of fast fashion is decimating the biannual seasonality that has long structured the fashion industry. In order to keep up, traditional apparel brands are now debuting around 11 seasons a year.
Fast fashion brands, on the other hand, may issue as many as 52 weekly “micro-seasons” per year. Topshop, for example, used to introduce around 500 styles per week on its website before its parent company, Arcadia Group, went bankrupt. Zara produces 20,000 new styles in a year.
Social media is accelerating this cycle. Influencer marketing and other social media strategies help new trends travel fast, creating rapid consumer demand for cheap fashions.
Shoppers act on that demand instantly, thanks to “See Now, Buy Now” tools on platforms like Instagram and Pinterest. Adept social media strategies on TikTok have translated to strong sales for companies like Fashion Nova, PrettyLittleThing, and Shein.
Fashion Nova is one example of a fast fashion e-commerce brand that has successfully leveraged social media to build its customer base and its brand. The company has 21M followers on Instagram, as well as more than 3,000 influencers, known as #NovaBabes, promoting its clothes. It reportedly spent $40M in 2019 on influencer marketing alone.
Cheaply made apparel can also cause environmental damage since rapid production runs of low-durability clothes promote excessive textile waste. According to the Environmental Protection Agency, some 12.8M tons of clothing are sent to landfills annually.
The global fashion industry emits 2.1B metric tons of greenhouse gases annually, according to McKinsey. This represents about 4% of total annual global greenhouse gas emissions — more than international flights and maritime shipping combined. It’s estimated that the fashion industry is responsible for up to 10% of global CO2 emissions, 20% of the world’s industrial wastewater, 24% of insecticides, and 11% of pesticides used.
While the sustainability issues within fashion — and fast fashion in particular — are not new, what’s changing is how the industry’s most influential customers are starting to respond. (We dig into this in “The push for sustainability in fashion” below.)
RAPID ITERATION & PRODUCTION
The costs of starting a fashion brand have gone down significantly, thanks to technology and e-commerce.
Manufacturing marketplaces, for one, can leverage AI to give feedback on whether designs are feasible and provide estimates on cost and production time, potentially eliminating months of back-and-forth with suppliers.
Further, the dawn of the Etsy online marketplace made it easy for anyone to start an online shop and build a following. Now, decreased production costs make it feasible for small or emerging brands to manufacture small runs of products at reasonable margins and build up online audiences from there.
In years past, fashion labels would have to manufacture hundreds or thousands of items in order to produce them at a reasonable price.
“In mass production, more product equals more
money. Vendors tend to be less responsive
to small-quantity orders unless they are
specifically set up for that scale.”
— ERIC SCHNEIDER FOR THE AMERICAN SOCIETY FOR MECHANICAL ENGINEERS
Now, startups like Sewport make it simple for small labels to find small-batch manufacturing partners that can meet their needs at scale, with transparent standards around pricing and sourcing. Emerging brands can weave small-batch runs (and transparent production standards) into their marketing.
“It’s about delivering on the instant gratification
that consumers are really seeking,” says Avery
Baker, chief brand officer at Tommy Hilfiger.
“Closing that gap between the visibility of a
fashion show and the moment of purchase.”
The Hilfiger brand has gone all in on making the Now line experiential and immediate.
NYC launch of the TommyXGigi collection
The first TommyNow collection — a collaboration with model Gigi Hadid — launched in 2016 with a 2-day Fashion Week extravaganza that supported a huge social media push. The event livestream was made “shoppable” for Facebook Live and was supplemented with instantly buyable product debuts on Pinterest, Instagram, and Snapchat.
Many other brands aim to follow TommyNow’s example, but this is no easy feat. Shortening an 18-month production window into just 6 months required the Tommy Hilfiger brand to overhaul its entire design, manufacturing, and distribution ecosystems.
Yet plenty of technologies are emerging to make scalable, sustainable production more feasible, at a faster pace.
STREAMLINING THE FASHION SUPPLY CHAIN
Some brands are “internalizing” production to quicken the pace of manufacturing and meet consumer demand more rapidly.
In April 2018, Gucci launched Gucci Art Lab, a 37,000-square- meter product development and lab testing center with in-house prototyping and sampling activity for leather goods, new materials, metal hardware, and packaging. The project’s aim is to bring the Gucci supply chain closer to home — ultimately giving the brand greater control over product development, sampling, and material development.
Vertical integration has helped companies from Peloton and Apple to Netflix and Tesla drive growth.
The Covid-19 crisis has also highlighted the risks of single sourcing. Companies saw production come to a complete standstill as the pandemic swept through China, where many multinationals source their items.
But in cases where supplier diversification isn’t possible, AI is becoming increasingly critical for supply chain monitoring.
AI companies are leveraging natural language processing (NLP) to scour news, government databases, trade journals, and more to monitor for supply chain disruptions, including natural disasters or factory mishaps. Machine learning is also being used to generate risk scores for vendors based on their supply chain network.