H&M, Zara, Fast Fashion Turn to Artificial Intelligence to Transform the Supply Chain (2024)

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Fashion is a form of self-expression. Whether it’s Meta CEO Mark Zuckerberg showing up in a hoodie for a meeting to signal he doesn’t need to dress up, someone wearing a bright red blazer to stand out from a sea of grey jackets at a job interview, or a teenager picking out a trendy outfit to wear to school to boost their popularity, what someone wears says a lot about who they are and how they want to be seen.

But how do clothing retail companies predict what trends will be popular ahead of the start of the season?

It turns out many of today’s leading fast-fashion companies, like H&M and Zara, are turning to artificial intelligence to help them predict tomorrow’s trends and stay ahead of the curve.

What Is Fast Fashion?

If you want the look for less, you want fast fashion. Examples of fast fashion include , Zara, Old Navy, Urban Outfitters, Topshop, Mango, and Uniqlo. Fast fashion is, typically speaking, the trendy pieces of clothing and accessories that you can purchase inexpensively from malls across the country.

“Inexpensive” is, of course, a relative term. Many fast fashion stores have bargain-basem*nt prices for clothing for younger shoppers seeking a look that won’t be in style beyond the season as well as higher price point looks for shoppers who are looking for outfits that are still considered affordable by the middle class but are more professional looking or made from better materials.

The word “fast” comes into play because these of-the-moment looks are often ripped from the catwalks and from celebrity culture and then found on Main Street faster than you can say “basic.” This means their production cycle is compressed as much as possible so brands can capitalize on trends before their customers declare them passé.

How Fast-fashion Finds Trends

How can a retailer predict a trend if the very nature of a trend is that it’s a short-lived fad? In the past, fast fashion brands have looked at what high fashion designers are doing during Fashion Week and emulated their looks. As well, they’ve looked to see what celebrities are wearing and even what streetwear looks like in cities like Paris and London.

Fashion labels also look to history. By and large, fashion is cyclical. What goes around, comes around. ’90s kids could be found wearing ’60s and ’70s bellbottoms along with their baggy JNCOs, and now Millennials and Gen Y are rocking the slip dresses and combat boots of the ’90s. As such, the fashion industry looks to the 20-year rule for inspiration.

They also look to current events. The hemline index theory proves that when the economy is booming, women’s skirts get shorter. Meanwhile, the lipstick effect suggests that when the economy slumps, women look to little luxuries like new lipstick to brighten up their styles.

With the rise of social media platforms like Instagram, influencers are posting their OOTD — outfit of the day. On the one hand, it allows the fashion industry to quickly capture trends through SEO and algorithms. On the other hand, it means trends may have shorter cycles, as everyone is looking for the next big thing instead of repeating looks.

Today, though, the fast fashion industry has artificial intelligence at its disposal.

What Is Artificial Intelligence?

When a machine seems to have the uncanny ability to be able to make decisions, that’s essentially artificial intelligence at work.

At its broadest definition, artificial intelligence (AI) uses computer science to simulate human, or natural, intelligence in machines. Algorithms enable machines to problem solve. Not just that, the algorithms allow the machines to learn.

Along with these abilities, AI can perform automated planning and scheduling.

How Fast Fashion Applies Artificial Intelligence

Artificial intelligence is transforming supply chain management.

There are a number of roles that AI can play in the supply chain. Chatbots could handle purchase requests, smart warehouses could manage inventory, and autonomous vehicles could help with shipping.

In 2019, a McKinsey survey found that most companies that use AI increased their revenue: “In supply-chain management, respondents often cite sales and demand forecasting and spend analytics as use cases that generate revenue.”

In 2021, global AI in the retail market was valued at USD $2,938,20 million. It's expected to reach $17,086.54 million by 2028.

This demand forecasting, or predictive analytics, is used throughout the fashion supply chain.

In the past, fast fashion had to gather insight from across the fashion world, taking into account the colors, patterns, materials, and cuts showcased on the runways in Paris, the streets of Berlin, the red carpet in Hollywood, and the dive bars of Brooklyn. It ran the risk of the Baader-Meinhof Phenomenon, in which a fashion buyer’s brain experiences selective attention and confirmation bias. Today, computers can gather and group information much more efficiently.

And more effectively: “In a recent Gallup study centered on predicting consumer demand, data was provided on NASDAQ, product and brand searches, underemployment, and standard-of-living indices. By combining these data sources, Gallup was able to create a predictive model that outperformed their client’s previous consumer demand model by more than 150%.”

How H&M Uses Artificial Intelligence to Predict Trends

The Swedish fashion empire H&M employs more than 200 data scientists to predict and analyze trends. Its AI algorithms obtain fashion trend data by capturing information on search engines and blogs. This information informs everything from how much they buy, when they buy, and where it should be placed in its stores.

Importantly, AI not only forecasts new trends the company’s buyers should be aware of but also informs them of whether they should restock currently popular merchandise. As Thomas has previously reported, H&M’s artificial intelligence therefore helps the company reduce waste and make more sustainable decisions.

Head of the H&M Club Samuel Holst said, “Knowing our customers — having this insight, knowing where, how and when they shop, knowing what they like — that is an important piece in how we will be able to predict trends.”

In 2019, then-CEO Karl-Johan Persson reported that H&M’s venture into artificial intelligence had already helped the company predict trends.

[For more information, read “”]

How Zara Uses Artificial Intelligence to Predict Trends

Like its competitor, the Spanish fashion outfitter Zara has turned to artificial intelligence to further its goals. The company uses it in a number of ways, including employing AI robots to fetch requested orders for customers using its Buy Online, Pick-Up in Store (BOPIS) or Click and Collect options.

Unlike H&M, which relies heavily on outsourcing to speed its production, Zara’s use of outsourcing is minimal. As Thomas has previously reported, one of the advantages of this tactic is that “Zara controls everything from design to display to shipping, allowing it to gather valuable data at every stage. This data can then be analyzed to identify inefficiencies, pinpoint areas of success, and create accurate forecasting.”

To further augment this, the company hired Tyco to install microchips into its clothing’s security tags so that they can identify where within the supply chain a particular style and size is located. This allows the company to have full visibility over the inventory it can sell, thereby informing its forecasting analytics.

Zara also has an initiative with Jetlore, a consumer behavior prediction platform that uses artificial intelligence. Founded by computer scientists from Stanford in 2011, the company was bought by PayPal in 2018. Crunchbase explains its use in fast fashion: “Jetlore’s AI-powered platform maps consumer behavior into structured predictive attributes, like size, color, fit, or style preferences, making it the only customer data platform for B2C businesses in the market. This structured data allows top tier retailers and large hospitality and media companies to optimize content and communication for the consumers, make better merchandising decisions, optimize search, and empower the next generation of customer service.”

[For more information, read Thomas’ “How the Zara Supply Chain Taps into Top Clothing, Retail Trends”]

AI Is Influencing Fashion

The irony is that artificial intelligence is doing more than just predicting trends. It’s also influencing them.

AI tracks shoppers’ behavior patterns online so it can offer a personalized shopping experience. Machine learning gathers data on what a shopper likes and when and how often they make certain purchases. Its predictive technology then allows it to anticipate a shopper’s needs and desires so it can offer them similar products. It even learns when online shoppers are more likely to be open to trying out a new brand.

In this way, AI can be used to introduce shoppers to new brands and styles, push bulging inventory, and drive trends.

Get More Insights on AI’s Role in the Supply Chain

• Predictive Analytics and Machine Learning in the Supply Chain
• The Pros and Cons of an AI-Based Supply Chain
• 4 Ways Artificial Intelligence Is Transforming Supply Chain Management

Image Credit: ESB Professional / Shutterstock.com

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