The Apparel Industry’s Stubborn Low-Tech Forecasting Problem

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The notoriously inefficient apparel industry’s low-tech approach to fashion forecasting was on display this week at New York’s cavernous Javits Convention Center during a semi-annual event called Tex World.

Approximately 600 exhibitors from countries like China, India, Vietnam, and Bangladesh displayed perhaps a million swatches, styles, and trimmings for an estimated 5,000 designers and buyers. These individuals face the unenviable task of trying to anticipate what consumers will want to buy next spring and beyond, then green-lighting millions in design and manufacturing expenses.

Technology, such as artificial intelligence, is playing a leading role in helping companies in many industries anticipate demand. However, in the apparel industry, the approach is still largely old-school and seat-of-the-pants, which helps explain why leading labels encounter issues like those experienced by Burberry this year. At this time last year, the company was riding a wave; for its 2023 first quarter, Burberry reported a sales surge of 18 percent. A year later, the company’s share price has fallen by more than half, its debt has doubled, and it is eyeing layoffs in the hundreds. According to analysts, Burberry’s problem is that it has lost the aspirational consumer. How did the company not see this coming at a time when economists were predicting a recession, and consumers were beginning to cut back and trade down?

How big a problem is this for the industry?

In a 2021 interview, Matthijs Crietee, secretary of the International Apparel Federation, described the fashion sector as “one big gambling machine, because who knows what people will want to wear in six months or a year from now?"

As someone steeped in developing technology to help retailers gamble less by testing new products and designs before they commit capital, what I observed among buyers at Tex World was a somewhat frantic rush to act, complicated by too many choices. It has to be an exhausting ordeal of too many possibilities during a week of scorching hot weather in the concrete canyons of the Big Apple—less than ideal conditions for good decision-making by ordinary mortals. The best use of technology thus far has been on the manufacturing end. Stretch, wrinkle-proof, and moisture-wicking materials have proliferated, but designs and styles are still largely decided by a handful of people who, like all of us, have biases and narrow points of view.

What is most confounding is that there are many ways to prevent small mistakes from becoming big ones.

AI makes it possible to create designs quickly and roll them out for online testing, without wasting a single yard of fabric. The feel of fabric cannot be replicated that way, but imagery and colorization could help narrow down those million or so choices at Tex World. This “rush to act,” I call the tyranny of now, as in “right now.” Technology has made life easier, more efficient, and frictionless. It has also created a sense of urgency that reflects itself in poorly worded text messages that sow confusion and important emails that go unread because they are too long or drown in a sea of junk mail.

Those who criticize the fashion industry for waste should know that, as things stand, the apparel industry is a long way from sustainable—if that’s even possible. Burberry, for example, has disclosed that between 2018 and 2023, it disposed of $115 million worth of its unsold products to keep them from showing up on the so-called gray market. Apparel retailers are slowly catching on, studying the tastes and habits of Gen Z and millennials, and experimenting with generating images of potential products using AI and online shopping platforms. For example, as the pandemic unfolded, companies like Tractor Supply were quick to recognize by studying their data that their stores were attracting more newly-arrived suburbanites who buy pet foods and Carhartt jackets.

Life and business today move at light speed—too fast for humans. The retail industry has a lot of catching up to do.

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