As metaverse developments create a huge hype, other technologies such as AI evolve. With AI improvements, more fashion brands are leaning into a data-driven “test-and-learn” model, and even automating tasks such as reorders, to better match supply with demand and minimize their inventory risks.
When you browse the site of a retailer like Revolve, and whether you realize it or not, you’ll probably see inventory that’s there because an algorithm decided it should be.
Fashion businesses haven’t only been leaning into AI to make smarter forecasts. A number are also embracing a data-driven “test-and-learn” approach, where they produce small batches of goods and then use algorithms to quickly crunch the data on top performers to reorder. Those reorders and tasks like distributing items to the right stores and warehouses can even be automated, helping brands push the speed limits set by their supply chains.
It’s like a tech-forward version of the model pioneered by Zara more than a decade ago and supercharged more recently by Shein. Still, even brands at higher price points and in categories other than clothing are leaning into variations of the approach to minimize their inventory risks.
Revolve has said this read-and-react model helped it weather the pandemic-induced market disruptions and supply chains that have hampered some of its competitors. The company highlighted it as a major contributor to its growth in sales and profits in 2021.
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“Since we have automated so many aspects of the decision-making process, we can leverage data much faster than others to identify trends and make merchandising decisions in a very quick, accurate, and efficient way,” co-founder Michael Mente told investors in February.
At Lulu’s Fashion Lounge, an online retailer that does much of its business selling items like $100 event dresses, 70 percent of sales in the six months through July 2021 came from reorders of items it previously tested in small batches, it revealed in its filing to go public last year. David Creight, the company’s chief executive, told investors last week while discussing the company’s latest quarter, which saw it raise its full-year outlook, that “algorithmic driven purchasing” accounted for 70 percent of its revenue.
“There’s been a transformation more broadly in terms of the merchant business model and rethinking the infusion of data science into inventory planning and analysis,” said Oliver Chen, a managing director and senior research analyst at Cowen.
Executing this model can be harder than it first appears since it requires having the fabric and materials available to reproduce orders and a fast-moving supply chain to manufacture and distribute items quickly. But brands have strong incentives to make it work, especially in recent months. After two years of being able to keep stock lean, many brands are seeing inventory pile-ups again. With inflation also making consumer spending less predictable, they’re seeking to be smarter and faster in matching supply to demand.
Companies able to put the model to use can reap benefits like freeing up capital, since less of it is tied up in large inventory orders, and importantly, selling more items at full price.
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