Say you’re looking for a new jacket that’s affordable and on-trend. What store comes to mind?
For many consumers, the answer is Zara. The Spain-based company is a leader in fast fashion, which refers to retailers that rapidly turn out inexpensive clothing inspired by what’s currently on the runway.
It turns out Zara’s success and popularity doesn’t stem from superior designers or unparalleled creative direction. The store is able to give customers what they want, when they want it, because of its supply chain.
Zara’s agile supply chain is the result of its decision to be vertically integrated, meaning it controls, rather than outsources, most pieces of the production process. The store’s success also comes from a substantial investment in artificial intelligence technology, said Nada Sanders, Distinguished Professor of Supply Chain Management at Northeastern.
“If I want to understand exactly how women’s long sleeve blue button-down shirts are selling at the Zara store on Newbury Street in Boston, I need to be able to hone in on that data,” Sanders explained.
An early example of Zara’s technology-driven approach was the use of personal digital assistants to collect data on product preferences from customers in real time. The data is then processed by algorithms to make the supply chain more efficient and responsive. The strategy worked so well, other companies began following suit.
“Seven-Eleven Japan has taken lessons from Zara, using technology to microsegment demand and to understand what customers want,” Sanders said. “They will literally reshuffle and change what the merchandising looks like in the course of one day, in one location, for different segments of customers.” For example, the store will rearrange milk cartons one way at 7 a.m. for adults commuting to work, and another way in the afternoon for students coming home from school, she explained.
Many organizations have leveraged Big Data and AI algorithms to improve their digital supply chain. Sanders cites Amazon, Uber, and FedEX as additional examples. Why aren’t more companies embracing these tools? Sanders said that although the technology exists, there is a lag across industries in adoption and implementation. She has been interviewing top executives to understand why.
“Every executive—without exception—has said the culture and the organization was the major bottleneck,” Sanders said.
Part of the problem is that the capability of algorithms is outstripping human intelligence. All the data in the world isn’t useful if a decision-maker can’t absorb it in a meaningful way. Sanders said the emerging domain of visual analytics can assist in solving this problem.
But another issue remains—people are inherently skeptical of artificial intelligence. Sanders said this is why it’s so important to educate students and workers that these new tools can be harnessed to complement their jobs, rather than feeding into fears that those jobs will be rendered obsolete. And that culture change has to come from the very top of the organization’s leadership.
“People need to feel that their security, their jobs, and their success is not threatened, but that all these algorithms and technology will help them succeed,” Sanders said. “Human beings will sabotage the implementation if they don’t feel that it’s helpful to them.”