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Suppose you have a product in a store that you want customers to be able to pick up and buy. How do you reconcile that with selling the same inventory to people ordering online? How do you decide how much of your inventory to sell online? When someone places an order online, how do you decide to process the order with inventory stores or warehouses of your company?

These are just a few of the many questions that retail giant Kohl’s has grappled with. According to Paul Gaffney, Kohl’s chief technology and supply chain manager, the retailer’s response has been to let AI make the decisions.

“When you start to authorize machine learning algorithms in making decisions, they sometimes make decisions that are not intuitive. They are not what people would do, ”Gaffney said.

AI makes a decision

Usually the deciding factor when trying to choose where to ship would be the shipping costs, Gaffney told VentureBeat Transform 2021 Virtual Summit. However, it also became clear to the company that when an item was left in inventory where it took longer to sell, it would end up being marked down, hurting the bottom line.

“We had this persistent suspicion that we were incurring more markdowns than we needed. Could we be smarter and say, “Hey, what if we were to sell the merchandise that we could have put months ago in a place where we now know it probably won’t sell in that store… then let’s choose- la in this store and let’s avoid future markdown, ”said Gaffney.

Kohl’s turned to partners to develop solutions to optimize their supply chain. Then came the leap of faith.

“What has opened up a lot of doors for us is the willingness to say, ‘OK, we are willing to risk a certain amount of money by believing in the algorithm, and even if that doesn’t work, this investment in learning has been pretty good, ”Gaffney said. “And it turned out that it paid off.”

With successes in hand, Kohl’s is reflecting on its use of AI, developing its internal capacity to exert more control over its AI tools, and are also considering other ways to optimize their stores beyond backend inventory management. For example, the data showed that each store has a different mix of customers, so the AI ​​decides what kind of things to display to account for different groups of customers. Allowing the algorithm to suggest changes to products for sale in different stores based on customer data resulted in “a huge positive benefit,” Gaffney said.

Human experience

People should “educate themselves” on what machine learning can do, but also understand how these advanced technologies can disrupt the way people work. Businesses need to think about ways to “deliberately re-engage” people in activities that are not conducive to machine learning.

“It’s tempting to treat the adoption of AI and big data as a technical issue,” Gaffney said. “But it’s much more a problem of managing human change.”


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