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Say you have a product at the store that you want customers to be able to buy off the shelf. How do you balance that by selling the same inventory to people who place orders online? How do you determine how much inventory you have in online sales? When someone places an online order, how do you decide whether to complete the order with inventory from stores or your company’s warehouse?

These are just some of the questions that retail giant Kohl wrestled with. The retailer’s response, according to Kohl’s chief technician and supply chain official Paul Gaffney, was to let AI shoot at the time of the decision.

“When you start allowing machine learning algorithms to make decisions, they sometimes make decisions that are not intuitive. That’s not what people will make, ”Gaffney said.

A.I. Decides

In general, the deciding factor when trying to choose where to carry is the shipping cost, Geffney said at VentureBet’s Transform 2021 Virtual Summit. However, it has also become clear to the company that when an item is left in inventory at a place where it takes longer to sell, it will eventually be drawn down, and that will damage the bottom line.

“It simply came to our notice then that there were more markdowns than we needed. Can we cleverly say, hey, if we sell at a place where we sold months ago where we now know we might not be able to sell in that store … then let’s pick it up from that store and avoid future markdowns, ” Gaffney said.

Kohl has turned to partners to develop their supply chain optimization solutions. Then came the leap of faith.

“What opened the door for us was to say, ‘Well, we’re willing to risk a certain amount in algorithm recognition, and even if it doesn’t work, that investment in education was good enough,'” Gaffney said. “And it turns out he pays.”

With the success, Kohl is reflecting on its use of AI, developing their inherent capacity for greater control over their AI tools, and considering more ways to optimize their stores in addition to backend inventory management. For example, the data showed that each store has a different makeup of customers, so the AI ​​decides what kind of items should be displayed for a different group of customers. Gaffney said allowing the algorithm to suggest changes to products on sale at different stores based on customer data, resulting in a “huge positive upside”.

Human experience

People should “educate themselves” on what machine learning can do, but to understand how these advanced technologies can disrupt people’s work patterns. Enterprises need to think about ways to purposefully re-engage people in activities that are not conducive to machine learning.

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

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