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Day 2 at the RLA

It’s moving slow, but Big Data, AI and machine learning are beginning to impact reverse logistics

It’s moving slow, but Big Data, AI and machine learning are beginning to impact reverse logistics

Wednesday was Day Two at the Reverse Logistics Association’s annual conference in Las Vegas..

Based on the conversations I had with other attendees at breakfast and lunch, including some from some very large companies, my visits with solution providers like Ben Stephenson from Optoro and an afternoon panel discussion on analytics and Big Data, I left the event with a couple of impressions.

One is that while companies have been dealing with returns forever, as a supply chain discipline, it’s very much “a maturing market.” “It used to be that Paul, the guy who sweeps the warehouse, oversaw returns,” said Harrison Loyd, a purchasing executive with Bargain Hunt Stores. “Now, when I talk to retailers about their overstocks, they have whole teams assigned to returns. They want to capture value up front.” I was also struck by how many of the exhibitors I spoke to were relatively new companies, with ten years or less in the market. What’s more, many had started out doing something different from what they are doing today. Or as one attendee put it, “we’ve had 50 years of investments in forward logistics, but very little in reverse logistics.”

The second is that as the field matures, Next Generation Supply Chain Technologies are finding their way into reverse logistics, albeit not nearly as quickly as they are being adopted by organizations in their forward logistics. That was certainly the case with Optoro and goTRG, two companies that are using the data they’re collecting about returned items to determine the best channel for resale and the best price for a returned item.

It was also one of the points made by Sylvie Thompson, an associate partner with Infosys Limited, who consults on supply chain issues with leading retailers and was a panelist on the Big Data session. “Brand owners (think the manufacturers of fashion items) are leading in innovation over retailers in part because their e-commerce systems are new versus legacy retail systems,” Thompson said. As an example, she noted that brand owners are using technology to utilize returns as part of their store fulfillment processes. At traditional brick and mortar retailers “reverse teams are not at the table.”

Interestingly, she noted that when reverse logistics falls under the supply chain team, it gets lost because supply chain is often focused on the logistics of moving product. When reverse logistics is owned by the merchandiser who are focused on maximizing revenue, it gets attention.

As an example, Thompson talked about new e-commerce models like the subscription startups that send 5 or 6 items to a customer each month. The subscriber can keep them all, keep some or return them all. In those models, returns aren’t considered a cost of doing business but are essential to the business model. Those companies can utilize artificial intelligence and machine learning to figure out the kinds of things a subscriber keeps and returns, then tweak what gets sent out the next month. Done right, those tweaks may get the subscriber to keep an extra item or two next time around, increasing revenue. Those are the kinds of use cases that can elevate reverse logistics.

“If you’re only going to get a few more pennies on the resale of an item, that won’t drive the use case for AI,” said Thompson. “But, if you can use returns pattern data to drive revenue, that can justify an investment and get returns a seat at the table.”