Companies deliver new orders faster than ever before — but the backend returns process continues to falter. Can AI deliver some badly needed assistance?
For the second time this month, I returned a pair of slacks I had ordered from an online retailer. The reason was the same: The slacks weren’t the size that I had ordered.
This surprised me, since I had already been through this scenario with the retailer two weeks earlier. It seemed that I would always encounter this issue. So I made a silent promise to myself that I wouldn’t place any more orders with this retailer.
Situations like this are reflected in the numbers. For online sellers, item returns are running as high as 30% according to assistant professor Yufei Zhang at the University of Alabama at Birmingham.
That’s a lot of money and a lot of returns, and it doesn’t begin to account for the problems that occur on warehouse floors, like items coming back with the wrong return slips and packages, or incoming returns simply being stacked in one area of the warehouse floor, because the warehouse personnel don’t have the time to get to the backlog that is building.
“Item returns have to be processed, inspected, repaired and dispositioned to determine whether or not they can be resold or reused in the future,” said Gaurav Saran, CEO of Reverse Logistix. “Oftentimes, companies rely on outdated and manual procedures that don’t provide a clear view of the returns cycle as a whole, which can prolong the process even further.”
Can an injection of AI help the situation?
Artificial intelligence has the potential to help if it’s used with a strong returns management system and if staff is trained in new return process workflows.
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A well-orchestrated RMS can automate workflows and create greater visibility into the end-to-end returns operation and how the items being returned are ultimately being dispositioned.
Companies can integrate this RMS system into other systems such as order entry or enterprise resource planning. Organizations can design their own returns workflows that optimize efficiencies and use fewer personnel.
On top of this, you can then add AI capability.
“Adding data analytics and business intelligence to your RMS system will give your company access to customized data reporting that is based on specific metrics or goals,” said Saran. “This additional BI will enable you to quickly identify and correct issues to streamline the process as a whole.”
This is true, and the addition of AI can do even more. For example, if I’m in marketing or sales, I can see which customers are making the most returns — and also check to see if they are among my best customers.
Do I want my best customers to be burdened with making returns all of the time if I want to keep them? Likely not. AI can inform me of the situation so I can proactively reach out to these customers before I risk losing them.
If I’m an engineering or manufacturing manager, and I am alerted to a high rate of returns for a particular high dollar item, I would want to look into that. If returns are due to a specific component that keeps failing, the product might need an engineering revision.
Can AI solve every issue in today’s complicated item return processes? No — but it’s a start.
“By adopting automated and AI-enabled returns management, companies get a more holistic view of returns data that will enable them to create informed and more efficient processes,” said Saran. “The use of technology alleviates the strain of labor shortages and limitations. It allows companies to maximize existing resources and to tackle sustainability by re-packaging and recycling.”