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Four ways to use AI and machine learning to improve reverse logistics

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President & CEO, Director of LocatorX

Return logistics is one of the trickiest operations a retailer will undertake. Getting returns from a customer’s home back to the warehouse without losing product en route or allowing counterfeit goods into the supply chain is time consuming and often at the expense of bottom line.

According to CNBC, in a 6% increase from 2020, retailers predicted customers would return about 16.6% of purchases in 2021, so without a comprehensive reverse logistics strategy is not an option. When properly managed, the reverse logistics process can be more than just a customer experience boost: it can help you get some of the money back that you had to pay back. That’s where advanced technology like machine learning (ML) comes into play.

In my last article, I gave an overview of the challenges of reverse logistics and how companies are rethinking their operations to overcome these obstacles. Now I want to take a deeper dive into four areas of reverse logistics and explore the technologies that are changing function. By enabling retailers to identify why products are returned, how to streamline the transportation process and how products can be reused, these new technologies can change the way you think about reverse logistics. As you adopt these technologies, ensure that your warehouse management application helps you in the following ways:

1. Avoid the return in the first place

It may be cliché, but when it comes to reverse logistics, a good defense is the best offense. Technology enables companies to reduce the number of products entering the returns process by reducing the likelihood that customers will have to return the goods in the first place. By tracking SKUs with smart tags as they enter the warehouse and scanning them as they leave, the packaging team can match the order to the item and avoid sending the wrong color or size.

Machine learning can also help reduce returns that are not caused by an error in the order. Take, for example, when a customer purchases a shirt that came in the correct size, but does not fit on arrival. I recommend collecting this kind of customer information so you can use machine learning technology to keep track of which styles are large or small versus standard sizes, and what materials were used to create them. You can then offer better sizing advice on the product page and help customers make informed decisions before purchasing.

2. Return products to the warehouse

The process of collecting returned goods is such a nuanced process that, as I mentioned in my last post, many companies will simply issue a refund instead of asking the customer to return the product. It trades the loss for potential gain and a better customer experience. But with the right technology, I think you can get it both ways.

Machine learning can help you understand all the costs associated with recovering an asset and determine how to optimize transportation to ensure that the value of a return is not lost in shipping costs (e.g. better use of space on distribution center-bound vans to cover empty kilometers). It can also help you detect patterns of fraud and raise the alarm when it suspects a customer is counterfeiting a return, halting the return process before money is spent returning the item.

3. Resell Products

Even if a fraudulent good makes it back to the warehouse, machine learning can help a retailer avoid putting it back on the shelf. Smart tags can make it harder for scammers to replicate an authentic item. And using data collected from past returns and known fraud, sensors can scan the returned goods to find even the tiniest flaw that betrays the fake.

You can also apply the same technology to determine the value of an item and determine whether it can go back on the shelf as is or need to be refurbished. Machine learning can measure damage to estimate repair costs and use market data to determine the typical price at which a specific good can be resold, helping you understand whether it is worth investing time and resources to renovate it properly.

4. Recycle Products

Suppose a company has confirmed the authenticity of the return, analyzed any defects and determined that it is ultimately not worth repairing the item. Machine learning can still play a key role in what happens to returns, especially since companies often have limited space in their warehouse and need to get rid of unusable returns quickly.

Using manufacturing data and historical component pricing, you can use ML tools to determine if it’s worth splitting the goods up for scrap; Be sure to use these tools to explore whether you can reuse materials or technology in other goods you are currently making, thus reducing your resource expenditure.

Finally, as more customers turn their attention to companies’ sustainability efforts, using reverse logistics processes to collect and recycle more unusable goods can positively impact your brand’s reputation. Make sure your tools give your business the data it needs to ramp up its recycling efforts.

Reverse losses on machine learning returns

Reverse logistics are often treated with a grin-and-bear-it attitude. Businesses must have it and it plays an important role in the customer experience. But in the end it is seen as a net negative function. Don’t let it be. By applying machine learning throughout the return logistics process, and especially using the methods I’ve outlined, you can reduce the cost of getting returned goods back to the warehouse and explore ways to recoup your lost profits. You can even reduce the chances of the product being returned.

If reverse logistics is a barrier to your business, consider new technologies to streamline and improve the process and make every return count.


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