How can fashion companies make better decisions when an item is returned? That was the question Team Fox took on during Implema’s hackathon. With the NoRagrets solution, Alice Stattin, Emma Landelius, Maja Wennstig and Stigove Borgmeister won over the jury by showing how AI and data can enable smarter returns flows, more efficient processes, and better control of a product’s path after a return.
For many retail companies, returns are a complex challenge. When a customer sends back a garment, a new process begins: Should the product go back to the regular warehouse? Should it be sold through an outlet? Does it need to be inspected, repaired, or handled in some other way?
Every decision affects costs, inventory management, and the ability to preserve the product’s value. This was exactly the challenge Team Fox wanted to solve.
What made you choose returns handling as the challenge?
– We wanted to find a real business problem where technology can deliver a tangible improvement. Returns are an area where many companies have complex flows, and where small improvements can have a big impact.
– When a product comes back, companies need to quickly decide what the best path forward is. Should it go back to the warehouse, be sold through an outlet, or be handled in some other way? We saw an opportunity to use AI to create a better basis for decisions and make returns handling more efficient.
What did you see AI could add specifically in the returns process?
– Returns handling is an example of a process where large amounts of information need to be weighed to make the right decision. Here, AI can help identify patterns and handle information in a way that’s difficult to do manually.
– At the same time, it was important for us to start from the problem and the user. The goal wasn’t to create a technically advanced solution for its own sake, but something that could actually make a difference in an operation.
So how does NoRagrets work in practice?
– NoRagrets is an AI-based solution where, by analyzing images and relevant information, you can support decisions about what should happen next with a returned product. For example, determining whether a product should go back to the warehouse, move on to an outlet, or be handled in some other way.
A key part of the solution is shifting the focus from simply processing returns to actively directing the products’ next step—and thereby reducing the risk of value being lost.
Can you give an example?
– Imagine a customer returns a pair of white sneakers because they were the wrong size. Photos are taken of the shoes, either by an employee using a phone or automatically with a camera in the flow. The AI analyzes the images together with the return and the customer’s order, assesses the condition, and verifies that it’s the correct product and appears authentic.
– After a few seconds, a recommendation comes back with a rationale: condition B, to outlet, because the sole is slightly worn and the original box is missing. The employee confirms or changes it, and the shoes are sent to the right shelf. If the AI is uncertain or suspects the item is a counterfeit, the return is routed to a person for review.
– The value is most visible in the assessment being equally thorough for every return, regardless of who does it or how hectic it is in the warehouse. That means more products end up in the right place, and items that would otherwise have been discarded can be resold through an outlet, repaired, or given a second life in second-hand.
The name NoRagrets stands out. How did you come up with it?
– The name NoRagrets is a play on words with two meanings. On the one hand, it comes from the phrase “no regrets”—helping companies make better decisions so value isn’t lost in the returns flow. On the other hand, we play on the word “rags,” which connects to textiles and the apparel industry and the problem we wanted to solve.
– The core idea is that a return shouldn’t be the end of a product. With the right decisions, the garment can take a new path forward instead of losing value.
How did you work during the hackathon to go from idea to a working concept? What was the biggest challenge?
– It was intense hours where we quickly had to go from an idea to something concrete. We had to prioritize and constantly think about which part created the most value.
– At the same time, it was very inspiring to see how much you can accomplish in a short time when different perspectives and skill sets come together.
If you could continue developing NoRagrets, what would the next step be?
– The next step would be to test the solution against real returns flows and real data. Then you can further develop the model and see how the solution can best be integrated into companies’ existing processes and systems. It would also be exciting to see how accurate the AI actually is when it needs to assess different types of products and returns.
The jury consisted of:
- Fanny Widman, speaker, moderator and founder of Fannys Förebilder
- Patrik Sundeborn, Sales Director at Implema
- Petra Ranhem, CMO at Implema
The jury’s rationale:
“The solution presented was as value-creating as it was easy to understand. Returns handling would work tremendously better tomorrow if the solution were implemented. The jury members focused more on future potential than on remarks in their feedback, which says a lot. Congratulations, Team Fox!”
