Retailers have spent the last decade trying to make the forward supply chain faster.
The next margin battle may be going the other way.
The National Retail Federation projected that U.S. retail returns would reach $849.9 billion in 2025. Online return rates were expected to reach 19.3%. That is no longer an edge process hiding behind the loading dock. It is an economic system large enough to influence inventory, labor, transportation, fraud, customer loyalty, working capital, and ultimately margin.
And I think the industry is still framing the problem incorrectly.
We tend to talk about reverse logistics as a transportation and warehouse problem: How do we get the item back? Where do we process it? What does the return cost?
Those questions matter. But they are not the most important question.
The critical variable in reverse logistics is time-to-disposition: how quickly can the company determine what the returned product is worth now and put it on the best economic path?
That changes reverse logistics from a cost center into a decision system.
A Returned Product Is a Depreciating Asset
Think about what happens when a $300 product comes back.
At the moment of return, the retailer does not necessarily know whether it has a $300 asset, a $240 open-box item, a $180 refurbished product, a $100 liquidation unit, a source of spare parts, or waste.
Until someone or some system makes that determination, the item is economically frozen.
It may physically sit in a store, a returns cage, a trailer, a consolidation center, or a distribution center. But economically it is stranded inventory.
And for many categories, the value of that inventory declines with time. Fashion moves. Electronics age. seasonal products miss their window. Packaging gets damaged. Inventory already in the forward network competes with the returned unit.
That is why a company can have a highly efficient transportation process and still have a bad reverse supply chain.
If it takes ten days to decide what to do with a product that could have been resold on day two, the problem was not freight. The problem was decision latency.
The Returns Network Is Not the Forward Network in Reverse
One of the most important messages coming out of NRF Rev 2026 was that reverse flows need their own strategies, metrics, partners, and operating models.
That should be obvious, but many networks still behave as though returns are simply outbound fulfillment run backward.
They are not.
A forward shipment usually has a known item, known destination, known customer, and known service commitment. A return begins with uncertainty. The product condition may be unclear. The best destination may be unknown. The disposition may depend on demand, price, repair cost, transportation cost, fraud risk, and available secondary channels.
The physical move is only one component of a much larger decision.
NRF Rev offered several good examples. IKEA has expanded buyback, resale, and spare-parts programs. Target described moving away from a one-size-fits-all reverse strategy. Best Buy has used customer data to change how open-box products are merchandised rather than treating them as a separate pile of damaged goods.
Those examples point in the same direction: the best reverse supply chains are trying to recover value, not merely process units.
The Market Gets One Thing Wrong: Cost per Return Is Not the North Star
Cost per return is useful. It is also incomplete.
Imagine two operations.
Operation A processes a return for $8 and takes twelve days to decide the disposition.
Operation B costs $11 but makes the disposition decision in twenty-four hours and recovers $35 more resale value.
Which one is better?
The answer is obvious, yet many return operations are still optimized around handling cost instead of total value recovery.
A better operating model should measure:
time from return initiation to disposition decision;
percentage of returned inventory recovered to primary sale;
recommerce or secondary-market recovery value;
markdown avoided;
days inventory remains economically unavailable;
fraud loss avoided;
transportation and handling cost by disposition path; and
customer retention after the return.
That is a very different scorecard.
Returns Are Becoming an Inventory Problem
This is where reverse logistics starts colliding with mainstream supply chain planning.
Returned inventory is not automatically available inventory. But it may become available inventory very quickly if the company can inspect, classify, and reposition it.
That means planners need some level of visibility into the return stream.
How many units are likely to come back? How many will be sellable? Where will they re-enter inventory? How long will that take? Is there demand in that location? Should the company replenish a product while hundreds of units are already on their way back?
Once returns reach the scale they have today, those questions stop being operational trivia.
They become part of inventory policy.
This is also why the traditional divide between order management, warehouse management, transportation, planning, and returns systems is getting harder to defend. The disposition decision requires information from all of them.
Fraud Makes the Decision Harder, Not Less Important
The 2025 NRF study estimated that 9% of returns were fraudulent. NRF’s 2026 discussion of return fraud also described a shift toward more organized and adaptive schemes.
The easy response is to tighten the return policy for everyone.
That can reduce fraud. It can also punish profitable customers.
The better response is to make the decision more granular.
A known customer returning a low-risk product should not necessarily face the same process as an anonymous high-risk transaction. A product with a serial-number mismatch should not follow the same flow as an unopened item returned within hours of purchase.
In other words, return policy is becoming another form of segmentation.
The company needs to determine not only what the product is worth, but also how much trust to place in the transaction.
Recommerce Changes the Economics
The growth of resale, refurbishment, repair, and recommerce makes the disposition problem more interesting.
Historically, many retailers had a limited number of paths: restock it, send it back to the vendor, liquidate it, or dispose of it.
That is changing.
Secondary markets create more possible recovery paths, but they also increase the need for better product condition data, pricing, channel selection, and inventory synchronization.
A return is no longer simply an exception to the original sale. It can become the beginning of a second commercial cycle.
That is why I would expect reverse logistics and recommerce systems to become more closely connected to pricing, order management, planning, and inventory availability over time.
The Closed Loop Is the Real Competitive Advantage
The strongest returns operation does something else that is easy to miss: it feeds information back into the forward business.
Why did the customer return the product?
Was the description wrong? Was sizing inconsistent? Was the product damaged in fulfillment? Was packaging inadequate? Did a supplier quality problem create repeat returns? Is one distribution center producing more damage than the others?
If the return reason disappears into a reverse-logistics system and never changes the forward process, the company is paying to learn the same lesson repeatedly.
The real loop should look like this:
sale → return → diagnosis → disposition → value recovery → root-cause correction.
That is not a returns process. It is a supply chain learning system.
Logistics Viewpoints has been writing about omnichannel returns management for years. What has changed is the scale and the economics.
At nearly $850 billion, returns are too large to remain a back-room workflow.
The companies that win will not necessarily be the ones with the cheapest reverse transportation. They will be the ones that make the disposition decision fastest, recover the most value, and use what came back to improve what goes out next.
That is when reverse logistics stops being a cost center and starts becoming a margin engine.
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