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The $850 Billion Returns Problem: Why Reverse Logistics Is Becoming a Margin Engine

Published

3 semaines ago

16 septembre 2026

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.

The post The $850 Billion Returns Problem: Why Reverse Logistics Is Becoming a Margin Engine appeared first on Logistics Viewpoints.

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The New Economics of Logistics Visibility

Published

16 heures ago

5 octobre 2026

Knowing that a shipment will arrive six hours late is information. Knowing it early enough to reschedule labor, protect a customer commitment, avoid detention, or change an inventory decision is economic value.

That distinction is becoming central to the logistics visibility market. The earlier argument that exceptions are becoming the real unit of work explains why visibility economics depend less on event volume than on whether the organization can convert important events into timely resolution.

The first era of visibility was largely about answering a basic question: Where is my shipment? The next era is about a harder question: What should I do because its state has changed?

Visibility Is Not the Outcome

Location and status data can be valuable, but they are intermediate products. A business does not earn a return because a dot moved across a map more accurately. The return appears when information changes an operational decision. A useful way to think about visibility is as a chain: signal -> interpretation -> decision -> intervention -> economic outcome. If any link is missing, much of the potential value disappears.

A Signal Has to Arrive Inside the Decision Window

Timing matters.

A delay discovered after the customer has already missed production is history. The same delay identified early enough to expedite an alternate shipment may be actionable. An ETA update received after warehouse labor has reported for a shift may have less value than the same update received while the schedule can still be changed.

This means visibility quality is not only about accuracy. It is about whether the signal arrives with enough lead time to support an intervention.

Not Every Exception Deserves Attention

As visibility improves, organizations often discover a new problem: too many exceptions. A network with thousands of shipments will always contain delays, deviations, missed scans, changing ETAs, and incomplete data. If every deviation creates an alert, planners become the bottleneck. The more important capability is prioritization.

Which late shipment threatens a high-value order? Which delay creates a stockout? Which container risks demurrage? Which arrival change will disrupt a dock schedule? Which event is likely to self-correct without intervention?

Visibility becomes intelligence when the system can distinguish operational consequence from mere deviation.

ETA Is a Decision Input

Estimated time of arrival is a good example of how the economics are changing. ETA was once primarily a customer-service or tracking metric. Increasingly it can influence warehouse scheduling, yard planning, labor, inventory, customer promises, and downstream transportation. That makes ETA a shared operating variable.

The value increases when the prediction is connected to the systems that can respond. A changing ETA that remains trapped in a visibility dashboard creates less value than one that can trigger a workflow or decision elsewhere.

Dwell, Detention, and Demurrage Make the Economics Visible

Some visibility use cases have direct financial consequences. Better awareness of arrival, dwell, free-time windows, and container status can help organizations manage detention and demurrage exposure. Yard visibility can reduce unnecessary trailer search and moves. Earlier exception detection can protect delivery appointments and reduce costly service recovery. These cases make an important point: visibility value is often realized outside the visibility platform itself.

More Visibility Can Increase Work

This is the uncomfortable side of digital transparency. If a company exposes ten times as many events but does not improve prioritization or workflow, it may create ten times as many things for people to inspect. The result can be an expensive monitoring layer sitting on top of the same manual decision process.

That is why visibility and autonomous exception management are converging. The system must increasingly help decide which events require action, assemble context, recommend a response, and automate routine resolution where appropriate.

Measure Intervention, Not Just Coverage

Visibility programs are often measured by tracking coverage, data completeness, ETA accuracy, or number of connected carriers. Those are necessary operating metrics, but they do not fully describe business value.

Organizations should also ask: How many material exceptions were identified early enough to act? How quickly were they resolved? How often did intervention protect service or avoid cost? How many alerts required no useful action? How much planner time was consumed per exception?

Those measures connect visibility to economics.

The Market Is Moving Toward Action

This shift has strategic implications for technology providers. Pure visibility is becoming less differentiated as location and event data become more widely available. The higher-value layer is interpretation and action: understanding what an event means to a specific operation and helping execute the appropriate response.

That pushes visibility platforms toward orchestration, workflow, decision intelligence, and AI. It also pushes TMS, WMS, and other execution systems toward richer external event awareness.

The Bottleneck Moves

For years, logistics organizations complained that they could not make better decisions because they could not see what was happening. Increasingly, they can see more.

The bottleneck is moving.

When a network can identify exceptions continuously, the constraint becomes the speed and quality with which the organization can interpret and resolve them. That is precisely the environment in which AI agents become interesting—not because logistics needs another conversational interface, but because it needs more capacity to do operational work.

Related Logistics Viewpoints research

The New Architecture of Logistics
Systems Engineering in Logistics
2026 Autonomous Exception Management Market Map
The Economics of Decision Latency
Previous in this series: Transportation Is Becoming Computational

Request The New Architecture of Logistics Client Edition

If your organization is assessing connected execution, orchestration, AI, observability, decision velocity, or selective autonomy, I would be glad to provide the complete client edition and discuss the implications for your logistics operating model and technology architecture.

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The post The New Economics of Logistics Visibility appeared first on Logistics Viewpoints.

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o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions

Published

16 heures ago

5 octobre 2026

Supply chain decision-making is difficult partly because the relevant information is distributed across products, locations, suppliers, orders, capacities, policies, and external events. A system may have access to all of those records and still struggle to understand the relationships among them quickly enough to support a consequential decision.

o9 Solutions addresses that problem through its Digital Brain architecture, including an enterprise knowledge graph and in-memory modeling designed to connect demand, supply, inventory, planning, and operating context. That semantic layer is important because it gives analytics and AI a structured representation of how supply chain entities relate to one another rather than treating the environment as a collection of independent tables and documents.

The company combines this architecture with integrated planning, optimization, scenario modeling, machine learning, and human oversight. The strategic direction is toward a continuous decision environment where changes can be interpreted quickly, alternatives can be modeled, and recommendations can be traced back to the assumptions, events, and constraints that produced them.

As with any broad planning and intelligence platform, the value depends on implementation quality. Knowledge models need strong data governance, entity resolution, process ownership, and clear decision rights. A sophisticated model of the supply chain is useful only if the organization can keep it current and use it consistently in real operating workflows.

o9 Solutions appears in the Logistics Viewpoints Supply Chain Decision Intelligence MarketMap and Autonomous Exception Management MarketMap. The two MarketMaps highlight the relationship between integrated decision intelligence and the faster exception-response capabilities now developing around it.

The post o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions appeared first on Logistics Viewpoints.

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AI Will Not Transform the Supply Chain Until the Architecture Around It Catches Up

Published

19 heures ago

5 octobre 2026

Executive thesis. The model is becoming the least durable layer of the AI stack. Sustainable advantage will come from the operating architecture around AI: authoritative context, governed tools, permissions, observability, and connection to enterprise workflows.

The model is only one component

Supply chain AI discussions often begin with model capability: prediction accuracy, reasoning quality, computer vision performance, or the fluency of a generative system. Those capabilities matter, but operational value depends on everything around the model. The system still needs authoritative context, enterprise tools, permissions, workflow, observability, and a reliable path from recommendation to action.

Different AI patterns solve different problems

Prediction, optimization, generative AI, vision, and agents should not be treated as interchangeable technologies. Forecasting demand, selecting a route, extracting information from a document, interpreting an image, and executing a multi-step workflow require different evidence, control, and performance measures. A mature architecture starts with the decision or task and chooses the AI pattern that fits it.

Context is the operating fuel

An AI system can produce a plausible answer while using stale or incomplete operational context. In logistics, that can be dangerous because the truth may reside across orders, inventory, rates, carrier status, warehouse state, supplier records, and policy documents. Retrieval, master data, identity resolution, and system access therefore become part of the AI architecture, not secondary data-engineering concerns.

Tool access turns intelligence into consequence

The moment an AI system can create a shipment, change an order, contact a carrier, release inventory, or approve an exception, governance becomes an operational requirement. Tool permissions, financial limits, approval gates, idempotency, retries, and rollback are the mechanisms that separate an interesting demonstration from a dependable production workflow.

Measure workflow performance

A fluent response is not the right success metric for operational AI. Supply chain leaders should measure decision latency, manual context gathering, exception closure, override behavior, error recovery, tool failure, and the business outcome being improved. That measurement discipline also creates a rational basis for expanding autonomy as evidence accumulates.

The Logistics Viewpoints AI in Logistics: Use Cases, Architecture, and Implementation Guide separates the major AI patterns and connects them to data, tools, governance, workflow, observability, and ROI—the architecture required to move from model capability to operating value.

Executive implication

AI strategy should separate model selection from control architecture and measure value through workflow performance, decision quality, and operational outcomes.

Go deeper: provides the durable buyer, architecture, and implementation reference for this topic. AI & Advanced Analytics connects this analysis to the broader Logistics Viewpoints research architecture.