---
title: "TMS-Generated Return Visibility in 2026: What Adding Carriers Means"
id: "26625"
type: "post"
slug: "tms-return-transport-visibility-carrier-status-2026"
published_at: "2026-09-16T16:30:00+00:00"
modified_at: "2026-09-17T07:26:17+00:00"
url: "https://locus.sh/blogs/tms-return-transport-visibility-carrier-status-2026/"
markdown_url: "https://locus.sh/blogs/tms-return-transport-visibility-carrier-status-2026.md"
excerpt: "A unified returns view shows only the states every carrier emits. Across four carriers that is 4 of 11, which is why the return timeline has to be generated by your TMS rather than inherited."
taxonomy_category:
  - "General"
---

#### [General](https://locus.sh/blogs/category/general/)

# TMS-Generated Return Visibility in 2026: What Adding Carriers Means

[Anas T](/author/anas_locus/)

Sep 16, 2026

17 mins read

Return transport visibility is the ability to track the reverse leg of an order with the same fidelity as the outbound one, from the moment a customer requests a return to the moment the refund is released. Buyers increasingly ask for it in those terms, with normalized carrier statuses so that a single view means the same thing regardless of who is carrying the item. The difficulty is structural rather than technical: returns fragment across more carriers and more collection modes than deliveries do, every carrier emits its own set of status events, and a merged view can only reliably display the states that all of them emit. Adding a carrier therefore subtracts resolution. The way out is to stop treating the carrier feed as the source of the return timeline and start generating it from the transportation management system that planned the collection, because a TMS holds the events carriers never send. Locus, the world’s first Decision-Intelligent, Agentic TMS, generates return status from its own execution record against more than 250 real-world operating constraints.

## Key Takeaways

- A unified returns view shows only states every carrier emits: about 4 of 11 across four carriers, under 3 across eight.
- Adding carriers degrades resolution monotonically, which inverts the usual assumption that a broader carrier mix improves service.
- The states that survive are the location ones. Collection booked, collection attempted, inspection complete and refund released all die.
- Three of seven common customer questions remain answerable, and the four that fail are the ones about time and money rather than location.
- Surviving states are unreliable as timestamps too: event-to-report lag averaged 5.9 hours with a 7.5-hour spread.
- A TMS that planned the collection already holds the booking, assignment and attempt events, which are exactly the states normalization loses.
- Locus derives return status from its own dispatch and execution record, so states exist whether or not a carrier reports them.

## Why Return Transport Visibility is Hard: The Business Case

The reverse flow is large enough to need its own tracking rather than an exception process. The National Retail Federation’s 2025 Retail Returns Landscape, produced with Happy Returns, puts [returns at 15.8% of annual retail sales in 2025, worth $849.9 billion, with online returns at 19.3%](https://nrf.com/research/2025-retail-returns-landscape)
. It also found [82% of consumers cite free returns as a major consideration when buying, up from 76%](https://nrf.com/research/2025-retail-returns-landscape)
, which makes the returns experience part of the purchase decision rather than an aftercare matter.

The fragmentation that breaks tracking has been increasing. The [Pitney Bowes Parcel Shipping Index](https://www.pitneybowes.com/us/shipping-index.html)
 records non-major carriers doubling their share of US parcel revenue from 3.4% to 7.2% in a 23.1 billion parcel market. Every regional operator added to a returns program is useful capacity and another status vocabulary to reconcile.

The cost of the movement underneath has risen too. ATRI’s 2026 analysis puts the [industry-average operating cost at $2.336 per mile in 2025, up 3.4% and the highest in the report’s history](https://truckingresearch.org/2026/07/new-atri-report-details-accelerating-costs-and-low-profitability-despite-cuts/)
, which pushes retailers toward broader carrier mixes for price, and broader mixes are exactly what degrades the tracking view.

Density is what makes the alternative work. McKinsey finds that [raising drops per stop from one to five cuts labor and vehicle cost by more than 50%](https://www.mckinsey.com/de/publikationen/2024-10-28-ooh-delivery)
, and a collection made on a route you already run is both the cheapest option and the one where you generate the status yourself.

Returns also fragment across modes in a way deliveries do not. A single retailer may collect from the door, accept drop-off at a carrier point, take returns in store, and run a consolidator, each generating a different event stream for the same customer promise.

The economics reward consolidating that flow onto routes you already run, which also happens to be where you own the data. Locus data puts standalone return pickups at [$4 to $6 per item against under $1.50 when the collection joins an existing route](https://locus.sh/blogs/ai-reverse-logistics-retail-returns-optimization/)
, and a collection made by your own plan produces a status you generate rather than one you wait for.

| Also Read: AI in Reverse Logistics: Turning Returns into a Competitive Advantage |
| --- |

## What Normalization Actually Costs You

The model below takes eleven states in a canonical return journey and assigns each a likelihood of being emitted by any given carrier, high for core events and low for peripheral ones. A merged view can display a state only where every carrier in the mix emits it. Values are illustrative; substitute your own carrier event lists and the structure holds.

### 1 A unified view can only show what every carrier emits

Normalization means mapping each carrier’s vocabulary onto one shared set of states. Where a carrier does not emit an event at all, there is nothing to map, and the merged view has to either omit the state or infer it. Omitting is honest and leaves a gap. Inferring is convenient and produces a status that is sometimes wrong, which is worse.

The consequence is that the resolution of a unified view is set by the least informative carrier in the mix rather than the most informative one.

### 2 Every carrier added subtracts resolution

| Carriers in the mix | States all of them emit | Resolution retained |
| --- | --- | --- |
| 1 | 7.1 | 64% |
| 2 | 5.4 | 49% |
| 3 | 4.6 | 41% |
| 4 | 4.0 | 36% |
| 6 | 3.3 | 30% |
| 8 | 2.8 | 25% |

This is the finding that matters, because it runs against the direction of most carrier strategy. Diversifying the carrier mix improves cost, coverage and resilience, and it degrades the customer-facing tracking experience at the same time. Both things are true simultaneously, and the second is usually discovered after the first has been decided.

### 3 The states that die are predictable, and they are the ones customers ask about

Across a four-carrier mix, these survive: return authorized, label or QR issued, collected or dropped off, in transit, arrived at hub.

These do not: collection booked, emitted by all four only 9% of the time. Collection attempted, 2%. Departed origin, 24%. Handed to receiving, 3%. Inspection complete, effectively never. Refund released, effectively never.

The pattern is clean. What survives is the middle of the journey, where the item is physically moving and carriers have commercial reasons to scan it. What dies is either end: the booking and attempt events before collection, and the processing events after arrival. Those two ends are where the customer’s real questions live.

| Also Read: US Returns Hit $850 Billion in 2025: Why US Retailers Are Restructuring Reverse Logistics in 2026 |
| --- |

### 4 Three of seven customer questions remain answerable

| Customer question | State required | Answerable across four carriers |
| --- | --- | --- |
| Has my return been picked up? | Collected or dropped off | Yes |
| When will someone collect it? | Collection booked | No |
| Did the driver come and miss me? | Collection attempted | No |
| Where is it now? | In transit | Yes |
| Has it arrived? | Arrived at hub | Yes |
| Has it been checked? | Inspection complete | No |
| When do I get my money? | Refund released | No |

The three that work are location questions. The four that fail are about time and money. That is the precise shape of the problem buyers describe when they say “where is my refund” becomes a second unanswered question after “where is my order”: the tracking answers the question the customer has stopped asking and not the one they now have.

It is also why adding a tracking page to a returns portal rarely reduces contacts. The page displays what the carrier feed contains, which is the movement, and the contacts are about the timing.

There is a second-order effect worth naming. A tracking page that answers three questions well trains customers to check it, which is good, and then fails them on the fourth, which is worse than not having offered it. A customer who has looked twice and found nothing about their refund does not conclude that the information is unavailable. They conclude that nobody is handling it, and they contact support in a different frame of mind from the one they would have had without the page.

### 5 The same status word does not mean the same moment

Even the states that survive are unreliable as timestamps, because carriers report at different points in their own process.

| Carrier | Typical lag to status | Spread |
| --- | --- | --- |
| Real-time scan at collection | 0.1h | 0.2h |
| Hourly batch | 0.5h | 0.4h |
| End-of-day upload | 7.0h | 4.5h |
| Next-morning file | 16.0h | 6.0h |

Blended across that mix, the lag to a collected status averages 5.9 hours with a standard deviation of 7.5 hours. A refund clock started on that event therefore starts anywhere between minutes and a full day after the item actually left the customer, which means two customers with identical real experiences can see refund timings that differ by a day for reasons neither the retailer nor the carrier did anything to cause.

This is the part that makes status normalization harder than a mapping table. Two carriers can both emit collected, the mapping can be correct, and the resulting timeline still not be comparable.

### 6 The missing states are the ones your TMS already has

Nothing above is an argument against a multi-carrier returns program. The cost advantages are real and the capacity is needed. The point is that carrier feeds are the wrong source for the states customers care about, because those states are not commercially interesting to a carrier.

A carrier scans an item because the scan governs its own liability and billing. It has no reason to emit collection booked, because the booking happened in somebody else’s system, or inspection complete, because inspection happens in somebody else’s building. Those events exist, and they exist in systems the retailer owns.

That reframes the problem usefully, and it points at a specific system. The states that die in normalization are precisely the ones a transportation management system produces as a by-product of planning the work: the collection booking is a dispatch record, the assignment is a route decision, the attempt is a driver event, the arrival is a receiving scan. A TMS that scheduled the collection does not need to be told the collection was booked, because the booking is its own output.

That is the architectural point underneath the buyer requirement. Normalized carrier statuses are worth having and they will never be sufficient, because the normalization can only ever reach the intersection. A return timeline assembled in the TMS starts from the events the TMS generated and treats each carrier feed as an additional contributor, which inverts the dependency: the carrier adds detail to a timeline that already exists rather than being the reason one exists at all.

### 7 What to audit before buying a tracking product

Three things, using data you already hold. First, list every status code every returns carrier sends you and map them onto a canonical journey. The count of states that survive the intersection is your current ceiling, and it is usually lower than people expect.

Second, take your returns contact reasons for a month and mark each against the state that would have answered it. That shows which missing states are actually costing you contacts, which is rarely the same as the ones that look most conspicuously absent.

Third, for a single status such as collected, compare the carrier timestamp against an independent record of when the item actually moved. The gap and its spread tell you whether your refund clock is measuring the event or the reporting of it. An independent record is easier to find than it sounds: a customer confirmation, a driver app event, or a store scan will all do, and any of them beats comparing two carrier feeds to each other.

The audit usually produces one uncomfortable finding and one useful one. The uncomfortable finding is that the customer-facing timeline has fewer real states than the interface suggests, because some of what appears there is inferred rather than reported. The useful one is that most of the missing states already exist somewhere in the estate, in a dispatch table or a receiving log, and have simply never been connected to the customer view because nobody owned the join.

## Carrier-Sourced Status vs Execution-Sourced Status: Key Differences

| Dimension | Status inherited from carrier feeds | Status generated by the TMS |
| --- | --- | --- |
| Resolution | Set by the least informative carrier | Set by what your plan records |
| Effect of adding a carrier | Resolution falls | Unchanged |
| Pre-collection states | Usually absent | Booking and attempt both available |
| Post-arrival states | Usually absent | Receiving, inspection, refund available |
| Timestamp meaning | Varies by carrier reporting practice | Consistent across the network |
| Failure mode | Silent gaps, or inferred states that are wrong | Gaps only where you have no record |

| Also Read: Reverse Logistics Explained: Process, Types and Benefits in 2026 |
| --- |

## What to Look for in Return Transport Visibility

**States generated by the TMS, not only ingested from carriers.** Ask where each state on the customer-facing timeline originates. If every one traces to a carrier feed, the view will thin out as the carrier mix broadens, and no amount of mapping work will stop it.

**Explicit handling of missing states.** A platform should show a gap where it has no information rather than inferring a state. Ask what the system displays when a carrier goes quiet for a day, and whether the customer sees a guess.

**Timestamp provenance on every event.** The same status from two carriers can be hours apart in meaning. The system should record when the event happened separately from when it was reported, and use the first for any clock the customer sees.

**Coverage of the two ends, not just the middle.** Booking, attempt, receiving, inspection and refund are the states customers ask about and carriers do not send. The first three sit in the TMS if it planned the collection. Confirm the platform captures them rather than waiting for a feed that will not carry them.

**A normalization map you can inspect.** Ask to see how each carrier’s codes map onto the canonical set, and what happens to codes that do not map. Mappings maintained invisibly tend to drift as carriers change their APIs.

## Return Visibility in Action: Real-World Results

A Canadian grocery brand delivering fresh and perishable orders across more than 30 cities ran its last mile through contracted third-party fleets, which meant the brand’s visibility depended on partners it did not control. In the [carrier orchestration deployment](https://locus.sh/case-studies/grocery-carrier-orchestration/)
, moving allocation and tracking into a single orchestration layer produced 33% faster deliveries, 15% lower fulfillment cost, 25% less manual shipping time and customer support resolution 10 to 20 times faster. That last figure is the visibility result stated in customer terms: contacts resolve quickly when the answer is already in one place rather than being assembled from several feeds.

A leading North American retailer with several hundred stores consolidated six legacy systems in a [multimodal automation program](https://locus.sh/case-studies/retailer-multimodal-logistics-automation/)
, resolving exceptions in under two hours against on-time store delivery above 99%, with route compliance above 95% and more than $1M returned in year one. Sub-two-hour exception resolution requires a single authoritative state for each shipment, which is the same requirement a normalized return timeline has.

## Common Return Visibility Mistakes to Avoid

**Assuming a broader carrier mix improves the customer experience.** It improves cost and coverage and degrades tracking resolution at the same time. Both effects are real and they need managing together.

**Inferring a state the carrier did not send.** A guessed status that turns out wrong costs more trust than an honest gap, because the customer acted on it.

**Building the timeline entirely from carrier feeds.** The states customers ask about sit at the two ends of the journey, and carriers have no commercial reason to emit them. Those events live in your own dispatch, receiving and finance records.

**Treating a status mapping as a one-off integration task.** Carrier APIs change, codes get added and retired, and an unmaintained map degrades quietly until a state silently stops appearing.

| Also Read: Top Reverse Logistics Companies and Software Solutions of 2026 |
| --- |

## How Locus Generates Return Visibility From the TMS

Locus, the world’s first Decision-Intelligent, Agentic TMS, generates return status inside the TMS rather than assembling it from carrier feeds. Because the collection was planned in the [route planning system](https://locus.sh/route-planning-system/)
, the booking, the assignment, the attempt and the completion are all events Locus produced rather than events it is waiting to receive, which is why the states that normally die in normalization survive here. The Customer and Dispatch agents timestamp when an event occurred separately from when it was reported, so a refund clock measures the movement rather than the paperwork, and where a third-party carrier is used its feed contributes to a timeline that already exists instead of being the whole of it.

Locus is [recognized by Gartner for seven consecutive years](https://locus.sh/analyst-recognition/)
, featured in the 2026 Hype Cycle for Supply Chain Execution and Logistics Technologies, named a Leader in TMS by QKS Group in the SPARK Matrix, and ranked #1 in Route Planning on G2’s 2026 Best Software Awards. In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Locus continues to operate independently.

Return transport visibility fails for a structural reason rather than a technical one. A unified view can only display the states every carrier in the mix emits, so resolution falls from about 7 of 11 canonical states with a single carrier to 4 across four and under 3 across eight, and the states that die are the ones at either end of the journey: collection booked, collection attempted, inspection complete, refund released. What remains answers where the item is, which leaves three of seven common customer questions answerable and the four about time and money unanswered. Even surviving states are inconsistent as timestamps, with a collected event arriving anywhere from minutes to a day after the item moved. The fix is to generate the timeline in the TMS that planned the collection and treat carrier feeds as contributors to it, because the booking, assignment and attempt events a customer asks about are the TMS’s own output rather than something a carrier has any reason to send. [Request a Locus return visibility assessment](https://locus.sh/schedule-demo/)
 to see how many states survive your own carrier mix.

## Frequently Asked Questions

**What is return transport visibility?**

Return transport visibility is tracking the reverse leg of an order with the same fidelity as the outbound one, covering collection booking, the collection itself, transit, arrival, inspection and refund. Buyers usually specify it with normalized carrier statuses, meaning one shared set of states that means the same thing regardless of which carrier moved the item.

**Why does adding carriers make returns tracking worse?**

Because a merged view can only reliably display states that every carrier emits. In this model resolution falls from about 7 of 11 canonical states with one carrier to 4 with four carriers and under 3 with eight. The broader mix improves cost and coverage while reducing what the customer-facing timeline can show.

**Which return statuses usually go missing?**

The ones at either end of the journey. Collection booked, collection attempted, inspection complete and refund released are rarely emitted by every carrier, because the booking happens in someone else’s system and the processing happens in someone else’s building. What survives is the movement in the middle.

**Why does tracking not reduce returns contacts?**

Because the surviving states answer location questions and the contacts are about timing. Of seven common customer questions, the three answerable across a four-carrier mix are all about where the item is, and the four that fail are when it will be collected, whether an attempt was missed, whether it has been checked, and when the refund arrives.

**Can two carriers reporting the same status mean different things?**

Yes. Reporting practice varies from real-time scanning to next-morning file uploads, so the lag between an event happening and being reported averaged 5.9 hours with a 7.5-hour standard deviation across a mixed carrier set. A refund clock started on a carrier timestamp measures the reporting rather than the movement.

**How do we get the missing states without changing carriers?**

Generate them in the TMS that planned the collection. The booking is a dispatch record, the assignment is a route decision, the attempt is a driver event, and arrival is a receiving scan, so a TMS that scheduled the work already holds them. Carrier feeds should contribute to that timeline rather than being the source of it.

MEET THE AUTHOR

Anas T

Senior Content Writer - Product Marketing

Anas is a product marketer at Locus who enjoys turning complex logistics problems into simple, clear stories. Outside of work, he’s usually unwinding with a book or catching a good movie or series.

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## TMS-Generated Return Visibility in 2026: What Adding Carriers Means

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