---
title: "Logistics Order Management Audit: Five Measured Numbers That Show What Caps Delivery Speed in 2026"
id: "27708"
type: "post"
slug: "logistics-order-management-audit-touchless-order-rate-2026"
published_at: "2026-10-26T14:30:00+00:00"
modified_at: "2026-10-27T09:59:20+00:00"
url: "https://locus.sh/blogs/logistics-order-management-audit-touchless-order-rate-2026/"
markdown_url: "https://locus.sh/blogs/logistics-order-management-audit-touchless-order-rate-2026.md"
excerpt: "Five measured numbers, from touchless order rate to re-decision time, show whether order-to-dispatch decisions cap delivery speed, with a worked model."
taxonomy_category:
  - "General"
---

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

# Logistics Order Management Audit: Five Measured Numbers That Show What Caps Delivery Speed in 2026

[Ishan Bhattacharya](https://locus.sh/blogs/author/ishan_locus/)

Last updated: Oct 27, 2026

14 mins read

A logistics order management audit is a measurement exercise that reads your order event log to show how many orders reach dispatch without a human touch, how long each decision waits and where people change what the system proposed. Most enterprises have automated picking, packing and labeling, but in logistics order fulfillment the order-to-node and order-to-dispatch decisions often still run on release waves, cut-off times and manual review queues, and those queues cap delivery speed in ways an automation percentage cannot show. The audit replaces the percentage with five numbers taken from timestamps most teams already hold. In the illustrative model below, an 81% touchless rate sits beside a 4.7-hour median wait for a dispatch decision.

## Key Takeaways

- Touchless order rate and decision speed are separate measurements: in an illustrative 10,000-order week, 81% of orders were touchless and the median order still waited 4.7 hours for a dispatch decision.
- Release waves and dispatch cut-offs set the median wait, while manual queues set the tail, so one fix does not cure both.
- Extending manual queue coverage from 12 to 24 hours cut the 90th-percentile re-decision time after a pick failure from 12.0 hours to 1.6 hours in the same model.
- An override rate means little until you compare outcomes for overridden and untouched orders.
- The five numbers are touchless rate, decision wait, override rate and value, re-decision time and exception resolution time.
- Locus logs each decision with its reasoning, which is the raw material for all five measurements.

## Why Order-to-Dispatch Decisions Matter: The Business Case

A decision that takes seconds can wait hours for a release wave, and the published evidence shows that waiting, not working, dominates most processes.

- APQC’s benchmarking data puts the median at [80% of sales orders](https://www.apqc.org/what-we-do/benchmarking/open-standards-benchmarking/measures/percentage-sales-orders-requiring-no) needing no human intervention across 425 companies, and APQC reports that top performers reach [94% of their orders](https://www.apqc.org/resource-library/resource-listing/benefits-touchless-orders) untouched. Both figures cover order-to-cash and not only order-to-dispatch.
- The Lean Enterprise Institute notes that [most processes spend under 10%](https://www.lean.org/the-lean-post/articles/why-lean-is-a-time-based-strategy/) of elapsed time on value-adding activities, so a short decision can sit inside a long wait.
- A study of real ship-from-store orders at large North American retailers found [pick-failure rates as high as 20%](https://www.contrib.andrew.cmu.edu/~ravi/msom22.pdf) at one retailer, with store inventory inaccuracy the main cause. An earlier study of 37 stores of one retailer found [65% of nearly 370,000 inventory records](https://ideas.repec.org/a/inm/ormnsc/v54y2008i4p627-641.html) inaccurate.
- Brick Meets Click data reported by Digital Commerce 360 shows that ultra-fast fulfillment of [one hour or less](https://www.digitalcommerce360.com/article/online-grocery-sales/) accounted for 18% of online grocery delivery orders in the first quarter of 2026.

Taken together, the figures describe an operation where most orders are expected to flow untouched, some picks will fail and the promise window keeps shrinking. The audit measures how your own order fulfillment supply chain behaves against that backdrop, using your timestamps and not an industry average.

| Also Read: Nobody is 70% Automated: Node Coverage and Flow Coverage Are Different Numbers |
| --- |

## How to Audit Order-to-Dispatch Decisions in Seven Steps

The audit runs on an extract of order events for a recent four-to-eight-week window, and it needs only timestamps and the person or service that caused each event.

### 1. Define a touch and extract the order event log

A touch is any event between order capture and dispatch commit that a person caused: an edit to the node, carrier, release time or delivery window, a manual release or a removed hold. Pull captured, node assigned, released, dispatch committed, exception opened and exception closed for each order, with the actor on every event. If the OMS and TMS keep separate logs, join them on order ID, and treat any missing actor field as the first finding.

### 2. Measure the touchless order rate

The touchless order rate is the share of orders with zero human-caused events between capture and dispatch commit. Report it by channel and by node as well as for the network, because a network average can hide the one channel that carries most of the touches. Also record touches per touched order.

### 3. Measure decision wait, split into waiting and working

Decision wait is the time from order capture to dispatch commit, leaving out physical pick and pack. Report the median and the 90th percentile, not the mean, and split each order’s wait into idle time and time spent deciding. A histogram of commit times by clock hour shows batching directly, because commits cluster just after each release wave and cut-off.

| Also Read: Logistics Automation: You Cannot Save More Time Than the Step You Automated |
| --- |

### 4. Measure the override rate and the override value

The override rate is the share of system-proposed node, carrier or window decisions that a person changed. The override value is the difference in outcome, such as cost per order or on-time delivery, between overridden and untouched orders of the same type. Research on human adjustments to statistical forecasts shows why the value test matters: a study of more than 60,000 forecasts from four supply-chain companies found that [judgmental adjustments increased accuracy](https://researchportal.bath.ac.uk/en/publications/effective-forecasting-and-judgmental-adjustments-an-empirical-eva/)
 in three of the four companies, yet upward adjustments were much less likely to help than downward ones, and a 2025 study of about 147,000 forecasts found adjustments [improved results for just over half](https://researchportal.bath.ac.uk/en/publications/forecast-value-added-in-demand-planning/)
 of SKUs. A separate study found people are [more likely to use an algorithm](https://ideas.repec.org/a/inm/ormnsc/v64y2018i3p1155-1170.html)
 when they can modify its forecasts, so allowing overrides can help adoption. These studies cover demand forecasts and not order decisions, but they pose the same question. Capture a reason code with each override, or you cannot separate informed corrections from habit.

### 5. Measure re-decision time after a pick failure

Re-decision time is the time from a short pick, stock-out or carrier rejection to a new committed node or carrier. Report it by hour of day, because failures that surface after the last staffed shift wait until the next one. The architecture question behind this number is covered in the guide to [where the node decision should live](https://locus.sh/blogs/oms-tms-omnichannel-node-decision-architecture-2026/)
.

### 6. Measure exception resolution time and touches per exception

Exception resolution time runs from exception opened to exception closed, and touches per exception counts the distinct people and handoffs in between. Sort exceptions by cause, such as address, hold, carrier rejection or inventory, because each cause has a different fix.

### 7. Read the five numbers together

The five numbers point to different causes, so read them as a pattern.

| Pattern | Likely cap | First fix to test |
| --- | --- | --- |
| Low touchless rate, short waits | People are fast but numerous | Rules or decisioning for the repeated touch |
| High touchless rate, long median wait | Release waves and cut-offs | Event-driven release and dispatch commit |
| Short median wait, long 90th percentile | Manual queues and coverage hours | Autonomous handling within limits, or longer coverage |
| High override rate | Distrust, stale data or missing constraints | Reason codes, then constraints or data fixes |

| Also Read: From 48 Hours to 4 Hours: How AI-Powered Order Orchestration Transforms Fulfillment Speed |
| --- |

## Automation Percentage vs Touchless Rate vs Decision Wait: Key Differences

| Measure | What it counts | What it hides | Question it answers |
| --- | --- | --- | --- |
| Automation percentage | Steps or nodes where a system proposes or executes | Whether a person changes the result downstream | How much of the process has software? |
| Touchless order rate | Orders with no human-caused event | How long each decision waited | How many orders flow without people? |
| Decision wait (median and 90th percentile) | Time from capture to dispatch commit | Whether the delay is a person or a batch rule | How long does an order wait for a decision? |
| Override rate and value | Human changes to system proposals and their outcomes | The reason for the change, unless coded | Do people improve on the system? |
| Re-decision time | Time from failure to a new committed plan | How often failures occur | How fast does the network recover? |

## What to Look for in Order Management and Dispatch Software

### An actor-stamped decision log

Every decision should record a timestamp, whether a system or a named user made it, the inputs and the outcome. Without the actor, the touchless rate cannot be computed.

### Override capture with reason codes

When a person changes a proposal, the system should store the original, the change and a reason. This turns the override from an anecdote into a measurable event.

### Event-driven release and dispatch commit

Release and commit should be triggered by readiness and by changes in capacity, with cut-offs modeled as constraints and not as batch jobs. Ask whether the system can commit an order in seconds when nothing else needs to change.

### A defined re-sourcing path

When a pick fails, the system should invalidate the route, release any tender, preserve or renegotiate the customer promise and re-plan, without a person rebuilding the order.

### Queue visibility and coverage

Exception queues should show owner, age and cause, and the system should handle routine exceptions within defined limits when no one is on shift.

| Also Read: Fulfillment Automation Scorecard: 7 Criteria to Separate Workflow Scripts From Order Orchestration in 2026 |
| --- |

## Order Management Audits in Action: Real-World Results

**An illustrative model on stated assumptions.** The figures below are arithmetic on assumptions and not a benchmark. The model simulates 10,000 orders a week, averaged over five runs, captured between 06:00 and 22:00. The system proposes a node in 30 seconds; 7% of orders are held for manual node review and 60% of those reviews change the node. Orders then wait for a release wave every two hours, and pick and pack takes 90 minutes. A pick fails on 3% of orders, and 30% of failures are re-sourced automatically in five minutes while the rest go to a planner. A further 6% of orders get a manual carrier review, half of which change the carrier, and dispatch commits at three daily cut-offs (11:00, 15:00 and 19:00). An exception hits 5% of orders and needs one to three touches. Manual queues are staffed from 07:00 to 19:00.

| Measure | As modeled: release waves and cut-offs | Same touches, event-driven release and commit |
| --- | --- | --- |
| Touchless order rate | 81.3% | 81.3% |
| Median decision wait | 4.7 hours | About 1 minute |
| 90th-percentile decision wait | 16.1 hours | 0.8 hours |
| Orders waiting more than 4 hours | 58.5% | 4.2% |
| Re-decision after pick failure, median and 90th percentile | 47 minutes and 12.0 hours | 44 minutes and 11.8 hours |

The touchless rate did not move, yet the median wait fell from 4.7 hours to about a minute once release and commit were triggered by events. The remaining 4.2% of orders waiting more than 4 hours are the touched ones, and their tail comes from queue hours. When the same model staffed manual queues around the clock, the 90th-percentile re-decision time fell from 12.0 hours to 1.6 hours and the 90th-percentile exception resolution time from 16.2 hours to 5.6 hours, with the touch rates unchanged. Three levers, none of which changes the touchless rate, moved three different numbers.

**A Canadian grocery brand.** The brand delivers fresh food to homes in more than 30 cities through several contracted carriers. Before Locus, warehouse associates spent hours each day logging into each carrier’s portal to create orders and print labels, and the team checked each order against serviceability sheets line by line, so manual coordination became the ceiling on both speed and cost. Locus’s agents now create the order and label as soon as a shipment is ready and select the carrier on live rates, contracted SLAs, ETAs and serviceability. The brand reported 33% faster fulfillment from order ready to delivered, 15% lower fulfillment costs and 25% less time on manual shipping tasks. [Read the Canadian grocery case study](https://locus.sh/case-studies/grocery-carrier-orchestration/)
.

**A North American retailer.** The retailer, with a multi-hundred-store footprint, replaced six disconnected systems with one decisioning layer, while its existing ERP and WMS stayed the systems of record. It reported 80%+ less manual dispatch, 99%+ on-time store delivery and exceptions resolved in under two hours. [Read the retailer case study](https://locus.sh/case-studies/retailer-multimodal-logistics-automation/)
.

## Common Order Management Audit Mistakes to Avoid

**Counting a system proposal as automation.** If a person reviews and approves every proposal, the step is manual in effect, however much software sits behind it.

**Reporting averages.** A mean wait hides the batch effect in the median and the queue effect in the tail, so report both the median and the 90th percentile.

**Treating every override as a failure, or as proof of expertise.** An override is a signal, and it only becomes a verdict when you compare outcomes for overridden and untouched orders.

**Stopping the audit at node assignment.** The delay often sits between release and dispatch commit, so measure through to the commit.

| Also Read: The Slow Revert: How Logistics Automation Goes Back to Manual Without Anyone Deciding |
| --- |

## How Locus Makes Order-to-Dispatch Decisions Measurable

Locus, the world’s first Decision-Intelligent, Agentic TMS, runs the decision chain from order readiness to dispatch inside one platform, and its agents choose the carrier, create the shipment and label and track the order to proof of delivery within the policies a customer defines. Every autonomous decision stays under six governance mechanisms, including explainability, traceability, autonomy levels and human-in-the-loop override, and Locus records the trigger, context, reasoning, action and outcome of each decision. Locus [Control Tower](https://locus.sh/control-tower-software/)
 raises alerts on SLA breaches before they happen, and [ShipFlex](https://locus.sh/ship-flex/)
 orchestrates carriers from one decision layer. Locus has optimized 1.5B+ deliveries for 360+ enterprise customers across 30+ countries.

**Recognized by analysts and selected after evaluation.** Locus was recognized by Gartner for seven consecutive years and appears in the 2026 Hype Cycle for Supply Chain Execution and Logistics Technologies, as detailed on its [analyst recognition page](https://locus.sh/analyst-recognition/)
. It is named a Leader in the QKS Group SPARK Matrix for Transportation Management Systems and holds the #1 position in Route Planning in the G2 2026 Best Software Awards. In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Ingka’s [press release](https://locus.sh/press-releases/ingka-group-acquires-locus/)
 says that after an extensive evaluation of logistics software worldwide, it selected Locus for its strength in combining advanced route optimization, real-time visibility and smart use of vehicles and resources. Locus continues to operate independently.

**What this means for the audit.** A decision recorded with its actor, reasoning and outcome is the input every one of the five measurements needs, and a human override becomes an event with an owner and not a silent edit. Locus integrates with existing ERP, OMS, WMS, FMS and TMS systems, which stay the systems of record, and it has [public API documentation](https://docs.locus.sh/)
.

**What to confirm.** Locus’s public pages do not publish a touchless-order-rate report, override reason codes or the time it takes to re-decide after a pick failure. Ask to see the decision log for a production customer, whether overrides carry reason codes, how autonomy levels are set per decision type and what the system does when a pick fails outside staffed hours.

To see your own order-to-dispatch decisions measured, [schedule a demo](https://locus.sh/schedule-demo/)
.

For operations leaders asking what caps their delivery speed, Locus offers automated logistics solutions that pair autonomous order-to-dispatch decisioning with a full decision log in one platform, so the five numbers can be read from the system and not rebuilt from spreadsheets.

## Frequently Asked Questions

**What is a touchless order rate?** The share of orders that move from capture to a defined end point with no human-caused event. In this audit the end point is dispatch commit, and a touch is any edit, release or hold removal made by a person. APQC measures a broader version across order-to-cash.

**What is a good touchless order rate?** APQC’s median across 425 companies is 80% of sales orders with no human intervention, and its top performers reach 94%, but that covers order-to-cash through invoicing. We found no published benchmark for capture to dispatch commit, so set your own baseline by channel and track the trend.

**How do you measure order-to-dispatch decision time?** Take the time from order capture to dispatch commit, leave out physical pick and pack, and report the median and 90th percentile. Split each wait into idle time and decision time, and plot commit times by clock hour to see whether release waves and cut-offs are setting the median.

**Is a manual override always a bad sign?** No. An override can correct stale data or a missing constraint. The audit tests it by comparing the outcome of overridden and untouched orders of the same type, with a reason code on each override.

**How is this different from a fulfillment automation scorecard?** A [scorecard](https://locus.sh/blogs/fulfillment-automation-scorecard-order-orchestration-2026/)
 rates a tool on how deeply it decides, while this audit measures what your own operation does, using timestamps from your order log. The two work together: the scorecard picks the tool and the audit shows whether it changed the numbers.

**Does Locus report the touchless order rate?** Locus’s public pages do not describe a touchless-rate report. Locus records each decision with its actor, reasoning and outcome, which supplies the data the five measures need, and the report itself should be confirmed in a demo.

MEET THE AUTHOR

Ishan Bhattacharya

Lead - Content

Ishan, a knowledge navigator at heart, has more than a decade crafting content strategies for B2B tech, with a strong focus on logistics SaaS. He blends AI with human creativity to turn complex ideas into compelling narratives.

[https://locus.sh/blogs/driver-eligibility-dispatch-constraint-clearinghouse-2026/](https://locus.sh/blogs/driver-eligibility-dispatch-constraint-clearinghouse-2026/)
[General](https://locus.sh/blogs/category/general/)

## [Automated Dispatch and Driver Compliance: Why Eligibility Status Should Be a Dispatch Constraint in 2026](https://locus.sh/blogs/driver-eligibility-dispatch-constraint-clearinghouse-2026/)

Why driver compliance status, from Clearinghouse and CDL status to medical certificates, belongs inside automated dispatch and not in a back-office check, with the real 2026 rules and an exposure model.

[Aseem Sinha](https://locus.sh/blogs/author/aseem_locus/)

Last updated: Oct 27, 2026

[Read more](https://locus.sh/blogs/driver-eligibility-dispatch-constraint-clearinghouse-2026/)

[https://locus.sh/blogs/effective-freight-rate-routing-guide-tender-rejections-2026/](https://locus.sh/blogs/effective-freight-rate-routing-guide-tender-rejections-2026/)
[General](https://locus.sh/blogs/category/general/)

## [Effective Freight Rate: What a Static Routing Guide Really Costs When Tenders Are Rejected in 2026](https://locus.sh/blogs/effective-freight-rate-routing-guide-tender-rejections-2026/)

A routing guide prices every load at the primary carrier's rate until a tender is rejected. A model of the effective rate gap at 5%, 14% and 28% rejection.

[Anas T](https://locus.sh/blogs/author/anas_locus/)

Last updated: Oct 27, 2026

[Read more](https://locus.sh/blogs/effective-freight-rate-routing-guide-tender-rejections-2026/)

Logistics Order Management Audit: Five Measured Numbers That Show What Caps Delivery Speed in 2026

- Share
  - [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Flocus.sh%2Fblogs%2Flogistics-order-management-audit-touchless-order-rate-2026%2F)
  - [Twitter](https://twitter.com/intent/tweet?url=https%3A%2F%2Flocus.sh%2Fblogs%2Flogistics-order-management-audit-touchless-order-rate-2026%2F&text=Logistics%20Order%20Management%20Audit%3A%20Five%20Measured%20Numbers%20That%20Show%20What%20Caps%20Delivery%20Speed%20in%202026)
  - [LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flocus.sh%2Fblogs%2Flogistics-order-management-audit-touchless-order-rate-2026%2F)
  - [Email](mailto:?subject=Logistics%20Order%20Management%20Audit%3A%20Five%20Measured%20Numbers%20That%20Show%20What%20Caps%20Delivery%20Speed%20in%202026&body=https%3A%2F%2Flocus.sh%2Fblogs%2Flogistics-order-management-audit-touchless-order-rate-2026%2F)

- Print
- Download
- [Schedule a Demo](https://locus.sh/schedule-demo/)

### Is your team spending more time on fixing logistics plan than running the operation?

- Agentic transportation management from order intake to freight settlement
- Route optimization built on 250+ real-world constraints
- AI-driven dispatch with automatic execution handling

20%Cost Reduction

66%Faster Planning Cycles

[Schedule a demo](/schedule-demo/)

Insights Worth Your Time

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

## [Locus 2026 UK Consumer Survey: Why Returns Visibility is Now the Conversion Engine for AI-Driven Shopping in UK Retail](https://locus.sh/blogs/returns-visibility-conversion-engine-ai-shopping-uk-retail-locus-q2-2026-consumer-survey/)

[Aseem Sinha](https://locus.sh/blogs/author/aseem_locus/)

Last updated: Sep 11, 2026

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

## [Locus 2026 US Consumer Survey: Generative AI isn’t Just Changing How Consumers Shop, it’s Breaking the Demand Patterns US Retail Was Built On](https://locus.sh/blogs/generative-ai-shopping-effect-retail-fulfillment-operations-locus-q2-2026-consumer-survey/)

[Ishan Bhattacharya](https://locus.sh/blogs/author/ishan_locus/)

Last updated: Jun 2, 2026

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

## [Embedded vs Bolted-On AI: The Architecture Question European Logistics Buyers Are Asking](https://locus.sh/blogs/embedded-vs-bolted-on-ai-european-logistics-platform-architecture-business-benefits/)

[Aseem Sinha](https://locus.sh/blogs/author/aseem_locus/)

Last updated: Jul 17, 2026

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

## [Hybrid Fleet Management: How Owned, 3PL, Gig, ICE, and EV Capacity Actually Operate at Most Enterprises](https://locus.sh/blogs/three-workforce-fleet-reality-owned-3pl-gig-drivers/)

[Aseem Sinha](https://locus.sh/blogs/author/aseem_locus/)

Last updated: Jul 18, 2026

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

## [US Returns Hit $850 Billion in 2025: Why US Retailers Are Restructuring Reverse Logistics in 2026](https://locus.sh/blogs/850-billion-us-returns-ai-routing-reverse-logistics-2026/)

[Ishan Bhattacharya](https://locus.sh/blogs/author/ishan_locus/)

Last updated: Jul 18, 2026