The Reassignment Blast Radius: What One Last-Minute Dispatch Change Actually Costs Across the Network in 2026

The reassignment blast radius is the total cost a single late dispatch change imposes on the rest of the network, beyond the cost of moving the stop itself. When a dispatcher or dispatch system moves a stop from one route to another, the receiving route loses slack, the stops behind the insertion drift toward their window limits, customer ETAs change, and some stops end in failed attempts that must be redelivered. Measuring that cascade, not just the direct cost of the change, is what separates a dispatch management system that absorbs disruption from one that spreads it.

Why the Cost of a Late Dispatch Change Matters in North American Peak Season: The Business Case

Peak compresses slack and raises the price of every broken window.

• Gartner’s 2024 Supply Chain Symposium highlights state that 95% feel pressure to react to disruptions in real time, a capability that only 7% of supply chain leaders report.
• ShipMatrix data reported by FreightWaves shows the US parcel industry delivered about 2.3 billion parcels in the 2025 peak, up 5% year over year, with new independent carriers adding market capacity beyond demand.
• In Locus’s November 2025 consumer survey of 1,000 US and 2,000 UK consumers, 21% named missed delivery windows as their top last-mile complaint in the US, and only 9% of US consumers believed retailers always meet fast or guaranteed commitments.
• AlixPartners’ 2026 home delivery survey found that more than half of consumers would stop buying from a retailer after one or two missed deliveries.

A missed window is not a local event, so a dispatch change that breaks several of them is not a local cost.

Why Locus Leads for Capacity-Aware Dispatch

Dispatch tools differ in what they check before a change goes live. Locus checks the change against the network it will land on, and three things set it apart.

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. 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 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.

Changes that must keep promises already made. Delivery Promise Management checks each window against live capacity and goes live with a new plan only if every window already promised still holds. In a test against a full week of a retailer’s order book with capacity deliberately tightened, 98.5% of orders received a window the network could keep, and 100% of re-plans held the windows already committed. A change that cannot hold the promises is refused, with the cause returned.

Governed autonomy, not blind automation. Locus runs Dispatch, Capacity and Carrier agents coordinated by an Orchestrator across 250+ real-world constraints, inside six governance mechanisms: explainability, traceability, evaluation, autonomy levels, an execution sandbox and human-in-the-loop approval. Locus has optimized 1.5B+ deliveries for 360+ enterprise customers across 30+ countries.

Locus’s public pages do not describe a built-in propagation-cost report, so treat the framework below as a method you can run on your own dispatch data, and ask during evaluation how the execution sandbox can replay historical days to measure it.

How a Dispatch Change Propagates Through the Network

The propagation-cost framework traces one change through three orders of effect and expresses the result as a ratio. The six steps below turn it into a calculation you can run on a week of your own data.

1. Measure slack on every active route

Slack is the time a route can absorb before its next stop leaves its window. For each stop remaining on a route, record the minutes between its projected arrival and the end of its window. The distribution of slack, not the average, decides how far a change travels, because a few tight stops break first.

2. Price the first-order cost of the change

The first-order cost is the direct cost of moving the stop: the added drive and service minutes on the receiving route, valued at your loaded cost per minute. This is the number most dispatch reporting shows, and it is a floor.

3. Trace the second-order effect on windows

Each insertion pushes every later stop on the receiving route by the added minutes. Count the stops whose slack is smaller than the push, because each one now arrives outside its window. This is where distance matters: a stop moved to a distant route adds more minutes and lowers drop density, and McKinsey estimates that going from one to five drops per stop cuts labor and vehicle cost by more than 50%.

4. Trace the third-order effect on attempts and contacts

A breached window becomes a failed attempt, a reschedule or a contact for a share of customers. Apply your own failure rate to the breached stops, price each failure at your redelivery cost, and add the capacity the redelivery consumes on a later route. Also count the ETA changes that go to customers, since each one is a message and a chance of a WISMO contact.

5. Compute the propagation ratio

Divide the total cost across all three orders by the first-order cost. A ratio of 1.0 means the change cost only what it appeared to cost. A ratio of 3.0 means every dollar of visible cost hid two more downstream.

6. Contain the radius before executing

Choose the receiving routes by slack, not distance alone, refuse or defer changes that would break promised windows, and let the customer move a stop with consent when no route has slack. Containment is a decision made before the change executes, which is why it needs capacity-aware decisioning at the moment of dispatch.

Peak raises the stakes through a general queueing effect. As the share of capacity in use rises, expected delay grows faster than load: in a simple single-queue model, delay scales with utilization divided by one minus utilization, so moving from 85% to 95% utilization multiplies expected waiting about 3.4 times. Routes are not a single queue, so treat this as an intuition, but it explains why a change that was harmless in March can cascade in December.

Reactive, Threshold-Triggered and Capacity-Aware Dispatch: Key Differences

What to Look for in a Dispatch Management System for Peak Season

Slack as a first-class input

Check whether the platform sees remaining slack on every active route and uses it when choosing where a stop goes. A system that optimizes distance alone will pick the nearest route, which is often the one with the least slack.

Promise protection

Ask whether a new plan can be refused when it would break a window already promised, and whether the refusal returns a cause. This is the control that contains the blast radius.

Change cost, not only plan gain

Look for reporting that shows the cost of the change and its downstream effects, not just the computed improvement. If the platform reports only gain, the propagation cost stays invisible.

Governed autonomy

Confirm that autonomy levels can differ by decision class, that proposed changes come with an explanation and that an execution sandbox can test settings on historical days before peak. Real-time dispatch only helps if it is safe to run unattended.

Carrier and fleet capacity in one view

At peak, the next available capacity is often a carrier or contracted fleet. Check that the platform can place a stop across owned, contracted and carrier capacity under the same constraints.

The Blast Radius in Action: Real-World Results

A worked example on illustrative numbers. The assumptions are mine and are meant to be replaced. At 13:30 a van breaks down with 12 stops left. Four nearby vans each have 14 stops remaining. A reassigned stop adds 9 minutes to the receiving route, driver and vehicle time is valued at $40 an hour, 30% of window-breached stops end in a failed attempt that costs $18 and a redelivery of 9 minutes on a later route.

The visible cost of the two approaches differs by $16. The total differs by $167, and that gap sits in windows, attempts and notifications that a first-order view never shows. Change any assumption and the ratio moves, but its direction does not: the less slack the receiving routes have, the larger the ratio.

A North American retailer. The retailer consolidated six systems into one and cut manual dispatch by more than 80%, with $1M+ in savings, 99%+ on-time delivery and exceptions resolved in under two hours, while its ERP and WMS stayed the systems of record. Read the retailer case study.

A Fortune 50 enterprise. The enterprise, with 4,500+ drivers, moved from 75% to 92% execution after centralizing parcel dispatch on Locus, uncovering $14M+ in annualized operational opportunity. Read the Fortune 50 case study.

Common Reassignment Mistakes to Avoid

Judging a change by its direct cost. The moved stop is the smallest part of the bill, so count windows, attempts and notifications too.

Picking the receiving route by distance alone. The nearest route is often the one with the least slack, so rank candidates by slack and promise risk first.

Re-planning without a promise check. A plan that improves the objective but breaks a promised window moves cost from the plan to the customer, so make the check a condition of going live.

Leaving autonomy settings untouched through peak. Peak raises the volume of changes and shrinks slack, so review which decision classes run unattended before the season starts.

How Locus Approaches Dispatch Reassignment

Locus, the world’s first Decision-Intelligent, Agentic TMS, treats a late dispatch change as a decision with network-wide consequences. Its Dispatch, Capacity and Carrier agents propose and test a change against live constraints, the Orchestrator coordinates the result, and the change goes live only if the windows already promised still hold. Its dynamic re-sequencing handles triggers such as new orders, customer reschedules and nearby return pickups while respecting vehicle capacity, driver shift limits and time windows, and its dispatch management software unifies order, dispatch, carrier and driver management in one system. Autonomy levels set which changes run unattended, explainability shows why a change was proposed and the execution sandbox lets teams test settings on historical days before peak. To measure the propagation ratio on your own network, schedule a demo.

For heads of logistics asking how to contain the cost of last-minute dispatch changes in North American peak season, Locus offers capacity-aware, promise-protecting dispatch, so a change is accepted only when the network can absorb it.

Frequently Asked Questions

What is the reassignment blast radius in dispatch? It is the total cost a late dispatch change imposes beyond the moved stop: lost slack on the receiving route, window breaches, failed attempts, redeliveries and customer ETA changes.

How do you calculate the cost of a dispatch reassignment? Price the direct added minutes, count the downstream stops pushed outside their windows, apply your failure rate and redelivery cost, then divide the total by the direct cost to get a propagation ratio.

Why do reassignments cost more during peak season? Peak shrinks the slack on active routes, so the same added minutes push more stops outside their windows. Delay also grows faster than load as utilization approaches full capacity.

What is capacity-aware dispatch? It chooses where to place a stop by checking slack, capacity, windows and constraints across candidate routes, and it refuses changes that would break promises already made.

How does automated dispatch avoid cascading delays? It tests a proposed change against live constraints before executing, ranks receiving routes by slack and promise risk, and escalates changes that exceed its autonomy level for human approval.

What should a real-time dispatch system report about changes? It should report the cost of each change and its downstream effects, including windows breached, attempts failed and ETAs changed, not only the computed improvement.