Backhaul Route Optimization: Closing the Empty-Mile Gap in Retail Replenishment Networks in 2026

Backhaul route optimization is the practice of planning a return leg of a delivery route to carry a paying or cost-offsetting load, typically a reverse pickup such as returns, damaged stock, pallets or recyclable packaging, instead of running the vehicle back to the depot empty. In a retail replenishment network, where a fleet moves inventory from a distribution center to stores on a fixed cadence, the outbound leg is planned to the minute while the return leg is usually left to default routing. Backhaul route optimization treats both legs as one planning problem, sequencing the pickup stops so the return trip earns its mileage back, which is the same insertion-cost logic Locus, the world’s first Decision-Intelligent, Agentic TMS, applies to reverse volume inside an existing route.

Why Backhaul Route Optimization Matters: The Business Case

Deadhead mileage, the industry term for a truck running with no load, has stayed stubbornly elevated across for-hire and private fleets. ATRI’s operational cost research puts non-tank deadhead mileage at roughly 16.3 percent, and reports it remained elevated through 2025 as freight demand stayed soft. That is not a rounding error. For a truck covering 100,000 miles a year, ATRI’s cost modeling shows that roughly 16,000 non-revenue miles consume close to 2,400 gallons of diesel, more than $8,400 in fuel spend that produces nothing for the network. Fuel is the easy number to quantify. Driver hours, wear on the vehicle and the opportunity cost of a truck that could have carried a paying load are on top of it.

The reverse volume that could fill those empty miles is not scarce. The National Retail Federation’s 2025 Retail Returns Landscape forecasts $849.9 billion in US merchandise returns for 2025, with 19.3 percent of online orders sent back. Retail replenishment networks generate a parallel stream on top of that: damaged and expired stock pulled from shelves, pallets and totes that have to cycle back to the distribution center, and seasonal reset volume that moves opposite to the normal outbound flow. All of it needs a truck. Most of it is currently scheduled as a separate reverse logistics problem instead of as the return leg of a route that already exists.

The gap between these two facts, elevated deadhead mileage on one side and a large, physically nearby reverse volume on the other, is what backhaul route optimization closes. It is a sequencing problem, not a fleet-expansion problem, which is why the return on fixing it shows up faster than most network redesigns.

That speed matters because most retail networks already have the physical asset needed to close the gap: a truck passing the store on a fixed cadence. What they usually lack is the planning link between the two workflows. Outbound replenishment is scheduled against a warehouse management or transportation management system built around delivery windows and shelf-readiness commitments. Reverse logistics is frequently scheduled against a separate reverse-logistics or returns-management system built around processing cycles at the distribution center, not around which truck is nearest to a given store on a given day. Neither system was built to answer the question that actually determines whether a backhaul gets captured: given the route this truck is already running, does adding this specific reverse pickup cost less than leaving it for a dedicated collection later. Answering that question requires the outbound plan and the reverse volume to be visible to the same engine at the same time, which is a routing and data problem before it is anything else.

How Backhaul Route Optimization Works

Step 1: Tag reverse volume at the point of origin

Every unit that will eventually need to move backward, a return authorized at a store, a pallet flagged for exchange, packaging due for consolidation, gets a location, a ready time and a size the moment it is generated, not when a driver arrives to collect it. Without this, the planner has no visibility into what is available to backhaul until it is too late to route around.

Step 2: Separate hard outbound commitments from flexible return commitments

Outbound replenishment usually has a fixed delivery window because the store depends on it for shelf readiness. Reverse pickups are rarely time-critical in the same way. The optimizer needs to know which stops on a route cannot move and which can be resequenced, so it does not protect a soft reverse pickup at the expense of a hard delivery window.

Step 3: Build the outbound route first, then evaluate insertion cost for each reverse stop

Rather than planning two separate routes and hoping they overlap, the engine plans the outbound delivery sequence, then tests each candidate reverse pickup against that sequence to see what it actually costs to insert: added distance, added time, and whether it still respects vehicle capacity once the reverse load is added to whatever outbound capacity remains.

Step 4: Respect capacity and compartment constraints in both directions

A truck that delivers full pallets outbound may only have partial capacity left for the return leg, and returns often need to be kept separate from clean outbound stock for damage or contamination reasons. The optimizer has to treat outbound and reverse loads as sharing one vehicle’s finite capacity rather than assuming the return leg is unconstrained because the truck looks empty on paper.

Step 5: Re-optimize when a reverse pickup falls through

Stores do not always have the reverse volume ready when the truck arrives. A pallet flagged for exchange the night before might already have been cleared by a store associate, or a return authorization might come in after the route was already locked for the day. If a pickup is cancelled or short, the plan needs to absorb that in real time rather than leaving the driver idling at a stop that no longer has a load, or skipping a nearby pickup that could have filled the gap instead. This is the same live re-optimization discipline that dispatch teams already apply to outbound delivery exceptions, extended to cover the return leg.

Step 6: Measure backhaul capture rate, not just miles reduced

The metric that matters is the share of available reverse volume actually captured on an outbound route, not a single aggregate empty-mile percentage. A network can reduce its empty-mile percentage by running fewer routes altogether while still failing to capture the reverse volume sitting at its own stores.

Backhaul Route Optimization vs Standalone Reverse Logistics Planning

Consider a distribution center running 40 outbound replenishment trucks a day to a regional store cluster, each averaging 15 empty miles on the return leg out of roughly 90 total miles driven. At a rough all-in cost of $2.50 per mile for fuel, labor apportionment and wear, those return legs represent close to $1,350 a day in miles that produce nothing. If even half of that fleet can absorb a reverse pickup already waiting at a store it passes, without adding a single mile because the pickup sits directly on the existing route, the daily recovered cost is real money that required no new asset, only a planning decision that previously did not get made. The exact numbers will differ by network, which is why the metric to track is capture rate against your own baseline rather than a percentage borrowed from someone else’s fleet.

The practical difference between a network that captures backhauls well and one that does not is rarely the sophistication of its reverse logistics process on its own. A retailer can run clean tracking, fast processing at the distribution center and strong vendor recovery rates for returns, and still leave most of its backhaul opportunity on the table, because the routing decision of which truck picks up which pallet was never connected to the outbound plan. Backhaul route optimization is specifically the routing layer that sits between outbound and reverse processes, not a replacement for either one, which is why it is usually a faster fix than redesigning either process from scratch.

What to Look for in Backhaul Route Optimization Software

Joint planning of outbound and reverse legs in one engine. If the outbound delivery plan and the reverse pickup plan are generated by different systems and reconciled manually, the software is not actually doing backhaul optimization, it is doing two separate optimizations that happen to touch the same truck. Ask a vendor to show the actual data flow between outbound and reverse planning, not just a feature list that mentions both.

Real insertion-cost logic, not proximity matching. A system that assigns reverse pickups because a store is geographically near a route, without testing what that pickup actually costs to insert into the existing sequence, will frequently break delivery windows or leave capacity unusable at the point it is needed most. The test is whether the system can explain, for a specific rejected pickup, exactly why it was rejected in terms of time, distance or capacity.

Capacity and compartment awareness across both directions. The software needs to track remaining capacity on a truck after outbound loading is accounted for, and to respect separation requirements between clean outbound stock and returned or damaged reverse stock. A system that only checks total cubic volume or weight, without modeling which sections of the vehicle can physically hold which category of load, will approve backhauls on paper that get rejected at the loading dock.

Live re-optimization when reverse volume changes. Reverse pickups are less predictable than scheduled outbound deliveries. The system needs to absorb a cancelled or short pickup, or an unplanned one, without a manual replan.

Backhaul capture rate as a first-class metric. Look for reporting that shows what share of available reverse volume was actually picked up on an existing route, broken out by store and by week, not just an aggregate fleet utilization number that can improve for unrelated reasons.

Backhaul Route Optimization in Action: Real-World Results

A Fortune 50 parcel enterprise running more than 4,500 drivers across a 120-country network improved weekly execution rate from 75 percent to 92 percent and uncovered more than $14 million in annualized operational opportunity, $565,000 at one site alone scaled across 25 similar sites, once dispatch decisions, including reverse and exception volume, were planned inside the same system rather than handled as manual overrides. The gain came from making previously invisible capacity, including return-leg capacity, visible to the planning engine rather than from adding vehicles.

A leading North American retailer running a multi-hundred store network consolidated six legacy systems into one platform and reduced manual dispatch decisions by more than 80 percent while reaching 99 percent-plus on-time store delivery and 95 percent-plus route compliance, saving more than $1 million and reaching break-even within the first year. Consolidating outbound and reverse planning into a single system was part of what eliminated the manual reconciliation that had been absorbing dispatcher time between separate outbound and reverse-logistics workflows.

Across Locus’s enterprise deployments broadly, the platform has driven $320 million-plus in aggregate logistics cost savings across 1.5 billion-plus deliveries, evidence that resequencing decisions inside an existing fleet, rather than adding vehicles, is where a large share of durable savings comes from.

Common Backhaul Route Optimization Mistakes to Avoid

Treating empty-mile percentage as the only success metric. A fleet can lower its empty-mile percentage by consolidating routes or shrinking its network footprint while still leaving most of its capturable reverse volume behind. Track backhaul capture rate alongside empty-mile percentage, not instead of it.

Protecting reverse pickups as if they were hard delivery windows. Reverse stops are usually flexible. Planning them with the same rigidity as an outbound delivery commitment wastes the flexibility that makes backhaul insertion cheap in the first place.

Ignoring compartment and contamination constraints. Assuming a truck that delivered full has room for a return load without checking whether that return load needs to be physically separated from remaining outbound stock leads to rejected pickups at the dock, not savings.

Running reverse logistics and outbound replenishment on separate systems with a manual handoff. Even a well-designed reverse logistics process fails to compound with outbound routing if the two are reconciled by a person at the end of the week instead of planned together at the start of the route. The handoff itself, not either process individually, is usually where the capture rate is lost.

How Locus Approaches Backhaul Route Optimization

Locus, the world’s first Decision-Intelligent, Agentic TMS, plans outbound delivery and reverse pickup volume inside the same constraint set rather than as two separate workflows. Because Locus’s route planning system already models more than 250 real-world constraints simultaneously, including vehicle capacity, compartment separation and time windows, adding a reverse pickup to an outbound route is evaluated the same way any other stop is: on its actual insertion cost, not on proximity alone. Locus has been recognized by Gartner for seven consecutive years, featured in the 2026 Hype Cycle for Supply Chain Execution and Logistics Technologies, named a Leader in TMS by QKS Group’s SPARK Matrix, and ranked #1 in Route Planning on G2’s 2026 Best Software Awards.

A Fortune 50 parcel enterprise running Locus across more than 4,500 drivers improved execution rate from 75 percent to 92 percent and surfaced more than $14 million in annualized operational opportunity by making capacity that was previously invisible to dispatch, including reverse and exception volume, part of the same planning decision as outbound delivery. A leading North American retailer consolidated six legacy systems onto Locus and cut manual dispatch decisions by more than 80 percent while reaching 99 percent-plus on-time delivery and break-even in year one.

In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Locus continues to operate independently.

Empty miles and reverse logistics are usually planned as two separate problems because they sit in two separate systems, not because they are two separate routing decisions. Backhaul route optimization closes that gap by testing every reverse pickup against the insertion cost into a route the fleet already runs, and Locus does this as a native part of its constraint-based planning engine rather than as a bolt-on reverse logistics module. If empty miles and reverse logistics are currently planned as separate problems in your network, schedule a demo to see how Locus plans both legs of a route as one decision.

Frequently Asked Questions

What is backhaul route optimization? Backhaul route optimization is the practice of planning the return leg of a delivery route to carry a reverse load, such as returns, damaged stock or pallets, instead of running the vehicle back empty. It plans the outbound and return legs of a route together rather than as separate problems.

How much do empty miles cost a private fleet? For a truck running 100,000 miles a year, ATRI’s operational cost research shows roughly 16,000 of those miles are typically non-revenue, consuming more than $8,400 in fuel alone before labor, tolls and vehicle wear are added. Deadhead mileage for non-tank operations has stayed in the mid-teens percentage range through 2025.

Is backhaul route optimization the same as reverse logistics? No. Reverse logistics is the broader process of managing returns, exchanges and recovered inventory. Backhaul route optimization is the routing decision of where reverse volume gets picked up: on an existing outbound route rather than on a separately scheduled reverse-logistics run.

Does backhaul route optimization require new trucks or routes? No. It resequences pickup stops onto capacity a fleet already has on the road. New capacity is only needed once reverse volume at a given stop exceeds what the outbound vehicle has left after delivering its load.

What is a realistic backhaul capture rate to expect? There is no universal benchmark, because capture rate depends on how much reverse volume physically exists near existing routes and how tightly outbound capacity is already used. The metric to track is the trend in your own network’s capture rate over time, not a borrowed industry average.

What is the biggest barrier to backhaul route optimization in retail replenishment? Usually organizational, not technical. Outbound replenishment and reverse logistics are typically owned by different teams working from different systems, so the two legs of the same truck’s day never reach one planning engine unless that integration is deliberately built.