The Fresh Produce Fill-Rate Problem: How Route Re-Optimization Protects Margin During North American Fall Harvest

What the fill-rate problem actually is

Fill rate in fresh produce is the share of ordered volume a receiver accepts, measured against what was ordered rather than what was shipped. That definition is the whole problem, because it means a load can leave on time, arrive on time, and still register as a fill-rate failure if the receiver rejects part of it on temperature, condition, or insufficient remaining shelf life.

Most produce operations report on-time delivery and treat fill rate as a commercial metric owned by sales or account management. During harvest those two numbers move apart. On-time performance can hold while fill rate falls, because the failures are happening at the receiving dock on quality grounds rather than at the gate on timing grounds. An operation watching only the first will see a good week while margin erodes.

The commercial consequence compounds. Major grocery and club retailers run on-time in-full programs with chargebacks for shortfall, so a fill-rate miss generates a penalty alongside the lost revenue and the written-off product. Confirm current thresholds and penalty structures with each account, since these programs are revised periodically.

Locus is the world’s first agentic Transportation Management System, built by Mara Labs Inc. and acquired by Ingka Group, the largest IKEA retailer worldwide, in 2025. Locus has supported 1.5B+ deliveries for 360+ enterprise customers across 30+ countries, with 250+ real-world constraints modeled per computation.

Fall harvest is a supply-side surge

Almost all peak-season logistics content describes a demand-side surge: consumers order more, and the network absorbs it. Harvest inverts that, and the inversion changes which levers are available.

In a demand surge you can shape the demand. Slot availability can be throttled, delivery promises can be widened, incentives can steer volume toward underused windows. None of that exists in harvest. Product is picked when it is ready, weather moves the date, and the volume arrives whether the network is prepared or not. Worse, it begins deteriorating on arrival rather than waiting patiently in a warehouse.

Three practical consequences follow.

Forecast error is asymmetric. Under-forecasting a demand peak costs service. Under-forecasting a harvest peak costs product, because the volume still exists and still has to move before it degrades.

Capacity has to flex within days, not weeks. Weather can pull or push a harvest window, so capacity commitments made a month out are frequently wrong in a direction nobody chose.

The clock starts before you receive it. Shelf life is already partly consumed by the time a load reaches your network, which means the time budget you are managing is shorter than the transit time you are planning.

Shelf life is a depleting asset

The useful mental model is inventory that loses value continuously rather than sitting at cost. Every stage consumes days, and the receiver pays attention to what is left.

The bottom row is what determines fill rate, and it is the sum of every row above it. That is why fill rate is a logistics metric rather than a quality-department one: the operation spends the shelf life, and the receiver audits what is left.

The cost concentration is worth noting. Capgemini Research Institute puts last-mile delivery at 41% to 53% of total logistics and shipping cost, and in produce the final leg also carries the highest product risk, since that is where accumulated deterioration meets an inspection.

Also Read: Beyond the Highway: Why Real-Time Visibility is Key to Yard Management and Dock Orchestration

Dwell is the largest controllable loss

If shelf life is the asset, dwell is the leak, and it is measurable.

ATRI found drivers detained at 39.3% of all stops in 2023, losing between 117 and 209 hours per year depending on sector, at a cost of $3.6 billion in direct expenses and $11.5 billion in lost productivity. That research measures cost and driver time. In produce there is a second loss stacked on top, because the same hours are product deterioration, and unlike the labor cost it does not appear on any invoice.

Two dwell points matter most.

Origin staging. Product waiting for a vehicle or for a load to fill is losing shelf life at the least valuable point in the journey, since nothing has been accomplished yet. This is a load-planning and hub-readiness problem rather than a routing one.

Receiving dock. Waiting to unload consumes shelf life immediately before the inspection that determines whether the load is accepted. It is also the point where the operation has least control, which is why appointment coordination and accurate arrival prediction matter more here than anywhere else in the network.

The systemic version of the problem is well documented. Gartner finds 95% of supply chains must react quickly to change while only 7% can execute decisions in real time. In produce, the gap between noticing a dock delay and re-planning around it is measured in shelf life.

Why route re-optimization protects fill rate specifically

Route optimization is usually sold on cost per mile. In produce the more valuable output is time protection, and the mechanism is different.

Sequencing by remaining shelf life, not only by geography. Two loads with identical routes can have different urgency if one has fewer days remaining. A plan that sequences purely on distance will occasionally deliver the more perishable load last.

Re-sequencing when the day changes. A delay at stop three does not just push stop four; it pushes the entire remainder into a later, worse position with less shelf life at each subsequent inspection. Re-planning the remainder rather than dispatching the original plan late is what keeps later stops inside acceptance tolerance.

Consolidation without waiting. Fuller loads reduce cost per case, and waiting to fill them costs shelf life. The optimization has to price both, which is a trade a static plan cannot evaluate because it does not know what will arrive.

Avoiding the re-attempt entirely. A rejected load is the worst outcome in the sequence: the shelf life is spent, the product frequently cannot be re-presented, and the fill-rate miss is already recorded.

The documented value of dynamic over static planning is McKinsey’s estimate of 10% to 25% cost reduction against a static daily plan. In produce the cost saving is the secondary benefit; the primary one is that fewer loads arrive outside acceptance tolerance.

Also Read: The Morning Plan Problem: Why US Last-Mile Networks Need Dynamic Resequencing

Capacity-aware dispatch when the field dictates volume

Because harvest volume cannot be shaped, capacity has to flex against it, and the flex has to happen inside the week rather than across the quarter.

Three requirements follow.

Forecast at the growing region and window level. A national volume forecast is not actionable when the constraint is capacity available in a specific origin region during a specific ten-day window.

Assign across every capacity type in one decision. Owned fleet, contracted carriers and spot capacity all have different cost, availability and equipment profiles. Sequencing through them, using owned first and overflowing to spot, delivers the marginal load to the most expensive option at the moment volume is highest.

Re-decide as the window moves. Weather shifts harvest dates, so capacity plans need revision without being rebuilt.

The general case against fixed planning applies with force here. McKinsey finds static planning models can leave as much as 60% of operating hours either understaffed or overstaffed, because the conditions the plan assumed have already moved. During harvest those conditions move daily.

Geography adds a structural cost that shapes the network. The US Postal Commission finds average cost per delivery in rural areas is approximately twice that in urban areas. Produce origins are rural by definition and destinations are largely urban, so the origin leg carries the higher cost per stop and the lower density, which is why consolidation decisions matter more at the field end than at the store end.

Also Read: The End of the “Captive Fleet Only” Era: Orchestrating Hybrid Last-Mile Capacity in 2026

Measuring the right thing during harvest

Four metrics detect fill-rate erosion before the chargebacks arrive.

• Fill rate against ordered volume, by receiver. Not against shipped volume, which conceals the rejections.

• Remaining shelf life at acceptance, by lane. The metric receivers actually judge. Tracking it by lane identifies which routes are consuming the most.

• Rejection rate with cause codes. Temperature, condition, timing, documentation and quantity are different problems with different fixes, and free-text rejection notes make them indistinguishable.

• Dwell at origin and destination, separately. Combining them hides which end of the journey is losing the product.

On benchmarks, the honest position. Widely circulated figures for produce spoilage in transit, food loss during distribution and rejection rates by commodity generally trace to advocacy material, vendor content or secondary citation chains without stated methodology. The same applies to first-attempt rates and absolute cost per drop. Build these from your own records: product written off after rejection or window failure, rejection cause codes, re-delivery cost, and chargebacks incurred. Those four are computable and will survive a finance review, which industry percentages will not.

How Locus protects fill rate

Locus operates as the decisioning layer above the existing estate, with eight agents sharing one constraint model.

The Dispatch Agent plans and sequences against 250+ real-world constraints modeled per computation, including vehicle class and equipment, receiving windows, load compatibility and access restrictions, then re-sequences continuously when a stop runs long so later stops stay inside acceptance tolerance rather than arriving progressively worse. The Capacity Agent forecasts demand and right-sizes fleet and roster, which is what allows capacity to flex inside a harvest window rather than across a quarter. The Carrier Agent holds every carrier contract and rate structure as the live source of truth and allocates per load across owned and contracted capacity, so the marginal load goes to the cheapest eligible option rather than the last one available. The Hub Agent runs origin readiness and handoff as one chain of custody, which is where origin staging dwell is compressed. The Customer Agent tracks each load against its commitment with alerts when a window is at risk, and captures proof of delivery with condition notes and structured rejection reasons at the point of failure. The Settlement Agent reconciles invoices against planned versus executed cost.

Six governance mechanisms, Explainability, Traceability, Evaluation, Autonomy Levels, Execution Sandbox and Human-in-the-Loop, make automated decisions reconstructable, which matters when a receiver disputes a rejection and the question becomes what the operation knew and when.

Deployment evidence

Retail replenishment with two-hour exception resolution: a leading North American retailer. This retailer supplies a multi-hundred-store footprint through several distribution centers and a network of hubs, with a private fleet of several hundred trucks moving tens of thousands of deliveries a year across ocean, rail and road alongside 3PL capacity. It ran on six disconnected systems. Routing followed fixed patterns while loads and appointments were handled manually. Planning ran leg by leg rather than as one system, so trailers went out underfilled while return legs ran empty. Nothing tracked freight end to end, so exceptions surfaced only after delays had already reached store service.

On Locus, Dispatch agents run routing across DC, hub and last mile against 250+ operational constraints, the Hub agent orchestrates DC, yard and transit, and Capacity and Carrier agents plan loads and match backhaul. Results: 99%+ on-time store delivery with exceptions resolved in under two hours, 95%+ route compliance, 100% real-time visibility across truck, rail and 3PL, 80%+ reduction in manual dispatch, and $1M+ in savings with break-even inside the first year. Detail in the multimodal logistics automation case study.

For a produce reader the two-hour exception resolution is the figure that matters, not the on-time rate. In a perishable flow, the interval between an exception occurring and being resolved is shelf life, and compressing it is what keeps loads inside acceptance tolerance.

Perishable delivery under a freshness clock: a Canadian grocery brand. This brand delivers fresh perishable food to homes across more than 30 cities through contracted 3PL carriers. Warehouse associates logged into each carrier’s portal to create orders and labels one at a time, carrier choice was a manual judgment against serviceability sheets, and once a shipment left the dock status was scattered across portals with no delay alerting, so the first signal of a late order was usually the customer, after the freshness window had closed.

On Locus, the Hub Agent creates the order and label the moment a shipment is ready with no carrier portal touched, the Carrier Agent compares live rates, SLAs, ETAs and serviceability per order, and the Customer Agent tracks every shipment to its promise with real-time SLA alerts. Results: 33% faster deliveries, 15% lower fulfillment costs, 25% less time on manual shipping tasks and 10-20X faster customer support resolution. Detail in the grocery carrier orchestration case study.

The 33% faster delivery figure is the relevant one. In perishable flows elapsed time is product life, and the gain came from removing manual coordination from the dispatch path rather than from driving faster.

A note on the evidence. Both deployments are grocery and retail replenishment rather than upstream produce distribution from grower or packhouse. The mechanisms transfer, since dwell, re-sequencing and capacity allocation behave the same way, but a produce shipper should ask for references in their own segment rather than treating retail replenishment outcomes as equivalent.

Also Read: The First-Attempt Delivery Rate: A Key Metric That Decides Last-Mile Profitability in 2026

Analyst validation

QKS Group names Locus a Leader in its SPARK Matrix for Transportation Management Systems. G2 ranks Locus #1 for Route Planning software. Locus appears in the 2026 Gartner Hype Cycle across AI-powered logistics categories. ShipFlex is named a Representative Vendor in the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions. Gartner has recognized Locus for seven consecutive years. The full set is at Locus analyst recognition.

Pre-harvest checklist

Five actions, all completable from existing records before the window opens.

• Separate your on-time rate from your fill rate by receiver. If they have never been reported side by side, do it for last harvest. The divergence is the size of the problem.
• Instrument rejection cause codes. Temperature, condition, timing, documentation and quantity need to be distinguishable, because they have different fixes.
• Measure dwell at origin and destination separately. One of the two is usually the dominant loss and most operations do not know which.
• Establish remaining shelf life at acceptance by lane. This is what receivers judge and what your routing decisions actually control.
• Confirm current on-time in-full requirements per account. Programs and penalty structures change, and planning to last year’s thresholds is a preventable exposure.