Cost Per Drop Under Pressure: How Peak Season Density Changes Last-Mile Economics in Europe 2026

Peak density is uneven, and that is the whole point

The intuitive model of peak season is that volume rises and everything gets busier. The operational reality is that volume rises unevenly, and the unevenness is what moves cost per drop.

Some postcodes gain density sharply, because gifting concentrates in residential areas and particular categories cluster geographically. Others gain little. A few thin out, as commercial deliveries drop away over the holiday period while residential rises.

Cost per drop follows density, so during the same week it falls in the zones that gained and rises in the zones that did not. A network-level cost per drop figure averages those two movements and reports something close to flat, which is why most operations finish peak knowing their costs moved and not knowing where.

That is the first practical correction: report cost per drop by zone during peak, weekly rather than monthly. The aggregate is not a useful number in a period when the distribution is changing underneath it.

Density gains do not convert themselves

A zone with more stops per square mile should produce more deliveries per driver hour. Whether it does depends entirely on whether the plan exploits the change.

Two things have to happen. Routes have to be rebuilt against the new density rather than extended, since adding stops to an October route shape produces a longer route rather than a denser one. And service time assumptions have to hold, since a denser route completes only if the time at each stop matches what the plan allocated.

The second is where most of the gain leaks, because the stop is where most of the time goes. Urban Freight Lab research at the University of Washington, based on more than 1,800 real deliveries, found urban commercial vehicles spend around 80 percent of daily operating time parked, with most of a driver’s time spent outside the vehicle walking the final stretch. Density reduces the drive time between stops, which is the smaller share of the shift, and does nothing about the larger share unless the plan models it accurately.

The consolidation opportunity is real and it requires deliberate planning to capture. Chalmers University of Technology research indicates that optimised consolidation can raise vehicle fill rates from approximately 45 percent to approximately 74 percent. Peak density makes that easier to achieve and does not achieve it automatically.

Meanwhile the waste that density should eliminate persists. Eurostat reports that 21.6 percent of distances travelled by road freight vehicles in the EU were performed by empty vehicles in 2024, rising to nearly 26 percent for national transport.

Building the worked example on your own numbers

The method matters more than any published figure, and there are no credible published figures for density-driven efficiency gains anyway. Circulating percentages trace to software vendors rather than research.

Four steps, using data you already hold.

Step one: establish baseline density and cost per drop by zone. Stops completed per square mile per operating day, and cost per drop, for a normal month. Segment by the four density types: dense urban, mid-density metro, dispersed suburban, and rural.

Step two: pull last peak’s density by the same zones. The ratio between the two is your peak density multiple, and it will vary considerably across zones. That variation is the finding.

Step three: compare cost per drop movement against density movement, zone by zone. Where density rose and cost per drop did not fall proportionally, the gain was available and not captured. That gap is your addressable opportunity, expressed in your own numbers.

Step four: check service time in the high-gain zones. If cost per drop failed to fall where density rose sharply, the usual cause is that routes got denser while service time assumptions stayed flat, so drivers ran late and stops per hour did not improve.

The output is a zone-level opportunity map rather than a single percentage, and it is defensible in a way that a benchmark is not.

Two European conditions that change the maths

Out-of-home collection

In parts of Northern Europe and the Benelux, parcel lockers and pickup points are a first preference rather than a fallback, with dense networks and customers who actively choose them.

That changes density economics structurally. A pickup point is one stop serving many orders, which means a market with high out-of-home adoption already holds consolidated density that a home-delivery market has to construct through routing. Peak amplifies it, because gift volume routed to collection points concentrates further.

Two implications. In those markets, a meaningful share of peak density gain comes from collection point consolidation rather than from tighter routing, so the lever is share of volume routed to out-of-home rather than route optimisation alone. And cost per drop is not comparable across markets with different out-of-home adoption, which makes cross-market benchmarking within your own network misleading unless you segment for it.

Vehicle access where density is highest

Low emission zones, ULEZ, and city access windows determine which vehicle can serve which zone and when. That constraint bites hardest in exactly the dense urban cores where peak density gains are largest.

The operational consequence is a mismatch: the vehicle with available capacity during peak week may not be the vehicle permitted in the zone where the density is. Treating vehicle-to-zone eligibility as a hard allocation constraint rather than a compliance check is what prevents a plan that is efficient on paper and unexecutable in practice.

The returns density wave

This is the part almost nobody plans for, and in several European categories it is larger than the outbound peak.

Returns from the gifting period arrive concentrated in January, and they have density characteristics of their own. They cluster differently from outbound volume, because the return decision is made by the recipient rather than the buyer. They arrive over a compressed window driven by returns policies rather than by purchase timing. And in apparel and footwear particularly, the volume is substantial rather than marginal.

Three economic differences from outbound density.

Collection is less predictable than delivery. An outbound delivery has a promised window. A collection depends on the customer being ready, which makes the density less reliable even where the volume is there.

Drop-off shifts the density entirely. Where customers return via pickup points or drop-off locations, the return flow consolidates at those points, which is operationally cheaper and changes where the capacity is needed. A network planned for home collection and a network planned for drop-off consolidation need different capacity in different places.

The vehicle is running anyway. The cheapest return is one collected on a route already serving that area, which makes returns a fill-rate opportunity rather than a separate flow, provided the planning treats it that way. Networks that dispatch returns separately from deliveries pay twice for the same journey.

The service consequence of getting the whole peak wrong is measurable. Eurostat found that 35.4 percent of EU online shoppers reported a problem in 2025, with the most common being slower-than-expected delivery at 19.9 percent.

What to plan before the ramp

Five actions, in dependency order.

1. Classify zones by density type and establish baseline cost per drop for each. Everything downstream depends on the segmentation.
2. Pull last peak’s density multiple by zone, which tells you where the gains will concentrate this year.
3. Update service time assumptions in the high-gain zones, since that is where flat constants do the most damage.
4. Check vehicle eligibility against the high-density zones, and confirm the capacity you plan to use can legally enter them.
5. Plan the returns wave as a density problem, including whether return volume is collected on existing routes or dispatched separately.

Item three is the one most often skipped and the one that determines whether density gains convert. Item five is the one nobody does at all.

Where Locus fits

Locus, the world’s first Decision-Intelligent, Agentic TMS, plans against density as it actually is rather than against the route shapes that worked last month.

Within DiSCO, the Dispatch agent plans and re-sequences against 250+ real-world constraints, including vehicle eligibility and access restrictions that determine which asset can serve a dense zone; the Capacity agent matches available capacity to demand as it concentrates; and the Learn stage of the Sense, Decide, Execute, Learn cycle replaces assumed service times with observed ones, which is the input that determines whether density gains convert into stops per hour.

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 (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.

Two deployments show density being converted rather than merely observed. A global FMCG leader operating across ten countries eliminated 12,000+ trips each month through demand-matched capacity and fuller loads, with 15 percent less distance travelled and plan run time falling from three hours to five minutes. Fewer trips for the same volume is exactly what a captured density gain looks like.

A leading Canadian grocery brand delivering across more than 30 cities through contracted 3PL carriers moved carrier selection and order creation into autonomous allocation, producing 33 percent faster deliveries and 15 percent lower fulfilment costs on an unchanged carrier network.

The number to pull this week

Cost per drop by zone for last peak, against cost per drop by zone for the preceding October.

Where density rose and cost per drop did not fall with it, you have located a gain that was available and went unclaimed. That gap, expressed in your own zones and your own costs, is worth more than any published efficiency percentage, and it tells you precisely where to change the plan rather than telling you to improve in general.

FAQs

How does peak season density affect cost per drop? Unevenly, which is the operationally important part. Volume concentrates in some postcodes and thins in others, so cost per drop falls where density rose and rises where it did not, during the same week. A network-level figure averages the two movements and reports something close to flat, which is why cost per drop should be reported by zone and weekly during peak.

Why do density gains not automatically reduce cost per drop? Because the plan has to exploit them. Routes must be rebuilt against the new density rather than extended, since adding stops to an existing route shape produces a longer route rather than a denser one. And service time assumptions have to hold, since research on urban delivery indicates most of a driver’s shift is spent at the stop rather than driving, so density reduces the smaller component unless service time is modelled accurately.

How does out-of-home delivery change density economics? A pickup point is one stop serving many orders, so markets where lockers and collection points are a first preference already hold consolidated density that home-delivery markets have to construct through routing. In those markets a meaningful share of peak density gain comes from the share of volume routed to collection points rather than from route optimisation, and cost per drop is not comparable across markets with different adoption levels.

Why do vehicle access restrictions matter for peak density? Because they constrain exactly the zones where density gains are largest. Low emission zones, ULEZ, and city access windows determine which vehicle can serve which area and when, which creates a mismatch where the vehicle with available capacity during peak week may not be the one permitted in the dense zone. Vehicle-to-zone eligibility therefore belongs in the plan as a hard constraint rather than as a compliance check.

What is the returns density wave? The concentrated arrival of returns from the gifting period, typically in January, which in high-return categories such as apparel can exceed outbound peak volume. It has different density characteristics because the return decision is made by the recipient rather than the buyer, collection is less predictable than delivery, and drop-off returns consolidate at collection points rather than dispersing across homes.

What should European operations plan before the peak ramp? Five things in order: classify zones by density type and baseline cost per drop for each, pull last peak’s density multiple by zone to locate where gains will concentrate, update service time assumptions in the high-gain zones, verify vehicle eligibility against those zones, and plan the returns wave as a density problem including whether returns are collected on existing routes or dispatched separately.