Labour costs across the travel and logistics sectors rose by as much as 40% between 2018 and 2023. Over the same period, the 40 highest-grossing companies in the sector raised productivity, measured as revenue per employee, by 15%. Both figures come from McKinsey. Turnover among logistics employees is running 33% above pre-pandemic levels.
Operations pay substantially more for each shift and receive less output from it. Recruitment does not close that gap, because recruitment addresses availability while the gap is one of output per person.
There is also a cost that appears in no workforce report. It is created every time a plan meets a floor that has already changed. What follows sets out how the pressures compound, where that hidden cost accumulates, and what closing the gap requires.
The shortage is a throughput problem, not a hiring problem
The term describes two conditions running at once.
The first is availability. Roles go unfilled, tenure shortens, and daily execution depends increasingly on temporary and subcontracted staff. McKinsey attributes this to high attrition, an accelerating wave of retirements that removes decades of accumulated expertise, and training bottlenecks that limit how quickly replacements reach full complexity.
The second is capability, and it is the harder one. Deloitte’s 2026 Global Human Capital Trends research, covering more than 9,000 business and human resources leaders across 89 countries, treats human capacity itself as an increasingly scarce resource under demographic pressure.
The people leaving carry operational knowledge that nobody wrote down. The people arriving inherit the same processes without the same context.
MIXMOVE states the problem directly. A labour shortage is what happens when operational knowledge lives in individuals rather than in systems. When tenure falls, the knowledge leaves with the person. Throughput follows it out of the door, and cost per unit rises to meet it.
Takeaway
Availability is the visible half of the shortage. Capability is the expensive half. Systems that hold operational knowledge protect output when tenure does not.
The pressure is structural, and it predates the pandemic
Reading these figures as a pandemic aftershock is tempting. The evidence does not support it.
Analysis published by SupplyChainBrain traces the talent crisis well before 2020. Research from 2018 already projected substantial unrealised revenue from labour shortages by 2030. A survey that year found 70% of respondents said the profession lacked status and career growth opportunities.
The pandemic did not create the shortage. It removed the slack that had concealed it.
A second structural driver is more uncomfortable. The same analysis notes that digitisation has itself narrowed workforce options, because the automation intended to offset worker shortages requires technically skilled talent that is equally scarce.
Technology that demands specialist operators does not relieve a labour constraint. It relocates the constraint, usually into a role that is harder to fill than the one it replaced. Any system entering this market has to pass that test.
Demand, meanwhile, keeps rising. McKinsey research shows that between 10% and 20% of every e-commerce transaction flows to logistics, while consumer expectations on delivery speed and order transparency continue to raise the operational bar.
The operating model is shifting underneath all of it. In Deloitte’s 2026 research, 7 in 10 business leaders name speed and adaptability as their primary competitive strategy for the next three years. The two success drivers they rate most highly are accelerating how people and resources are orchestrated, and increasing the ability to adapt to change.
Deloitte names the shift explicitly. One of its three tipping points for 2026 is the move from static plans to dynamic orchestration, where strategy and execution merge and organisations orchestrate capacity against shifting demand rather than allocating it in advance.
For logistics this is not abstract. It is the difference between a shift plan written on Friday and a Tuesday morning where two people are absent, an inbound load is four hours late, and the plan is already fiction.
Takeaway
The shortage is structural, not cyclical. Adding technology that requires scarce specialists moves the constraint rather than removing it.
The labour cost nobody counts
Most logistics operations already run a transport management system, a warehouse management system, and a visibility platform. None resolves this, and the reason is documented.
Supply Chain Management Review reports that returns on supply chain technology fail at the handoff between planning and execution rather than inside the model. An accurate recommendation loses its value when the assumptions behind it no longer match real-time supplier, inventory, transport, or warehouse conditions.
Planning systems typically run on batch processes, overnight file transfers, or periodic warehouse syncs. They optimise against a version of reality that is hours or days old.
The execution environment runs in real time, against actual pick rates and actual dock schedules.
Then comes the part that belongs in every labour cost calculation and appears in almost none.
When the assumptions fail, the recommendation creates work rather than value. Someone in execution has to spot the gap, decide whether to follow the instruction or deviate, document that deviation or fail to, and absorb the consequences downstream.
That recognition, decision, and documentation loop stays invisible to the planning system, and often to the leaders who approved the investment.
This is the hidden labour cost of the shortage. Experienced staff spend their shifts reconciling instructions against conditions. None of that time appears on a productivity report. Headcount figures understate the problem, because they count people present rather than people producing.
McKinsey quantifies the same failure from the planning side. Sample data from one operator showed that static planning models left as much as 60% of operating hours either understaffed or overstaffed. McKinsey’s wider assessment is that many companies in the sector remain rich in data but underinvested in the tools to use it, with complex workforces managed through simplified models and spreadsheets.
The gap is definitional. Planning systems allocate labour in advance. Visibility platforms report on what already happened. Neither operates at the moment a worker stands in front of a pallet and decides what to do next. That moment is where throughput is created or lost.
Third-party evidence at a glance
60% of operating hours mis-staffed under static planning models, in one operator sample. McKinsey.
10% processing time reduction from removing duplicated manual data entry, with no change in headcount. McKinsey.
15% reduction in driver travel time from daily route optimisation, with no change in headcount. McKinsey.
Technology returns fail at the planning to execution handoff, not in the model. Supply Chain Management Review.
Four labour models, and why three of them fail
Analysis published by Supply Chain Management Review, written by a Gartner analyst, sets out four models for assigning and managing warehouse labour.
Fixed labour assigns specific roles, ensuring specialisation but limiting flexibility. Cross-trained labour adapts to volume swings by training employees across multiple roles.
Dynamic labour allocates using real-time data, supported by warehouse systems. On-demand labour covers seasonal peaks at higher cost.
| Labour model | Strength | Failure under a shortage |
|---|---|---|
| Fixed | Deep specialisation in each role | Knowledge concentrates in individuals, so every departure removes a capability |
| Cross-trained | Absorbs volume swings across roles | Requires training time that short tenure does not allow |
| Dynamic | Allocates against live conditions | Cannot be run manually, since it requires a system at the point of work |
| On-demand | Covers seasonal peaks | Solves availability and worsens cost per unit |
Read against a shortage, three of the four break down.
Fixed labour is the most exposed. Specialisation concentrates knowledge in individuals, so every departure removes a capability. Cross-training improves flexibility but takes time that an operation with short tenure does not have. On-demand labour solves availability and worsens cost, which is the trade every peak season forces.
Dynamic labour raises output without raising cost, and it does not depend on tenure. It is also the one model of the four that cannot run on a spreadsheet. Allocating against live conditions requires a system operating at the point of work rather than upstream of it.
The same analysis is clear about where cost accumulates. It attributes inflated logistics expense to the absence of a performance-driven culture, poorly designed layouts, and unnecessary activities that leave both space and labour underutilised. It also notes that warehouse automation workflows mostly remain incomplete, and that bottom-up decisions by operational teams are insufficient without a top-down vision.
Incomplete automation describes most operations today. That argues for a layer that orchestrates what already exists, rather than another point solution that adds a further island of capability.
Orchestration is neither hiring nor automation
Three responses to the shortage are commonly proposed, and they are not interchangeable.
Hiring adds capacity at market rates. It addresses availability, raises cost per unit, and depends on a labour pool that demographics are shrinking.
Automation replaces defined tasks with equipment. It works where volumes are high and processes are stable, requires significant capital, and introduces a dependency on technical staff who are themselves scarce.
Orchestration changes what each existing worker can accomplish in a shift. It carries the operational knowledge that departures remove, allocates against live conditions rather than a stale plan, and requires no specialist operator on the floor.
The three are compatible. They are not equivalent. Only the third raises output per person without raising cost per person, and that is the specific arithmetic this shortage has created.
Five steps that make dynamic labour operable
1. Guide execution at the point of work. A mobile-first execution layer presents the next correct action to the worker facing the task. The instruction carries the operational logic that previously lived only in tenured staff. New and temporary workers reach useful output early, and the system retains the knowledge when they move on.
2. Allocate against live conditions. The system assigns work against what is happening on the floor, not against a plan written before the shift began. Dynamic labour becomes operable rather than theoretical, and mis-staffed hours fall at source.
3. Consolidate against business rules. Cargo consolidation and reconstruction run against rules in real time. Throughput per square metre rises without additional headcount and without additional floor space.
4. Coordinate yard and trip execution. The system sequences yard movements and trip plans against live status, surfacing conflicts before they reach the floor. Supervisors handle genuine exceptions instead of reconciling instructions against reality.
5. Capture compliance in the same record. Every movement recorded for execution also stands as evidence. Structured deviation records close the feedback loop that most deployments leave open. Reporting obligations are met from operational data, not from a separate reconciliation exercise requiring staff the operation does not have.
Five steps to dynamic labour orchestration
1. Guide execution at the point of work
2. Allocate against live conditions
3. Consolidate against business rules
4. Coordinate yard and trip execution
5. Capture compliance in the same record
Each step raises output per person. None requires additional headcount.
Three layers, one data flow
The execution layer. MIXMOVE HUB OS orchestrates physical work at the hub: receiving, consolidation, cross-docking, yard movement, and dispatch. Maximum throughput. From node to network.
The orchestration layer. MIXMOVE HUB OS applies business rules across sites, so decisions at one node account for conditions across the network rather than at the dock alone.
The intelligence layer. MIXMOVE DI converts the execution record into audit-grade reporting for CSRD and ETS2 obligations, with Scope 3 reports structured to ISO 14083 methodology. Network intelligence. Audit-proof.
The three layers share one data flow. Compliance evidence becomes a by-product of execution rather than a separate workstream needing separate staff. That matters considerably when the staff are not available.
The evidence
Third-party research establishes the direction, and it points away from headcount.
McKinsey documents a logistics operator that found redundant scanning and duplicated manual data entry between collection and processing teams. Integrating the systems and removing the unnecessary steps cut processing time by 10%. A separate operator applying daily route optimisation reduced driver travel time by 15%.
Neither operator changed its workforce. Both changed the coordination burden placed on it.
Deloitte adds a finding that should govern implementation. In related research with 100 C-suite leaders, 59% took a technology-focused approach to AI. Those doing so were 1.6 times more likely to fall short of expected returns than those taking a human-centric approach. Deloitte concludes that the organisations that succeed are not those automating fastest, but those channelling efficiency into reinvestment and into work where humans create irreplaceable value.
That is the difference between a system that displaces the worker and a system that carries the knowledge the worker needs. The second returns more.
MIXMOVE results follow the same pattern. Across 35+ distribution companies operating in 20+ countries, MIXMOVE HUB OS has delivered up to 130% more warehouse hub throughput, up to 58% labour cost savings, and up to 80% fewer errors.
Deployments have also delivered up to 50% less warehouse space and up to 15% more billable output, captured between 8% and 15% more billable revenue, improved fill rates by 10% to 20%, and reduced dwell time by 40%. Existing teams delivered every one of those outcomes.
The longest-running deployment is at 3M in EMEA, now in its tenth year of collaboration, where the operation reports a 90% truck fill rate alongside the following result:
“By using the MIXMOVE software, 3M managed to reduce transport costs by 35% and CO₂ emissions by 50%.”
— Patrick Van De Vyver, Former Head of EMEA Logistics Operations, 3M
Where MIXMOVE fits
MIXMOVE is an Intelligent Logistics Orchestration Platform. It connects physical execution to network-level decisions and audit-grade compliance in a single data flow, built into that flow rather than bolted onto it.
It runs alongside an existing TMS, WMS, or ERP as an orchestration layer, or as a standalone platform where no adequate layer exists. Both routes are supported, and the choice belongs to the operation rather than to the software.
One design principle governs the rest. Operational failure is never a failure of the person on the floor. It is a failure of the system that left them without the information needed to act correctly.
Behind every missed slot and every reprocessed pallet is a supervisor absorbing the cost of a decision the system should have made simpler, and a worker deciding whether this job is worth keeping. Orchestration removes that burden rather than redistributing it. Retention improves for the same reason throughput does.
The argument
Labour costs rose 40% while output per person rose 15%, and that arithmetic does not correct itself. Throughput per person is decided at the point of execution, not in the hiring plan. Every quarter that layer stays manual, the gap compounds.
Book a MIXMOVE HUB OS walkthrough with your own throughput figures.
Or start with the MIXMOVE HUB OS platform overview for the full labour cost breakdown, and the 3M EMEA deployment story for ten years of measured results.
