How 3PL CFOs Can Quantify the ROI of Dispatch Automation

A CFO at a North American 3PL reviews the business case for dispatch automation submitted by the operations team. The deck is confident: “X% cost reduction,” “Y-month payback,” “$Z million in annual savings.”

The CFO has seen versions of this deck for the last decade across TMS upgrades, routing tools, warehouse systems, driver apps, and in-house planning projects. The deck is often right that the operation needs better tools. It is often weaker on the financial mechanics.

For 3PL CFOs, dispatch automation is not simply a technology purchase. It is a capital allocation decision with multi-year P&L implications. The investment case is structurally different from typical SaaS because dispatch automation changes how orders are assigned, routes are built, vehicles are utilised, SLAs are protected, and exceptions are managed.

In practical terms, 3PL dispatch automation ROI is calculated by comparing software and implementation costs against savings from planner productivity, route-mile reduction, better fleet utilisation, fewer SLA breaches, lower exception-handling cost, and deferred fleet capex.

Dispatch automation ROI = ****(Annual savings from labour + transportation + exception/penalty reduction + capex deferral value ? annual software and implementation cost) ÷ total investment cost**

The CFOs who build rigorous financial frameworks allocate capital better than CFOs who treat dispatch automation as a procurement decision. The framework matters as much as the vendor selection.

This article sets out a financial framework for North American 3PL CFOs evaluating 3PL dispatch automation ROI — covering cost categories, payback mechanics, IRR thresholds, sensitivity analysis, and the governance touchpoints that protect capital allocation decisions over multi-year horizons.

According to the Council of Supply Chain Management Professionals (CSCMP) State of Logistics Report, US business logistics costs run in the trillions annually, with transportation as the dominant segment. The operational levers affected by dispatch automation are therefore first-order P&L items, not marginal optimisation plays.

Why Dispatch Automation Is Structurally Different from Typical SaaS

Most SaaS purchases at 3PLs follow a relatively clean financial pattern: subscription cost in opex, modest implementation cost, productivity benefits concentrated in labour, and payback over 12–24 months. The financial framework is familiar and variance across implementations is relatively narrow.

Dispatch automation is different.

It affects five P&L line items simultaneously, with different timing profiles and different sensitivity to operational assumptions. Route optimisation influences fuel and mileage. Dispatch automation changes planner capacity. Capacity-aware allocation affects vehicle utilisation. Real-time exception handling supports on-time delivery and SLA adherence. Better asset productivity can delay fleet expansion.

This is why CFOs should distinguish dispatch automation from generic routing or planning tools. A modern auto dispatch logistics software layer covers order allocation, route optimisation, dispatcher workflows, driver assignment, live tracking, customer communication, exception management, and performance reporting.

The total impact compounds across these lines in ways that vendor-supplied projections often simplify. CFOs who model only labour savings or only variable transportation cost miss the actual financial substance.

The framework starts with understanding which P&L lines the investment touches — and how each one converts operational improvement into financial return.

Also Read: Automated Dispatch Software: Complete 2026 Guide

The Five Cost Categories Dispatch Automation Affects

Dispatch automation should be modelled as a multi-line P&L initiative. The table below maps the five cost categories to the operational KPIs CFOs should require from operations, transformation, and vendor teams.

Cost category	Operational lever	KPIs to baseline	Financial question for the CFO
Labour cost	Planner productivity and dispatch automation	Orders per planner, routes per dispatcher, manual interventions, overtime hours	Does automation reduce cost, absorb growth, or redeploy planning capacity?
Variable transportation cost	Route optimisation and mileage reduction	Miles per route, cost per mile, fuel per stop, empty miles, route adherence	How much of the mileage saving is measurable, repeatable, and attributable?
Fixed cost absorption	Fleet and capacity utilisation	Stops per vehicle, vehicle fill, driver hours, route density, planned versus actual capacity	Can the existing fleet handle more volume without proportional cost growth?
Exception and penalty cost	SLA adherence and proactive exception management	On-time delivery rate, SLA breach rate, failed deliveries, claims, customer credits	Which penalties and credits reduce, and when do they hit the P&L?
Capital intensity	Fleet capex deferral and asset efficiency	Fleet utilisation, peak capacity, replacement schedule, growth forecast	Can improved asset productivity delay or avoid marginal fleet investment?

Labour cost

Planner FTEs, dispatcher overtime, manual exception handling, weekend coverage, and customer escalation support all sit in this category. Dispatch labour is a meaningful cost line for many 3PL operations, particularly where routing is still handled through spreadsheets, static route templates, or manual dispatcher judgement.

Automation reduces this category, but rarely eliminates it. Exception management, customer-facing planning, supervisor intervention, and operational oversight remain. The honest financial framing is that labour reduction is real but often shows up as capacity leverage rather than pure headcount reduction: more orders, routes, facilities, or customers managed per planner.

For CFOs, the core question is not “How many dispatchers disappear?” It is: “How much more volume can the same planning team manage while maintaining service quality?”

Variable transportation cost

Fuel, mileage, tolls, subcontractor miles, driver hours, and route-level vehicle cost are usually the largest financial lever dispatch automation touches.

This is where route optimisation has direct P&L impact. Better sequencing, territory balancing, time-window adherence, capacity-aware vehicle selection, and reduced empty miles can lower miles per delivery and cost per route. CFOs evaluating these assumptions should understand how AI route optimization works, because the savings depend on constraints, data quality, service windows, vehicle capacity, and real-world route execution — not just algorithmic routing in isolation.

According to DAT Freight & Analytics trucking market data, US transportation cost per mile varies materially by lane and segment, making per-mile improvements directly relevant to margin.

For CFO modelling, the key is attribution. A route-mile reduction assumption should be based on baseline miles, planned versus actual route variance, delivery density, vehicle capacity constraints, and service-time assumptions — not a generic savings percentage.

Fixed cost absorption

Better capacity utilisation spreads fixed cost across more revenue. Existing vehicles handle more stops. Existing depots process more outbound routes. Existing planners manage higher volume. Growth can occur without proportional increases in fleet, labour, or facility overhead.

This is often one of the largest financial impacts of dispatch automation and one of the hardest to quantify upfront. It depends on growth trajectory, peak-day constraints, customer mix, route density, and capacity headroom.

CFOs should model this separately from variable transportation cost. A route-mile saving improves the current cost base. Fixed cost absorption changes the economics of future growth.

The fleet strategy question also matters. Operators comparing owned, subcontracted, and hybrid capacity should account for the economics of in-house fleet vs outsourced fleet management, because dispatch automation can change when external capacity is needed and how efficiently owned assets are used.

Exception and penalty cost

SLA misses create direct and indirect cost: contractual penalties, customer credits, redelivery cost, claims processing, escalation labour, and margin leakage from customer dissatisfaction.

This category often runs at meaningful single-digit percentages of revenue and is underweighted in financial models. Automated dispatch can reduce this cost through capacity-aware order allocation, realistic ETAs, dynamic re-routing, earlier exception detection, and proactive customer communication.

For CFOs, the right measure is not only on-time delivery. It is the cost of avoidable service failure: penalties, credits, repeat delivery attempts, claims, and escalation hours. Dispatch automation should improve the organisation’s ability to manage delivery exceptions before they become service failures, and it should reduce the downstream cost of failed deliveries.

Capital intensity

Better routing means each vehicle does more productive work. Marginal fleet expansion can be delayed or avoided. Replacement timing can change. Subcontracted capacity may be used more selectively.

Capex deferral has real NPV impact, particularly for asset-heavy 3PLs. This is the longest-term financial lever and often one of the largest in NPV terms, but it requires explicit modelling of growth trajectory, fleet replacement schedules, financing assumptions, and peak capacity.

CFOs should not bury this inside a generic “efficiency” line. Fleet capex deferral needs its own scenario model.

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Key Numbers for 3PL Dispatch Automation ROI

CFOs should avoid importing warehouse automation benchmarks directly into dispatch automation models, but adjacent automation data is useful for setting expectations around payback discipline, adoption risk, and capital governance.

• 12–24 months: realistic payback range for well-modelled 3PL dispatch automation implementations when the five P&L levers are measured and risk-adjusted.
• 18–36 months: broader logistics automation projects often see full ROI in this range, according to Balyo’s financial case for 3PL automation.
• 20–30%: recommended risk adjustment on the baseline business case to account for implementation, adoption, performance, and vendor risk.
• $35.9 billion to $39.5 billion: the global logistics automation market was valued at USD 35.9 billion in 2025 and expected to grow to USD 39.5 billion in 2026, according to Global Market Insights.
• 39% and 38%: in the 2025 NTT DATA Third-Party Logistics Study, 3PLs reported their greatest expected ROI from data and analytics investments would come from improved data accuracy (39%) and enhanced service-level performance (38%).

The takeaway: automation ROI is no longer evaluated only as labour arbitrage. For 3PL CFOs, the stronger business case comes from the combination of cost-to-serve reduction, better service performance, asset utilisation, and capital flexibility.

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The Financial Case Structure

A CFO-grade 3PL dispatch automation ROI model should separate investment cost, annual run-rate savings, payback, IRR, NPV, and risk-adjusted outcome.

Capex/opex profile

Most modern dispatch automation is delivered as SaaS, with predictable subscription cost in opex. Implementation services may be capitalised depending on accounting policy. Working capital impact is typically minimal.

The better comparison is not simply opex versus capex. It is the cost of automation versus the alternative operating model: continuing with manual dispatch, maintaining in-house tools, adding planning headcount, expanding fleet capacity earlier than necessary, or absorbing SLA leakage as volume grows.

A CFO should require the model to include:

• subscription cost;
• implementation and integration cost;
• internal project labour;
• training and change management;
• ongoing support and administration;
• any parallel-run cost during pilot and rollout;
• cost of maintaining legacy tools during transition.

Payback mechanics

Software cost runs as a manageable percentage of revenue for full platform deployment. Savings flow through the five cost categories at different speeds.

Labour productivity may be visible early. Route-mile reduction can be measured once live routes are compared with baseline routes. SLA improvements may take several operating cycles to stabilise. Fixed cost absorption and capex deferral usually compound over a longer horizon.

The honest payback range for well-modelled implementations is 12 to 24 months. Vendor claims of sub-12-month payback can be legitimate under specific conditions — high manual planning effort, poor route density, excess mileage, high penalty exposure, or rapid growth — but they should trigger rigorous validation rather than acceptance.

For broader 3PL automation investments, such as warehouse robotics or automated material handling, Balyo notes that most well-planned logistics automation projects see full ROI in 18–36 months. Dispatch automation can pay back faster when it directly reduces route miles, planning effort, SLA leakage, and capex pressure, but the model still needs operational proof.

IRR versus cost of capital

3PL weighted average cost of capital varies by ownership structure. PE-backed operators, family-owned logistics businesses, and public 3PLs carry different hurdle rates and return expectations.

Dispatch automation IRR typically clears the WACC threshold by a meaningful margin when operational modelling is accurate. The primary risk is not usually the theoretical adequacy of return. It is whether the operational assumptions hold after implementation.

CFO diligence should therefore focus on:

• baseline data quality;
• operational maturity;
• implementation scope;
• planner and dispatcher adoption;
• integration with TMS, WMS, OMS, ERP, telematics, driver app, and customer communication systems;
• ability to measure planned versus actual performance.

Sensitivity analysis

The most sensitive variable is the route-mile reduction assumption. A typical modelling range is conservative single digits to mid-double digits, depending on starting operational maturity.

Other sensitivity variables include:

• planner productivity improvement;
• dispatcher overtime reduction;
• fuel and cost-per-mile assumptions;
• on-time delivery and SLA breach reduction;
• vehicle utilisation improvement;
• customer growth and volume absorption;
• timing of fleet capex deferral.

CFOs should run conservative, base, and optimistic cases. The conservative case should still clear the company’s hurdle rate after risk adjustment. If the base case works but the conservative case fails, the investment may still be justified — but the governance model must be stricter.

This is also where automated route planning assumptions need to be tested carefully. Savings should be measured against baseline routes, actual driver adherence, route constraints, delivery windows, and post-implementation cost per stop — not against theoretical shortest-path outputs.

Also Read: Courier Dispatch Challenges & How to Solve Them

Risk-adjusted ROI

Four risk categories should be explicit: implementation risk, adoption risk, performance risk, and vendor risk.

CFOs should risk-adjust the baseline business case by 20–30% to produce a defensible expected-value calculation rather than a best-case projection. This does not mean assuming failure. It means protecting capital allocation from optimistic deployment timelines, overestimated adoption, incomplete integrations, and savings that cannot be cleanly attributed.

Example ROI Calculation for a 3PL Dispatch Automation Business Case

A CFO-grade model does not need to start with perfect precision. It needs a defensible structure that can be validated in pilot and recalibrated after rollout.

Assume a 3PL operation has:

• annual dispatch automation investment cost of $600,000, including subscription, implementation amortisation, support, training, and internal project labour;
• annual labour productivity value of $250,000 from higher planner capacity and lower overtime;
• annual transportation savings of $500,000 from route-mile reduction and better vehicle utilisation;
• annual exception and penalty reduction of $150,000 from fewer SLA breaches, claims, credits, and redelivery costs;
• annual capex deferral value of $300,000, based on delaying marginal fleet expansion or reducing subcontracted peak capacity.

The base annual benefit is:

$250,000 + $500,000 + $150,000 + $300,000 = $1,200,000

After subtracting the annual automation cost:

Net annual benefit = $1,200,000 ? $600,000 = $600,000

If the total first-year investment is $900,000, simple payback is:

$900,000 ÷ $600,000 = 1.5 years, or 18 months

A CFO should then apply a 20–30% risk adjustment. With a 25% haircut:

Risk-adjusted net annual benefit = $600,000 × 75% = $450,000

Risk-adjusted payback becomes:

$900,000 ÷ $450,000 = 2.0 years, or 24 months

This is the difference between a vendor-style ROI case and a CFO-grade capital allocation case. The first shows upside. The second shows whether the investment still works after implementation and adoption risk are priced in.

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Hard Savings, Soft Returns, and Where CFOs Should Draw the Line

Not every benefit belongs in the ROI numerator with the same confidence level. CFOs should separate hard savings, economically defensible soft returns, and strategic benefits.

Hard savings

Hard savings are benefits that can be tracked directly in the P&L or cash flow model. These include:

• lower planner overtime;
• reduced manual dispatch effort;
• lower cost per route;
• lower miles per delivery;
• fewer redelivery attempts;
• reduced SLA penalties and customer credits;
• lower claims processing cost;
• reduced subcontracted capacity;
• deferred vehicle or equipment capex.

These should carry the highest confidence weighting because finance can validate them against payroll, fuel, carrier invoices, penalty ledgers, claims records, and capital plans.

Economically defensible soft returns

Soft returns can still have financial value, but CFOs should require clear logic before including them in the base case. These include:

• improved customer visibility;
• fewer manual escalations;
• higher customer retention;
• faster onboarding of new accounts;
• better planner and driver experience;
• improved data accuracy;
• stronger control-tower visibility.

The 2025 NTT DATA Third-Party Logistics Study found that 3PLs expected the greatest ROI from data and analytics investments to come from improved data accuracy (39%) and enhanced service-level performance (38%). Those are not abstract benefits. They influence billing accuracy, exception cost, customer retention, and service reliability.

Strategic benefits

Strategic benefits are real, but they should usually sit outside the core ROI calculation unless they can be translated into a measurable financial driver. Examples include:

• greater ability to win complex omnichannel contracts;
• stronger differentiation in shipper RFPs;
• better network resilience during demand volatility;
• improved peak-season scalability;
• better readiness for AI-driven planning and execution.

These benefits strengthen the investment thesis, but they should not compensate for a weak hard-savings model.

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Dispatch Automation ROI vs Warehouse Automation ROI

Dispatch automation and warehouse automation both improve 3PL economics, but they do so through different operating levers.

Dimension	Dispatch automation	Warehouse automation
Primary operating domain	Transport planning, dispatch, route execution, exception handling	Picking, packing, sorting, storage, replenishment, material movement
Main ROI levers	Miles, planner productivity, vehicle utilisation, SLA adherence, capex deferral	Labour productivity, throughput, accuracy, space utilisation, safety
Typical payback logic	Often tied to route-mile reduction, cost per stop, penalties, and fleet utilisation	Often tied to labour reduction, throughput, error reduction, and equipment utilisation
Capital profile	Usually SaaS-led with implementation and integration cost	Often higher capex, though RaaS and leasing models may reduce upfront investment
Financial governance need	Route-level and SLA-level validation	Time-and-motion studies, throughput validation, accuracy measurement

Warehouse automation benchmarks are useful, but CFOs should not substitute them for dispatch-specific modelling. For example, SellersCommerce reports that AMRs in logistics can deliver payback in under 24 months and ROI above 250% in live deployments, while Open Sky Group cites Robotics-as-a-Service models that can reduce warehouse automation total cost of ownership by up to 30% and achieve AMR payback in as little as 12 months. Those numbers are relevant to automation strategy, but dispatch automation ROI must still be validated through route, dispatch, SLA, and fleet economics.

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Common Modeling Mistakes CFOs Should Avoid

Mistake 1: Counting only labour savings

Labour savings matter, but they are rarely the largest lever. A model that stops at planner FTE reduction misses route-mile savings, utilisation gains, SLA leakage reduction, and capex deferral.

Mistake 2: Treating mileage reduction as guaranteed

Route optimisation benefits depend on order density, service windows, driver adherence, traffic, vehicle capacity, territory design, and customer constraints. CFOs should require planned-versus-actual validation rather than accepting a generic mileage reduction percentage.

Mistake 3: Ignoring fixed cost absorption

If dispatch automation allows the same fleet, depots, and planning team to handle more volume, the benefit is not only current cost reduction. It is improved growth economics.

Mistake 4: Excluding penalties, credits, claims, and redeliveries

Service failures have direct financial consequences. SLA breaches, claims, customer credits, redelivery attempts, and escalation labour should be modelled explicitly.

Mistake 5: Over-crediting capex deferral too early

Capex deferral can be one of the largest NPV contributors, but it depends on growth and capacity assumptions. CFOs should separate near-term operating savings from longer-term capital benefits.

Mistake 6: Assuming perfect adoption

Dispatch automation changes planner workflows, dispatcher judgement, driver behaviour, and management routines. Adoption risk should be reflected in the 20–30% risk haircut.

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Financial Governance Touchpoints

Four governance gates protect dispatch automation capital allocation over the multi-year cycle.

Capital approval gate

The first gate establishes the baseline: current cost-to-serve, route miles, cost per mile, planner productivity, SLA breach cost, exception-handling cost, utilisation, fleet expansion plan, and implementation budget.

The approval pack should include sensitivity analysis, risk-adjusted IRR versus hurdle rate, payback period, NPV view, operating assumptions, and decision rights. This gate sets the baseline against which all future variance is measured.

Milestone-based release

Capital release should follow operational proof, not executive enthusiasm.

A practical rollout structure is:

• Phase 1: pilot or single facility. Validate baseline data, route optimisation assumptions, planner workflow, dispatch automation, driver adoption, and planned versus actual route performance.
• Phase 2: regional rollout. Validate scale economics, integration stability, exception workflows, and SLA adherence across multiple facilities or customer types.
• Phase 3: full rollout. Validate enterprise-level governance, performance reporting, and repeatability across the network.

Milestone-based release protects against the common failure mode: a platform is contracted at enterprise scale before operational assumptions have been proven.

Operational ROI validation

Quarterly review should compare actual savings against the approved model across all five cost categories.

The review should answer:

• Did route miles reduce as modelled?
• Did cost per stop and cost per route improve?
• Did planner productivity increase?
• Did on-time delivery and SLA adherence improve?
• Did exception-handling labour reduce?
• Did vehicle utilisation improve?
• Are savings visible in the P&L, or only in operational dashboards?
• Which assumptions need to be revised?

This is where operational reality meets the financial model.

Strategic review at 24 months

The 24-month review should assess compounding effects that are not always visible in the first two quarters: capex deferral, capacity headroom, growth absorption, improved customer retention, and network-level route density.

For many 3PLs, this is where the most material financial impact becomes visible. It is also where the next capital allocation cycle should be shaped: additional automation, broader AI routing, customer-specific SLA redesign, or fleet strategy changes.

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The CFO Evaluation Framework

Five questions for North American 3PL CFOs evaluating the investment case for dispatch automation:

1. Have we modelled operational impact across all five P&L line items — labour, variable transportation cost, fixed cost absorption, exception and penalty cost, and capital intensity — or are we modelling only one or two and missing the compounding effects?
2. Is our payback calculation based on validated operational assumptions and risk-adjusted by 20–30%, or are we accepting vendor-supplied best-case projections?
3. Does our IRR calculation use our actual cost of capital and clear the hurdle by a meaningful margin, with sensitivity analysis on the most movable assumptions?
4. Have we structured milestone-based capital release tied to validated operational outcomes rather than upfront commitment, with clear governance gates at pilot, regional, and full rollout?
5. Is our 24-month strategic review built into the governance framework to assess compounding effects — capex deferral, capacity utilisation, and growth absorption — that emerge only with operational maturity?

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Benefits of a CFO-Grade Dispatch Automation ROI Model

A disciplined ROI model improves more than the approval process. It changes how the business manages dispatch transformation after approval.

Better capital allocation

CFOs can compare dispatch automation against other uses of capital: warehouse automation, fleet expansion, TMS upgrades, M&A integration, or network redesign. The decision becomes a return profile, not a software preference.

Cleaner accountability

When savings are mapped to five cost categories, finance, operations, IT, and transformation teams know which outcomes they own. Route-mile savings, planner productivity, SLA adherence, and capex deferral cannot be blended into one vague “efficiency” number.

Faster variance detection

Quarterly ROI reviews expose whether the business case is underperforming because of data quality, adoption, routing constraints, customer mix, integration gaps, or unrealistic baseline assumptions.

Stronger customer economics

Dispatch automation can improve customer-level profitability by lowering cost-to-serve, reducing exception cost, and improving SLA performance. This matters in multi-client 3PL environments where revenue growth is not valuable unless it comes with margin discipline.

More resilient growth

The strongest automation cases do not only reduce today’s cost. They help the same operating platform absorb more volume without proportional increases in fleet, labour, or overhead.

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Key Features CFOs Should Look for in Dispatch Automation Platforms

CFOs do not need to evaluate every technical feature in depth, but they should understand which capabilities connect directly to ROI.

Capacity-aware order allocation

The platform should assign orders based on vehicle capacity, driver availability, service windows, geography, customer priority, and operational constraints. This supports route feasibility and reduces manual intervention.

Route optimisation with real-world constraints

The routing engine should account for delivery windows, traffic, service time, vehicle type, loading constraints, driver skills, customer rules, and route density. The financial output should be measurable in miles per route, cost per stop, and planned-versus-actual variance.

Dispatcher workflow automation

A strong system should reduce manual planning work, repetitive route adjustments, and exception triage. This is where planner productivity and overtime savings become visible.

Live execution visibility

Dispatch automation should connect planning to real-time execution. GPS tracking, driver app updates, route adherence, ETA management, and exception alerts are essential for SLA control.

Exception management

The system should detect delays, failed attempts, route deviations, capacity issues, and customer-impacting events early enough for intervention. This connects directly to penalty avoidance, customer credits, claims, and redelivery cost.

Performance reporting

CFOs should require dashboards that connect operational KPIs to financial outcomes: cost per route, cost per stop, miles per delivery, route adherence, SLA breach rate, claims cost, overtime, and utilisation.

Integration readiness

Dispatch automation must integrate with TMS, WMS, OMS, ERP, telematics, driver apps, customer communication tools, and billing systems. Without integration, ROI attribution becomes weaker and manual work persists.

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Why Choose Locus for Dispatch Automation ROI

At Locus, dispatch transformation is not framed as generic efficiency. It is validated through route-level, dispatcher-level, SLA-level, and P&L-level evidence.

Locus connects routing intelligence, dispatch workflows, driver execution, customer communication, exception management, and control-tower visibility into one operating model. That matters because dispatch automation ROI is not created at the planning screen alone. It is created when routes are executable, drivers follow them, exceptions are managed in real time, SLAs are protected, and performance data can be measured against the financial model the CFO approved.

For 3PLs, the core value is not simply reducing planning effort. It is building an operating layer that supports lower cost-to-serve, better utilisation, stronger SLA performance, more disciplined capacity decisions, and more reliable growth absorption.

Conclusion: Treat Dispatch Automation as Capital Strategy, Not IT Spend

Dispatch automation is a multi-year capital allocation decision affecting five P&L line items with different timing profiles. CFOs who treat it as a SaaS procurement decision allocate capital sub-optimally. CFOs who build rigorous financial frameworks — modelling all five cost categories, risk-adjusting projections, structuring milestone-based release, and reviewing compounding effects at 24 months — make decisions that compound across planning cycles.

The strategic question is not only which dispatch automation vendor to choose. It is:

Do we have a financial framework rigorous enough to allocate capital well — and to validate that the allocation worked over the multi-year horizon the investment actually plays out across?