Oleg Sargu
FivePlus Dispatch Strategy Engineering • 2025

Abstract

Flatbed carriers incur measurable costs before and after every revenue event from fuel burned in repositioning to staging, securement, unloading, and dispatcher coordination. The associated expenditures remain invisible when companies use traditional rate-per-mile metrics of profitability, but they can be formalized as Pre-Revenue Utilization Costs (PRUC)—the monetary expression of the Activation Tax a fleet pays to convert and close a dispatch cycle. Calculations of Pre-Revenue Utilization Costs integrate principles from operations research, lean logistics, and cost economics to quantify how activation frequency, dwell, and cognitive overhead erode margin. They model activation efficiency as the dominant driver of profitability.

Takeaway: Fewer, smarter activations create more predictable profit.

1. Why Pre-Revenue Costs Matter More Than Ever

It is a cold October morning in 2025. A driver backs into a steel mill for what should be a quick pickup. Two hours later—paperwork incomplete, crane occupied, HOS clock ticking, fuel idling at $4 per hour—he is still waiting. The 90-mile deadhead that brought him there has already cost $150. In operating a flatbed, this is not an anomaly. It is the game.

Profitability in open-deck freight is dictated not by nominal rate per mile but by how efficiently fleets convert assignments into revenues and return their assets to availability. Beginning at dispatch and ending only when the truck is emptied and released, each activation consumes fuel, labor, and administrative effort. Yet most accounting systems treat this entire interval as operational background noise.

Market Context. ATRI (2025) lists average operating cost at $2.26 per mile and non-fuel expenses at +3.6% YoY. Flatbed deadhead averages 18–22% of total miles—nearly double dry-van norms—with securement + dwell time at 2.5–4 hours per load and cancellation volatility for spot markets ≈ 10%. Field data show OTR operations closer to 7–10% deadhead, while high-RPM short-haul fleets reach roughly twice that share; thus, Pre-Revenue Utilization Costs (PRUC) impose a rising burden in terms of frequency and higher nonproductive mileage.

Table 1. Market Context

Metric	2025 Benchmark (Flatbed Premium)	Interpretation
Cost per Mile	≈ $2.26 (non-fuel +3.6%)	Each mile must outperform cost baseline
Deadhead Share	18–22% (vs 7–10% OTR)	One in five miles earns nothing
Securement & Dwell	2.5–4 h avg	≈ 2 × van wait times
Load Cancellations	8–10%	A cancellation can erase a day’s profit

Note. Adapted from An Analysis of the Operational Costs of Trucking, by ATRI, 2025, and from Spot Market Volatility Report, by DAT Freight & Analytics, 2025.

Fleets often counter by increasing load count—for example, by making six short runs instead of three long ones. They thereby maintain the appearance of productivity but double the number of Activation Tax events. Each new cycle restarts the sequence of deadhead, dwell, and dispatcher coordination.

Every load consists of two economic segments, as seen in Table 2.

Table 2. Flatbed Loads: Economic Segments

Segment	What Happens	Revenue	Economic Meaning
A: Activation	DH travel, check-in, tarping, staging, waiting	No	Pure cost
B: Execution	Loaded truck rolls toward delivery	Yes	Value creation

Because activation repeats with every load, more load cycles per week will mean more drag on revenue activation. This dynamic means the first part of every dispatch—the activation phase—is inherently unprofitable.

2. Why Pre-Revenue Utilization Cost Remained Unseen

Every flatbed load begins with a loss. Before a single revenue mile is driven, the fleet has already spent money on deadhead miles and staging, through administrative tasks and paperwork delays, tarping and securement time to idle fuel consumption and driver hours. It is because none of these appear in standard KPIs for finance and dispatch that PRUC is largely invisible. It is concealed in industry metrics.

The reason is systemic masking, not analytical neglect. Traditional pay models, accounting conventions, and KPIs absorb the effect into other aggregates and thereby hide the true economic loss.

2.1 Accounting and Payroll Masking

Percentage-of-gross pay hides dwell time by embedding it in the driver’s share of revenue. No hourly wage appears on the ledger, so idle hours appear to bear no cost. In reality, they still consume driver HOS, fuel, and opportunity value.

Daily-rate or salary models flatten waiting time into a fixed expense and thereby erase the marginal cost of activation delay. The fleet avoids line-item volatility but sacrifices measurable efficiency.

2.2 Metric Aggregation and the RPM Fallacy

Rate per mile (RPM) absorbs deadhead but ignores time as it averages total revenue over all miles. Managers assume their problems are solved once empty miles are normalized, yet dwell hours remain uncounted.

Cost per mile (CPM) inherits the same blindness by pricing movement, not readiness. Fixed-cost dominance further dilutes the visibility of dwell losses.

2.3 Resource Abundance Bias (Short-Trip Myopia)

Short-haul operations treat activation friction as trivial because resources appear abundant. The resources in question are driver hours, equipment, and distance.

Given the apparent abundance, 150-mile trip can afford two hours of dwell without jeopardizing HOS compliance. In long-haul work, however, the same degree of inefficiency would threaten an entire dispatch cycle. Perceived slack turns genuine waste into so-called “normal variability.”

2.4 Managerial Heuristics and Misaligned KPIs

In dispatch and operations, rewards are for activity but not for efficiency.

Such KPIs as loads booked per day or gross per truck favor cycle count over throughput quality. Meanwhile, PRUC accumulates silently inside the activity bias according to which more loads can create the illusion of productivity even though they multiply non-revenue hours.

2.5 Structural Data Fragmentation

The data required to expose PRUC rarely coexist in the same system. While ELD systems record dwell time and idling, it is TMS and accounting systems that track cost and revenue.

Because the datasets are siloed, hardly anyone joins idle hours with cost rates into a unified efficiency metric. What cannot be queried cannot be managed.

2.6 Cultural Normalization

Trucking culture has institutionalized waiting as background noise. Drivers describe “a day at the mill” the way sailors describe a change in the weather—as if it were inevitable. As long as RPM covers fuel and pay, the latent inefficiency escapes scrutiny. However, PRUC reframes the day at the mill as a measurable form of waste.

Table 3 summarizes the mechanisms that mask PRUC. They are summed up as hidden cost effects.

Table 3. Summary of Masking Mechanisms

Mechanism	How It Masks PRUC	Hidden Cost Effect
Percentage of gross pay	No explicit wage for dwell	Lower driver ROI, higher churn
RPM / CPM metrics	Normalizes miles, ignores time	Dwell invisible
Short-haul resource abundance	Illusion of hours left over	Inefficient use of HOS
Dispatcher KPIs	Focus on loads booked	More activations, lower yield
Data silos	Segregate ELD from TMS	Costs not captured
Industry culture	Assumes waiting is normal	Systemic complacency

As Table 3 shows, other metrics redistribute and obscure PRUC without removing it. The PRUC framework isolates the hidden layer and converts intuition into quantifiable loss.

3. Model Definition and Calculation

With PRUC it becomes possible to measure all resources consumed across a load’s full activation envelope, from dispatch acceptance through post-delivery release. It is calculated as follows:

PRUC=DHmVarCPMempty+tdhCdriverhr+tdw × Cidlehr+ Oadmin

where

• DHm = deadhead miles
• tdh = deadhead time
• tdw = combined dwell (pickup + delivery)
• VarCPMempty = variable cost per empty mile
• Cdriver/hr, Cidle/hr = hourly driver and idling costs
• Oadmin = admin overhead per load

Equation terms are explained in Table 4. Activation intensity rises with securement complexity, weather exposure, and infrastructure dependence. Average flatbed PRUC, ranging from $150 to $180 per load, is magnified by the Flatbed Adjustment Coefficient (FAC) which accounts for securement and weather, as follows:

PRUCflatbed=PRUC×FAC,FAC∈[1.15,1.40].

While PRUC traditionally reflects pickup activity, its logic applies to post-delivery operations as well. Unloading, dock congestion, inspections, and paperwork constitute exit-activation dwell in the form of delays to assets’ re-entry into the revenue stream. Combining both boundaries yields the Total Load Utilization Cost (TLUC):

TLUC=PRUCpickup+PRUCdelivery.

Delivery dwell frequently adds 20–30 percent to total activation time. This is particularly clear where cranes, escorts, or staging queues are involved. PRUC-in and PRUC-out together define the activation envelope—the full frictional boundary of a load’s life cycle.

For example, in a 2025 flatbed record, deadhead amounted to 90 miles, varCPM_empty to $1.07 (≈ 90% of the $1.19 loaded rate), and pre-load time to 2.5 hrs. @ $29/hr. (driver + idle fuel). As a result, PRUC was calculated as ≈ $96 (miles) + $73 (time), a total of $169 per activation.

That’s $169 spent before the first invoiceable mile. Three long hauls in a week cost $507. Six short runs cost $1,014. That’s the hidden spread between stability and burnout.

Table 4. Glossary of Variables

Symbol	Definition	Typical Range / Unit
DHm	Deadhead miles between loads	50–150 mi
VarCPMempty	Variation in cost per empty mile (fuel + wear)	$0.95–$1.10
tdh	Time spent driving while unloaded	1–3 h
tdw	Pickup + delivery dwell time	2–4 h
Cdriver/hr	Driver hourly value	$25–$30
Cidle/hr	Idle fuel burn cost	$4–$6
FAC	Flatbed Adjustment Coefficient (securement intensity)	1.15–1.40
TLUC	Total Load Utilization Cost (PRUC in + out)	< 7% of gross target
RAR	Revenue Activation Risk = PRUC × Volatility × Human factor	Risk index (no unit)

4. Flatbed Activation Intensity: Amplifiers and Economic Interpretation

4.1 Amplifiers

Amplifiers that raise the activation intensity include securement labor, weather exposure, and infrastructure dependency. Rain or winter conditions can double tarping time, and oversize permits add one to two hours. Due to these factors, identical rates can yield unequal effective margins.

4.2 Economic Interpretation

Economically, these amplifiers of intensity can be interpreted in five ways. When it comes to operations research, the issue is flow congestion, while in terms of lean logistics the issue is waste monetization. In microeconomic terms, the effects can be measured as diminishing returns. In transaction-cost economics the issue is coordination friction, and when it comes to strategic flexibility there is a question of the real options view.

In operations research, flow congestion is a measurable factor in flatbed activation intensity because each activation lengthens system cycle time without added throughput; PRUC measures that delay in financial terms. This measurement acknowledges the signature of Little’s Law. For lean logistics, waste is to be monetized where deadhead + dwell = waiting + motion + overprocessing waste. Using PRUC it is possible to assign explicit costs to these inefficiencies, and to transform the qualitative fact of waste into a quantitative variable measuring loss. As for the microeconomic impact of diminishing returns, drivers’ hours of service and daily freight windows are finite inputs; when activation frequency rises, each additional cycle yields a smaller marginal output, and that decrease in output manifests as declining profit per load. In PRUC, therefore, the monetary form of diminishing productivity in dispatch operations is expressed. Transaction cost economics, meanwhile, register the coordination friction of shippers with unpredictable dwell or scheduling who then externalize coordination costs onto carriers. Using PRUC it is possible to quantify this friction and thereby identify customer relationships that erode margin. Even acceptable nominal rates are measurable. With a real options view of strategic flexibility, time spent in activation reduces strategic choice. Because delays eliminate the option to pursue higher-yield freight later in the cycle, PRUC monetizes the lost option value. This amounts to the economic cost of constrained flexibility.

Collectively, these perspectives demonstrate that activation inefficiency converts operational slack into measurable financial drag.

5. Dispatchers’ Cognitive Bandwidth and Reliability

Every activation consumes cognitive energy. A dispatcher managing multiple concurrent loads must coordinate more ELD alerts, ETAs, phone calls, problem resolution, and schedule deviation. Because these tasks expand exponentially with activation count, they create attention overhead—a legitimate economic input which cannot be reduced to an incidental task.

In PRUC, dispatcher bandwidth functions as an operational input comparable to fuel or labor: It is finite, quantifiable, and expensive when saturated. As activation frequency rises, coordination overhead shows faster growth than output does, and the situation reproduces the same diminishing-return geometry seen in capital utilization curves.

When bandwidth becomes saturated, service quality and reliability decline. This can mean missed check-ins, incomplete updates, and eventually broker penalties, FreightGuard incidents, or Do-Not-Use status. Such reputation losses compound PRUC into long-term revenue risk, linking direct costs and Revenue Activation Risk (RAR).

Quantifying the Coordination Layer

Dispatcher oversight adds a modest but persistent overhead to every activation. Field observations suggest that the administrative coordination of check-ins, broker communication, ETA updates, and issue resolution may raise activation costs by $20–$40 per load. This scalar can be expressed within the PRUC equation using the administrative constant Oa₋admin:

PRUC=DmCmile+TdwellChour+TdeadheadChour+Oadmin

where

Oa₋admin represents the dispatcher coordination expense. For multi-dispatcher fleets, multiplying this term by the average number of loads per dispatcher per week provides a first-order estimate of cognitive and labor saturation costs.

Table 5. Strategic Differentiation and Entry Barrier

Strategy	Loads / Week	PRUC Events	Weekly Activation Cost	Revenue Stability
Gross Focus (Long Haul)	3	3	≈ $507	High
RPM Focus (Short Haul)	6	6	≈ $1 014	Low

Flatbed cancellations for spot marker ≈ 8–10%; each extra activation doubles exposure to loss and fatigue.

As demonstrated in Table 5, three loads per week constitute the structural entry cost for viable asset use. Beyond this threshold, incremental activations lead to rapid accumulations in deadhead, dwell, delivery delays, and administrative overhead. Hence, if pickup and delivery dwell are included, the fifth and sixth loads can consume $600–$1 000 in activation expenses and therefore often surpass small fleets’ weekly profits.

6. Measurement and Implementation

6.1 Data Acquisition

Many small or legacy fleets still operate without full ELD–TMS integration. In such environments, PRUC remains partially invisible because dwell and deadhead data are fragmented across paper logs, messaging apps, and fuel receipts. Yet even basic proxies—arrival and departure timestamps, odometer notes, driver check-in calls—can, with reasonable accuracy, make activation windows measurable. Establishing visibility at any resolution is the first step; precision can evolve once digital systems mature. After all, PRUC is a lens and not a software requirement, and ELD and TMS can be integrated to capture deadhead, pickup dwell, and delivery dwell via geo-fencing.

6.2 Benchmarks

Effective discipline may result from implementation of PRUC, but effective implementation depends on measurable thresholds by which to establish how abstract cost dynamics translate into field diagnostics. Table 6 defines reference benchmarks derived from 2025 flatbed averages and internal calibration models.

Table 6. Reference Benchmarks for PRUC

KPI	Target	Comment
TLUC (Total Load Utilization Cost)	< 7% of gross	Healthy operation zone
Activations per Truck per Week	≤ 3–4	Beyond this, diminishing returns
PRUC per Load (Flatbed Avg.)	$150–$180	Pickup & delivery dwell included
Dwell per Load (Pickup + Delivery)	≤ 4 hrs.	Signals customer efficiency
Dispatcher Load Bandwidth	4–6 active loads	Beyond this, cognitive risk

These values serve as markers for orientation, not as absolutes. Each fleet should calibrate PRUC sensitivity to its own operating density, lane design, and driver pay structure.

6.3 Managerial Applications

1. Pricing: It is possible to include both pickup and delivery dwell in lane bids.
2. Customer selection: Partners can be scored on reliability and securement time.
3. Labor allocation: Dispatcher headcount can be balanced with activation load.
4. Continuous improvement: Variance in PRUC measurements can be treated as a lean waste indicator.

7. Mitigation Playbook

Reducing PRUC requires both tactical and behavioral alignment. Table 7 summarizes core levers and their expected directional effects in flatbed operations.

Table 7. Actions and Outcomes of Implementing PRUC

Lever	Action	Outcome
Customer Scoring	Monitor dwell and securement by facility; penalize chronic delays	Predictability ↑ ≈ 25%
Lane Design	Build weeks with fewer stops; link OTR & backhauls to reduce activation count	Activation events ↓ ≈ 40%
Rate Terms	Negotiate paid dwell after 90 min ($50 / hour flatbed premium)	Convert delay into revenue stream
Dispatcher KPIs	Base a bonus on PRUC efficiency (< 6% gross)	Aligns team behavior
Automation	Set ELD dwell alerts and smart ETA dashboards	Real-time PRUC control
Training & Feedback	Integrate PRUC reviews into weekly ops meetings	Sustains awareness and adaptive behavior

The golden rule in this playbook is a matter of valuing fewer starts, hence smarter cycles. Following these principles, fleets convert operational friction into competitive advantage because they treat PRUC as a controllable variable and not as an unavoidable cost.

8. Conclusions and Path Forward

PRUC translates pre-revenue inefficiency into a financial metric that connects operational behavior to profit stability. By identifying activation frequency and duration as the primary drivers of margin erosion, the framework redefines dispatch strategy based on load cycle optimization rather than rate maximization.

High-cycle RPM approaches generate diminishing returns due to excessive activation costs and the strains they place on dispatchers’ bandwidth. Gross-focused strategies anchored in PRUC discipline produce lower volatility, higher driver satisfaction, and greater operational resilience.

By integrating pickup and delivery dwell, dispatcher overhead, and cognitive limits, PRUC describes the full activation envelope of flatbed economics. Each activation consumes scarce capital, labor, and attention; beyond roughly three cycles per week, marginal cost rises in nonlinear fashion. Measurements of PRUC therefore represent both load-specific overhead and human-capacity constraints. Managing PRUC transforms dispatch from reactive load booking in favor of engineered revenue activation.

Future work will extend the model through revenue activation risk (RAR) to link activation exposure with probability-weighted revenue outcomes. Together, PRUC and RAR form the Load Cycle Profitability FrameworkTM—a disciplined approach to understanding how operational rhythm, not rate alone, determines financial performance in contemporary trucking.

RAR=PRUC×(1+Volatilitymarket)

References

American Transport Research Institute. (2025). An analysis of the operational costs of trucking: 2025 Update. https://truckingresearch.org/2025/07/an-analysis-of-the-operational-costs-of-trucking-2025-update/

DAT Freight & Analytics (2025). Spot Market Volatility Report. Portland, OR.

Lean Enterprise Institute (2023). Lean Lexicon, 8th Edition. Cambridge, MA.

Williamson, O. E. (1985). The Economic Institutions of Capitalism. Free Press.

U.S. Department of Transportation (1997). Brake Wear and Service Life Analysis (FHWA-RD-97-058). Washington, DC.

Appendix
Calibration and Robustness, Simplified: Three vs Six Loads

A1. Why We Need This Appendix

This appendix tests whether the logic of PRUC (based on activation cost per load) holds under realistic numbers. It compares gross-focused OTR with short-haul–focused RPM.

Type	Description	Weekly Loads	Weekly Miles	Deadhead (%)	Total CPM	Remarks
OTR (Gross Focus)	3 long hauls	3	4 500	≈ 8%	$1.90	Steady, predictable
RPM (Short-Haul Focus)	6 short hauls	6	1 000	≈ 18%	$3.45	High frequency, high overhead

Both produce similar driver utilization, but the short-haul strategy doubles the number of activations, meaning twice as much activation tax.

A2. What PRUC Actually Covers

To  be clear, PRUC already includes the following:

• Deadhead miles (fuel + wear + driver time) between loads
• Dwell time (waiting, loading, unloading, paperwork)
• Dispatcher / coordination effort

So, PRUC = deadhead cost + dwell cost + admin overhead.

Deadhead can be presented separately, but only to show its share of PRUC—not to add it again. In short, deadhead cost ⊂ PRUC (not ⊕ PRUC). Basically, PRUC as the total activation penalty to get a truck from dispatched to loaded and rolling and then back to empty and available.

A3. How We Calibrate PRUC for Both Models

Using the activation envelope calculation (pickup + delivery dwell),

PRUCperactivation=$211 ($169 × 1.25 = $211)

The $211 value already represents deadhead + dwell + admin, so it is never necessary to list them separately again.

Hence the following comparison of OTR and RPM orientations:

Metric	OTR	RPM
Loads per Week	3	6
PRUC per Activation	$211	$211
Weekly PRUC	3 × $211 = $633	6 × $211 = $1 266
Share of Gross	$633 / $9 990 = 6.3%	$1 266 / $4 500 = 28.1%

This alone explains most of the performance gap between the two orientations. As the table shows, rigid adherence to an RPM strategy costs roughly $633 more, every week, just in activation friction.

A4. How Cost per Mile Magnifies It

The difference in cost per mile explains the greater short-haul compromise associated with each PRUC event.

• OTR: $1.90 / mi over 4 500 mi
• RPM: $3.45 / mi over 1 000 mi

Because the truck and driver exist in either scenario, the short-haul operation spreads fixed costs across fewer miles and thereby inflates its per-mile baseline. Consequently, even before PRUC is applied, the RPM fleet imagined here starts out heavier on a  per-mile basis.

With PRUC, the calculation is as follows:

Orientation	Base Gross	Miles per Week	Base Cost	PRUC	Total Cost	Net Result per Week
OTR	$9 990	4500	$8 550	$633	$9 183	$807 (≈ 8%)
RPM	$4 500	1000	$3 450	$1 266	$4 716	−$216 (loss)

These are not literal P&Ls, but they demonstrate the directional effect of PRUC. Even though the RPM case shows high gross per mile, the extra activations consume the margin.

A5. Sensitivity (Simple and Transparent)

Next, here is a test by which to prove the outcome changes if conditions vary.

Scenario	Change	OTR Net	RPM Net	Remarks
Base Case	As above	$ + 807	$ − 216	Baseline gap = $1 023 per truck per week
RPM Under 10% Faster Turnarounds	PRUC ↓ to $190	+807	+ (−46)	Still worse than OTR
OTR Under One Extra Load (Four in Total)	PRUC ↑ to 4 × $211 = $844	+643	−216	Gap narrows but remains
RPM Reduces Dwell by 25%	PRUC ↓ to $158 per load	+807	++198	Finally breaks even (unrealistic in flatbed world)

As these results show, the break-even point is only achievable if RPM activations become as smooth and delay-free as OTR, but that is a condition which contradicts flatbed reality. Hence the conclusion is robust.

A6. The Real-World Amplifiers

Even the numbers above are conservative. In practice, the following values are more likely:

• Deadhead% for RPM fluctuates with market availability, often > 20%
• Maintenance CPM runs 15¢ higher in short haul (stop-start) urban driving
• Delivery-side dwell is harder to control during multi-stop weeks
• Dispatcher bandwidth hits a ceiling; reliability scores drop
• RAR factor: When volatility spikes, each activation’s failure probability compounds, as seen in the following equation

RAR=PRUCVolatilitymarketHumanfactor

Hence, instead of closing, the gap widens.

A7. Summary in Plain Language

To sum up in plain language, there are five key points to consider in order, as follows:

1. PRUC already includes deadhead. We don’t add it twice.
2. With only three activations, OTR pays ≈ $630 per week in activation friction (≈ 6% of gross).
3. With six activations, RPM pays ≈ $1 260 (≈ 28% of gross).
4. High CPM and short mileage amplify that penalty and wipe out RPM’s on-paper advantage.
5. Even optimistic improvements can’t fully erase the gap—so, activation frequency governs profitability and rate per mile does not.

The fundamental takeaway is that every extra load adds cost more quickly than it adds profit. And the math presented here is conservative; in real operations, the difference is even bigger.