Wert-Berater, Inc. is an independent truck service center feasibility study consultant preparing lender-grade analyses for heavy-duty repair centers, fleet maintenance facilities, truck tire and preventive-maintenance operations, mobile service businesses, and projects incorporating fleet fueling. Our studies evaluate carrier and truck populations, service-bay demand, technician availability, labor productivity, parts and service revenue, fleet accounts, equipment requirements, stabilization, debt-service coverage, and sensitivity.
Prepared for lenders, CDCs, and federal agencies to SBA SOP 50 10 8, USDA 7 CFR Part 5001, and conventional underwriting standards. Fiduciary duty runs to the lender and the agency, never the borrower. 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value. So far in 2026: 41 engagements and $1.54 billion evaluated — 17 SBA, 11 USDA.
Truck-service feasibility is fleet-relationship analysis: the carrier domiciles and lanes within the service radius, bay-utilization economics across tire, PM, and repair lines, parts and mobile-service revenue, national-account and warranty work, and — for cardlock and fleet fueling — the committed fleet accounts behind gallon projections. Technician availability is treated as a binding constraint, because service capacity is labor before it is bays. Where cardlock or fleet fueling anchors the site rather than supplementing it, the engagement is scoped as an independent truck stop study so diesel volume and capture carry their own demand logic.
Methodology combines fleet-population and carrier data, competitive service-capacity census, labor-market analysis for technicians, and industry benchmarks for bay revenue and fuel margins. Coverage is tested with utilization and labor-cost sensitivity.
Every Wert-Berater financial model is fully linked with no hardcoded values, so any reviewer can stress any input. Deliverables comprise a complete narrative report and the linked Excel model, with ten-year pro forma, sensitivity analysis at ±5, 10, and 15 percent, interest-rate stress from +0.5 to +3.0 percent, and ratio analysis benchmarked against RMA and IBISWorld data.
SBA engagements are prepared to SOP 50 10 8, including its debt-service-coverage minimums of 1.15x operating and 1.00x global. USDA engagements follow RD Staff Instruction 5001 across the Business & Industry, Community Facilities, REAP, and Value-Added Producer Grant programs. Conventional engagements are built to the lender's stated coverage standard, typically 1.20x. Roadside and transportation-service projects arrive under SBA 504 and 7(a) for owner-operators — with SOP 50 10 8 special-purpose property treatment addressed where it applies — USDA B&I at qualifying rural locations, and conventional structures for multi-site operators.
Truck service and fleet fueling projects are evaluated within the firm’s travel-center and freight practice. That record includes a $48,571,365 interstate travel center and truck stop programmed at 1,000 spaces under USDA Regulation 5001, and a $13,500,000 major-brand truck stop on a 6-acre site with gasoline and diesel canopies — engagements in which fueling volume, carrier demand, and on-site service capacity were analysed as a single operating system rather than as separate businesses.
The firm has not published a standalone heavy-duty repair or fleet-maintenance engagement, and none is claimed. What transfers directly to a service-center study is the carrier-demand methodology developed in that freight work: FMCSA-based fleet population analysis, corridor commercial traffic classification, and the discipline of separating committed fleet volume from prospective accounts. Independence is non-negotiable: determinations follow the evidence and are not revised under pressure, and studies are built to pass lender, agency, and third-party review without exception items.
A truck service center feasibility study consultant analyses whether enough commercial vehicles in a defined market need enough billable work, at rates the market will pay, to keep the proposed bays productive and cover debt service. The demand base is fleet activity, not population, and capacity is governed by technicians as much as by bays.
A truck service & fleet fueling feasibility study is not a generic business plan review. Every engagement is scoped to the specific revenue lines the project will operate — heavy-duty preventive maintenance, tire service, DOT inspection lanes, drivetrain and electrical repair, mobile road-service dispatch, cardlock or retail diesel, and DEF dispensing — and the financial model is built line by line around those lines, not imported from a template.
Population density is not the relevant demand base for a truck service center. Commercial fleet activity is. A market with strong residential growth and no carrier presence generates little heavy-duty service demand, while a modest town hosting several terminals and a drayage operation can support a substantial shop. The study measures the commercial base directly rather than inferring it from demographics.
Where legitimately available, the analysis draws on FMCSA carrier registration records to identify carriers domiciled within the service radius and their reported power-unit counts, then profiles local fleet domiciles and terminals, corridor truck traffic and commercial vehicle classification counts, and state or IFTA information where it is lawfully accessible. Equipment age and type matter, because a fleet running newer units under warranty sends different work to an independent shop than one operating older equipment. Owner-operator concentration is examined separately, since that segment buys service differently from a managed fleet and is more price-sensitive.
Bay demand is analysed by work type, because the categories consume very different amounts of bay time and carry different rates. Preventive maintenance is schedulable and predictable. Tires turn quickly and often need no lift. DOT inspection is time-boxed. Brake, drivetrain, and electrical work varies widely in duration, and electrical diagnostics in particular can occupy a bay for hours with limited billable output. Unscheduled repair arrives without warning and disrupts the schedule. Mobile service consumes technician time without consuming a bay at all.
The model works in productive hours rather than open hours. It sets a technician-to-bay ratio, applies realistic efficiency and comeback rates, derives revenue per productive hour by work type, and arrives at bay utilisation from the bottom up. Open-to-close hours multiplied by bay count is not capacity: scheduling gaps, parts waits, road-call interruptions, and equipment downtime all reduce it, and a shop that looks busy can still be selling far fewer billable hours than its bay count suggests.
Unused bays cannot create revenue without technicians. This is the constraint most often understated in a sponsor’s projection, and in tight labor markets it decides the outcome. A six-bay shop staffed for four bays is a four-bay business, and the capital in the extra bays earns nothing until the shop can hire.
The analysis examines diesel technician supply in the local labor market, prevailing and offered wages against what fleets and dealers in the area pay, the certification and skill level each work type requires, overtime dependence, recruitment channels including any relationship with a technical training programme, expected productivity by experience level, and technician-to-bay coverage through the staffing ramp. Where the projection assumes full staffing from opening, the study models a realistic hiring path instead and reports coverage under it. Where wage inflation is a live risk, it is carried into sensitivity rather than held constant.
Signed commercial fleet relationships and prospective accounts are modelled separately, and only supported commitments are carried in the base case. An executed maintenance agreement, a national-account authorisation, or a letter of intent is evidence. A sponsor’s expectation that a nearby carrier will move its work is not, however plausible it may be.
National-account participation is examined for what it actually delivers: it brings volume and referral flow but usually at discounted labor rates, with prescribed parts sourcing, mandated warranty terms, and payment cycles that affect working capital. The study models those terms rather than the rack rate. Concentration is tested directly — where one fleet or one national account carries a large share of projected revenue, the study runs its loss as a downside case and reports whether the project still services debt.
Parts, tires, and mobile service are modelled as distinct revenue lines with their own gross margins, because blending them into a single service figure obscures both the earnings profile and the working-capital requirement. Parts carry inventory investment, obsolescence risk, and margins that vary sharply between stocked items and special orders. Tires turn on volume at thinner margins, require inventory space and disposal handling, and are frequently tied to a supplier programme with its own pricing terms and casing-credit arrangements.
Mobile service and road calls are analysed separately again: they generate premium billing and win fleet relationships, but they consume technician hours that would otherwise be sold in a bay, add vehicle and equipment cost, and introduce scheduling volatility. Where mobile service is a material part of the plan, the study reports the trade-off explicitly rather than treating road-call revenue as incremental to full bay utilisation.
Where fueling is supplemental to a service business, it is analysed on its own terms: committed fleet gallons supported by agreements rather than expectation, the fleet accounts and card networks involved, margin per gallon net of card and network fees, DEF sales and dispensing infrastructure, tank and dispenser capital cost, and environmental and compliance obligations attaching to fuel storage. Cardlock volume is unattended and predictable but thin-margin, and the study models it that way.
If retail diesel or travel-center fueling is the primary business model rather than a supplement, this is not the applicable page and the study is scoped differently. Those projects are addressed in the travel center & truck stop feasibility study, where fuel volume, convenience-store sales, and parking capacity drive the economics and the service bays are the secondary component.
Break-even is calculated for the specific project rather than quoted from a rule of thumb. The study reports the billable-hour volume and corresponding bay utilisation required to cover operating expense, and the higher level required to cover operating expense plus debt service at the proposed loan structure, both derived from the project’s own labor rate, work mix, parts and tire margins, and staffing plan.
Because the answer depends on the revenue mix, no generic utilisation threshold is published. A shop weighted toward preventive maintenance at contract rates reaches break-even at a different utilisation than one weighted toward unscheduled repair at full retail, and a shop carrying significant tire and parts margin different again. The projected utilisation path through the staffing ramp is set against both thresholds so the lender can see when coverage is first achieved and how much headroom exists if hiring runs slow.
Demand for truck service is not estimated from population density or rooftop counts. It is built from the commercial vehicle population that actually operates within the service radius and the structural reasons those vehicles need service at a fixed location rather than at a carrier terminal or a competing shop.
The carrier-population count begins with FMCSA operating-authority and safety-fitness records, which identify domiciled carriers, registered power units, and interstate versus intrastate operation. State motor-carrier licensing registries and IFTA-reporting data provide a secondary cross-check on the active fleet in the region. Traffic counts from state DOT weigh-station and classification records establish the volume of commercial vehicles on the corridors the project intends to serve, distinguishing through-traffic from locally domiciled units, because the two populations generate very different capture assumptions.
Fuel-volume demand is built from fleet-account interviews and letter-of-intent documentation where available, not from corridor traffic alone. Cardlock projections without committed accounts are stress-tested at conservative capture rates. Competitive supply is assessed through a field census of existing truck-service providers — independent shops, dealer service centers, national-account locations, and carrier terminal facilities — rated by bay count, service capability, and hours of operation. Trade association data from organizations serving the trucking and commercial-tire industries supplements the field census where published capacity data is available. The result is a supply-gap analysis specific to the service lines the project will operate, not a generic market-share assumption.
Four inputs move the debt-service-coverage ratio for this project type more than any others. Each is tested explicitly in the sensitivity analysis rather than held at a single point estimate.
Every assumption is sourced and documented so a lender or agency reviewer can evaluate the basis without accepting it on faith.
Lender and agency concerns for this project type are specific and recur across engagements. Understanding them shapes how the study is structured.
Under SBA SOP 50 10 8, the first question is whether the facility qualifies as special-purpose property. A truck-service center with fuel infrastructure, wash bays, or a cardlock canopy may meet the definition, which affects collateral treatment and the documentation the study must address. Coverage minimums of 1.15x operating and 1.00x global are non-negotiable; the study demonstrates compliance at the base case and identifies the input levels at which coverage falls below threshold.
USDA Business & Industry engagements at rural locations require the study to address the public-benefit test — job creation, service to an underserved area, or support for agricultural supply chains — alongside the financial projections. Fuel and service infrastructure serving agricultural carriers or rural freight corridors can satisfy that test, but the analysis must be explicit.
Conventional lenders typically require 1.20x coverage and focus heavily on fleet-account quality: the creditworthiness of committed accounts, contract terms, and the concentration risk if a single carrier represents a large share of projected gallons or service revenue. National-account and warranty-work revenue is scrutinized for the contractual basis behind the projection. In all structures, the independence of the study matters: a determination that follows the evidence and is not revised under pressure is the standard a lender or agency reviewer expects.
The fee for a truck service & fleet fueling feasibility study is fixed and quoted in writing within one business day of receiving the project description. No fee is contingent on the finding, and the quote does not change based on the outcome of the analysis. That structure is a direct consequence of the firm's fiduciary duty to the lender and reviewing agency, not to the borrower.
Standard delivery is ten to fifteen business days from the date a complete data room is received. The data room for this project type typically includes site control documents, equipment and construction cost estimates, fleet-account letters of intent or contracts, technician hiring plans or employment commitments, fuel-supply agreements, and any existing traffic or weigh-station count data the sponsor has assembled. Incomplete data rooms delay delivery; the engagement letter specifies exactly what is required before the clock starts.
Rush delivery is available and is quoted at the time of engagement. Every engagement is published to a secure client portal where the fully linked Excel model stays live and recalculates when inputs change. A lender or agency reviewer can request a specific stress scenario and see the result without waiting for a revised report. The bound narrative report, the ten-year pro forma, sensitivity tables, interest-rate stress schedules, ratio analysis benchmarked against RMA and IBISWorld data, and the explicit statement of conditions are all delivered together as a single package. Wert-Berater, Inc. has completed 4,000+ engagements representing $41.2 billion in evaluated project value since 1998.
Related project types analysed by the same team, each with its own demand model and its own report structure:
The consultant analyses whether enough commercial vehicles in the market need enough billable work, at rates the market will pay, to keep the proposed bays productive and cover debt service. That covers carrier and power-unit populations, fleet domiciles and equipment profiles, service demand by work type, productive hours and bay utilisation, diesel technician supply, parts and tire margins, fleet and national accounts with concentration risk, fueling economics where applicable, and debt-service coverage under base and downside cases.
Demand is calculated from the commercial vehicle base rather than from population. The analysis establishes the power units domiciled or operating within the service radius, profiles them by type and age, applies service intervals and observed failure rates by work category, and converts that into annual billable hours by work type. Corridor breakdown and transient repair demand is added where traffic supports it. The result is a bottom-up hour count, not a share of a market-size estimate.
Where legitimately available, FMCSA carrier registration records are used to identify carriers domiciled within the service radius and their reported power-unit counts. That is supplemented with local terminal and fleet-domicile identification, corridor truck traffic and commercial vehicle classification counts, and state or IFTA information where lawfully accessible. Equipment age and type are profiled, since a fleet running newer units under warranty sends different work to an independent shop than one operating older equipment.
Utilisation is forecast in productive hours rather than open hours. The model sets a technician-to-bay ratio, applies realistic efficiency and comeback rates, and builds utilisation from work-type demand upward. Scheduling gaps, parts waits, road-call interruptions, and equipment downtime are deducted explicitly. Open-to-close hours multiplied by bay count is not treated as capacity, because a shop that appears busy can still be selling far fewer billable hours than its bay count implies.
It is treated as a hard capacity constraint, because unused bays cannot generate revenue without technicians. The study examines local diesel technician supply, prevailing versus offered wages, required certification levels by work type, overtime dependence, recruitment channels, and productivity by experience level, then models a realistic hiring ramp instead of assuming full staffing at opening. Coverage is reported under that ramp, and wage inflation is carried into sensitivity.
Signed relationships and prospective accounts are modelled separately, and only supported commitments enter the base case. Executed maintenance agreements, national-account authorisations, and letters of intent are evidence; expectations are not. National-account work is modelled at its actual discounted labor rate with prescribed parts sourcing and payment cycles rather than at rack rate. Where one account carries a large revenue share, its loss is run as a downside case.
Each is forecast as a distinct line with its own gross margin. Parts carry inventory investment and obsolescence risk, with margins differing between stocked and special-order items. Tires turn on volume at thinner margins with inventory, disposal, and supplier-programme terms attached. Mobile service bills at a premium and wins fleet relationships but consumes technician hours that would otherwise be sold in a bay, so the trade-off is modelled rather than treated as incremental revenue.
Where fueling supplements a service business, the analysis covers committed fleet gallons supported by agreements, the fleet accounts and card networks involved, margin per gallon net of card and network fees, DEF sales and dispensing infrastructure, tank and dispenser capital cost, and compliance obligations attaching to fuel storage. Cardlock volume is modelled as unattended, predictable, and thin-margin. Where retail diesel is the primary business rather than a supplement, the project is scoped as a travel center or truck stop study instead.
Break-even is calculated twice from the project’s own labor rate, work mix, parts and tire margins, and staffing plan: the billable-hour volume and bay utilisation required to cover operating expense, and the higher level required to cover operating expense plus debt service. No generic utilisation threshold is published, because a shop weighted toward contract-rate preventive maintenance breaks even at a different utilisation than one weighted toward full-retail unscheduled repair.
The fee is fixed and quoted in writing within one business day of receiving the project description. It does not change based on the outcome of the analysis, and no portion is contingent on a favorable finding. Contact Wert-Berater directly with the project scope — loan program, facility type, and revenue lines — to receive a written quote.
Standard delivery is ten to fifteen business days from receipt of a complete data room. The data room for this project type includes site control documents, cost estimates, fleet-account letters of intent, fuel-supply agreements, and technician hiring documentation. Rush delivery is available and is quoted at engagement. An incomplete data room is the most common source of delay.
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Wert-Berater, Inc. is an independent provider of feasibility studies and other related services. The firm does not provide financing or equity investment advice, and does not arrange, broker, or place debt or equity capital of any kind.
All appraisal assignments are performed by Bruce E. Jones, MAI, ASA-GC, BCA, CMEA, a member of the Appraisal Institute since 2006, a staff member of Wert-Berater, Inc. and owner of Special Purpose Realty Valuation.