How an independent feasibility study for a greenfield or brownfield U.S. manufacturing plant seeking EXIM financing is actually built — from the demand evidence hierarchy through the capacity model, capital and operating budgets, job-year sizing, and the debt-service and sensitivity analysis a credit officer will read line by line.
This page walks through a manufacturing plant feasibility study in the order it is genuinely assembled. A plant converts demand into product at a cost that leaves enough margin to service debt, and the study has to prove that conversion works before a dollar is committed. For an application to the Export-Import Bank of the United States (EXIM) — typically under the Make More in America Initiative for a domestic export-oriented plant, or through the Structured and Project Finance Division for a limited-recourse structure — the study must be built so that an independent engineer and market consultant, hired specifically to test it, cannot pull the thread that unravels the case. What follows is our method, the analysis behind each section, and the three failure modes that sink applications most often. For the broader program context see the EXIM feasibility study hub.
The study begins with the product, defined precisely: the specification, the grades or variants, the unit of sale, and the price at which each unit clears. Everything downstream — capacity, capital cost, staffing, coverage — is denominated in those units, so ambiguity here propagates through the model.
The harder question is demand, and here we impose a strict hierarchy of evidence, because a serious reviewer does not weight all demand the same:
The weighting is not cosmetic. Contracted volume flows into the base case at close to face value; letters of intent are haircut; modeled demand is discounted more heavily still and labeled a projection. A model in which the early revenue years rest on contracts and only the later ramp rests on modeled demand is defensible. A model in which year one already depends on demand nobody has committed to buy is not, and it is the first thing we tell a sponsor to fix.
Because EXIM is an export credit agency, the plant's output has to reach foreign buyers, and the study must evidence that by destination rather than in the aggregate. Under the Make More in America Initiative eligibility turns on the percentage of production or shipments tied to exports: 15 percent for small businesses, transformational export areas and climate-related transactions, and 25 percent of output exported or expected to be exported for other sectors. Export suppliers can qualify where the same nexus standards are met.
The analytical weight sits on the words expected to be exported. A projection is permitted, but it must be evidenced: named destination markets, the tariff and registration barriers into each, the distribution route, and demand tied to identifiable buyers by country. We build the nexus as a schedule — volume by destination, the evidence tier behind each, and the running percentage — so a reviewer can see exactly where the export share comes from and stress it. Detail on the nexus mechanics lives on the MMIA feasibility study page.
Nameplate capacity is the sum of the vendors' rated throughputs under ideal conditions. It is almost never achievable and should never anchor a revenue model. We work it down through a defined chain:
The result is effective annual capacity. On top of that sits the utilization ramp — the share of effective capacity the plant reaches in each period. A greenfield does not start at full utilization; it commissions, qualifies, hires and trains, and sells into markets that take time to develop. The ramp is therefore governed by the slower of two constraints: how fast the plant can run reliably, and how fast the sales organization can place the output. Setting it by the first constraint alone is a classic and fatal error, discussed below.
The capital budget is assembled bottom-up and then tested top-down. Bottom-up, it is the sum of:
The top-down test is benchmarking against comparable facilities: cost per unit of capacity, cost per square foot, and the equipment-to-total-project ratio, each adjusted for location and scale. Where a project sits far off the benchmark, the study explains why or corrects the estimate. Finally, the budget is reconciled to the engineer's estimate or FEED package line by line. EXIM's project finance criteria expect total project cost to be comparable to projects of similar type and size for that market, and a capital budget that does not reconcile to the engineering is the second failure mode below. For limited-recourse structures the interaction between CAPEX, the EPC contract and coverage is covered on the project finance feasibility study page, and the Attachment F misconceptions on the Attachment F page.
Operating cost is where thin studies are thinnest, because it is laborious to build honestly. We assemble it from:
Costing labor at BLS occupational wage data grounds the largest controllable cost in a public, defensible source, and it feeds the job-year computation that sizes the loan in the next section.
Under the Make More in America Initiative the amount of EXIM financing available to a project is scaled to the U.S. jobs it supports, during construction and over the life of the financing. Each job-year — one job sustained over five years being five job-years — allows for up to $229,502 in financing, and for domestic transactions this replaces the U.S. content requirement that governs traditional foreign-buyer deals.
The employment schedule is therefore not administrative detail; it sizes the loan. We build it in two layers: the construction labor schedule over the build period, and the operating staffing plan over the financing term, both by occupation and headcount. Multiplying sustained jobs by years supported yields job-years, and the job-year total against the $229,502 figure establishes the financing the project's employment can support. A careless staffing plan can understate or overstate the eligible loan; it deserves as much attention as the revenue line.
Everything above converges in a fully linked financial model with no hardcoded values, so any reviewer can move an input and watch coverage respond. The model carries the utilization ramp, unit pricing, contribution margin, fixed cost, the amortization of the proposed EXIM facility, and the resulting debt-service coverage ratio (DSCR) by period, on both a project and a global basis.
The sensitivity suite stresses the drivers individually and in combination: price and volume at plus and minus 5, 10 and 15 percent, input cost at the same steps, schedule slippage, and interest rate from +0.5 to +3.0 percent. Each run is reported against the coverage floor, and the breakeven — the utilization, price or cost level at which DSCR reaches 1.00x — is identified explicitly so the reviewer sees how much cushion exists before the project cannot pay its debt.
The table below shows how utilization, unit price, contribution and DSCR interact across the first five years of a plant's operation. It is ILLUSTRATIVE ONLY: the figures are placeholders that demonstrate the mechanics of the model, not results from any project. Real figures come from the project's own contracts, engineering, cost data and financing terms.
| Metric (ILLUSTRATIVE) | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|---|---|---|---|---|---|
| Utilization of effective capacity | 40% | 60% | 75% | 85% | 90% |
| Units shipped (index, Yr5 = 100) | 44 | 67 | 83 | 94 | 100 |
| Unit price (index, Yr1 = 100) | 100 | 101 | 102 | 103 | 103 |
| Contribution margin per unit | 28% | 31% | 34% | 36% | 37% |
| Debt-service coverage ratio (DSCR) | 0.9x | 1.2x | 1.5x | 1.8x | 2.0x |
| Coverage status vs. 1.25x floor | Below — funded from reserve/equity | At/near floor | Above | Above | Above |
The point the table makes is structural, not numeric. Early-year coverage below the floor is common and manageable when the ramp is honest — it is planned for with a debt-service reserve, an interest-only period, or equity, all of which the finance plan must show. It becomes a problem when the ramp was drawn to reach the floor faster than the sales organization can deliver, because then the reserve runs out before the revenue arrives. Rising contribution margin reflects fixed-cost absorption as utilization climbs; if the base case assumed high early margin, the sensitivity cases will expose it.
The risk register lists each material risk, its owner, the mitigant, and the residual exposure once the mitigant is applied — construction and completion risk, offtake concentration, input price and supply, technology and ramp risk, foreign-exchange exposure on export revenue, and permitting. EXIM's approach expects risk to be allocated to the parties best suited to manage it, and the register is where that allocation is made visible.
The implementation schedule sequences permitting, construction, commissioning, equipment qualification and commercial operation, and it is tied to the drawdown profile so that funding is drawn against milestones rather than a calendar. The schedule and the utilization ramp must agree: the month the plant reaches commercial operation on the schedule is the month the ramp can begin in the model, and any gap between the two is a financing cost the model has to carry.
The single caveat that matters most. A feasibility study prepared for EXIM is read adversarially by EXIM's own independent engineer and market consultant, retained at the sponsor's cost, and it is reviewed within five to ten business days on submission only to decide whether it contains enough information to proceed to evaluation — incomplete applications are returned with an explanation of the deficiencies. Every number in the study must therefore be traceable to a source the sponsor can produce on request: a contract, a quotation, an engineering document, a wage table. An input that is asserted rather than evidenced does not merely weaken the study; it invites the reviewer to distrust everything around it. EXIM requirements change, and neither this page nor any study guarantees an outcome, which is EXIM's determination alone.
Across manufacturing studies, three errors account for most first-pass rejections. Each is avoidable, and each is the reason we ask the questions we ask before an engagement begins.
The most common failure is a revenue model whose early years rest on demand no buyer has committed to. Industry growth statistics and a large addressable market are context, not evidence. A credit officer wants to know who buys year one's output, at what price, and under what document. When the answer is a projection where a contract should be, the study fails at the demand evidence hierarchy in section one, and no downstream modeling repairs it.
The second failure is a capital budget that cannot be tied back to the equipment quotations and the engineer's estimate. Numbers that appear from nowhere, contingency chosen for comfort rather than sized to estimate maturity, and owner's costs omitted entirely all signal an estimate built to fit a financing rather than to build a plant. Line-by-line reconciliation and benchmarking against comparable facilities is how the study earns the reviewer's confidence in the largest figure in the transaction.
The third failure is an engineering ramp presented as a commercial ramp. The equipment may reach 90 percent utilization in year two, but if the sales organization cannot place that volume — because contracts, distribution and export registrations are not yet in place — the revenue will not arrive on the schedule the coverage assumes. The ramp must be governed by the slower of production readiness and market development; when it is not, the debt-service reserve is exhausted before the plant is earning. Building the ramp against the sales pipeline, not the nameplate, keeps the coverage honest.
We are exact about this, because export finance rewards precision and penalizes overstatement. Wert-Berater has not to date completed an engagement financed by EXIM, and we make no claim that EXIM has approved, accepted or reviewed our work. Our studies are prepared to address EXIM's published requirements using an EXIM-aligned methodology; whether an application succeeds is EXIM's determination. What we bring to a manufacturing plant study is directly adjacent and verifiable: manufacturing facility studies in Bulgaria, Finland, the Czech Republic, Germany and Saudi Arabia; oil and gas refinery studies in Qatar and Dubai, including a 50,000-barrel-per-day refinery and a used-lube-oil re-refinery; a 150,000-square-foot indoor food production facility in Qatar; and an extensive record of SBA 504/7(a) (SOP 50 10 8) and USDA Business & Industry (RD Instruction 5001) manufacturing and processing studies. Every engagement is delivered through a secure client portal, with standard delivery of 10 to 15 business days from complete project data. For the full program map, return to the EXIM feasibility study hub.
EXIM facts on this page are summarized from EXIM published guidance: Make More in America Initiative, Our Approach to Project Finance, Guidelines for Submitting a Successful Project Finance Application, and Economic Impact Procedures. Occupational wage figures are developed from U.S. Bureau of Labor Statistics data. EXIM requirements change; confirm current guidance with EXIM or your lender before relying on any summary, including this one.
Tell us the product, the plant, the destination markets and the financing structure you are contemplating. We will tell you whether an EXIM application is realistic, which evidence you are missing, and what the study will cost — before you commit to anything.
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