Feasibility Analysis Process and Evidence Standards

Introduction

A business idea is not yet a business opportunity. An idea becomes investable only when systematic investigation provides sufficient evidence that customers will value the proposed offering, the venture can deliver it, the organization can operate lawfully and reliably, and the expected returns justify the risks. Feasibility analysis is therefore a disciplined process for deciding whether to proceed, redesign, postpone, or abandon a proposed venture.

Feasibility analysis differs from a business plan. A business plan explains how an entrepreneur intends to create and capture value; feasibility analysis tests whether the underlying assumptions are credible enough to justify detailed planning and resource commitment. It is also different from forecasting. Forecasting estimates what may happen, whereas feasibility analysis evaluates whether the estimates are supported by evidence and whether the venture remains viable under unfavorable conditions.

The central principle is proportionality: the more capital, regulatory exposure, technical complexity, and irreversible commitment a venture requires, the stronger and more independent its evidence should be.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain the purpose, stages, and logic of feasibility analysis.
2. Formulate explicit assumptions and convert them into testable propositions.
3. Design investigations covering market, product, technical, operational, organizational, legal, and financial dimensions.
4. Distinguish primary from secondary evidence and assess evidence quality.
5. Apply criteria, thresholds, sensitivity analysis, and staged go/no-go decisions.
6. Recognize ethical, regulatory, and governance responsibilities in feasibility work.

1. The Logic of Feasibility Analysis

A feasibility investigation asks four connected questions:

• Desirability: Do identifiable customers have a significant problem or unmet need?
• Viability: Can the venture generate sufficient revenue, margin, cash flow, and return?
• Feasibility: Can the product or service be developed and delivered with available resources and capabilities?
• Legitimacy: Can it operate within applicable laws, ethical standards, and stakeholder expectations?

These questions are interdependent. Strong demand does not rescue a product that cannot be manufactured economically. Technical capability does not establish a market. A profitable projection may be unacceptable if it depends on unlawful data use or an unavailable licence.

A useful conceptual model is:

This is a conceptual relationship rather than an empirical production function. It emphasizes that a sophisticated spreadsheet cannot compensate for weak evidence or hidden assumptions.

Feasibility analysis normally follows an iterative sequence:

1. Define the opportunity and decision to be made.
2. Map stakeholders, assumptions, constraints, and alternatives.
3. Investigate the market and customer problem.
4. Test the product or service concept.
5. Assess technical and operational deliverability.
6. Examine organization, law, ethics, and governance.
7. Build an integrated financial model.
8. Stress-test assumptions and identify risks.
9. Make a documented go, revise, hold, or no-go decision.

The sequence is not strictly linear. Evidence from a technical assessment may change the product concept; legal analysis may eliminate a market; customer testing may alter the required operating model.

2. From Ideas to Testable Assumptions

Entrepreneurs often use statements such as “the market is large,” “customers will pay,” or “the technology is scalable.” These are hypotheses, not facts. A feasibility study makes them explicit and testable.

An assumption register should record:

A strong assumption is specific, measurable, time-bound, and linked to a decision. “Young professionals like the app” is weak. “At least 30 of 50 target users complete a task prototype and 20 indicate willingness to pay at the proposed monthly price” is testable, although the sample and threshold must still be justified.

Assumptions can be classified as:

• Desirability assumptions: customers experience the problem and value the solution.
• Demand assumptions: a sufficient number of customers can be reached and converted.
• Price assumptions: customers accept the price and payment terms.
• Cost assumptions: input, labor, logistics, and acquisition costs remain within limits.
• Capability assumptions: the team can obtain required skills, technology, suppliers, and funding.
• Regulatory assumptions: licences, approvals, data permissions, and standards are attainable.
• Timing assumptions: development, launch, and customer adoption occur within planned periods.

Critical assumptions should be tested first. A simple prioritization method scores each assumption on impact and uncertainty. High-impact, high-uncertainty assumptions deserve early experiments because they have the greatest potential to invalidate the venture.

3. Evidence Standards

Evidence is information that reduces uncertainty about a decision. Not all information is equally persuasive. A founder’s belief, a customer’s stated interest, a signed purchase order, and audited sales data represent different evidence strengths.

3.1 Primary and secondary evidence

Primary evidence is collected directly for the feasibility question. Examples include customer interviews, observations, surveys, prototype tests, supplier quotations, laboratory tests, pilot transactions, and expert consultations.

Secondary evidence already exists. Examples include government statistics, industry reports, academic studies, competitor disclosures, regulatory publications, and market databases. Secondary evidence is efficient but may be outdated, aggregated, biased, or based on definitions that do not match the venture’s target market.

The best studies triangulate: they compare multiple sources and methods. For example, customer interviews may reveal a problem, behavioral observation may confirm its frequency, and a paid pilot may test whether the problem is commercially important.

3.2 Dimensions of evidence quality

Evidence should be assessed using the following dimensions:

• Relevance: Does it answer the specific decision question?
• Validity: Does the method measure what it claims to measure?
• Reliability: Would a similar method produce reasonably consistent results?
• Recency: Does it reflect current market, technology, and regulation?
• Representativeness: Does the source reflect the target population or operating context?
• Independence: Is it free from incentives that could distort the conclusion?
• Verifiability: Can another person inspect the source, method, and calculations?
• Sufficiency: Is there enough evidence for the consequence of the decision?

A large survey with a biased sample may be less useful than a smaller but carefully selected set of observations. Likewise, a supplier’s verbal estimate is weaker than a written quotation specifying capacity, price, lead time, and conditions.

3.3 Evidence hierarchy and triangulation

Evidence strength is context-dependent, but the following progression is generally useful:

1. Speculation or internal opinion.
2. Desk research and broad market indicators.
3. Structured interviews and documented expert assessments.
4. Prototype or usability tests with target users.
5. Observed behavior, letters of intent, or pilot commitments.
6. Paid transactions, repeat purchases, binding contracts, or operating data.

Higher levels do not automatically prove feasibility. A paid pilot may be subsidized, unusually favorable, or too small to generalize. Evidence must be interpreted in relation to the hypothesis, sample, incentives, and conditions.

4. Market and Customer Feasibility

Market feasibility evaluates whether a sufficiently attractive customer opportunity exists and whether the venture can reach customers better than alternatives.

4.1 Problem and customer analysis

The investigation should identify:

• the customer or decision-maker;
• the job, problem, or desired outcome;
• current alternatives and their weaknesses;
• frequency and urgency of the problem;
• purchasing authority and buying process;
• switching costs and adoption barriers;
• willingness and ability to pay.

Interviews should investigate actual behavior rather than invite hypothetical praise. Questions such as “How did you solve this problem the last time it occurred?” are generally more informative than “Would you buy our product?”

4.2 Market size

Market size must be defined rather than asserted. Three common concepts are:

• Total addressable market (TAM): all potential demand if the venture achieved unrestricted reach.
• Serviceable available market (SAM): the portion compatible with the venture’s geography, capabilities, and offering.
• Serviceable obtainable market (SOM): the realistically attainable portion within a specified period.

A top-down estimate may use credible industry totals. A bottom-up estimate is often more decision-useful:

Each variable must use compatible definitions and units. If 10,000 customers make four purchases per year at $25 each, estimated annual revenue is 10,000 × 4 × $25 = $1,000,000. This is potential market revenue, not the venture’s forecast sales.

Market share should be derived from customer acquisition capacity, sales channels, conversion, retention, and competition—not selected merely because it produces an attractive forecast.

4.3 Competition and positioning

Competition includes direct rivals, substitute products, internal solutions, and customer inaction. Analysis should compare value proposition, price, quality, convenience, trust, switching cost, distribution, intellectual property, and regulatory advantages.

A competitor matrix is useful, but it should not confuse features with customer value. A feature matters only if it improves a customer outcome that is recognized and valued.

5. Product and Service Feasibility

Product feasibility examines whether the proposed solution actually addresses the validated problem and can achieve acceptable performance.

A minimum viable product (MVP) is not simply the cheapest version of a product. It is the smallest credible offering that tests the most important assumptions. The MVP should specify:

• target users;
• core job to be completed;
• essential features or service elements;
• measurable success criteria;
• excluded features;
• safety, quality, and accessibility requirements.

Useful metrics may include task completion, defect rate, response time, customer satisfaction, repeat use, conversion, retention, or willingness to pay. Metrics should be linked to decisions. A prototype that receives compliments but produces no use or payment evidence may not support commercial feasibility.

6. Technical Feasibility

Technical feasibility asks whether the proposed solution can be designed, built, integrated, maintained, and scaled at acceptable cost and performance.

The study should examine:

• technology readiness and maturity;
• required hardware, software, infrastructure, and data;
• performance, reliability, safety, and cybersecurity;
• interoperability with existing systems;
• development skills and intellectual property;
• testing, maintenance, and upgrade requirements;
• capacity constraints and scaling behavior;
• technical dependencies and single points of failure.

A proof of concept demonstrates that a technical principle can work. A pilot demonstrates performance in a realistic context. Neither necessarily proves commercial scalability. Testing should specify operating conditions, acceptance criteria, sample size where relevant, failure modes, and remediation plans.

For capacity planning, a basic utilization measure is:

Both numerator and denominator must use the same period and output unit. “Maximum practical capacity” should account for maintenance, downtime, staffing, changeovers, and quality losses; theoretical capacity can create an unrealistically optimistic result.

7. Operational Feasibility

Operational feasibility concerns the repeatable system through which value is created and delivered. It includes suppliers, procurement, production or service processes, inventory, logistics, customer support, quality control, information systems, and continuity planning.

Process mapping should identify activities, inputs, outputs, responsible roles, handoffs, bottlenecks, controls, and failure points. Entrepreneurs should distinguish one-time launch work from recurring operating work. A business may be technically possible but operationally infeasible if each customer requires excessive customization.

Supplier analysis should consider price, minimum order quantity, lead time, quality, payment terms, geographic concentration, substitution options, and resilience. A low quoted price may be misleading if it excludes shipping, duties, inspection, installation, or waste.

Service ventures should model labor availability and scheduling. If one employee can complete 8 service appointments per day and expected demand is 80 appointments per week over five days, at least 2 employee-equivalents are required before allowing for absence, training, and non-service work. Such calculations are planning estimates, not guarantees of productivity.

8. Organizational and Human Feasibility

Organizational feasibility evaluates whether the founding team and proposed structure can perform the required work.

The analysis should identify capabilities in product development, sales, finance, operations, compliance, leadership, and industry knowledge. A capability gap is not automatically a reason to stop; it may be addressed through hiring, partnerships, outsourcing, advisory support, or staged development. However, the cost, timing, and availability of those remedies must be evidenced.

Governance becomes important as soon as the venture involves co-founders, investors, employees, sensitive data, regulated activities, or material safety risks. The study should clarify authority, accountability, decision rights, reporting, conflicts of interest, and escalation procedures.

9. Legal, Ethical, and Regulatory Feasibility

Legal feasibility should be investigated early, not treated as a final checklist. Relevant issues may include business registration, licences, zoning, tax, employment law, consumer protection, advertising, product safety, environmental requirements, intellectual property, competition law, contract terms, privacy, cybersecurity, and sector-specific regulation.

Legal requirements differ by jurisdiction and industry; entrepreneurs should obtain qualified professional advice where necessary. A feasibility report should identify the applicable authority, required approval, estimated time, cost, conditions, and residual uncertainty rather than simply state “compliant.”

Ethical feasibility extends beyond minimum legal compliance. Questions include:

• Is customer consent meaningful and informed?
• Are vulnerable groups exposed to disproportionate harm?
• Are claims accurate and evidence-based?
• Are workers, suppliers, and communities treated fairly?
• Are automated decisions explainable and contestable?
• Are environmental impacts measured and responsibly disclosed?

When AI is used for customer analysis, forecasting, or product delivery, the study should address data provenance, privacy, bias, security, human oversight, model limitations, and accountability. AI-generated outputs should not be treated as evidence without checking the underlying sources and performance.

10. Financial Feasibility

Financial feasibility integrates the findings of other dimensions. It should include assumptions, startup investment, operating costs, revenue drivers, working capital, funding requirements, cash timing, and downside scenarios.

10.1 Revenue, cost, and contribution

Revenue is commonly estimated as:

Variable cost changes with output; fixed cost does not change in the relevant short-run range. Contribution per unit is:

If fixed costs are $60,000, price is $50, and variable cost is $30, contribution is $20 and the accounting break-even volume is:

Thus, break-even units = $60,000 ÷ $20 = 3,000 units. This formula assumes constant price and variable cost per unit, fixed costs within the relevant range, all units produced are sold, and a single product or a constant sales mix. It excludes financing costs and taxes unless those are included consistently in fixed costs.

For multiple products, the contribution margin ratio is often used:

Break-even sales revenue equals fixed costs divided by the contribution margin ratio, provided the sales mix remains stable.

10.2 Cash and profitability

Profit is not the same as cash. A venture may report accounting profit while customers pay late, inventory absorbs funds, or equipment requires large investment. A feasibility model should therefore include a monthly or quarterly cash budget during the launch period.

Net cash flow for a period can be represented as:

Opening cash plus net cash flow gives closing cash, subject to financing flows and any defined cash restrictions. The model should show the lowest projected cash balance and the funding needed before that point.

Discounted cash-flow measures may be appropriate for larger investments. Net present value is:

where CF_t is net cash flow in period t, r is the discount rate per period, and n is the final period. Cash flows and the discount rate must use the same time units. NPV assumes the cash-flow estimates and discount rate are appropriate for the project’s risk and timing. A positive NPV means the projected cash flows exceed the present value of the investment at the selected rate; it does not guarantee success.

10.3 Sensitivity and scenarios

Sensitivity analysis changes one assumption while holding others constant, such as price, volume, cost, or launch delay. Scenario analysis changes several related assumptions to describe optimistic, base, and adverse conditions. Stress tests should examine events such as demand being half the base case, a major supplier failing, approval being delayed, or customer acquisition cost doubling.

A decision should not depend on a single precise forecast. It should remain defensible across a reasonable range of outcomes or include milestones that limit exposure.

11. Decision Criteria and Go/No-Go Governance

A go/no-go decision should be based on pre-defined criteria rather than enthusiasm at the end of the investigation. Criteria may be absolute, comparative, or staged.

A staged decision may be superior to a single binary decision:

• Go: proceed to the next stage with resources and controls.
• Conditional go: proceed only after specified conditions are satisfied.
• Revise and retest: change the proposition, segment, technology, or operating model.
• Hold: delay while waiting for evidence, regulation, funding, or a partner.
• No-go: terminate or archive the opportunity because critical assumptions fail.

The final report should distinguish facts, estimates, judgments, unresolved questions, and recommendations. It should disclose conflicts of interest and preserve an audit trail of sources, methods, versions, and calculations.

12. Applied Case: FreshRoute Produce Delivery

The following is an illustrative case, not a report of an actual company.

Background

A student venture proposes a subscription service delivering locally sourced produce to urban households. The founders believe that customers want convenient, healthy food and that small farms need better access to consumers.

Problem and assumptions

The initial assumptions are that households will pay $35 per weekly box, farms can supply consistent quantities, delivery can occur within a four-hour window, and customer acquisition cost will remain below $25 per subscriber. The team identifies delivery density, seasonal supply, and repeat purchase as high-impact uncertainties.

Investigation

The team conducts 32 interviews, observes purchasing behavior at two markets, and runs a landing-page test. Interviews confirm interest but also reveal that convenience and predictable contents matter more than local sourcing alone. A prototype ordering process is tested with 20 target users; 15 complete an order without assistance. A two-week paid pilot with 40 households produces 34 completed deliveries and 26 renewal commitments.

Three farms provide written seasonal supply schedules, but one cannot guarantee winter volume. Two logistics providers quote prices based on delivery density. The team maps packing, order confirmation, route planning, delivery, complaint handling, and refunds. A food-safety adviser identifies temperature control and traceability requirements. Legal review identifies registration, labelling, consumer protection, and insurance obligations.

Financial analysis

The proposed weekly price is $35. Variable costs per box are estimated at $21: $12 for produce, $6 for delivery and packing, and $3 for payment processing, spoilage, and customer service. Weekly contribution is therefore $14. Monthly fixed costs are estimated at $8,400, including coordination labor, software, insurance, and storage. Using four weeks as a planning approximation, weekly fixed-cost equivalent is $2,100, so the approximate weekly break-even volume is:

The result is sensitive to delivery density and spoilage. At 100 boxes per week, the venture would not cover the stated fixed-cost equivalent. At 180 boxes, it would generate approximately 180 × $14 − $2,100 = $420 weekly contribution after the allocated fixed costs, before taxes, financing, and unplanned costs.

Decision

The evidence supports a conditional go, not an unrestricted launch. Conditions include securing a second winter supplier, confirming food-safety procedures, achieving at least 150 weekly boxes in a controlled neighborhood, and tracking renewal, spoilage, delivery cost, and complaints for eight weeks. The venture should not extrapolate the 26 renewal commitments directly to a citywide forecast because the pilot participants may be unusually interested and the operating conditions were limited.

Learning points

The case demonstrates that feasibility is integrated. Customer interest alone was insufficient; supply reliability, delivery economics, compliance, and repeat behavior determined the decision. It also illustrates why thresholds should be set before scaling and why a staged commitment can preserve learning while limiting financial exposure.

13. Challenges and Limitations

Feasibility analysis cannot eliminate uncertainty. New ventures often lack historical data, face changing competitors, and influence the market they are trying to predict. Stated intentions may not translate into behavior, pilots may not represent full-scale operations, and financial models can create false precision.

Common analytical errors include confirmation bias, survivorship bias, double-counting market segments, confusing TAM with attainable sales, ignoring working capital, treating competitors as static, omitting compliance costs, and using unsupported conversion rates. Another error is excessive research without a decision rule: information is useful only when it changes understanding or action.

The remedy is not to demand certainty but to make uncertainty visible. Use ranges, document assumptions, conduct disconfirming research, seek independent review, and design reversible experiments. Evidence should be strong enough for the commitment being requested.

Best-Practice Checklist

A professionally prepared feasibility study should:

• define the decision, scope, time horizon, and alternatives;
• maintain a dated assumption and evidence register;
• prioritize high-impact uncertainties;
• use both behavioral and stated evidence;
• triangulate independent sources;
• distinguish market potential from obtainable sales;
• test technical and operational performance in realistic conditions;
• obtain qualified legal and regulatory advice;
• reconcile operational assumptions with the financial model;
• include cash timing, sensitivity, scenarios, and downside exposure;
• state criteria and decision rights in advance;
• disclose limitations, conflicts, and unresolved risks;
• recommend a staged action with measurable milestones.

Chapter Summary

Feasibility analysis is a systematic investigation of whether a proposed venture is desirable, viable, technically and operationally deliverable, organizationally capable, and legally legitimate. It converts vague entrepreneurial beliefs into explicit assumptions, testable hypotheses, evidence requirements, thresholds, and decisions.

The process covers market and customer need, product performance, technical capability, operations, organization, law and ethics, and finance. Evidence should be relevant, valid, reliable, current, representative, independent, verifiable, and sufficient for the decision at hand. Primary and secondary research should be triangulated, with observed behavior and paid pilots generally providing stronger commercial evidence than opinions alone.

Financial analysis integrates the investigation but must distinguish revenue, contribution, profit, and cash. Break-even analysis, cash budgets, sensitivity analysis, scenario analysis, and—when appropriate—NPV help reveal the consequences of assumptions. The appropriate conclusion is not always “go” or “no-go”; conditional go, revise and retest, or hold may be more rational. Professional feasibility work is transparent, ethical, staged, and designed to reduce uncertainty before irreversible commitment.

Key Terms

Feasibility analysis: A structured investigation of whether a proposed venture can and should proceed.

Assumption: A proposition accepted provisionally and requiring validation.

Hypothesis: A testable statement about an expected relationship or outcome.

Primary evidence: Information collected directly for the investigation.

Secondary evidence: Existing information used to inform the investigation.

Triangulation: Comparing multiple sources or methods to strengthen conclusions.

TAM: Total addressable market under unrestricted reach.

SAM: Serviceable available market compatible with the venture’s scope.

SOM: Serviceable obtainable market realistically attainable.

Minimum viable product: The smallest credible offering used to test important assumptions.

Technical feasibility: The ability to develop and scale a solution to required standards.

Operational feasibility: The ability to deliver value reliably through repeatable processes.

Organizational feasibility: The availability of capabilities, roles, governance, and resources.

Contribution margin: Revenue remaining after variable costs.

Break-even point: Output or sales level at which relevant revenue equals relevant costs.

Working capital: Funds tied up in operating items such as inventory, receivables, and payables.

Sensitivity analysis: Changing one assumption to observe its effect on results.

Scenario analysis: Evaluating outcomes under coherent sets of assumptions.

Conditional go: Permission to proceed only after specified conditions are met.

Evidence standard: The defined level and quality of support required for a decision.

Review Questions

Short Answer Questions

1. How does feasibility analysis differ from a business plan?
2. What is the difference between primary and secondary evidence?
3. Distinguish TAM, SAM, and SOM.
4. Why is cash flow analysis necessary even when a venture appears profitable?
5. What is a conditional go decision?

Long Answer Questions

1. Explain how an assumption register improves the quality of a feasibility investigation.
2. Discuss the criteria used to evaluate evidence quality and explain why triangulation is valuable.
3. Describe a comprehensive process for assessing technical and operational feasibility.
4. Explain how legal, ethical, and regulatory issues can affect the feasibility of a venture.
5. Discuss the strengths and limitations of break-even, sensitivity, scenario, and NPV analysis in entrepreneurial decision-making.

Analytical / Application Questions

1. A venture sells a service for $80 per unit, incurs variable cost of $50 per unit, and has monthly fixed costs of $12,000. Calculate its contribution per unit and monthly break-even volume. State the assumptions of your calculation.
2. Design three tests for the assumption that small retailers will pay $100 per month for an inventory application. Rank the tests according to evidence strength and justify your ranking.
3. A pilot produces strong customer interest but depends on one supplier, has unresolved licensing requirements, and shows negative cash flow for the first six months. Recommend a go, conditional go, revise-and-retest, hold, or no-go decision, and identify the evidence required before the next stage.

Multiple Choice Questions with Answers

Question 1

Which statement best describes the primary purpose of feasibility analysis?

A. To guarantee that a venture will succeed

B. To test whether a venture is sufficiently viable to justify further commitment

C. To produce promotional material for investors

D. To replace all legal and accounting advice

Correct Answer: B. To test whether a venture is sufficiently viable to justify further commitment

Question 2

Which is an example of primary evidence?

A. A government population report

B. An academic article published five years ago

C. A paid pilot with target customers

D. A competitor’s annual report

Correct Answer: C. A paid pilot with target customers

Question 3

Which market concept represents the realistically attainable portion within a specified period?

A. TAM

B. SAM

C. SOM

D. GDP

Correct Answer: C. SOM

Question 4

A product sells for $40 and has a variable cost of $25. What is its contribution per unit?

A. $15

B. $25

C. $40

D. $65

Correct Answer: A. $15

Question 5

If fixed costs are $30,000 and contribution per unit is $15, what is the break-even volume?

A. 450 units

B. 1,500 units

C. 2,000 units

D. 3,000 units

Correct Answer: C. 2,000 units

Question 6

Which evidence would generally provide the strongest indication of actual willingness to pay?

A. A founder’s personal belief

B. A customer saying the idea sounds useful

C. A broad industry statistic

D. A completed paid transaction by a target customer

Correct Answer: D. A completed paid transaction by a target customer

Question 7

What does sensitivity analysis normally do?

A. Changes one assumption while holding others constant

B. Eliminates all uncertainty from a forecast

C. Measures only legal risk

D. Replaces customer research

Correct Answer: A. Changes one assumption while holding others constant

Question 8

Which issue is most directly associated with operational feasibility?

A. Whether a process can deliver consistently at the required capacity

B. Whether the logo is visually attractive

C. Whether the founder prefers partnership finance

D. Whether the market report uses colorful charts

Correct Answer: A. Whether a process can deliver consistently at the required capacity

Question 9

Why should a feasibility study distinguish profit from cash flow?

A. Profit and cash are always identical

B. Cash timing, receivables, inventory, and capital spending can create funding needs

C. Cash flow is relevant only to nonprofit organizations

D. Profit cannot be estimated mathematically

Correct Answer: B. Cash timing, receivables, inventory, and capital spending can create funding needs

Question 10

What is the best description of a conditional go decision?

A. Immediate unrestricted expansion

B. Permanent rejection of the opportunity

C. Proceeding only after specified evidence or conditions are satisfied

D. Ignoring unresolved risks until launch

Correct Answer: C. Proceeding only after specified evidence or conditions are satisfied