Since fixed costs are unavoidable, only variable costs are relevant for the make-vs.-buy comparison. Variable cost to make = $320,000 / 10,000 = $32 per unit. Buy price = $38 per unit. Making saves $6 per unit x 10,000 units = $60,000. Option A reaches the wrong conclusion. Option C uses total cost ($45 per unit) which incorrectly includes unavoidable fixed costs, making the buy option appear cheaper than it actually is on a relevant-cost basis. Option D reaches the correct conclusion but uses total cost, which is the wrong analytical framework.

CM per machine hour: Product A = $18/2 = $9/hr; Product B = $12/1 = $12/hr. Product B is more efficient per constrained resource hour, so maximize B first. B: 3,000 units x 1 hr = 3,000 hrs; CM = $36,000. Remaining hours = 1,000; A: 1,000/2 = 500 units; CM = $9,000. Total CM = $45,000. Option A does not optimize the resource constraint. Option B fills all hours with A first, ignoring B's higher contribution per hour. Option C uses 4,000 hours (1,000 x 2 + 2,000 x 1 = 4,000) but does not rank by CM per hour.

Relevant costs are future costs that differ between alternatives. For a make-vs.-buy analysis, relevant costs include: all variable manufacturing costs (which are avoided if outsourced), fixed costs that can actually be eliminated or redeployed if production stops, and any opportunity costs from capacity that would become available. Unavoidable fixed costs are irrelevant because they occur regardless of the decision. Option A includes irrelevant unavoidable fixed costs. Option C includes allocated overhead that may be unavoidable. Option D understates relevant costs by excluding overhead components that may vary or be avoidable.

Lease: After-tax payment = $40,000 x 0.75 = $30,000/yr. PV = $30,000 x 4.355 = $130,650. Purchase: Straight-line depreciation = $180,000/6 = $30,000/yr. Annual tax shield = $30,000 x 0.25 = $7,500. PV of tax shield = $7,500 x 4.355 = $32,663. Net PV cost = $180,000 - $32,663 = $147,337. Leasing is $16,687 cheaper on a PV basis. Option A identifies the purchase cost correctly but recommends the more expensive option. Option B introduces a non-quantitative factor that does not override the financial analysis. Option C is incorrect; a $16,687 difference exists.

Contract direct CM = $42,000 - $18,000 = $24,000. Opportunity cost = 500 hours x $35 = $17,500. Net benefit of accepting = $24,000 - $17,500 = $6,500. Despite the opportunity cost, the contract still generates $6,500 of net incremental value and should be accepted. Option B reaches the wrong conclusion about revenue adequacy. Option C reaches the correct accept conclusion but relies only on the gross margin without netting the opportunity cost. Option D reaches the wrong conclusion; the direct profit ($24,000) exceeds the opportunity cost ($17,500).

A 17-percentage-point gross margin decline is a serious signal that warrants root cause analysis before committing more capital to that business. If margins are declining because of competitive pricing pressure, more volume at lower prices will accelerate the problem. If input costs are rising faster than pricing power, volume alone will not restore margins. The analytically sound approach is to diagnose the cause of margin decline and redirect resources where returns are higher - a recommendation supported by the data showing the smaller line's superior margin stability. Option B uses revenue as the investment criterion rather than return on investment. Options C and D make unsupported assumptions about operational leverage and self-correction.

The recommendation must account for contractual obligations (Customer C cannot be immediately dropped) and should be data-driven across all three relationships. Customer B's profit-to-revenue ratio of 36% far exceeds Customer A's 10%, making it the most efficient relationship to grow. Customer C requires a structured remediation plan: understand the cost-to-serve drivers, seek contract improvements, and definitively exit at contract end if economics cannot improve. Option A ignores the contractual constraint. Option B prioritizes absolute revenue over return. Option D treats all customers as equivalent despite dramatically different economics.

Relevant data meets three tests: it is future-oriented (specific to the decision at hand), it differs between alternatives (data that is the same regardless of choice is irrelevant), and it affects the outcome (it changes the financial or operational result of the decision). Presenting non-relevant data adds complexity and noise without improving the recommendation. Option B defines relevant too broadly - not all quantifiable financial data is relevant to every decision. Option C confuses data quality (accuracy) with relevance. Option D is a procedural definition that has nothing to do with the analytical concept of relevance.

CM per unit = $40 - $26 = $14. Units for target profit = (Fixed costs + Target profit) / CM per unit = ($480,000 + $140,000) / $14 = $620,000 / $14 = 44,286 units. Option A is the break-even volume ($480,000 / $14), which covers fixed costs but produces zero profit. Option B uses an incorrect denominator. Option C uses an incorrect calculation.

At the new price of $45 and volume of 12,000: CM per unit = $45 - $30 = $15. Total CM = 12,000 x $15 = $180,000, which is $20,000 less than the current $200,000. Although revenue increases and volume grows, total contribution margin declines because the margin per unit falls from $20 to $15 and the volume increase is insufficient to offset the per-unit reduction. Option A focuses on revenue rather than the relevant profit metric. Option C reaches the wrong conclusion from the data. Option D identifies a valid threshold but recommends a policy rather than a recommendation based on the actual data.