CPA(U) MDC Exam 1

Contribution per pair (fixed overhead absorbed is excluded because total fixed costs do not change): Executive Oxford 185,000 − 60,000 − 18,000 − 18,000 − 7,000 = Shs 82,000; Safari Boot 240,000 − 96,000 − 22,000 − 27,000 − 10,500 = Shs 84,500; School Shoe 96,000 − 36,000 − 10,000 − 9,000 − 3,500 = Shs 37,500.

Which resource binds? Leather needed for maximum demand = 3,000 × 2.5 + 2,500 × 4.0 + 6,000 × 1.5 = 7,500 + 10,000 + 9,000 = 26,500 square feet against 22,000 available, so leather is short. Stitching hours needed for maximum demand = 6,000 + 7,500 + 6,000 = 19,500 hours against 18,000, so stitching also looks short. Rank on leather first and then test the plan against stitching hours.

Contribution per square foot of leather: Executive Oxford 82,000 / 2.5 = Shs 32,800; School Shoe 37,500 / 1.5 = Shs 25,000; Safari Boot 84,500 / 4.0 = Shs 21,125. Executive Oxford is ranked first, School Shoe second and Safari Boot third, even though the Safari Boot earns the highest contribution per pair.

Allocation of 22,000 square feet: the contractual 1,000 School Shoes come first (1,500 sq ft), leaving 20,500. Executive Oxford 3,000 pairs use 7,500 (leaving 13,000). The remaining 5,000 School Shoes use 7,500 (leaving 5,500). Safari Boot gets 5,500 / 4.0 = 1,375 pairs.

Stitching check: 3,000 × 2 + 6,000 × 1 + 1,375 × 3 = 6,000 + 6,000 + 4,125 = 16,125 hours, within the 18,000 available, leaving 1,875 hours slack. Stitching is therefore not binding at this plan, and the single-constraint ranking gives the optimum.

The maximum monthly profit is Shs 192,187,500, with 1,125 pairs of Safari Boot demand left unmet.

📋 Objective: 3.4 (Applying relevant cost analysis to decisions)

Minimum price = 74,400,000 / 400 = Shs 186,000 per pair. At this price Kawempe is exactly as well off as under its optimal plan.

Advice: the proposed Shs 178,000 is below the minimum by Shs 8,000 a pair, so accepting would reduce profit by 8,000 × 400 = Shs 3,200,000. The sales manager has ignored the opportunity cost of the scarce leather. Quote at least Shs 186,000, plus a margin, or decline.

📋 Objective: 3.3 (Relevant costs of materials and labour)

Unmet demand: under the optimal plan only 1,375 Safari Boots are made, so 2,500 − 1,375 = 1,125 pairs of demand are unmet.

Make or buy for boots Kawempe can make: variable cost of making = 96,000 + 22,000 + 27,000 + 10,500 = Shs 155,500 a pair; cost of buying = 198,000 + 2,000 = Shs 200,000 a pair. Making is cheaper by Shs 44,500 a pair, and the leather freed by buying in would have no better use because Executive Oxford and School Shoe demand is already met. Kawempe should continue making its 1,375 pairs.

Buying to fill unmet demand: each bought-in pair earns 240,000 − 200,000 = Shs 40,000 per pair. The supplier's cap of 800 pairs is below the 1,125 unmet, so buy 800 pairs: 800 × 40,000 = Shs 32,000,000 extra contribution.

Revised profit = 192,187,500 + 32,000,000 = Shs 224,187,500. Advice: continue making Safari Boots in house and accept the offer for 800 pairs to serve the unmet demand, subject to the qualitative factors.

📋 Objective: 3.4 (Applying relevant cost analysis to decisions)

Outsourcing offer. Kawempe loses direct control of quality, so poorly made bought-in boots could damage its brand reputation; it should insist on inspection and quality control at delivery. Kireka Lastcraft sells competing boots in Kampala, so the design could leak and Kawempe could create a rival; there is also a risk of dependence on one supplier and doubts over its reliability on delivery dates and future prices once Kawempe stops planning for full Safari demand. Meeting the unmet Safari demand protects customer goodwill, whereas turning customers away may push them to competitors. Stitchers may worry about job security, affecting morale.

Hotel order. A prestige hotel customer could bring repeat business and publicity, but a low price sets a precedent for future orders and may anger regular customers; the order also displaces 300 Safari Boots.

If stitching hours were also binding. Ranking by contribution per unit of one resource works only when a single constraint binds. If stitching hours were cut so that both leather and stitching bound at the optimum, each resource could give a different ranking, and the mix must be found by linear programming: formulate the objective function and constraints, then solve by the graphical method (two products) or the simplex method (three or more). The solution also gives the shadow price of each binding resource, the most Kawempe should pay for an extra square foot of leather or an extra stitching hour.

📋 Objective: 3.1 (Relevant and irrelevant costs and revenues; qualitative factors)

Sales price variances (actual price − standard price) × actual cases: Mango (1,150 − 1,200) × 26,000 = KES 1,300,000 adverse; Passion (1,560 − 1,500) × 16,000 = KES 960,000 favourable; Pineapple (980 − 1,000) × 30,000 = KES 600,000 adverse. Net sales price variance = KES 940,000 adverse.

Standard contribution per case: Mango 420, Passion 570, Pineapple 300. Budgeted contribution = 8,400,000 + 8,550,000 + 7,500,000 = KES 24,450,000 on 60,000 cases, a weighted average of 24,450,000 / 60,000 = KES 407.5 (407.50) per case.

Sales mix variances: actual total 72,000 cases in the budgeted mix 20:15:25 gives Mango 24,000 cases, Passion 18,000 cases and Pineapple 30,000 cases. Mango (26,000 − 24,000) × 420 = KES 840,000 favourable; Passion (16,000 − 18,000) × 570 = KES 1,140,000 adverse; Pineapple nil. Net sales mix variance = KES 300,000 adverse.

Sales quantity variance = (72,000 − 60,000) × 407.5 = KES 4,890,000 favourable.

Check: mix + quantity = 4,890,000 F − 300,000 A = 4,590,000 F, the total sales volume contribution variance.

📋 Objective: 10.3 (Variance analysis, reconciliation and criticisms)

Proof: actual revenue 26,000 × 1,150 + 16,000 × 1,560 + 30,000 × 980 = KES 84,260,000; less variable costs 56,700,000 and fixed costs 16,450,000 = KES 11,110,000. 8,450,000 + 4,590,000 − 940,000 − 540,000 − 450,000 = 11,110,000.

📋 Objective: 10.3 (Variance analysis, reconciliation and criticisms)

The managing director is right that volume helped: the favourable quantity variance of KES 4,890,000 is the main reason profit beat budget. But the mix moved away from Passion, the highest-margin juice (KES 570 a case), towards Mango, a less profitable mix that cost KES 300,000. Part of the extra Mango volume was bought with a price cut, which gave an adverse price variance of KES 1,300,000; whether that was worth it depends on price elasticity and whether the new supermarket shelf space is kept at the normal price later.

The mix variance is meaningful only if the juices are substitutes, so that a customer who buys Mango would otherwise have bought Passion. If they are bought independently the mix variance has little meaning and product volume variances are more useful.

The Passion price rise and lower Passion volume followed a poor harvest, which is not controllable by the sales manager; growth in the whole market may also explain the quantity variance, so the company should also look at its market share before praising the sales team.

📋 Objective: 10.3 (Variance analysis, reconciliation and criticisms)

Net payoffs (inflows less outlay): new line 5,200,000 − 1,800,000 = GHS 3,400,000 (high), 3,000,000 − 1,800,000 = 1,200,000 (medium); converted line 2,800,000 − 600,000 = GHS 2,200,000 (high), 1,500,000 (medium), 600,000 (low); selling the formula 1,100,000 in every state.

Decision tree: a square decision node at the start has three branches. The new line and converted line branches each lead to a circular chance node with high (0.3), medium (0.5) and low (0.2) outcomes. On the new line low-demand branch there is a second decision node: continue (900,000 − 1,800,000 = −900,000) or abandon by selling the line (1,500,000 − 1,800,000 = −300,000). Rolling back, the directors would abandon, so the low branch value is −300,000.

Expected values: new line = 0.3 × 3,400,000 + 0.5 × 1,200,000 + 0.2 × (−300,000) = 1,020,000 + 600,000 − 60,000 = GHS 1,560,000. Converted line = 0.3 × 2,200,000 + 0.5 × 1,500,000 + 0.2 × 600,000 = 660,000 + 750,000 + 120,000 = GHS 1,530,000. Selling the formula = GHS 1,100,000.

Recommendation: on expected value, build the new line (EV GHS 1,560,000). Without the option to sell the line its EV would be only 1,020,000 + 600,000 − 180,000 = GHS 1,440,000, below the converted line, so the flexibility to exit adds GHS 120,000 and changes the decision. The margin over conversion is small (GHS 30,000), so the choice is sensitive to the estimates.

📋 Objective: 7.3 (Decision tree analysis)

With perfect information the directors would pick the best option for each demand level: high demand, the new line (3,400,000); medium demand, the converted line (1,500,000); low demand, sell the formula (1,100,000).

EV with perfect information = 0.3 × 3,400,000 + 0.5 × 1,500,000 + 0.2 × 1,100,000 = 1,020,000 + 750,000 + 220,000 = GHS 1,990,000.

Value of perfect information = 1,990,000 − 1,560,000 (best EV without information) = GHS 430,000.

Advice: the survey fee of GHS 500,000 exceeds the value of even perfect information, so the survey is not worth commissioning. A real survey is imperfect, so its value would be lower still. The finance director's concern about the downside is better met by the option to sell the line, which already limits the loss.

📋 Objective: 7.5 (Decisions with perfect and imperfect information and under uncertainty)

Expected value is a long-run average: this is a one-off decision, so the actual result will be one of the payoffs, never GHS 1,560,000 itself. The probabilities are subjective estimates by the marketing director, and the small gap between the two lines means a sensitivity analysis is needed.

The two cautious directors are risk averse and may prefer the converted line, which cannot make a loss (worst outcome GHS 600,000): this is the maximin choice. The chief executive is risk seeking and would choose the new line, which offers the best possible outcome of GHS 3,400,000 (the maximax choice).

📋 Objective: 7.2 (Probability and expected value)

Financial perspective: ROCE fell from 179.2 / 800 = 22.4% to 172.8 / 960 = 18.0%, below the benchmark. Revenue grew by 12.5% but the operating margin fell from 14.0% to 12.0%, so growth was bought at lower profitability and with more capital.

Customer perspective: on-time shipment fell from 414 / 450 = 92.0% to 408 / 480 = 85.0%, against 95% for the best performer. Late shipments put repeat orders from European buyers at risk.

Internal business process perspective: garments returned rose from 64,000 / 3,200,000 = 2.0% to 126,000 / 3,600,000 = 3.5%, three and a half times the benchmark, showing weak stitching quality and rework costs.

Learning and growth perspective: operator turnover rose from 460 / 2,300 = 20.0% to 696 / 2,400 = 29.0%, and training fell from 12 to 7.5 hours per operator. New styles fell from 48 to 36, so innovation is weakening.

Overall: on every perspective Hawassa trails the benchmark (ROCE 18% against 21%, on time 85% against 95%, turnover 29% against 10%). The scorecard shows a chain of cause and effect: less training and high turnover lead to more faults and late shipments, which damage customer loyalty and margins. Profit alone would have hidden the causes.

📋 Objective: 15.10 (Balanced scorecard)

Types: comparing with the best member of the club is competitive benchmarking; Hawassa could also use internal benchmarking between its own sewing lines, and functional benchmarking against firms in other industries that excel at, for example, logistics or staff retention.

Process: agree the areas to compare (on-time shipment, returns, turnover); choose the partner; collect comparable data on measures and on practices; carry out a gap analysis to quantify the shortfall and find the causes; learn the best practice behind the gap (training, line supervision, quality checks); set targets and implement changes; then monitor and repeat.

Difficulties: the best performer may treat its practices as confidential and share only headline figures; differences in product mix, buyers and accounting policies limit comparability; data collection takes management time; and copying a rival may only reach parity rather than create advantage.

📋 Objective: 15.3 (Benchmarking)

Bonuses tied to targets can encourage gaming and data manipulation, for example recording a late order as shipped on time. Managers may favour measures they can move quickly and cut training to save cost, which is short-termism. Measures can conflict (speed against faulty garments), and too many measures cause information overload.

Recommendation: use a few measures with a clear weighting across all four perspectives, include only items the managers control, set targets with participation and benchmark evidence, and keep profit as a gateway condition.

📋 Objective: 8.4 (Setting financial performance targets)