Unit: Advanced Management Accounting
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Login to Access| Month | Number of croissants (units “000”) | Production cost Sh.“000” |
| 1 | 60 | 1,350 |
| 2 | 180 | 2,100 |
| 3 | 60 | 900 |
| 4 | 30 | 900 |
| 5 | 180 | 2,700 |
| 6 | 150 | 2,250 |
| 7 | 30 | 1,050 |
| 8 | 150 | 1,950 |
| 9 | 90 | 1,350 |
| 10 | 120 | 1,950 |
| 11 | 120 | 1,800 |
| 12 | 90 | 1,500 |
| Number of croissants | Production cost (Sh.) | |
| Monthly total | 1,260,000 | 19,800,000 |
| Monthly average | 105,000 | 1,650,000 |
| Month | Material handling cost (Y) | Number of orders | Number of kilograms ordered |
| “Sh.000” | \(\mathbf{(X_1)}\) | \(\mathbf{(X_2)}\) | |
| June 2024 | 2,000 | 100 | 6,000 |
| July 2024 | 3,090 | 125 | 15,000 |
| August 2024 | 2,780 | 175 | 7,800 |
| September 2024 | 1,990 | 200 | 6,000 |
| October 2024 | 7,500 | 500 | 29,000 |
| November 2024 | 5,300 | 300 | 23,000 |
| December 2024 | 4,300 | 250 | 17,000 |
| January 2025 | 6,300 | 400 | 25,000 |
| February 2025 | 5,600 | 475 | 12,000 |
| March 2025 | 6,240 | 425 | 22,400 |
| 1. | Summary output of regression statistics: | |
| Multiple R | 0.999420 | |
| R Square | 0.998841 | |
| Adjusted R Square | 0.998509 | |
| Standard Error | 75.76272 | |
| Observations | 10 | |
| 2. | The analysis of variance (ANOVA) table: | ||||
| df | SS | MS | F-statistics | ||
| Regression | 2 | 34613020 | 17306510 | 3015.076722 | |
| Residual | 7 | W | 5739.99 | ||
| Total | 9 | 34653200 | |||
| 3. | The parameter estimate table is as follows: | |||
| Variable | Coefficients | Standard error | t-ratio value | |
| Intercept | 507.3097 | 57.3225 | 8.850098 | |
| X variable 1 | 7.835162 | Z | 33.47672 | |
| X variable 2 | 0.107181 | 0.003742 | 28.6464286 | |
| Actual cost incurred: | Sh. |
| Direct material used (165,000 kilograms) | 15,675,000 |
| Direct labour (80,000 hours) | 5,800,000 |
| Variable production overheads | 16,800,000 |
| Fixed production overheads | 6,750,000 |
| Favourable | Adverse | |
| Planning and operating variances | Sh. | Sh. |
| Direct material price variance | 825,000 | |
| Direct material usage variance | 1,500,000 | |
| Direct labour rate variance | 200,000 | |
| Direct labour efficiency variance | 350,000 | |
| Variable production overhead: | ||
| Expenditure variance | 800,000 | |
| Efficiency variance | 1,000,000 | |
| Fixed production overhead: | ||
| Expenditure variance | 1,250,000 | |
| Volume variance | 6,250,000 |
| Product | Betax | Zetay | Alphaz | Total |
| Sales mix | 40% | 30% | 30% | |
| Sh.“per unit” | Sh.“per unit” | Sh.“per unit” | Sh. | |
| Selling price | 2,000 | 3,000 | 2,500 | |
| Variable cost | 1,000 | 1,800 | 1,500 | |
| Total fixed costs | 15,664,000 | |||
| Total sales revenue | 45,000,000 |
| Product | Betax | Zetay | Alphaz | Total |
| Sales mix | 50% | 20% | 30% | |
| Sh.“per unit” | Sh.“per unit” | Sh.“per unit” | Sh. | |
| Selling price | 2,000 | 2,800 | 2,500 | |
| Variable cost | 1,000 | 1,680 | 1,500 | |
| Total fixed costs | 15,664,000 | |||
| Total sales revenue | 45,000,000 |
| Year 2023 | Equivalent production | Overheads |
| Month | Units “000” | Sh.“000” |
| January | 1,425 | 12,185 |
| February | 950 | 9,875 |
| March | 1,130 | 10,450 |
| April | 1,690 | 15,280 |
| May | 1,006 | 9,915 |
| June | 834 | 9,150 |
| July | 982 | 10,133 |
| August | 1,259 | 11,981 |
| September | 1,385 | 12,045 |
| October | 1,420 | 13,180 |
| November | 1,125 | 11,910 |
| December | 980 | 10,431 |
| Predictor | Coefficient | SE Coef | t |
| Constant | 0.93050 | 0.3670 | \(X_a\) |
| Months | 0.38762 | \(X_b\) | 6.20 |
| Type | \(X_c\) | 0.3141 | 4.02 |
| S = 0.459048 | R – Sq = 85.94% | R Sq (adj) = 81.9% |
| Source | DF | SS | MS | F |
| Regression | 2 | 9.0009 | \(X_d\) | 21.36 |
| Residual error | 7 | \(X_e\) | 0.2107 | |
| Total | 9 | 10.4760 |
| 1. | The demand for the company’s’ product is dependent on disposable income and price of the products. |
| 2. | The analysis of variances table: | ||
| Source | Degrees of freedom | Sum of squares | |
| Model | 3 | 187 | |
| Error | 9 | 4 | |
| Total | 12 | 191 | |
| 3. | The parameter estimates and their errors: | ||
| Variable | Estimate | Standard error | |
| Constant | 1.5 | 2.000 | |
| Price | -1.4 | 0.1934 | |
| Income | 5 | 0.2700 | |
| X | Y | Z | |
| Units produced and sold | 12,000 | 16,000 | 8,000 |
Sh. | Sh. | Sh. | |
| Sales price per unit | 50 | 70 | 60 |
| Direct material cost per unit | 16 | 24 | 20 |
| Direct labour cost per unit | 8 | 12 | 8 |
Product overhead costs | Total Sh. | Cost drivers | |
| Machining costs | 102,000 | Machine hours | |
| Production scheduling | 84,000 | Number of production runs | |
| Set up costs | 54,000 | Number of production runs | |
| Quality control | 49,200 | Number of production runs | |
| Receiving materials | 64,800 | Number of components receipts | |
| Packaging materials | 36,000 | Number of customer orders | |
| Information on the cost drivers is given as follows: | |||
| X | Y | Z | |
| Direct labour hours per unit | 1 | 1.5 | 1 |
| Machine hours per unit | 0.5 | 1 | 1.5 |
| Number of components per unit | 3 | 5 | 8 |
| Number of component receipts | 18 | 80 | 64 |
| Number of customers orders | 6 | 20 | 10 |
| Number of production runs | 6 | 16 | 8 |
| 1 | Regression analysis performed using MS Excel in a computer yielded the following results: |
| Summary of output Regression statistics | ||
| Parameter | Output | |
| Multiple R | 0.984523 | |
| R square | 0.969285 | |
| Adjusted R square | 0.961607 | |
| Standard Error | 32.196570 | |
| Observations | 6 | |
| 2. | The analysis of variance (ANOVA) output was as follows: | |
Predictor | df | SS | MS | F | Significance F | ||
| Regression | 1 | 130853.5 | 130853.5 | 126.2311 | 0.000357 | ||
| Residual | 4 | 4146.476 | X | ||||
| Total | 5 | 135000 | |||||
Variable | Coefficients | Standard error | t-statistic | P-value | Lower 95% | Upper 95% | |
| Intercept | 509.9119 | 45.55789 | Y | 0.000363 | 383.4227 | 636.4011 | |
| Variable X | 29.40529 | 2.617232 | 11.23526 | 0.000357 | 22.13867 | 36.6719 |
| Where: | Y = Cumulative average time per batch a = time taken to produce initial batch x = cumulative units of batches b = learning curve index |
| Details | Sh. |
| Material cast per chair | 250 |
| Labour cost per hour | 80 |
| Fixed overheads per annum | 300,000 |
| Capital investment | 830,000 |
| Where: | F = Total monthly factory costs, and |
| M = Machine hour per month. | |
| Regression sum of squares = 41,437,500 | |
| Residual sum of squares = 7,312,500 |
| Year | Quarter | Quarter number | Units produced |
| 2019 | 1 | 1 | 2,000 |
| 2 | 2 | 2,500 | |
| 3 | 3 | 3,000 | |
| 4 | 4 | 6,000 | |
| 2020 | 1 | 5 | 5,000 |
| 2 | 6 | 4,000 | |
| 3 | 7 | 6,000 | |
| 4 | 8 | 10,000 |
| Where; | X represents units produced per quarter. |
| Q represents the quarter number |
| Cost item | Relationship |
| Office rent | TC = 500,000 |
| Office salaries | TC = 200,000 + 2x |
| Fuel cost | TC = 45,000+ 6x |
| Transport wages | TC = 62,000 + 8x |
| Sundry costs | TC = 29,965 + x |
| Where; | TC represents the total cost per quarter |
| x represents the number of units produced per quarter |
| Budget | Actual | |
| Output (batches) | 6 | 6 |
| Labour hours | 2,400 | 1,950 |
| Total labour cost (Sh.) | 1,680,000 | 1,365,000 |
| Total labour cost variance | Sh.315,000 |
| Labour rate variance | Nil |
| Labour efficiency variance | Sh.315.000 |
| Project lifetime sales volume (units) | 300,000 |
| Sh. | |
| Target selling price | 8,000 |
| Target profit margin (30%) | (2,400) |
| Target cost | 5,600 |
| Projected cost | 7,000 |
| Manufacturing costs: | Sh. | Sh. |
| Direct materials (bought in parts) | 3,900 | |
| Direct labour | 1,000 | |
| Direct machining costs | 200 | |
| Ordering and receiving | 80 | |
| Quality assurance | 600 | |
| Rework | 150 | |
| Engineering and design | 100 | 6,030 |
| Non-manufacturing costs: | ||
| Marketing | 400 | |
| Distribution | 300 | |
| After sales service and warranty costs | 270 | 970 |
| Total cost | 7,000 |
| Month | Machine hours "000" | Fuel oil expense Sh."000" | Month | Machine hours "000" | Fuel oil expense Sh."000" |
| July 2020 | 34 | 640 | January 2021 | 26 | 500 |
| August 2020 | 30 | 620 | February 2021 | 26 | 500 |
| September 2020 | 34 | 620 | March 2021 | 31 | 530 |
| October 2020 | 39 | 590 | April 2021 | 35 | 550 |
| November 2020 | 42 | 500 | May 2021 | 43 | 580 |
| December 2020 | 32 | 530 | June 2021 | 48 | 680 |
| Machine hours "000" | Fuel oil expense "000" | |
| Annual total | 420 | 6,840 |
| Monthly average | 35 | 570 |
| Sh. | |
| Materials | 6,000 |
| Assembly labour (12 hours at Sh.300 per hour) | 3,600 |
| Manufacturing overheads (150% of labour cost) | 5,400 |
| Profit mark-up | 6,000 |
| Selling price | 21,000 |
| 1 | It is expected that material cost per bicycle is to remain constant irrespective of the number of bicycles manufactured. |
| 2 | The management expects the assembly time to gradually improve with experience and has therefore estimated an 80% learning curve. |
| 3 | A racing team has approached the club's assembly department and made enquiries on the following quotations:
|
| Demand (units) | Probability |
| 2 3 4 5 6 7 8 | 0.02 0.08 0.22 0.34 0.18 0.09 0.07 |
| Lead time (weeks) | Probability |
| 1 2 3 4 5 | 0.23 0.45 0.17 0.09 0.06 |
| 1 | The production manager expects an 80% learning effect for this type of work. |
| 2 | The company uses standard absorption costing. |
| 3 | The costs attributable to the centre in which Product Aye is manufactured are as follows: |
| Direct materials | Sh.30 per unit | |
| Direct labour | Sh.6 per hour | |
| Variable overheads | Sh.0.50 per direct labour hour | |
| Fixed overheads | Sh.6,000 per four-week operating period |
| Amount spent per week Sh. | Annual income of head of household per year Sh. | Household size No. |
| 2,000 1,700 500 0 300 800 1,400 1,900 3,200 1,700 900 800 400 2,000 1,000 900 700 1,400 5,900 700 | 600,000 500,000 1,000,000 1,400,000 2,500,000 1,000,000 2,100,000 1,700,000 2,900,000 1,400,000 700,000 900,000 1,400,000 1,900,000 1,300,000 1,000,000 900,000 1,100,000 3,400,000 1,000,000 | 1 2 1 4 2 5 1 1 2 3 1 3 2 1 1 2 3 3 6 2 |
| Regression statistics | |
| Multiple R | 0.6691961 |
| R square | 0.447817 |
| Adjusted R square | 0.382855 |
| Standard error | 10.196161 |
| Observations | 20 |
| Anova | df | ss | ms | F | significance F |
| Regression Residual Total | 2 17 19 | 1432.03 1765.77 3197.80 | 716.0149 103.8688 | 6.893453 | 0.006423 |
| Coefficients | Standard error | t stat | P-value | Lower 95% | Upper 95% | |
| Intercept Income Size | -4.099268 0.985764 1.762415 | 5.583689 0.313508 1.716065 | -0.734151 3.144306 1.027009 | 0.472862 0.005915 0.318808 | -15.87984 0.32432 -1.858171 | 7.681302 1.647208 5.383002 |
| Year and Month 2016 | Machine hours Sh."000" | Fuel expenses Sh."000" |
| July August September October November December 2017 January February March April May June | 34 30 34 39 42 32 26 26 31 35 43 48 | 640 620 620 590 500 530 500 500 530 550 580 680 |
| Machine hours Sh."000" | Fuel expenses Sh."000" | |
| Annual total | 420 | 6,840 |
| Monthly average | 35 | 570 |
| Expected life (production) | 256,000 units |
| Sh. | |
| Selling price per unit | 123 |
| Direct material cost per unit | 36 |
| Total direct labour cost (first batch) | 52,500 |
| Variable overhead costs per unit | 24 |
| Total specific fixed costs | 3,875,000 |
| (i) | The expected profit to be earned from the product over its lifetime. |
| (ii) | It has now been established that the learning effect will continue for all ofthe 256 batches that will be produced. Required: The "learning curve" required to achieve a lifetime product profit of Sh.10 million, assuming that a constant learning rate applies throughout the product's life. |
| "Sh.000" | |
| Materials | 25,000 |
| Labour (2,000 hours x Sh.15,000 per hour) | 30,000 |
| Overhead (50% of labour cost) | 15,000 |
| 70,000 | |
| Profit mark-up (25%) | 17,500 |
| Selling price | 87,500 |
| (i) | If the customer above paid Sh.87,500,000 for the first machine, determine the price he would have to pay later for a second machine. |
| (ii) | Advise the management of Innovators Ltd. on the price quotation per machine if the customer above places an order for the third and the fourth machines as a single order. |