Question
Harry Draco Ltd manufactures laboratory equipment that requires a highly skilled labour force. The management has been experiencing inexplicable variances in terms of labour efficiency
Harry Draco Ltd manufactures laboratory equipment that requires a highly skilled labour force.
The management has been experiencing inexplicable variances in terms of labour efficiency for quite some time and is interested when they hear that other, similar manufacturers, have implemented the learning curve which potentially provides better costing than that used currently.
Management has decided to test the potential of the learning curve on a particular product, which is relatively new to the organisation.
The current standard cost is specified in the table below:
Material costs per unit | 5,000 |
Standard hours per unit | 140 |
Standard wage rate per hour | 22 |
Standard variable overhead rate per labour hour | 5 |
Total Fixed production cost for May | 10,000 |
Detailed records have been kept on the production of this item of equipment so the management accountant can extract the following information:
Cumulative production units | Cumulative average hours per unit | Cumulative total hours |
1 | 160 | 160 |
2 | 152 | 304 |
4 | 144.4 | 577.6 |
During the eighth month of production (May), the following data was recorded:
Cumulative production at the beginning of May | 50 units
|
Production within May | 10 units |
Total Material costs | 49,500 |
Total labour cost | 28,000 |
Total Variable cost | 6,000 |
Total fixed cost | 10,000 |
The management of Harry Draco Ltd would like to achieve profit maximisation but is unsure of how to achieve this and has currently set the price for this item of equipment based on a profit margin of 25%.
Required:
- Calculate the expected costs for May based upon:
- the current standard cost
- applying the learning curve formula.
- If the learning curve effect stops after 60 units, with the 60th unit representing the standard cost, prepare a standard cost for once the learning curve ends.
Note : y = b = the learning index = log learning rate/log 2
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