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CHAPTER 4
Standard Costing1 Setting standardsThe nature of standardsA standard cost will comprise two elements.
♦ Technical standards for the quantities of material to be used and theworking time required.
♦ Cost standards for the material prices and hourly rates that should bepaid.
Standards from past recordsPast data can be used to predict future costs.
The main disadvantage with this method is that past data may containinefficiencies which would then be built into the standards.
Engineering standardsThis involves engineers developing standards for materials, direct labour andvariable overheads by studying the product and the production process.
Types of standardIdeal standards. In some cases standards are established on the assumptionthat machines and employees will work with optimal efficiency at all times.
Attainable (expected) standards. In other cases the standards set will be thosewhich are reasonably attainable.
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Current standards. These are standards established for use over a short periodof time, related to current conditions.
Basic standards A basic standard is one which, having been fixed, is notgenerally revised with changing conditions, but remains in force for a longperiod of time.
2 Revision of basic variancesA formula approachThe following operating statement will be used to revise direct materials, directlabour, machine cost and production overhead variances.
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Operating statement with flexed budget
Period ____________
Std perunit
Budgetfixed
Flexedbudget
Actual Budgetvariances
Number of units 1 1,000 1,100 1,100
£ £ £ £ £Sales 70.0 70,000 77,000 82,500
____ ______ ______ ______Direct materials: 1 kg @ £15
15.0 15,000 16,500 17,000
Direct labour: 1 hr@ £10
10.0 10,000 11,000 11,250
Machine cost: 1 hr@ £2.5
2.5 2,500 2,750 3,050
Fixed productionoverhead: 1 hr @ £5
5.0 5,000 5,000 5,300
____ ______ ______ ______Factory profit 37.5 37,500 41,750 45,900
____ ______ ______ ______Other fixed costsMarketing cost 12,500 12,500 12,950Admin cost 13,000 13,000 13,550
______ ______ ______Operating profit 12,000 16,250 19,400
====== ====== ======
♦
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♦ Direct materials actual usage = 1,200 kg
♦ Direct labour actual hours worked = 1,250 hours
♦ Machine hours actually worked = 1,250 hours
All direct cost variances can be calculated using a simple formula approach.
Formula approach for basic variances
Formula
Price type Quantity type
(SP – AP) AQ (SQ – AQ) SP
Key: SP = standard price AQ = actual quantity AP = actual price SQ = standard quantity
Applying the above formula to direct materials, labour and machine cost givesresults shown below.
Direct materials variances
Total £500 A
Price Usage(SP – AP) AQ (SQ – AQ) SP
−
1,20017,00015 1,200
(1,100 – 1,200) 15
= £1,000 F = £1,500 A
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Machine cost variances
Total £300 A
Expenditure Efficiency(SR – AR) AH (SH – AH) SR
−
1,2503,0502.5 1,250
(1,100 – 1,250) 2.5
= £75 F = £375 AKey: SR = standard rate SH = standard hours AR = actual rate AH = actual hours
Direct labour variances
Total £250 A
Rate Efficiency(SR – AR) AH (SH – AH) SR
−
1,25011,25010 1,250
(1,100 – 1,250) 10
= £1,250 F = £1,500 A
Given that absorption costing principles are being used in the operatingstatement, the following production overhead variances can be calculated.
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Analysing production overhead variances
Total
Volume Expenditure
Efficiency Capacity
A tabular approach will be used to calculate the above variances.
Calculating overhead variances
Std AB5,500
AFO5,300
Total200 F
Std AB5,500
Volume500 F
BFO5,000
Std AB5,500
AFO5,300
Expenditure300 A
Std AHW6,250
BFO£5,000
Efficiency750 A
Capacity1,250 F
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Key
Std AB = Standard absorbed in actual output £5 x 1,100 = £5,500
AFO = Actual fixed overhead = £5,300
BFO = Budgeted fixed overhead (flexed if necessary) = £5,000
Std AHW = Standard absorbed in actual hours worked
= 1 hour @ £5 x 1,250 hours = £6,250
3 Investigation of variancesGeneralised reasons for variancesIt has been suggested that the causes of variances can be classified under fourheadings.
♦ Planning errors (see Chapter 5)
♦ Measurement errors (errors caused by inaccurate completion oftimesheets or job cards, inaccurate measurement of quantities issuedfrom stores, etc – the rectification of such errors will probably not giverise to any cost savings though this is a generalisation)
♦ Random factors (elimination of these may well not save costs)
♦ Operational factors (elimination of these may save costs so we willconcentrate on them now)
Thus it is the specific operational causes of variances to which we will devoteour attention and which we will consider rectifying.
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Trend and materiality of variancesBy expressing each variance as a percentage of some base, such as thestandard cost, then both trend and materiality will become more evident.
Both of these characteristics should be important to management whendeciding whether to investigate a variance and the production process whichgave rise to it. A discernible trend could suggest either that the standard iswrong or out of date, or that there is a fault in the production process whichshould be rectified. The materiality of the variance will help to decide whetherthe benefits of investigation outweigh the costs.
The use of decision treesDecision trees can be used to aid management in deciding whether toinvestigate variances.
ExampleBruff Ltd manufactures a cure for sore throats which is sold in 300ml bottles. Inview of the problems being experienced in the mixing and bottling plant, acareful check is carried out on the quantity of materials used. Decisions thenneed to be made on whether or not to investigate the variance to determinewhether it was caused by a fault that might be correctable. The followingestimates have been made.
♦ Cost of carrying out an investigation = £2,000
♦ If a fault is detected, the cost of trying to correct the fault = £500
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♦ Cost incurred if a fault is not corrected = £6,000
♦ Probability that a variance is caused by a fault = 0.6
♦ Probability that the fault can be corrected = 0.8
Calculate whether the variance should be investigated.
SolutionTo decide whether to investigate the variance, a decision tree is drawn asfollows.
B
D
C
A
Investigate(£2,000)
No fault
Don’tInvestigate
Fault(£500)
Corrected
Uncorrected(£6,000)
No fault
Fault(£6,000)
0.8
0.2
0.4
0.6
0.4
0.6
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To evaluate the decision tree we start from the right hand side and workbackwards, calculating the expected values of costs and benefits at each fork inthe tree. As an example, the expected value of costs at fork C is given by 0.6 x£6,000 = £3,600. We call this EVC. Similarly, we can calculate the followingvalues.
EVD = 0.2 x £6,000 = £1,200
EVB = 0.6 x (£500 + £1,200) = £1,020
EVinv = £1,020 + £2,000 = £3,020
EVC = 0.6 x £6,000 = £3,600
Therefore investigate, because the expected costs of investigating are £3,020,whereas the expected costs of not investigating are higher at £3,600.
4 The learning curve effectThere are certain types of industry where the unit of production changes atregular intervals rather than a standard unit of production being manufacturedfor a very long period of time.
In such industries it is known that the time taken to produce a unit in the earlystages of a new product is significantly longer than the time per unit once theitem has been manufactured for some time.
For any operation which is repeated, the overall average time for the operationwill decrease by a fixed percentage as the number of repetitions is doubled.
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A typical reduction percentage is 20 per cent, so that an operation would havethe following time characteristics.
Time for first unit = 100 hoursOverall average time per unit for 2units
= 100 x 0.8 = 80 hours(ie 100 hours –20% x 100hours)
Overall average time per unit for 4units
= 80 x 0.8 = 64 hours
Overall average time per unit for 8units
= 64 x 0.8 = 51.2 hours
Overall average time per unit for 16units
= 51.2 x 0.8 = 40.96 hours
This situation is said to follow an 80 per cent learning curve. (Note that the 20per cent reduction in time is not quoted as the learning factor.)
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Graphical presentationThe learning curve
ExampleThe distinction between overall average time per unit and time to produce thelatest unit (or batch of units) is demonstrated in the following example of a firmexperiencing a learning factor of 80 per cent (or 0.8).
A trial batch of 100 units of a new product is produced in an average time of 20minutes per unit. Production times based on continually doubling the batch sizewould be as follows.
0102030405060708090
100
0 1 2 4 8 16Number of units
Overallaverage time
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Batchquantity
Cumulativequantity
Overall average timeper unit
Totaltime
Batchtime
(units) (units) (minutes) (minutes) (minutes)100 100 20.00 2,000 2,000100 200 20 x 0.8 = 16.00 3,200 1,200200 400 16 x 0.8 = 12.80 5,120 1,920400 800 12.8 x 0.8 = 10.24 8,192 3,072
If the planned production of this item for the coming month was, say, 300 (overand above the initial 100 thus making cumulative production 400), the total timeand average time per unit for this 300 would be:
Total time for 300 units = 5,120 – 2,000 = 3,120 minutes
Average time for these units = 3,120 ÷ 300 = 10.4 minutes
General formulaIn practice, it will frequently be necessary to estimate the effect of learning for alevel of production which is not a doubling point. This can be done using ageneral formula, as follows.
If a = time taken to produce the first unit (or first batch)
x = the cumulative production in units (or in batches of the samesize as the first batch)
and –b =2ofLog
ratelearningtheofLog
then y, the average time per unit (or per batch) to date is given by:
y = ax –b
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As an example, consider the 800 unit cumulative quantity and our initial batchsize of 100 (note that x is expressed as batches).
100800 = 8
–b =2Log
0.8Log = –3010.00969.0 = –0.3219
then y = 2,000 x 8 -0.3219
= 2,000 x 0.512
= 1,024 minutes
∴ Cumulative time to date = 8 x 1,024
= 8,192 minutes
The formula gives the average time per batch of 100 to date, and we musttherefore multiply by eight to get the total time to date. Also note that mostcalculators allow you to find two types of log: log buttons (to base 10) and lnbuttons (natural logarithms). It doesn’t matter which you use, as long as youare consistent.
Effect on budgetingIt would make a considerable difference to production planning, delivery datesand costing for this product and therefore, if it is known that a learning effect islikely to occur, it should be taken into account when preparing budgets.
