P1 Management Accounting

Module: 11

Variance Analysis – Yield

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1. Mix and yield variances

Direct material mixes and yield variances

Let’s imagine we own a juice factory. In recent weeks we’ve managed to launch a new product, which is a blended mixture of fresh apple and orange juice.

We typically juice together 8kg of oranges and 4kg of apples, which, after filtering out all the seeds and skin and pulp, produces a 10kg barrel of our finished product.

However, there are times when our local fruit supplier seems to run low on oranges. When this happens we usually have no choice but to adjust our production process slightly, instead using 6kg of apples and 6kg of oranges.

This is known as changing the input mix. By doing this, we deviate from our standard levels of input which can cause a change in our costs. This change is known as a mix variance.

We also notice that when we use more apples than oranges in our juice mixture, the output is slightly less. This is probably due to there being more pulp and waste in our apple juice than there is in our orange juice. As a result, using 6kg of apples and 6kg of oranges only gives us a 9kg barrel of juice.

This reduced output is known as a yield variance. To illustrate these concepts better, we’ll go through our example numerically and calculate the actual value of these variances.

Example:

The details for producing one barrel of juice are as follows:

Standard data Standard Input

kg

£/kg

Total cost Apples 4 £4.00 £16.00 Oranges 8 £7.00 £56.00

£72.00

Average cost of input (£72/12kg) £6.00 per kg Expected output (kg) 10

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Standard Output

Total standard cost of input £72.00 Expected output (kg) 10 Standard cost per kg of output £7.20

At the end of the production run, our actual results are as follows:

Actual data Input

kg

£/kg

Total cost

Apples 6 £4.00 £24.00 Oranges 6 £7.00 £42.00

£66.00

Actual output (kg) 9

Let’s go ahead and work out our direct material cost variance. You should recall this is simply the difference between the standard cost of our actual output and the actual cost.

Standard cost of output (9kg x £7.20 per kg) £64.80

Actual cost of output £66.00

Variance £1.20 adverse

This tells us that the barrel of juice we produced cost £1.20 more than standard, which is due to us spending more on materials than expected. By looking at our information, we can also see that the actual per kg price of material was the same as the standard price (£4/kg for apples and 7/kg for oranges), meaning our direct material price variance will be zero. Therefore, our entire variance will relate to a direct material usage variance.

We can now split our direct material usage variance into its mix and yield components:

Direct material mix variance

This is the difference between the cost of the actual input mix quantities and the standard input mix quantities at standard cost.

To calculate this variance, we need to compare the cost of our actual input to that of our standard input:

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Apples. Oranges Actual input (kg) 6 6

Standard input (kg) 4 8 Variance (kg) (2) 2 Standard price/kg £4.0 £7.00 Variance £8.00 £14.00 £6.00 adverse favourable favourable

We can see that we made slight changes to our standard mix of 4kg apples and 8kg oranges. We used fewer oranges and more apples to bring our mix to 6kg of each. This resulted in our costs being £6 less than standard, as the favourable variance suggests.

Direct material yield variance

This is the difference between the actual output quantity and the expected output quantities at the standard cost of input.

12kg of input should have yielded (units) 10 12kg of input actually yielded (units) 9 Variance (units) 1 adverse x standard cost of input £7.20 Variance £7.20 Adverse

To calculate this variance, we simply take the standard output expected and compare it to the actual output. To express the variance in monetary terms, we then multiply the variance in units by our standard cost of input per unit, which is calculated above.

In this example, we produced 1kg lower than the output that was expected from our level of input. The result is an adverse variance, indicating a £7.20 reduction in our profit.

Reconcile

As always, we should reconcile our variances. Our mix and yield variances should add up to the direct material usage variance that we calculated earlier:

Direct material mix variance £6.00 favourable Direct material yield variance £7.20 adverse Direct material usage variance £1.20 adverse

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Interpretation

It is important that you understand the interrelationship between the material mix and yield variances. A change in mix will almost certainly change the yield, and a change in yield is most likely due to a change in the mix, so it doesn’t really make sense to consider the two variances individually.

If a high adverse mix variance eventuates, management should consider the effect this has had on the yield. It is possible the ‘adverse’ product mix has actually improved the yield significantly, which might mean the overall effect is favourable. In some cases, it can even indicate that standards should be revised, and a new standard product mix should be set.

2. Labour mix and yield variances There are often times when different classes of labour are required in the production of any one product. For example, you may require specialists who cost significantly more than your average worker, or you might be able to use unskilled labour for some tasks, and so pay less.

In such scenarios, it is helpful to split your direct labour efficiency variance into mix and yield components. Let’s take a look at an example.

Example

Producing our juice involves two main processes – juicing and bottling. The bottling process is rather simple and involves little more than pushing a few buttons. However, the juicing process is a little more complicated and requires specially trained staff. Naturally this results in juicing workers being more expensive to hire than bottling workers.

The budget and actual labour results for the period are as follows:

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Standard data Input

Hours

£/hour

Total cost

Juicing labour (per unit) 2 £10.00 £20.00 Bottling labour (per unit) 3 £5.00 £15.00 5 £35.00

Average cost per hour (£35/5 hours) £7.00

Actual data Units produced

50

Juicing labour hours 90

Bottling labour hours 180

Wages paid £1,900.00

Let’s begin by calculating our labour rate and efficiency variances:

Labour cost variance

Actual labour cost £1,900.00

Standard labour cost (50 units x £35 per unit) £1,750.00

Labour cost variance £150.00 adverse

We’ll then split this into its labour rate and labour efficiency components:

Labour rate variance

90 juicing hours at standard cost (£10/hour) £900.00

180 bottling hours at standard cost (£5/hour) £900.00

Total standard cost of labour used £1,800.00

Actual labour cost £1,900.00

Variance £100.00 adverse

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Labour efficiency variance

You should recall that this is calculated by finding the difference in cost between the standard hours required for our actual output and the actual hours used. We’ll need to do this separately for each class of labour:

Standard juicing hours (50 units at 2 hours/unit) 100

Actual juicing hours 90 Variance 10 Standard juicing labour rate £10.00 Variance £100.00 favourable

Standard bottling hours (50 units at 3 hours/unit)

150

Actual bottling hours 180

Variance 30

Standard bottling labour rate £5.00

Variance £150.00 adverse

Total labour efficiency variance

£50.00 adverse

We’re now in a position to split our labour efficiency variance into its mix and yield components.

Labour mix variance

This is the difference between the cost of the actual labour mix quantities and the standard labour mix quantities at the standard labour rates.

Actual hours used

Juicing Bottling Total 90 180 270

Standard mix of hours

(40% juicing, 60% bottling) 108 162 270 Variance 18 (18) Standard labour rate per hour £10.00 £5.00 Labour mix variance £180.00 £90.00 £90.00 favourable adverse favourable

Let’s go through this calculation step by step. We begin with the actual hours used for each class of labour, in this case, 90 hours for juicing and 180 hours for bottling for a total of 270 hours.

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We then determine what the standard mix would’ve been for our actual labour hours. In this case, we used 270 total hours of labour. We know that the standard amount of hours to produce each unit is 5, which consists of 2 hours for juicing and 3 hours for bottling. This works out to be 40% juicing time and 60% bottling time.

We then apply this ratio to our actual hours of 270, which gives us a standard mix of 108 juicing hours and 162 bottling hours.

From there we calculate the difference between the standard mix and the actual mix and multiply it by the standard rate of labour.

In the above example, we can see that we had a favourable juicing mix of £180 and an adverse bottling mix of £90, for a total favourable variance of £90.

Labour yield variance

270 hours should have yielded (5 hours/unit) 54 units 270 hours actually yielded 50 units Variance in units 4 adverse x standard cost per unit £35.00 Labour yield variance £140.00 adverse

This variance is calculated by working out the difference between the standard yield and the actual yield. We know that our standard labour input is 5 hours per unit (2 hours juicing, 3 hours bottling). Therefore, at our standard rate, 270 hours of labour should have produced 54 units (270 hours/5 hours per unit). However, our actual output was only 50 units, giving us an adverse variance of 4 units. To express this variance in monetary terms we multiply it by the standard labour cost per unit of £35, giving us an adverse variance of £140.

Reconcile

It’s important to check the mix and yield variances are correct by ensuring they add up to our direct labour efficiency variance:

Direct labour mix variance £90.00 favourable Direct labour yield variance £140.00 adverse Direct labour efficiency variance £50.00 adverse

Interpretation

You should always consider the interrelationship between our labour mix and yield variances. We know that we have a £90 favourable labour mix variance. This indicates that we enjoyed a £90 cost benefit due to using a slightly different labour mix than the standard. Perhaps management

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decided to use the cheaper bottling staff in the juicing process, or simply tried using fewer juicing hours wherever possible.

However, the favourable mix variance is more than offset by an adverse labour yield variance of £140. This suggests that the changes in our labour mix translated into less efficient processing, ultimately leading to a lower output.

It should be clear that the variances are almost always interdependent, and a favourable change in one variance can easily contribute to an adverse change in the other. Therefore, it’s important that you understand the impact that the mix can have on the yield and vice versa.

Step 1: Calculate variance between actual and budgeted input

Calculating standard hours in this step is just a matter of multiplying the standard hours per unit by the actual units produced. We know that we produced 50 units during the period. The standard juicing hours per unit is 2, therefore the total juicing hours required is 100 (50 units x 2 hours per unit). Similarly, the standard bottling hours per unit is 3, which gives us a total of 150 bottling hours (50 units x 3 hours per unit).

Juicing Bottling Actual hours 90 180 Standard hours for actual output 100 150 Variance (hours) (10) 30

We simply subtract these totals from the actual number of hours to find our variance.

Step 2: Calculate difference between standard and average cost

In this step we are comparing the price of each class of labour to the average price of labour. It simply illustrates that the cost of labour for juicing is higher than average and the cost of labour for bottling is lower.

Juicing Bottling Average hourly labour rate £7.00 £7.00 Standard hourly labour rate £10.00 £5.00 Variance (£3) £2.00

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Step 3: Calculate variance

Juicing Bottling Input variance (hours) (10) 30 Cost variance (£3) £2.00 Total variance £30.00 £60.00 £90.00

favourable favourable favourable

The labour mix variance is £90 favourable, which is the same result we came to use our earlier method. However, once again the logic is a little different here. Although we used more than the standard bottling hours allowed, this is actually the cheaper input. Conversely, we required less juicing hours, which is the more expensive input. Using more of the cheaper input and less of the expensive input both contributed to the total cost being lower than standard. As a result, both input variances are considered favourable.

3. Sales volume variances You should already be familiar with sales volume variances from our previous chapter.

An example of a sales volume variance is the sales volume profit variance, which determines the effect on profit that arises from unit sales being different than expected. You should recall that we calculate this variance as follows:

(Budgeted unit sales – actual unit sales) x standard profit per unit

Other variations of the sales volume variance are the sales volume contribution variance, which is calculated by replacing ‘profit’ in the above equation with ‘contribution’, and a sales volume revenue variance which is calculated by replacing ‘profit’ with ‘revenue’.

Each of these variances can be broken down into mix and quantity variances for further analysis.

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Example:

Let’s assume that we sell our juice in two forms – cans and bottles. Details for the period are as follows:

Cans Bottles Total Standard mix (units) 8 12 20 Standard profit £2 £3 Total profit £16 £36 £52

Average profit per unit (£52/20 units) £2.60

Budgeted sales

Cans 300

Bottles 450

Total 750

Actual sales 270 465 735

Sales volume profit variance

Our first step is to calculate our sales volume profit variance (alternatively you could calculate a sales volume contribution variance or sales volume revenue variance).

Cans Bottles Budgeted sales (units) 300 450 Actual sales (units) 270 465 Variance (units) 30 adverse 15 favourable x standard profit per unit £2 £3 Sales volume profit variance £60 adverse £45 favourable

Total sales volume profit variance £15 adverse (£60 adverse + £45 favourable)

You should already be familiar with the calculation of this variance. Notice that we calculate the variance separately for each product line and then add the two together to find our total variance.

This variance can now be divided into its mix and quantity components:

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Sales quantity profit variance

Total budgeted sales (units) 750 Total actual sales (units) 735 Variance 15 adverse x average standard profit per unit £2.60 Sales quantity profit variance £39 adverse

This variance is calculated by taking the total number of units sold and deducting it from the budgeted number unit sales. To express this value in monetary terms we then multiply it by the average standard profit per unit. Note how the variance does not concern itself with the type of unit sold. We are simply trying to determine the profit impact of the variance in sales quantity – whether bottles or cans were sold is irrelevant.

Sales mix profit variance

Standard mix (units)

Cans 294

Bottles 441

Actual mix (units) 270 465 Variance (units) 24 Adverse 24 favourable x standard profit per unit £2 £3 Variance £48 Adverse £72 favourable

Sales mix profit variance (£48 adverse + £72 favourable)

£24 favourable

Calculating this variance is very straightforward once we have calculated our standard mix.

To calculate our standard mix, we work out each product’s portion of the mix as a percentage, and then multiply this amount by the actual output. This is laid out in the workings below:

Cans Bottles Total Standard mix (units) 8 12 20 Standard mix (percentage) 40% 60%

Multiplied by actual output (735 units) 294 441

From there it is simply a matter of calculating the difference between the actual mix and standard mix and multiplying the resulting figure by the standard profit.

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Reconcile

To ensure our variances are correct, we add them together to see that they add up to our original sales volume profit variance:

Sales volume quantity variance £39 adverse Sales volume mix variance £24 favourable Sales volume profit variance £15 adverse

Interpretation

By breaking down the sales volume profit variance into quantity and mix components, we can see that selling a lower quantity of units adversely impacted our profit by £39. This was partially offset by a favourable change in the actual mix of products sold, which provided a benefit of £24.

A typical explanation for this scenario might be that there was a major sales drive on the more expensive product (bottles). However, while increased sales of bottles increased our profit somewhat, sales of cans suffered to a greater extent which resulted in an adverse effect overall.

Once again you should see that both variances have the ability to heavily influence each other. This reinforces the idea that variances need to be analysed together in order to make the best possible decisions.

4. Planning and operational variances Standards are typically set at the beginning of the year and are based on assumptions that were true at that point in time. However, the modern business environment is fast changing, and it is unlikely that all factors of production will remain unchanged for any given period. Therefore, when conducting variance analysis, it is important to distinguish the components of variances that are within the control of management from those that are not. We make this distinction by using planning and operational variances.

Planning variances

Planning variances relate to the portion of a variance that is outside management’s control. For example, if we are heavily dependent on oil and there is a large increase in the price of oil, the result will be a large adverse direct material price variance. However, this is outside the control of the management as it is due to external factors rather than poorly managed operations. In this case, the standard is now out-dated and needs revising. Therefore, the portion of the variance that relates to the price increase will be isolated as a planning variance and not negatively impact our assessment of our managers.

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Operational variances

These are variances that are within the control of management. In our above example, any adverse variance that extends over and beyond the oil price increase will be considered an operational variance. These could be due to anything from careless purchasing to using higher quality materials.

It should be obvious that planning variances are seldom investigated, as they are often outside the organisation’s influence and are generally unavoidable. By identifying these variances, management are able to dismiss them and instead focus their attention on rectifying operational variances, which are more likely to involve factors within the management’s control.

The following example illustrates in more detail the importance of identifying planning and operational variances.

Example:

We’ve just opened a new factory to manufacture our new toy car. We’ve employed a new manager to run the factory and would like to carry out a variance analysis to assess the efficiency of his operation. The details are as follows:

Standard data Standard selling price per unit

Quantity Price Total £100

Raw materials per unit(kg) 5 £5 £25 Labour hours per unit(hours) 3 £4 £12 Standard contribution per unit £63

Standard production (units)

500

Actual data

Quantity

Price

Total

Actual sales 500 £160 £80,000

Raw materials (kg) 2,700 £9 £24,300 Labour (hours) 1,450 £6 £8,700 Actual contribution 500 £94 £47,000

Actual production (units)

500

Let’s start off by calculating our key variances:

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Selling price variance

Actual selling price £160 Standard selling price £100 Variance £60 favourable x sales volume (units) 500 Selling price variance £30,000 favourable

As our standard and actual sales volume was the same, the sales volume contribution variance will be nil.

Material price variance

Standard material price per kg £5

Actual material price per kg £9 Variance £4 adverse x actual quantity (kg) 2,700

Material price variance £10,800 adverse

Material usage variance

Standard quantity (kg) 2,500

Actual quantity (kg) 2,700

Variance (kg) 200 adverse x standard price per kg £5

Material usage variance £1,000 adverse

Labour rate variance

Standard labour rate £4

Actual labour rate £6

Variance £2 adverse x actual hours 1,450

Labour rate variance £2,900 adverse

Labour efficiency variance

Standard labour hours 1,500 Actual labour hours 1,450 Variance (hours) 50 favourable x standard labour rate £4 Labour efficiency variance £200 favourable

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Reconciliation of budget to actual profit

Budgeted contribution (£63×500 units) £31,500 Selling price variance £30,000 Material price variance (£10,800) Material usage variance (£1,000) Labour rate variance (£2,900) Labour efficiency variance £200 Actual contribution £47,000

(Negative figures signify adverse variances)

With our typical variance analysis, it appears that our new manager has achieved a high level of efficiency. The favourable variances outweigh the adverse variances, which have produced a contribution almost £15,000 higher than standard.

However, upon review our manager informs us of several changes that were beyond his control:

1. An increase in the minimum wage resulted in labour rates increasing to £7 per hour.

2. There was a market increase in the price of metals required to produce the cars. This raised the market price of our raw material to £8 per kg.

3. Demand for the product surged significantly. Retailers were

scrambling for the limited supply, which allowed us to sell each unit at a premium price of £165.

Now that we’ve been made aware of changes in several factors, it is prudent to revise the standards that were originally in place at the start of the period. Following this it is important that we break our variances down into planning and operational components. The process for doing this is outlined below:

Selling price variance

Our standard selling price at the start of the period was £100 per unit. However, we now know that it was viable to expect a standard selling price of £165 per unit during the period. This represents what we will now use as our new standard price, or ‘revised’ standard price.

In order to determine how much of the increased revenue related to the price increase and how much related to effective operations, we’ll separate our variance into planning and operational components:

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Selling price planning variance

Standard selling price £100 Revised standard selling price £165 Variance £65 favourable x standard quantity 500 Selling price planning variance £32,500 favourable

This planning variance is calculated by finding the variance between the original standard price and the revised standard price, and then multiplying it by the standard volume of sales.

In the above example, the variance is £32,500 favourable. This indicates that profit increased by £32,500 simply due to a change in standard. The increase was not due to any action taken by management.

Selling price operational variance

Actual selling price 160 Revised standard selling price £165 Variance £5 adverse x actual quantity 500

Selling price operational variance £2,500 adverse

This variance is calculated in the same way as our usual selling price variance, only this time we use the revised standard price rather than the original standard price.

In the above example the variance is £2,500 adverse. This suggests that our profit was reduced by £2,500 due to receiving a lower selling price than we could have expected in the market. This is considered to be the portion of the variance that management had control over.

Material price variance

The standard price of raw materials at the start of the period was £5/kg. However, external factors outside management’s control led to the market price increasing to £8/kg, which we will now adopt as our revised standard price.

In order to distinguish between the portion of our material price variance that relates to the price increase and the portion that relates to operations, we’ll separate our variance into planning and operational components:

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Material price planning variance

Standard price per kg £5 Revised standard price per kg £8

Variance £3 adverse x standard quantity (kg) 2,500 Material price planning variance £7,500 adverse

This variance is calculated by finding the difference between the original standard price and the revised standard price of materials, and then multiplying it by the standard quantity of material used.

In the above example the variance is £7,500 adverse. This indicates that due to a price increase in materials that management had no control over, we spent £7,500 more on materials than expected.

Material price operational variance

Actual price (per kg) £9 Revised standard price (per kg) £8 Variance £1 Adverse x actual quantity (kg) 2,700 Material price operational variance £2,700 Adverse

This variance is calculated by finding the difference between the revised standard price and the actual price of materials and multiplying it by the actual quantity of materials used.

In the above example we have an adverse variance of £2,700. This indicates that we paid £2,700 more for materials than was expected, due to factors that management had control over.

Labour rate variance

The standard price of labour at the start of the period was £4 per hour. However, changes to minimum wage led to the price of labour increasing to £7 per hour, which we will now use as our revised standard labour rate.

In order to distinguish between the portion of our labour rate variance that relates to the minimum wage increase and the portion that relates to operations, we’ll separate our variance into planning and operational components:

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Labour rate planning variance

Standard hourly rate £4 Revised standard hourly rate £7 Variance £3 adverse x standard hours 1,500 Labour rate planning variance £4,500 adverse

This variance is calculated by finding the difference between the original standard rate and the revised standard rate and multiplying it by the standard hours required. In the above example the variance is £4,500 adverse. This represents the effect on our profit that arises from the change in standard, i.e. a change in minimum wage, which is unrelated to operational efficiency.

Labour rate operational variance

Actual hourly rate £6 Revised standard hourly rate £7 Variance £1 favourable x actual hours 1,450 Labour rate operational variance £1,450 favourable

This variance is calculated in the same way as our usual labour rate variance, only this time we use our revised standard rate rather than our original standard rate. The favourable variance of £1,450 indicates that due to greater operational efficiency by management, we managed to pay a lower rate of labour than expected, resulting in a saving of £1,450.

Other variances using revised standards

Because our standards have changed, we’ll also need to recalculate our material usage and labour efficiency variances. However, because the actual standard of usage hasn’t changed (the standard quantity of materials and the standard hours of labour per unit is still the same), we do not need to split these variances into planning and operational components.

Material usage variance

Standard quantity (kg) 2,500 Actual quantity (kg) 2,700 Variance (kg) 200 adverse x revised standard price £8 Material usage operational variance £1,600 adverse

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Labour efficiency variance

Standard hours 1,500 Actual hours 1,450 Variance 50 favourable x revised standard rate £7 Labour efficiency variance £350 favourable

Reconcile

Let’s reconcile our budget to actual profit using our revised standards (negative figures signify adverse variances):

Budgeted contribution £31,500 Planning variances

Selling price planning variance £32,500

Material price planning variance (£7,500)

Labour rate planning variance (£4,500)

Total planning variances £20,500

Operational variances

Selling price operational variance (£2,500)

Material price operational variance (£2,700)

Material usage operational variance (£1,600)

Labour rate operational variance £1,450

Labour efficiency operational variance £350

Total operational variances (£5,000) Actual contribution £47,000

You should take a moment to compare this with our original reconciliation:

Budgeted contribution (£63×500 units) £31,500 Selling price variance £30,000 Material price variance (£10,800) Material usage variance (£1,000) Labour rate variance (£2,900) Labour efficiency variance £200 Actual contribution £47,000

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Analysis

The above reconciliation illustrates the importance of distinguishing between planning and operational variances.

We now are able to benchmark management’s performance against standards that were genuinely achievable (operational variances). We can also identify which variances were due to external factors that management could not have been expected to control (planning variances).

The labour rate variance, which we originally found to be adverse, is actually favourable from an operational viewpoint. This indicates that management was effective at keeping labour rates to a minimum, even though our original analysis led us to believe otherwise.

Our material price variance under traditional variance analysis was £10,800 adverse, suggesting that management had been largely ineffective in controlling material purchase prices. Further analysis uncovered that the operational portion of this variance was only £2,700, and, while still adverse, this paints a much more encouraging picture of material purchasing processes.

Our original selling price variance was a favourable variance of £32,500. This gives us the impression that our management were highly effective at researching and negotiating the highest possible selling price for our product, leading to revenues of £32,500 above our budgeted amount. Further analysis tells us that the operational portion of this variance is actually adverse, indicating that management were in fact unable to take advantage of the market’s highest possible prices.

You should also understand that although planning variances are considered uncontrollable from an operational standpoint, it is actually important that they too are investigated. There are times where certain factors could reasonably be expected to change, and therefore the fault lies not with an “uncontrollable” factor but with the standard setting process itself.

In our example above, it is unlikely that politicians woke up one day and decided instantly to change the minimum wage. Such a significant legal issue would have been discussed in the media for weeks or even months before it was finally agreed upon, and business owners would most likely be made well aware of the date the new law was to become effective.

Therefore, it could be argued that this cost increase was not unexpected at all but should have been known to management and considered during the standard setting process. In these situations, the variances are not actually “uncontrollable”, but are rather due to faulty standard setting which should be addressed during the planning process.

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5. Standards and variances in non-manufacturing organisations

Service industries

Variance analysis through standard costing has been criticised for being poorly suited to service industries, with critics claiming that in such industries the human influence is too great to allow accurate standards to be set. Moreover, establishing a measurable cost object for certain services is even more difficult, as in many service industries each cost object is never truly the same (consider how a tour company will need to modify each itinerary to suit diets, weather, number of passengers etc.).

Nevertheless, standard costing is still used in the service industry today, aided by the use of certain techniques to help standardise services and identify measurable cost objects. One such phenomenon is the idea of ‘McDonaldisation’.

McDonaldisation

McDonaldisation lies in direct contrast to the criticism that standard costing is ineffective because of its lack of incentive for constant improvement. Instead, it tries to argue that offering high volumes of highly homogeneous products that never change (similar to McDonald’s fast food offerings) is highly advantageous in the modern business environment. This ideology is often applied to the service industry in an effort to standardise services and reduce the element of human influence wherever possible. McDonaldisation aims to deliver four key advantages:

Calculability

Every McDonald’s meal is identical in weight, size and price, serving as a measurable cost object for which a standard price can be set.

Efficiency

Due to the homogeneous nature of the products, they are cheap and fast to produce, and are efficient in their use of resources.

Predictability

Customers can be confident of what to expect when they purchase the product, regardless of time or place. A Big Mac from Australia will be the same as a Big Mac from the UK.

Control

Human influence is removed wherever possible. Each item is produced using a known set of materials and a predetermined set of tasks. This allows for effective comparisons between actual and standard inputs and outputs.

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In applying the concept of McDonaldisation to service industries, providers aim to homogenise their offerings as much as possible. If every service is the same as the last, it is possible for a standard cost to be set that can be meaningfully be compared to actual outputs. The calculability factor can be overcome by establishing a measurable cost object, a good example being the use of ‘passenger miles’ in the airline industry.

Public services

In the healthcare industry there have been instances of standard costing being employed in order to help evaluate performance and determine remuneration levels of healthcare providers. This is done by determining a standard cost for treating patients with specific medical conditions.

A common method of implementing this model is through the use of Diagnostic Reference Groups (DRG’s). A DRG is established for each medical condition, with patients then being classified into DRG’s upon this basis. It is then assumed that each patient within the DRG is suffering from the same condition and therefore will require similar treatment.

Healthcare funders (such as the government or health insurance providers) then proceed to apply standard costing principles based on these groups. If treating a patient for heart disease is believed to have a standard cost of £5,000 and a hospital runs up costs of £8,000, this is considered an adverse variance and the hospital’s operations will be considered inefficient.

While it does exist in the real world, the use of standard costing in such an environment has been widely criticised. Every patient is different, and similar symptoms will not always translate into similar treatments. In the above example, there may be one patient with heart disease who requires emergency surgery, while another simply requires a dietician consultation and a slap on the hand for eating too many cheeseburgers. Using such a system can also encourage healthcare providers to offer “standard” treatments where it may not be completely appropriate, all in an effort to cut costs or secure funding.

Using evaluation measures that can encourage such practises is a sensitive issue, particularly in healthcare where the need for quality and complete service is arguably more important than cost or profit. Needless to say, a costing system in such an environment is a delicate issue and must be carefully implemented in order to be viable.

Professions

In this chapter we explored the value of using a labour mix variance. While often used in manufacturing environments, this variance is unique in that it is actually best suited to the service industry.

Consider an audit firm’s everyday operation and how it employs the use of several different levels of staff. Graduates may carry out much of the basic

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work of collating data and preparing simple summaries, while the partner in charge will typically consider the big picture issues and sign his name to the final report. Somewhere along the line the seniors, assistant managers and managers will take care of everything in between.

As every level of staff has a different pay rate, determining the mix of staff that would incur the lowest cost can be a challenging issue. Perhaps tasks undertaken by seniors could be done just as quickly by graduates, or perhaps the partner is spending too much time on tasks which could reasonably be delegated.

Using the labour mix variance can be a good indicator of the efficiency of professional practices, as a key operational challenge in such environments is the effective integration of junior and senior staff.