HAPTER

VARIANCE ANALYSIS PART 3

1. Sales volume variances
A sales volume variance arises from a difference in a company’s income as a consequence
of a different quantity of a product being sold in reality than budgeted. 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”.

For companies that sell multiple substitutable products, each of these variances can be
broken down into mix and quantity variances for further analysis. Let’s use an example to
see how.

Example:
Let’s imagine we own a soft drink factory, where we produce our popular blended mixture
of fresh apple and orange juice.

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

Cans Bottles Total

Budgeted sales 300 450 750
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 of 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

Cans Bottles
Standard mix (units) 294 441
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 £24 favourable

(£48 adverse + £72 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:

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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 the standard mix, and then multiplying the resulting figure by the standard
profit.

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.

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2. 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 outdated 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.

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, managers are able to dismiss them and instead focus
their attention on rectifying operational variances, which are more likely to involve
factors within management’s control.

The following example illustrates in more detail the importance of identifying

planning and operational variances.

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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 Quantity Price Total

Standard selling price per unit £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:

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

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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

Revised for the new standard price:

Standard quantity (kg) 2,500

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

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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

Revised for the new standard labour price:

Standard labour hours 1,500

Actual labour hours 1,450
Variance (hours) 50 favourable
x Standard labour rate £7
Labour efficiency variance £350 favourable

Reconciliation of budget to actual profit

Budgeted contribution (£63×500 units) £31,500j

Selling price variance £30,000j
Material price variance (£10,800)
Material usage variance (£1,000)
Labour rate variance (£2,900)
Labour efficiency variance £200j
Actual contribution £47,000j

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(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 of almost £15,000 higher than the standard.

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

 An increase in the minimum wage resulted in labour rates increasing to £7 per

hour.

 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.

 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 is related to the price
increase and how much is 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 revised
standard sales (or original standard if it isn’t possible to deduce the revised
standard).

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.

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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:

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
revised standard quantity of material used (or the original standard if it isn’t possible
to deduce the revised standard). Again, it is important to ensure that you use the
same quantity when calculating the material price operational variance to ensure that
the two variances reconcile correctly.

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 £8 -
100 more on materials than expected.

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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 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 the 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:

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

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This variance is calculated by finding the difference between the original standard
rate and the revised standard rate and multiplying it by the revised standard hours
(or original standard if it isn’t possible to deduce the revised standard).

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 the actual standard of usage hasn’t changed for the standard quantity of
materials and the standard hours of labour per unit, we do not need to split these
variances into planning and operational components. In this case, we can use
our original figures for the material usage variance and the labour efficiency
variance.

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

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Budgeted contribution £31,500j

Planning variances
Selling price planning variance £32,500j
Material price planning variance (£7,500)
Labour rate planning variance (£4,500)
Total planning variances £20,500j

Operational variances
Selling price operational variance (£2,500)
Material price operational variance (£2,700)
Material usage variance (£1,600)
Labour rate operational variance £1,450j
Labour efficiency variance £350)
Total operational variances (£5,000)
Actual contribution £47,000j

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

Budgeted contribution (£63×500 units) £31,500j

Selling price variance £30,000j
Material price variance (£10,800)
Material usage variance (£1,000)
Labour rate variance (£2,900)
Labour efficiency variance £200j
Actual contribution £47,000j

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

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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 managers 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 important that they are
investigated too. There are times when 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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3. Control reports
So, now you’re familiar with variance analysis, you need to be able to use it to help
management make key decisions that will guide the business to meeting its
objectives. Preparing control reports for management will enable them to
understand where costs have increased/decreased, the reasons why, and what
actions to take. Here’s a step-by-step guide to creating a useful control report:

1. Define the purpose and scope of the report

Determine the specific objectives of the control report. What aspects of the
business are you monitoring? Common areas include budget variances, cost control,
efficiency of operations, and overall financial performance.

2. Collect relevant data

Gather accurate and up-to-date information from various departments. This data
should include:

• Actual performance data (e.g. actual costs, actual revenues)

• Budgeted or standard performance data (e.g. budgeted costs, expected

revenues)

• Operational data (e.g. production volumes, labour hours)

3. Perform variance analysis

Compare actual performance against the budgeted or standard performance.

Identify variances, which are differences between actual and expected performance.

Types of variances:

• Favourable variance: When actual performance is better than expected (e.g.

costs are lower, revenues are higher)

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• Unfavourable variance: When actual performance is worse than expected

(e.g. costs are higher, revenues are lower)

4. Analyse the variances

Determine the causes of significant variances. This analysis could involve:

• Price variance: Differences due to changes in the price of materials or labour

• Quantity variance: Differences due to the amount of materials used or labour

hours worked

• Efficiency variance: Differences due to the efficiency of operations (e.g.

machine utilisation, labour productivity)

5. Summarise findings

Create a summary of key findings from the variance analysis. Highlight significant
variances and their potential causes.

6. Develop recommendations

Based on the analysis, recommend appropriate control actions to address

variances. Recommendations might include:

• Adjusting budgets or forecasts to reflect more accurate data

• Implementing cost-saving measures (e.g. sourcing cheaper materials, reducing

waste)

• Improving operational efficiencies (e.g. better scheduling of labour, optimising

production processes)

• Enhancing revenue strategies (e.g. adjusting pricing, marketing efforts)

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7. Prepare the report

Organise the control report into a clear and concise format. Include the following
sections:

1. Executive summary - Brief overview of the report’s purpose, key findings, and
main recommendations

2. Introduction - Background information on the budget or operational goals

3. Variance analysis - Detailed analysis of variances, including tables and charts

to illustrate key data points

4. Findings and discussion - Explanation of the causes of variances and their

impact on the business.

5. Recommendations - Specific, actionable steps to address the identified issues

6. Conclusion - Summary of the main points and a call to action for

management

8. Presentation to management

When presenting the report to management:

• Use visual aids (e.g. graphs, charts) to highlight key data

• Focus on the most critical variances and their implications

• Clearly articulate your recommendations and the expected benefits

• Be prepared to answer questions and provide further explanations if needed

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Example of a control report

Executive summary

This report analyses the variances in our production budget for the first quarter of
20X4. Key findings indicate a £20,000 unfavourable variance in raw material costs due
to increased prices and a £15,000 favourable variance in labour costs due to
improved efficiency. Recommendations include renegotiating supplier contracts and
implementing additional training programmes for staff to maintain efficiency levels.

Introduction

The purpose of this report is to monitor and control our production costs by
comparing actual performance against budgeted figures for Q1 20X4.

Variance analysis

• Raw materials cost variance: £20,000 unfavourable

• Labour cost variance: £15,000 favourable

Budgeted
Variance Actual (£) Variance (£)
(£)
Raw materials 130,000 150,000 (20,000) Unfavourable
Labour 90,000 75,000 15,000) Favourable

Findings and discussion

The raw material cost variance is primarily due to an unexpected increase in plastic
prices. The favourable labour variance is attributed to enhanced productivity
following the recent implementation of efficiency measures.

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Recommendations

• Raw materials: Negotiate long-term contracts with suppliers to lock in lower

prices and explore alternative suppliers.

• Labour: Continue with the current training programme to sustain productivity

improvements.

Conclusion

Addressing the raw material cost issue and maintaining labour efficiency will help
achieve our budget targets in the upcoming quarters.

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