# Variance Analysis – Part 3

Now that we’ve dealt with the direct material and direct labour parts of production, what about looking at overheads? Of course, we have variance calculations for overheads as well. We even split it up between fixed and variable overheads.

Let’s now make it more challenging and do both fixed and variable overheads in the same calculation with a more complex example.

Example information:
Budgeted production output was 600 units. Budgeted variable overheads were £900. Budgeted fixed overheads were £480. Overheads are absorbed according to machine hours. Machine hours were budgeted at 0.50 hours per unit. Actual production output was 580 units, actual variable overheads were £825, actual fixed overheads were £520, total machine hours were 270.

Step 1 – Since it is not given, we need to calculate what the standard absorption rates are for fixed and variable overheads. We first take the budgeted production output and multiply it by the machine hours per unit = 600 units x 0.50 hours = 300 total budgeted machine hours.

Step 2 – We then take the budgeted variable overheads and divide it by the total budgeted machine hours = £900 / 300 = £3.00 is the variable overhead absorption rate. We will do the same for the budgeted fixed overheads and divide it by the total budgeted machine hours = £480 / 300 = £1.60  is the fixed overhead absorption rate.

Step 3 – We then take budgeted machine hours per unit and multiply it with the rates. For variable overhead rate, it is 0.50 hours x £3.00 = £1.50. For fixed overhead rate, it is 0.50 hours x £1.60 = £0.80. These are the standard rates/costs.

Step 4 – Now let’s do actuals! To calculate the expenditure variances, we will need to know the actual overheads figures and we will need to use the actual machine hours spent and multiply it with the absorption rates.

Actual quantity (number of machine hours) is 270 hours and multiply this with the variable overheads absorption rate of £3.00 = £810
The difference of £825 – £810 = £15 is the variable overheads expenditure variance and is an adverse one in this case.

Note that I will now start denoting an adverse variance with an (A) after the figure, and a favourable variance will have an (F) after the number.

Actual quantity (number of machine hours) is 270 hours and multiply this with the fixed overheads absorption rate of £1.60 = £432
The difference of £520 – £432 = £88 is the fixed overheads expenditure variance and like above variance, is an adverse one as well.

Step 5 – To calculate the volume and efficiency variances, we will need to alter the way we look at the information a little bit. For some students, it can be an easy switch in outlook, but for others, it can leave you scratching your heads. So I’ll try and make it practical.

For efficiency, we should be looking at how much hours did we spend producing vs how much hours it should have been. Have a look at below calculation:

Actual output is 580, which took 270 actual hours to produce. As per budget, each unit should have taken 0.50 hours to make. If we take the actual output and multiply it with the budgeted machine hour per unit (580 x 0.50), it should have taken us 290 hours. But instead, we only took 270 hours. This is a favourable difference because it has taken us faster to produce more output. We are more efficient!

To value this efficiency difference, we will need to take the differences in hours (290 – 270 = 20) and multiply this with the variable overhead absorption rate of £3.00 to place a value (20 x £3.00). The difference of £60.00 is a favourable variable overheads efficiency variance.

Let’s go a bit further and calculate total variable overheads variance. I probably should point out that having an adverse variance on one side and favourable variance on the other would matter in the total calculation. Adverse and favourable variances have the opposite effect on each other. So if you ever encounter this scenario (as we have above), we would need to calculate the difference rather than the sum of the rate and efficiency variances.

For volume, we should be looking at how much we produced (quantity of units) vs how much was budgeted for.

To calculate the fixed overheads volume variance, we should find the difference between the budgeted output of 600 units and actual units of 580. This particular difference is an adverse one since we have produced less than what was planned (600 – 580 = 20). To place a value, we should take this difference and multiply it with the fixed overheads standard rate of £1.60 to get the result of £32.00 (20 x £1.60).

And for the total fixed overheads variance:

Shall we stop here? There are more sets that I’d like to cover the next few weeks – planning, mix and yield variances. Whoever thought that there would be so much variance analyses? I certainly did. Didn’t I say that when I first started this series?

Operational OT course available here.

# Variance Analysis – Part 2

So if I haven’t made you run away with Part 1, then I must have done something right. Welcome to Part 2 of Variance Analysis.

Remember our memorised visual two weeks ago? If you’ve got it stuck in your mind, then that’s good. That’s how we wanted it all along. For those who’ve forgotten, here it is again.

We made use of this table for a couple of variances. This week, we will look at direct labour and overheads variances. The concept is not different from how we worked out direct material variances. Only the terms change with regards to whether it is a price variance, usage variance, efficiency variance, volume variance, expenditure variance, etc. But I’m getting way aheadof you guys.

Let me start by introducing 2 direct labour variance – direct labour rate variance and direct labour efficiency variance.

In this instance, because we are dealing with rates, scratch out the price on the table and call it rate for a second, like so.

So now let’s start with our variances and take direct labour rate variance. Similar to how we did our calculations when dealing with direct material, the same goes for direct labour. We never like overcomplicating things so we tweak the same example information with just slight changes to the numbers. We will also note that the quantity is now based according to how many hours and the rate is the labour rate per hour.

Example information: Actual value of production hours was £7000. Actual number of production hours was 1000 hours. It was originally forecast at £5 per hour.

So we will do the calculations as follows:
Actual value at actual rate = £7000 (we can easily calculate this out since we know the actual production hours, and if we do, we work out that actual rate used was £7000/1000 hours = £7)
Actual production hours = 1000 x standard rate at £5 = £5000

The difference of £2000 is a rate difference (because we are calculating the difference between rate rather than quantity or number of actual hours). And since actual rate is much higher than the standard rate, this direct labour rate variance is an adverse one.

Looking at above table, what happens to Columns 2 and 3? The difference between these 2 columns affect the actual and standard quantity (number of spent hours) and so will generate the direct labour efficiency variance.

Example information: Actual number of production hours spent was 1000. It was originally budgeted to spend 1200 at a rate of £5 per hour.

The calculations would be as follows:
Actual number of production hours = 1000 x standard rate at £5 = £5000
Standard number of production hours = 1200 x standard rate at £5 = £6000

The direct labour efficiency variance of £1000 arises from producing the goods in less time than originally budgeted for and so this variance is considered to be a favourable one.

The sum of direct labour rate variance and direct labour efficiency variance is the total direct labour variance. In this calculation, the total direct labour variance is then calculated as (Adverse) £2000 + (Favourable) £1000 = (Adverse) £1000.

To check:
Actual value at actual rate = £7000
Standard number of production hours = 1200 x standard rate at £5 = £6000
The difference is £7000 – £6000 = £1000 adverse, which confirms above result.

The next segment of variance analysis will be a  longer one dealing with overheads and so I’m going to end this one off here for now. Tune in next week for our third instalment.

P.S. If you’d like to get your hands on the P1 study text, why not visit Astranti to have a look at what they have on offer?

# CIMA Exam Pass Rates – June 2017

CIMA has recently released the latest pass rates for June 2017.

Gateway level pass rates continue to be below 50%. But the good news is that after last quarter’s dismal performance and percentage, all the pass rates seem to have gone up for all case studies levels this quarter. Some jumped up more so than others (management level). Strategic level maintains its average of 60+% for the last year.

The exam pass rates are calculated on total exams passed over total exams taken for the periods stated below. Highlighted in blue are the subjects/courses that have less than 50% pass rate.

It is important to note that the Certificate level percentages are based on the new Cert BA 2017 syllabus and only cover the period from 11 January 2017 to 30 June 2017.

Don’t be deterred or disheartened by the low pass rates for some of the courses/ subjects/ levels. If you’d like to get your hands on some study materials, visit our Resources page for some helpful links.

For a graphic view of this, check out our CIMA Exam Pass Rates page.

Level
Operational
Management
Gateway
Strategic
Aug 2016
64%
71%
39%
63%
Nov 2016
67%
71%
33%
65%
Feb 2017
45%
62%
26%
64%
May 2017
47%
73%
39%
66%

### For Objective Tests

These results are total exams taken from 1 July 2016 to 30 June 2017.

Passed
vs Taken
E1
75%
P1
45%
F1
68%
E2
84%
P2
46%
F2
50%
E3
65%
P3
52%
F3
53%

### For Certificate Level

These results are total exams taken from 11 January 2017 to 30 June 2017.

Exam Pass Rate
BA1
72%
BA2
59%
BA3
67%
BA4
59%

# Variance Analysis – Part 1

If you find variance analysis to be a confusing topic, then you are not alone in this world. I’ve been teaching P1 for a while and most students I find tend to get muddled up when calculating the different variances. It also doesn’t help that there are just so many of them to remember.

But in my years of teaching, I’ve discovered a general model that’s used by many tutors and students alike to understand variances better. I’d like to share this with you today and I hope this helps you in some little way.

Below is a visual tip on what you need to remember when looking at variances. Please memorise. It will become useful in a second once we start using it.

So we have a lot of different variances – sales variances, material variances, labour variances, and overhead variances. We also have price variances, usage variances, efficiency variances, expenditure variances, yield variances, mix variances, volume variances, etc. See how it can easily get confusing?

Do you still remember the table a couple of minutes ago? Good! Let’s try it on some Sales Variances. We have 2 main ones we will do today. One is called the selling price variance and the other is the sales volume profit variance.

Let’s bring back our table and determine which difference yields which variance. If we look at the first 2 columns, we have the same actual quantity. The price is the difference here. We have an actual price and a standard price. If we multiply the quantity and the price, and then subtract column 1’s result to column 2, we will get the price variance.

Example information: Actual quantity sold was 500 at £5 per piece. It was originally forecast as £2 per piece.

So we will do the calculations as follows:
Actual quantity = 500 x actual price at £5 = £2500
Actual quantity = 500 x standard price at £2 = £1000
The difference of £1500 is a price difference (because we are calculating the difference between price rather than quantity). This is the selling price variance and it is a favourable variance.

Why did I say that? Well, the actual total sold in terms of quantity and price was a lot higher than what we were half expecting. If we look at the 3 columns, we could classify them as:

Column 1 – Actuals (because it is actual quantity and actual price)
Column 2 – Half Expected (because it is actual quantity but with standard price)
Column 3 – Budgeted (because it is standard quantity and standard price)

A simpler way to remember is, if the first column’s total (the actuals) is higher than the second (the half expected), then it is a favourable selling price variance. If it’s the other way around, then it is an adverse selling price variance.

Of course, this only works if the table and the cell descriptions do not change in any way.

Now let’s try the other side of the table where the quantities are the ones that’s changing. That is column 2 and 3 – see below table.

Example information: Actual quantity sold was 500. It was originally forecast to sell 400 at £2 per piece.

So we will do the calculations as follows:
Actual quantity = 500 x standard price at £2 = £1000
Standard quantity = 400 x standard price at £2 = £800
The difference of £200 is a volume difference (because we are calculating the difference between quantities rather than the price). This is a sales volume profit variance. And it is a favourable one.

Again, as per prior explanation, if the second column’s total (the half expected) is higher than the third (the budgeted), then it is a favourable selling price variance. If it’s the other way around, then it is an adverse selling price variance.

Still with me on this? Ready for the next lot?

So let’s do Direct Material Variances this time. And let’s not overly complicate our example. We will use the same example as above except now instead of being sold, it is being used in production.

Example information: Actual quantity used was 500 with cost price of £5 per piece. It was originally forecast with cost of £2 per piece.

The calculations would be as follows:
Actual quantity = 500 x actual price at £5 = £2500
Actual quantity = 500 x standard price at £2 = £1000
The difference of £1500 is a price difference. Because this pertains to direct material, the difference is referred to as direct material price variance.

So here’s the kicker. With regards being adverse or favourable, the direct material price variance in this case is an adverse one. You may wonder why it is the other way around compared to the selling price variance. The simple reason is that the selling price variance deals with income and so if the actuals are higher than the half expected and budgeted, that means we are getting more income. With direct material, it is an expense. So if the actuals are higher, this would mean we are spending more.

Let’s go onto our second direct material variance – the direct material usage variance. Again, let’s tweak the same example.

Example information: Actual quantity used was 500. It was originally budgeted to use 400 at cost price of £2 per piece.

The calculations would be as follows:
Actual quantity = 500 x standard price at £2 = £1000
Standard quantity = 400 x standard price at £2 = £800
The difference of £200 is usage difference and is commonly referred to as direct material usage variance. It is an adverse one.

The sum of direct material price variance and direct material usage variance makes up direct material total variance.

We have a few more variances to deal with, and I’ll put more up in the coming weeks. I’ll let you mull these variances and calculations over for now.

I hope you’ve gained a better understanding of the topic.

See you next week!

Operational OT course available here.

# What’s in a Paragraph?

Dear Mary Jane,

I am currently preparing for the SCS in August.

Can you give me an idea how many paragraphs per question would be needed to pass and how many sentences? I understand 3 sentences in a paragraph are not enough?

Thanks
Alina

Good day Alina

To pass a CIMA case study exam is a little different. Number of paragraphs and sentences are not the only factors that you will need to think of. Students are required to fulfill competency skills (Technical, Business, People, Leadership) on a case study exam as well.

I normally don’t want to set exact numbers of paragraph and sentences as a goal post. Students tend to treat them as targets to be reached, not realizing the more important part is that the answers within should be in-depth.

A good paragraph should be on point and relevant. This does not normally mean 3 sentences. Sentences are structured differently each time so I would tell you rather to focus on the content of the paragraph. If you’ve properly explained your key points through, sometimes 2 long sentences can be enough. It all depends on whether the paragraph is addressing the requirements or not.

I prefer to keep my paragraphs simple so that my explanation and analysis don’t become too convoluted for the examiner/marker to read through and follow.

The length of your answer is determined by the number of marks available in a question. If we set aside skills and solely discuss paragraphs, then I’d say that one good paragraph deserves one full mark. One and a half or two marks, if stretched. Working on this assumption, then paragraphs would need to be in the range of 15 to 25 good ones to gain a decent score on a 25-mark question.

I hope this helps.