Performing variance analyses is an important task of management accounting. In a recent post, the benefits of such a budget vs actual analysis were analysed. Now it is about time to dive a bit deeper and have a look at the most important variance analysis formulas.
Generally, a management accountant will analyse a set of figures and compare all of them against some kind of a plan, for example, budget. This process is called variance analysis. The methods used are different from company to company and it is likely that many theoretical variance analysis formulas are not used in order to simplify the work.
The following high-level extract of some financial figures will be used in this article as an example:
Variance analysis formulas for earnings
Comparing actual earnings to plan is essential in order to figure out whether the company’s sales are ahead of or under the plan. How profound an analysis can be is depending on the given dataset. Theory suggests analysing the sales mix, sales units and, of course, the sales value. In practice, however, a management accountant is often only seeing $ values and no units on the data sheet (as shown in the example above).This means the only possible comparison is actual sales value against planned sales value. Indicating reasons (price, amount or mix) explaining a deviation is not possible. In the example, it can be seen that computer sales performed very well. It can’t be seen though whether this is due to a higher sales price or an increase in sales numbers.
It is likely that the variance of computers will be classified as material and consequently analysed further by requesting additional data. Assuming it was possible to figure out than 110 computers were sold, compared to only 100 in the plan and the average unit price was $ 1318 compared to $ 1200 in the original estimation, a more qualified analysis can be done:
As the illustration shows the total variance of $25k can be divided in price influence and volume influence. This allows for a higher quality analysis. The table on the right side indicates the variance analysis formulas applied.
Regarding the example, the same logic can be used to examine the rest of the earnings:
Formulas for COGS
Costs of goods sold can be analysed in a similar way as the earnings. Considering the example, on the first view, the dataset indicates higher costs and thus an adverse variance. However, positive sales volume variance should be considered as well. An increase of 10 % in computer sales, will also increase costs. Taking this into account, COGS for computers are completely fine ($ 100k planned costs + 10% extra sales = $ 110k).
The output of other products has been doubled but COGS remained the same. This is a possible cost driver that should be investigated further. It could be that the buying price was very favourable, less material was used to produce one unit or an accounting error occurred. Once more, in practice, it can be difficult to receive all the information needed for a deep analysis.
All overhead costs can be analysed by comparing actual results to plan. In the above example, there is a small adverse variance in the area of sales. This could be related to an increase of sales bonus, unexpected costs or other reasons. A breakdown of these costs into categories or even accounts will provide more detailed information. In normal cases, a management accountant works with a more detailed data sheet and can apply variance analysis formulas on several accounts to determine the nature of such a financial difference.
The end result of using variance analysis formulas should be a report which highlights the most important deviations and indicates their reason. This paper will then be a base for management discussion.