Variance analysis is the accounting process that compares planned or projected performance in the business to actual results.
It is a quantitative tool that is intended to identify deviations and their underlying causes.
Variance analysis looks at total costs or volumes for a particular account, such as purchases or sales, to identify differences between planned and actual numbers.
It is not uncommon for actual numbers to deviate. A variance analysis will also look at trend lines (patterns of deviation over time) from one reporting period to the next, to identify dramatic changes or spikes.
These are the significant results that will demand further examination. Depending on the numbers examined, the analysis will also offer an interpretation or explanation for the variance.
A variance analysis involves several steps:
The first step is to gather all relevant information in a centralized location. For example, if a sales variance analysis is to be performed, then sales totals for a particular unit in the business will be gathered. The information will be aggregated for a particular time frame or reporting period and include similar numbers for previous reporting periods to establish trends.
Next, accountants will compare data sets to establish variances. This may be as simple as subtracting totals for one set from another. In the sales example above, actual sales totals would be subtracted from the total for projected sales. Usually, a positive variance—actual sales are greater than projected—is considered a favorable variance. A negative difference is considered an unfavorable variance.
After variances have been established, accountants will attempt to evaluate and ascertain the cause of the discrepancies. Some businesses establish thresholds to determine at what point a variance is a cause for concern or requires further analysis. Factors such as profit margin (low or high) or materials costs can influence where those thresholds are set. Accountants will also drill down to the lowest common denominator, such as vendor prices, to determine the root cause of a variance.
Once the variance has been identified, isolated, and analyzed, accountants can prepare reports for upper management which will inform its decision-making and support future planning adjustments.
As an example of a variance analysis, if a manufacturing company budgeted for 1,000 widgets at a cost of $.50 per widget, its total budgeted costs for widgets would be $500. If the company actually spent $700 on widgets, the variance analysis would reveal that the company had an unfavorable (negative) variance of $200.
The company would then break down the analysis and compare budgeted and actual figures for both costs and volume of widgets purchased to determine the root cause of the variance.
Management can perform a variance analysis on many different functions in the business:
Purchase variance analysis compares actual purchases by the business against those that were planned and budgeted. If a business is purchasing more or less than planned, further analysis is required to determine the causes.
A sales variance analysis will look at discrepancies between expected and actual sales volume for a certain period of time. A sales analysis is typically used to assess performance of a particular unit within the business.
An overhead variance analysis examines the difference between planned and actual expenses on overhead costs like space rental and utilities. An overhead variance analysis is a good way for a business to identify potential savings in the cost of operations.
A business that requires materials for manufacturing, or to otherwise produce a product for sale, will perform a material variance analysis to look at the cost of purchasing those materials.
The analysis will examine changes in the purchase price and the volume of materials purchased, either or both of which could contribute to a variance.
A labor variance analysis looks at the variances in the cost of employing the workforce. A number of factors may contribute to a labor variance. The demands of the business or the amount of time required for the business to operate may exceed what management had expected.
The business may also be paying too much for the work that is required. In other words, highly compensated employees may be doing work that less skilled and lower-paying employees could perform.
Manufacturing companies perform efficiency variance analyses to assess the efficiency of their operations. This will look at labor, machine time, materials, and other factors that impact the process of production.
Variance analysis is a valuable tool for accountants and management that supports their ability to exercise effective oversight by:
Monitoring the overall performance of the business
Maintaining accuracy in the planning process
Exercising control over budgeting
Properly assigning responsibility to departments and programs within the business
Providing a proper check on expectations and projections
In the most basic form, a variance analysis involves the simple calculation of actual costs minus projected costs, which might be expressed as P – A = Variance.
If an analysis involves multiple variables, such as rates or costs and quantities, the calculation becomes more complex. To determine a variance in quantity, the analysis would calculate the variance between actual quantity multiplied by a projected cost and projected quantity multiplied by the projected cost.
To determine the variance in cost, the analysis would then calculate the variance between actual quantity multiplied by the projected price and the actual quantity multiplied by the actual price. The analysis would then add the two variances together to arrive at the total variance.
This calculation would be expressed as:
Step 1. (Actual quantity x projected price) - (projected quantity x projected price) = quantity variance
Step 2. (Actual quantity x projected price) - (actual quantity x actual price) = price variance
Step 3. Quantity variance + price variance = overall variance
Request a demo with us and see how your company can Continuously monitor for risk with automated fluctuation analysis.