Question

Prepare a 2-page research paper defining and discussing price, efficiency, volume, and utilization variances including the formula for calculating each of them. Also, discuss how monitoring and analyzing each of them can be used by management in healthcare organizations.

40 points

Answer 1

Variance Analysis, in managerial accounting, refers to the investigation of deviations in financial performance from the standards defined in organizational budgets.

Variance analysis typically involves the isolation of different causes for the variation in income and expenses over a given period from the budgeted standards.

So for example, if direct wages had been budgeted to cost $100,000 actually cost $200,000 during a period, variance analysis shall aim to identify how much of the increase in direct wages is attributable to:

• Increase in the wage rate (adverse labor rate variance);
• Decline in the productivity of workforce (adverse labor efficiency variance);
• Unanticipated idle time (labor idle time variance);
• More wages incurred due to higher production than the budget (favorable sales volume variance).

Types of Variances

Main types of variances are as follows:

• Sales Price Variance

Definition

Sales Price Variance is the measure of change in sales revenue as a result of variance between actual and standard selling price.

Formula

Sales Price Variance:

=	(Actual Price - Standard Price)	x	Actual Unit
=	Actual Price x Actual Units Sold	-	Standard Price x Actual Units Sold
=	Actual Sales Revenue	-	Standard Revenue of Actual Units Sold

• Sales Volume Variance

Definition

Sales Volume Variance is the measure of change in profit or contribution as a result of the difference between actual and budgeted sales quantity.

Formula

Sales Volume Variance (where absorption costing is used):

=

(Actual Unit Sold - Budgeted Unit Sales)

x

Standard Profit Per Unit

Sales Volume Variance (where marginal costing is used):

=

(Actual Unit Sold - Budgeted Unit Sales)

x

Standard Contribution Per Unit

• Sales Mix Variance

Definition

Sales Mix Variance measures the change in profit or contribution attributable to the variation in the proportion of the different products from the standard mix.

Formula

Sales Mix Variance (where standard costing is used):

=

(Actual Unit Sold - Unit Sales at Standard Mix)

x

Standard Profit Per Unit

Sales Mix Variance (where marginal costing is used):

=

(Actual Unit Sold - Unit Sales at Standard Mix)

x

Standard Contribution Per Unit

• Sales Quantity Variance

  Definition

  Sales Quantity Variance measures the change in standard profit or contribution arising from the difference between actual and anticipated number of units sold during a period.

  Formula

  Sales Quantity Variance:

  =

  (Budgeted sales - Unit Sales at Standard Mix)

  x

  Standard Contribution*

  *Where marginal costing is used

  Sales Quantity Variance:

  =

  (Budgeted sales - Unit Sales at Standard Mix)

  x

  Standard Profit*

  *Where absorption costing is used

• Direct Material Price Variance

Definition

Direct Material Price Variance is the difference between the actual cost of direct material and the standard cost of quantity purchased or consumed.

Formula

Direct Material Price Variance:

=	Actual Quantity x Actual Price	-	Actual Quantity x Standard Price
=	Actual cost	-	Standard Cost

Where:

• Actual Quantity is the quantity purchased during a period if the variance is calculated at the time of material purchase
• Actual Quantity is the quantity consumed during a period if the variance is calculated at the time of material consumption

• Direct Material Usage Variance

Definition

Direct Material Usage Variance is the measure of difference between the actual quantity of material utilized during a period and the standard consumption of material for the level of output achieved.

Formula

Direct Material Price Variance:

=	Actual Quantity x Standard Price	-	Standard Quantity x Standard Price
=	Standard Cost of Actual Quantity	-	Standard Cost of Standard Quantity
=	(Actual Quantity - Standard Quantity)	x	Standard Price

Since the effect of any variation in material price from the standard is calculated in the material price variance, material usage variance is calculated using the standard price.

• Direct Material Mix Variance

Definition

Direct Material Mix Variance is the measure of difference between the cost of standard proportion of materials and the actual proportion of materials consumed in the production process during a period.

Formula

Direct Material Price Variance:

=	Actual Quantity x Standard Price	-	Standard Mix Quantity x Standard Price
=	Standard Cost of Actual Mix	-	Standard Cost of Standard Mix
=	(Actual Mix Quantity - Standard Mix Quantity)	x	Standard Price

As material mix variance is an extension of the material usage variance, the variance is based on the standard price rather than actual price since the difference between actual and standard material price is accounted for separately in the material price variance.

• Direct Material Yield Variance

Definition

Direct Material Yield Variance is a measure of cost differential between output that should have been produced for the given level of input and the level of output actually achieved during a period.

Formula

Direct Material Price Variance:

=

(Actual Yield - Standard Yield)

x

Standard Material Cost Per Unit

• Direct Labor Rate Variance

Definition

Direct Labor Rate Variance is the measure of difference between the actual cost of direct labor and the standard cost of direct labor utilized during a period.

Formula

Direct Labor Rate Variance:

=	Actual Hours x Actual Rate	-	Actual Hours x Standard Rate
=	Actual Cost	-	Standard Cost of Actual Hours

• Direct Labor Efficiency Variance

Definition

Direct Labor Efficiency Variance is the measure of difference between the standard cost of actual number of direct labor hours utilized during a period and the standard hours of direct labor for the level of output achieved.

Formula

Direct Labor Efficiency Variance:

=	Actual Hours x Standard Rate	-	Standard Hours x Standard Rate
=	Standard Cost of Actual Hours	-	Standard Cost

• Direct Labor Idle Time Variance

Definition

Labor Idle Time Variance is the cost of the standby time of direct labor which could not be utilized in the production due to reasons including mechanical failure of equipment, industrial disputes and lack of orders.

Formula

Direct Labor Idle Time Variance:

Idle Time Variance:

=

Number of idle hours x Standard labor rate

• Variable Overhead Spending Variance

Definition

Variable Overhead Spending Variance is the difference between variable production overhead expense incurred during a period and the standard variable overhead expenditure. The variance is also referred to as variable overhead rate variance and variable overhead expenditure variance.

Formula

Variable Overhead Spending Variance:

=	Actual Manufacturing Variable Overheads Expenditure
Less
=	Actual hours	x	Standard Variable Overhead Rate per hour

where:

Actual Hours is the number of machine hours or labor hours during a period.

• Variable Overhead Efficiency Variance

Definition

Variable Overhead Efficiency Variance is the measure of impact on the standard variable overheads due to the difference between standard number of manufacturing hours and the actual hours worked during the period.

Formula

Variable Overhead Efficiency Variance:

=	Standard hours	x	Standard Variable Overhead Rate per hour
Less
=	Actual hours	x	Standard Variable Overhead Rate per hour

where:

Hours refers to the number of machine hours or labor hours incurred in the production of output during a perio

• Fixed Overhead Total Variance

Definition

Fixed Overhead Total Variance is the difference between actual and absorbed fixed production overheads during a period.

Formula

Fixed Overhead Total Variance:

=	Actual Fixed Overheads	x	Absorbed Fixed Overheads
			Actual Output x FOAR*

* Fixed Overhead Absorption Rate

• Fixed Overhead Spending Variance

Definition

Fixed Overhead Expenditure Variance, also known as fixed overhead spending variance, is the difference between budgeted and actual fixed production overheads during a period.

Formula

Fixed Overhead Expenditure Variance:

=

Actual Fixed Overheads

-

Budgeted Fixed Overheads

• Fixed Overhead Volume Capacity & Efficiency Variance

Fixed Overhead Volume Capacity Variance

Fixed Manufacturing Overhead Volume Variance quantifies the difference between budgeted and absorbed fixed production overheads.

Formula

Fixed Overhead Volume Variance:

=	Absorbed Fixed overheads	-	Budgeted Fixed overheads
=	Actual Output x FOAR*	-	Budgeted Output x FOAR*

* Fixed Overhead Absorption Rate per unit of output

Fixed Overhead Capacity Variance

Fixed Overhead Capacity Variance calculates the variation in absorbed fixed production overheads attributable to the change in the number of manufacturing hours (i.e. labor hours or machine hours) as compared to the budget.

The variance can be calculated as follows:

Fixed Overhead Capacity Variance:

= (budgeted production hours - actual production hours) x FOAR*

* Fixed Overhead Absorption Rate / unit of hour

Fixed Overhead Efficiency Variance

Fixed Overhead Efficiency Variance calculates the variation in absorbed fixed production overheads attributable to the change in the manufacturing efficiency during a period (i.e. manufacturing hours being higher or lower than standard ).

The variance can be calculated as follows:

Fixed Overhead Efficiency Variance:

= (standard production hours - actual production hours) x FOAR*

* Fixed Overhead Absorption Rate / unit of hour

Use of variances by the managemnt in the health care organizations

Healthcare managers, clinical researchers and individual patients (and their physicians) manage variation differently to achieve different ends. First, managers are primarily concerned with the performance of care processes over time. Their time horizon is relatively short, and the improvements they are concerned with are pragmatic and ‘holistic.’ Their goal is to create processes that are stable and effective. The analytical techniques of statistical process control effectively reflect these concerns. Second, clinical and health-services researchers are interested in the effectiveness of care and the generalisability of findings. They seek to control variation by their study design methods. Their primary question is: ‘Does A cause B, everything else being equal?’ Consequently, randomised controlled trials and regression models are the research methods of choice. The focus of this reductionist approach is on the ‘average patient’ in the group being observed rather than the individual patient working with the individual care provider. Third, individual patients are primarily concerned with the nature and quality of their own care and clinical outcomes. They and their care providers are not primarily seeking to generalise beyond the unique individual. We propose that the gold standard for helping individual patients with chronic conditions should be longitudinal factorial design of trials with individual patients. Understanding how these three groups deal differently with variation can help appreciate these three approaches.

Clinical and health-services researchers

While quality-management thinking tends towards the use of data plotted over time in control-chart format, clinical researchers think in terms of true experimental methods, such as RCTs. Health-services researchers, in contrast, think in terms of regression analysis as their principal tool for discovering explainable variation in processes and outcomes of care. The data that both communities of researchers use are generally collected during fixed periods of time, or combined across time periods; neither is usually concerned with the analysis of data over time.

Take, for example, the question of whether age and sex are associated with the ability to undertake early ambulation after hip surgery. Clinical researchers try to control for such variables through the use of entry criteria into a trial, and random assignment of patients to experimental or control group. The usual health-services research approach would be to use a regression model to predict the outcome (early ambulation), over hundreds of patients using age and sex as independent variables. Such research could show that age and sex predict outcomes and are statistically significant, and that perhaps 10% of the variance is explained by these two independent variables. In contrast, quality-improvement thinking is likely to conclude that 90% of the variance is unexplained and could be common-cause variation. The health-services researcher is therefore likely to conclude that if we measured more variables, we could explain more of this variance, while improvement scientists are more likely to conclude that this unexplained variance is a reflection of common-cause variation in a good process that is under control.

The entry criteria into RCTs are carefully defined, which makes it a challenge to generalise the results beyond the kinds of patients included in such studies. Restricted patient entry criteria are imposed to reduce variation in outcomes unrelated to the experimental intervention. RCTs focus on the difference between point estimates of outcomes for entire groups (control and experimental), using statistical tests of significance to show that differences between the two arms of a trial are not likely to be due to chance.

Understanding variation is one of the cornerstones of the science of improvement

This broad understanding of variation, which is based on the work of Walter Shewart in the 1920s, goes well beyond such simple issues as making an intended departure from a guideline or recognising a meaningful change in the outcome of care. It encompasses more than good or bad variation (meeting a target). It is concerned with more than the variation found by researchers in random samples from large populations.

Everything we observe or measure varies. Some variation in healthcare is desirable, even essential, since each patient is different and should be cared for uniquely. New and better treatments, and improvements in care processes result in beneficial variation. Special-cause variation should lead to learning. The ‘Plan–Do–Study’ portion of the Shewhart PDSA cycle can promote valuable change.

The ‘act’ step in the PDSA cycle represents the arrival of stability after a successful improvement has been made. Reducing unintended, and particularly harmful, variation is therefore a key improvement strategy. The more variation is controlled, the easier it is to detect changes that are not explained by chance. Stated differently, narrow limits on a Shewhart control chart make it easier and quicker to detect, and therefore respond to, special-cause variation.

The goal of statistical thinking in quality improvement is to make the available statistical tools as simple and useful as possible in meeting the primary goal, which is not mathematical correctness, but improvement in both the processes and outcomes of care. It is not fruitful to ask whether statistical process control, RCTs, regression equations or longitudinal factorial design of experiments is best in some absolute sense. Each is appropriate for answering different questions.

Forces driving this new way of thinking

The idea of reducing unwanted variation in healthcare represents a major shift in thinking, and it will take time to be accepted. Forces for this change include the computerisation of medical records leading to public reporting of care and outcome comparisons between providers and around the world. This in turn will promote pay for performance, and preferred provider contracting based on guideline use and good outcomes. This way of thinking about variation could spread across all five core systems of health,¹² including self-care and processes of healthy living.