The labor efficiency variance formula is referenced under several names. To be accurate, the formula is used to measure direct labor rate variance. That sounds pretty technical, but it’s a simple enough concept. Direct labor variance is a means to mathematically compare expected labor costs to actual labor costs.
More specifically, the formula looks at the direct labor hours invested into an outcome (such as the number of units produced) and relates them to the projected labor hours. The formula assumes standard rates for both. In simpler terms, the variance tells you exactly how many hours you invested as compared to expectations. Then, it tells you how much that difference cost. This can help with budgeting when you have fixed overhead.
There are two common ways to look at the variance formula. While they appear different, they are mathematically identical. The first version highlights the importance of comparing the actual hours to the expected hours:
(actual hours – standard hours) x standard rate.
It’s pretty easy to see at a glance that the variance will hinge entirely on the difference between the two variables.
The second form of the equation highlights the fact that both the actual hours and standard hours are charged at the standard rate:
(actual hours x standard rate) – (standard hours x standard rate)
This way of writing the formula emphasizes an important fact. The direct labor efficiency variance does not analyze changes in labor rates. It focuses on standard costing to carefully scrutinize time management.
Using the Formula
When you apply the formula to financial accounting, you get meaningful results at a glance. Your variance will either be positive or negative. If the number is negative, then it reflects a cost savings over your expectations. It’s typically called a favorable variance. By convention, the negative sign is usually dropped, and the word “favorable” is attached to the variance instead.
Conversely, when the calculation yields a positive number, it demonstrates an unfavorable variance and shows that the work was done inefficiently.
You are remodeling a bathroom. After getting multiple quotes, you have determined that the standard cost of the job will be 20 hours of labor at $60 per hour. With this expectation, you hire a contractor. When the job is finished, you find that you paid for 33 hours of labor at $60 per hour. When you plug this into the formula, you get a direct labor efficiency variance.
(33 hours of direct labor – 20 hours of standard labor) x $60/hour = $780
The efficiency variance is $780. Note that this is a positive number, so you have unfavorable variance. That’s easy to justify since you spent 13 more hours on labor than you expected. The job was done less efficiently than your projections.
Managing Labor Efficiency
Calculating the efficiency variance is a clear way to determine areas of labor that need to improve, but the number can only do so much. Ultimately, changes have to be made to labor in order to improve efficiency. That’s best done after considering the most common sources of inefficiency.
You can break labor inefficiency into two broad categories: personnel and logistical. If workers don’t have the tools, materials or resources necessary to complete a task, you get disruptions. Those disruptions can create idle time for the workers, and that will always kill efficiency.
On the other side of the coin, personnel efficiency problems usually stem from poor morale, low learning curves or a lack of skill. Any of these issues can prevent workers from using their time as well as competitors in the industry.
Clearly, efficiency improvements stem from two places. First, logistics have to maintain a steady stream of resources that are sufficient to keep workers from hitting stoppages. Secondly, hiring and training need to take labor efficiency into account. Continued learning and more-selective hiring are invaluable tools to this end.
When you make the most of variance analysis, you can quickly find efficiency problems and resolve them. That is essential to running a good business.