What is 'Yield Variance'

Yield variance is the difference between actual output and standard output of a production or manufacturing process, based on standard inputs of materials and labor. The yield variance is valued at standard cost. Yield variance is generally unfavorable, i.e., actual output is less than the standard or expected output, and only rarely favorable.

The formula for yield variance is straightforward: yield variance = standard unit cost x ( actual yield – standard yield )

BREAKING DOWN 'Yield Variance'

Generally, yield variance uses direct materials, which are raw materials that are made into finished products. These are not materials used in the production process. Direct materials are goods that physically become the finished product at the end of the manufacturing process. In other words, these are the tangible pieces or components of a finished product.

If a firm overestimates or underestimates how much material it requires take to generate a certain amount, the material's yield variance will be less than or greater than zero. If the standard quantity is equal to the quantity actually used, then the variance will be zero.

If the direct materials yield variance proves that the company is producing less than originally planned for a given level of input, the company can review their operations for ways to become more efficient. Intuitively, producing more products with the same level of inventory while keeping quality constant can help the organization improve profitability.

Example of Yield Variance

For example, if 1,000 units of a product are the standard output based on 1,000 kilograms of materials in an 8-hour production unit, and the actual output is 990 units, there is an unfavorable yield variance of 10 units. If the standard cost is $25 per unit, the unfavorable yield variance would be $250.

Yield variance is a common financial and operational metric within manufacturing industries. To improve or enhance the measure, it's fairly regular for an analyst to adjust inputs for special scenarios. For instance, during a raw material price spike, it may not make sense to use temporary price inputs experiencing short-term jumps in prices, as these results would be distorted from normal levels. Here, like any other analysis, it is part art and science.

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