How to Calculate Inventory Material Usage Variance

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Direct materials usage variance records the difference in cost between expected and actual production. Managerial accountants use this information to determine if the variance is favorable or unfavorable. This process is usually quite common as production companies create budgets for estimating the costs associated with manufacturing goods. Variances often help a company determine how efficient operations are in managing costs. For example, unfavorable variances can indicate a problem that needs further analysis or other review.

Estimate the total costs for the upcoming year’s materials production budget by looking at last year’s number and adding a percentage for expected growth. Only direct materials — those items that are necessary to create finished goods — should be in this amount.

Determine the amount of production output under ideal conditions. One way to compute this figure is to take an average from previous years. Adding a percentage for expected production output growth is also possible.

Divide estimated production costs by expected production output to determine the standard materials cost for the production process.

Review production reports to determine the amount of direct materials are necessary to produce a batch of goods.

Multiply the standard cost by the amount of materials to produce one good. The result is the total standard materials cost for the batch production process.

Record actual materials used throughout the actual production period. Managerial accountants typically record these costs by batch, depending on the company’s manufacturing process.

Multiply the actual materials used by the standard materials cost. The total is the actual costs to produce the batch of goods.

Compare actual production costs to standard production costs. The difference between the two costs is the materials usage variance. Higher actual costs are unfavorable, while lower actual costs are favorable.

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References

  • "Cost Management: Strategies for Business Decisions"; Thomas P. Edmonds, et al.; 2006
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