The purpose of this paper is to illustrate and discuss the limitations related to traditional standard costing overhead variance analysis within the manufacturing environment. Stammerjohan points out that “standard costing has been criticized for being inflexible, motivating excess production, and failing to provide meaningful information on a timely basis” and suggests that by combining techniques from both activity-based costing and traditional standard costing we can provide more relevant information.

Types of Overhead Variances

1.  Variable Overhead Efficiency Variance (difference between actual usage and allowed usage multiplied by a standard rate per cost driver unit)

2.  Variable Overhead Spending Variance (difference between actual cost and the expected cost of variable overhead)

3.  Production Volume Variance (difference between applied fixed overhead and budgeted fixed overhead)

4.  Fixed Overhead Spending Variance (difference between actual fixed overhead and budgeted fixed overhead)

ABC Modeling Company Example¹

The author uses this example to show the limitations of traditional standard overhead variance information and how the information can be improved by incorporating ABC techniques into the calculations.

Scenario:     ABC Modeling Company has a single injection molding machine and a single operator. The machine and operator can produce, at full capacity, 125 parts per hour or 1,000 parts per eight-hour shift. During the test month (contains 20 eight-hour shifts) the expected production is 18,000 parts, not 20,000, because the operator is expected to perform 2 setups that each require an eight-hour shift.

                   Table 1 compares the following budgeted and actual information for the three components of overhead cost:

The Limitations of Traditional Standard Overhead Costing Variance Analysis

“The limitations of the traditional standard overhead costing variance do not extend to the unit-level variable cost that is driven by a single, basic cost driver”, but, as we will see from analyzing the table below, limitations will arise when treating all overhead items as unit-level variable costs. 

From the table above we can see that limitations arise particularly because the setup costs are treated as unit-level variable costs instead of batch-level costs. The efficiency variance results for both operator cost setup hours and solvent are highly distorted and hide the true efficiencies and inefficiencies. The results treat the extra setups as inefficiencies, even though they may have a valid business purpose, and conceals the fact that solvent cost per setup was actually less than expected even though the solvent was more expensive. Due to these limitations management may not investigate whether or not using the more expensive solvent led to the savings since less solvent was needed per setup (four gallons versus six gallons).

The second limitation of the above information is that management may be inclined to think that the unfavorable spending variance for electricity is beyond their control since there was an increase in utility prices. Even though the utility price does have an unfavorable effect on the spending variance, this is only one of the two factors that caused this unfavorable variance. The variance was also caused because the machine consumed 4,800 more kilowatt-hours than expected, which could signal a problem with electrical efficiency of the molding machine that needs to be corrected.

If we treated the batch-level costs as fixed-overhead costs the results would not yet be useful for decision making because it combines the many factors that are aggregated into one number. For example, the unfavorable spending variance associated with the solvent costs arises from the extra two setups performed, using less solvent per setup, and paying more per gallon of solvent.

ABC Standard Overhead Analysis

The variances in the table below where calculated by combining ABC and traditional standard costing techniques. As mentioned earlier the limitations do not extend to operator production hours since this is driven by a single cost driver, but it is evident that the results for electricity are a lot more useful for decision making purposes.

This analysis clearly separates the cost resulting from the price increase in utilities ($168U spending variance), from the cost of using more electricity than expected ($288U 2^nd level cost driver) and from the cost reduction gained by producing 16,000 parts using less electricity ($48F basic cost driver). With these more refined results management can focus their attention on the areas that can be improved to increase company performance. The same is true when applying ABC and traditional standard costing techniques to the batch-level costs, the results become more refined and are more useful for decision making purposes.

System Building Blocks

The author states that this combination of techniques “does not represent the ultimate sophistication in overhead analysis”, but it does provide the basic building blocks for a system “capable of producing more relevant, more sophisticated information”. Companies must realize that in order to take full advantage of these techniques, they must continuously update standard quantities and standard prices because learning curves and business environments are always changing. The information produced should help management focus on potential areas where costs can be reduced and inefficiencies can be improved.

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¹ Titles and information in recreated tables were taken directly from Stammerjohan’s article. Although most of the technical details were omitted, the purpose of this summary is to convey the main ideas in the paper. The reader must consult the article to understand the specific calculations required for each set of variances presented.

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