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Updating standard cost systems; making them better tools for today's manufacturing environment.

In recent years standard cost systems have been criticized by many theorists. Many of these systems measure variables no longer considered important. They need to be updated to measure variables tht are currently significant in manufacturing.

Traditional standard cost systems came out of the production-driven past, when favorable price and efficiency variances were achieved through large volumes of material and intensive labor. Just-in-time systems now have led manufacturers to recognize the dangers of overproduction and poor quality. Concentrating only on input can lead to poor decisions about output--and to increased costs. Updated systems should focus on quality and production as well as price and efficiency.



A traditional standard cost analysis appears in exhibit 1 on page 59. In this example, two cost variances -- price and efficiency -- are computed. (Variances for fixed costs are not covered in this article.) The price variance is computed on the basis of pounds purchased. In this case 2,700 pounds were purchased at a price that was $.10 above the standard, resulting in a $270 unfavorable variance.

Also, the computed efficiency variance indicates that 2,400 pounds should have been used to produce 1,200 good units but that 2,600 pounds actually were used. The extra 200 pounds at $1 per pound results in a $200 unfavorable efficiency variance.

The problem with this analysis is that it gives information only on inputs (the components of the product, such as materials and labor). Focusing exclusively on inputs is inconsistent with sales-driven concepts. In a sales-driven company, managers produce to meet specific sales orders rather than to accumulate masses of inventory. When input information is incomplete and misleading, erroneous decisions can be made. For instance, managers trying to avoid unfavorable price and efficiency variances may produce large amounts of unneeded inventories and minimize of unconcerns, costing the company money.

In a production-driven environment, managers tended to avoid unfavorable price and efficiency variances by buying materials in quantity and keeping workers busy. The idea of shutting down the assembly line for any reason was abhorrent to most production managers. The result was un-needed inventories that cost companies interest on the investment, storage, insurance and obsolenscence.

In contrast, the just-in-time concept of production recognizes that costs associated with excess inventories need to be minimized and that efficiency and flexibility in production can better be achieved by producing smaller batches.

In the past, managers' philosophy was that costs should be minimized while quality merely had to be acceptable. This lack of attention to quality hurt the reputations of many companies and the need to rework defective units or sell them as seconds increased their costs.

Detecting and measuring quality deficiencies are as important as determining price and efficiency variances. Unfortunately, most standard cost systems being used in manufacturing ignore both production and quality concerns.

A better analysis is presented in exhibit 2 on page 60. In this illustration, price and efficiency variances are isolated as are the output variances of quality and production. In this case, there were 50 defective units at a cost of $2 each, resulting in a $100 unfavorable quality variance.

There also were 200 units produced in excess of current needs. At an investment of $2 each, the extra units resulted in $400 in excess inventory, also an unfavorable production variance.

Why is the system in exhibit 2 superior to the traditional analysis in exhibit 1? Its primary advantage lies in its attention to both output and input. Price and efficiency variances also are revised to suit a just-in-time system better.


Quality variances focus attention on resources invested in units that must be reworked, scrapped or sold as seconds. In exhibit 2, 50 finished units did not meet quality standards, which resulted in a $100 unfavorable quality variance.

Under the traditional standard costing in exhibit 1, the cost of these detective units is buried in the efficiency variance. Without additonal analysis, there is no way to tell how many inputs were invested in defective units or misused in other ways.

It is recognized now that a lack of quality results in higher production costs. Measurable warranty costs and the intangible costs of customer ill will also might increase if defects are not detected before the product is sold. Quality is a completely different concern from efficiency and it should be recognized and measured separately.

In exhibit 2, it is assumed that all units were 100% complete. Equivalent production calculations should be used if the units are partially complete. For example, if the 50 defective units were only one-fourth complete, then the equivalent unit figure would be 12.5 units and the unfavorable quality variance would be only $25 unfavorable (12.5 units times $2) instead of $100. Thus, unfavorable quality variances can be reduced by detecting defects earlier in the manufacturing process. Computing the


variance in this fashion motivates managers to take early corrective action rather than allowing defective units to accumulates costs until they are found on final inspection.


Production variances show deviations from scheduled production numbers. In exhbit 2, the production variance is $400 unfavorable because 200 excess units were produced. Viewing excess production as unfavorable follows the just-in-time philosphy of minimizing investories at all production stages. Some authorities go so far as to view the excess as a liability, rather than an asset, for internal use. (For external use, the traditional treatment for assets under generally accepted accounting principles is necessary.)

As discussed in the article "Reporting the Effects of Excess Inventories" (JofA, Nov.89 page 131), the production variance can be used to calculate the cost of capital on excess inventories to determine the real cost of overproducing. Some companies that use a production variance view any deviation from the scheduled amount, whether over or under, as unfavorable. Underproduction is unfavorable because the company cannot meet sales demands. However, there is a greater likelihood that managers will ignore the costs of overproduction--which is why this variance also is labeled unfavorable. Actually, labeling a production variance as either over or under the scheduled amount is equally satisfactory, assuming management considers both kins of unfavorable variances.


In the new manufacturing literature, price variances have been critized for motivating managers to ignore quality and buy low-priced materials and parts. However, the real problem is that price variances have been separated frm production-related variances. Price is certainly a valid concern but it cannot be viewed in isolation or ignored. It makes more sense to evaluate price variances in terms of trade-offs.

In other words, a decision to buy high-quality materials for a particular production run may result in an unfavorable price variance but in favorable efficiency and quality variances. Or a decision to employ highly skilled workers may result in an unfavorable price variance for labor but in favorable efficiency and quality variances.

In exhibit 2, the price variance for materials is computed on the basis of materials used in production. Under the traditional standard costing in exhibit 1, the price variance is based on materials purchased because this approach isolates the variance earlier so that corrective action can be taken.

Computing the price variance on the basis of materials used in production is recommended because the trade-offs between price and efficiency or quality are more apparent if the same batch of materials is used for all variances. Suppose a company purchased a large amount of low-quality materials this week because it got a very good price. Using the traditional system, it would recognize a large favorable price variance for the week. But the potential unfavorable efficiency and quality variances would not be isolated until the materials were put into production, perhaps several weeks later. There is a much better match of the trade-offs if materials put into production are used for all comparisons.


Historically, efficiency variances have been computed by multiplying excess inputs by the standard price (see exhibit 1). In recent years, this approach has been criticized for motivating managers to ignore concerns to avoid unfavorable efficiency variances. In other words, there is an incentive to produce a low-quality product by minimizing the amount of material used or the time spent in production.

The approach in exhibit 2 separates the efficiency variance from the quality variance. Inputs


consisting of conversion time or material used in defective units are captured in the quality variance.

Separating the two variances allows production decision makers to evaluate the trade-offs between efficiency and quality. They can minimize production time to gain a favorable efficiency variance but this probably will increase the number of defective units and result in an unfavorable quality variance. Likewise, trying to minimize the number of defective units may result in investing more time and more material and therefore having an unfavorable efficiency variance.

In the case of hidden defects in materials, an unexpected number of units may be rejected on final inspection. Ordinarily, this would affect the efficiency variance. However, if the quality variance is separate from the efficiency variance, the quality variance would reflect the rejects but the efficiency variance would be unchanged. In other words, the efficiency variance computed in this fashion shows true efficiency. The production department would not be penalized by an unfavorable efficiency variance for working on units that appeared to be good. In effect, the efficiency variance becomes a tool for detecting waste that disappeared during the production process, while the quality variance detects waste that remains in the form of finished or semi-finished goods.

In production processes in which there is normal shrinkage due to evaporation or similar processes, the shrinkage would be part of the efficiency rather than the quality variance because the standard inputs allowed for total production would be adjusted. A separate yield variance could be broken out from the efficiency variance to show the effect of shrinkage.


The new manufacturing environment does not require dismantling standard cost systems, which have helped companies achieve their production goals for many years. However, when these goals change, the old systems need to be updated. In this case, updating consists of incorporating new variance measures into the standard cost system and focusing on key issues--inventory and quality control--that are important in the production process. Revision also narrows the scope of the traditional price and efficiency variances, making them more useful tools because their causes are easier to identify.

CAROLE CHEATHAM, CPA, PhD, is a professor of accounting at Northeast Louisiana University, Monroe. A member of the American Institute of CPAs, the National Association of Accountants and the American Accounting Association, she is the author of Cost Management for Profit Centers (Institute for Business Planning, 1981).
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Author:Cheatham, Carole
Publication:Journal of Accountancy
Date:Dec 1, 1990
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