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Manufacturing Costing Page 5

Several methods are used to estimate total cost equations, including account analysis, engineering approach, high-low approach, and linear regression analysis. Account analysis classifies costs as fixed or variable but may not accurately capture semivariable costs. The engineering approach estimates costs from product specifications but is less accurate for other costs. The high-low approach uses total costs at high and low outputs to estimate a linear relationship, but additional data points may not fit the line well. Linear regression fits a line to minimize differences between all data points, but requires the most data.

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81 views1 page

Manufacturing Costing Page 5

Several methods are used to estimate total cost equations, including account analysis, engineering approach, high-low approach, and linear regression analysis. Account analysis classifies costs as fixed or variable but may not accurately capture semivariable costs. The engineering approach estimates costs from product specifications but is less accurate for other costs. The high-low approach uses total costs at high and low outputs to estimate a linear relationship, but additional data points may not fit the line well. Linear regression fits a line to minimize differences between all data points, but requires the most data.

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ESTIMATING TOTAL COSTS

Several methods are used in manufacturing to estimate total cost equations, in which total costs
are determined as a function of fixed costs per time period, variable costs per unit of output, and
the level of output. These methods include account analysis, the engineering approach, the high-
low approach, and linear regression analysis. In all these methods, the central issue is how total
costs change in relation to changes in output.

ACCOUNT ANALYSIS.

In account analysis, all costs are classified as either strictly fixed or variable. This has the
advantage of ease of computation. However, some costs may be semivariable costs or step costs.
Utility bills are typically semivariable in that they contain fixed and variable components. Step
costs increase in discrete jumps as the level of output increases. In account analysis, such costs
are typically categorized as either fixed or variable depending which element predominates.
Thus, the accuracy of account analysis depends in large part on the proportion of costs that are
not strictly fixed or variable. For many manufacturing firms, account analysis provides a
sufficiently accurate estimation of total costs over a range of output levels.

ENGINEERING APPROACH.

The engineering approach infers costs from the specifications of a product. The approach works
best for determining direct material costs and less well for direct labor costs and overhead costs.
The advantage of the engineering approach is that it enables manufacturers to estimate what a
product would cost without having previously produced that product, whereas the other methods
are based on the costs of production that has already occurred.

HIGH-LOW APPROACH.

In the high-low approach, a firm must know its total costs for previous high and low levels of
output. Graphing total costs against output, total costs over a range of output are estimated by
fitting a straight line through total cost points at high and low levels of output. If changes in total
costs can be accurately described as a linear function of output, then the slope of the line
indicates changes in variable costs. The problem with the high-low approach is that the two data
points may not, for whatever reasons, accurately represent the underlying total cost-output
relationship. That is, if additional total cost-output points were plotted, they might lay
significantly wide of the line connecting the two initial high-low points.

LINEAR REGRESSION.

Linear regression analysis addresses the shortcomings of the high low approach by fitting a line
through all total cost-output points. The line is fitted to minimize the sum of squared differences
between total cost-output points and the line itself, in standard linear regression fashion. The
drawback of this approach is that it requires more data points than the other approaches.

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