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ADMS 3510
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Jamison Aldcorn
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Administrative Studies

ADMS 3510

Jamison Aldcorn

Fall

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CHAPTER 10. QUANTITATIVE ANALYSES OF COST FUNCTIONS
Direct cost: control is easy, straightforward due to observable cause effect relationship
Indirect cost: benefit shared unequally among different products. Neither feasible nor cost effective
General issues in estimating cost functions
- Indirect cost allocation rate – use it to predict future MOH value
- Cost driver rate : cost per unit of benefit received by each distinct type of output from unequal
use of shared resources
- A single cost pool total that includes many different costs is a heterogeneous cost pool.
- Goal is to choose a method to use to describe the cost benefit relationship that best reflects the
unequal sharing of common MOH resources between distinct types of outputs.
Linear and curvilinear cost functions:
- A linear cost function for MOH : X- quantity of benefit received
Y- Dollar value of cost pool
- Curvilinear cost function: data points are joined to form a curve
Variable cost Fixed cost mixed cost
Curvilinear unitized fixed cost function
With x axis for activity level and y axis for the cost
- Correlation: relation between two or more variables- change in one quantity that explains but
does not cause changes in another quantity.
- Choice of quantity of direct resource as the best measure of benefit is assessed based on its
economic plausibility.
- Better analysis matching value added = reliability and confidence in prediction.
- Y = a +bX
Where a = value of Y when = 0 b = slope coefficient – rate of change in Y when X used changes by 1 unit
y = future MOH cost pool
Cost function estimation based on quantitative data analysis
Use MIS reports on historical MOH costs and quantity of direct resources used, giving a
quantitative data analysis. The steps are:
1. Identify the value of indirect MOH cost pool and potential measures of direct resources used
that may best measure the unequal benefit sharing from the common indirect resources
used.
2. Gather existing historical data on actual cost driver(X) quantities and corresponding actual
indirect cost pool value (Y)
3. Test whether these data match three basic assumptions:
a. Economic plausibility is reflected in the relevant range of X observed and reported and in
Y, the actual costs reported. It should function as y = a + bX
b. Systematic correlation between X and Y can be defined as linear
c. The XY data pairs represent a continuous change between one set of data points and the
next.
4. Graph the XY or X,y pairs , analyze and evaluate the cost driver
5. Estimate the continuous linear cost function using the best cost drive, estimate the normal
range of future MOH
Indirect manufacturing overhead cost pools (MOH)
- There is usually more than one indirect MOH cost drivers because there is no economically
plausible association of the cost drivers of these many costs to the unit of output sold.
- But DMLH is an economically plausible measure of benefit from sharing MOH resources used.
- The more capital/ machine intensive a production process, the more likely it is that maintenance
and supplies to keep the machines in working order -> increase indirect MOH
Managers problems:
- Selecting the best cost driver among alternatives available
- Testing their decision rigorously enough to provide evidence the choice was indeed the best.
- Using the relationship to predict total indirect cost pool value.
Statistics: gives managers information to predict normal variance – unfavourable or
favourable around the predicted value Y
Variance -> signal for attention-> Management by exception
Cost estimation methods using historical data
A discontinuous cost (step fixed cost) function arises when a single resource consumed does not form a
straight line with a constant slope. A step variable cost function is a function in which the

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