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Chapter10.docx

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School
Department
Course
Professor
Jamison Aldcorn
Semester
Fall

Description
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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