Accounting for Operational Activities: Illustrative Transactions and Financial Statements Answers

CHAPTER FIVE COST ESTIMATION Introduction When managers light up findings they need to compare the addresss (and bene conniptions) among alternative actions. In this chapter, we discuss how to estimate the termss required for decision making (Lanen, 2008). attainment purposes According to Lanen (2008), after completing Chapter 5 you should 1. Understand the reasons for estimating fixed and variable woos. 2. por feed constitute exploitation engineering estimates. 3. Estimate be using account synopsis. 4. Estimate be using statistical analysis. 5. give the results of degeneration go forthput. 6.Identify potential problems with reversal entropy. 7. Evaluate the prefers and disadvantages of alternative woo estimates. 8. (Appendix A) Use Microsoft jump to answer a infantile fixation analysis. 9. (Appendix B) Understand the mathematical congressship describing the learning phenomenon. Why Estimate follow? Managers make decisions and need to compare be and benefits among alternative actions. Good decision requires corking information near prices, the better these estimates, the better the decision managers will make (Lanen, 2008).. Key Question What adds value to the firm? understand thisFinancial StatementsGood decisions. You saw in Chapters 3 and 4 that good decisions require good information near monetary value. Cost estimates are important divisions in helping managers make decisions that add value to the company (Lanen, 2008). Learning verifiable One Understand the reasons for estimating fixed and variable lives The reasons for estimating fixed and variable costs The underlying idea in cost estimation is to estimate the relation in the midst of costs and the variables affecting costs, the cost drivers. We focus on the relation mingled with costs and one important variable that affect them action (Lanen, 2008).Basic Cost Behavior Patterns By now you understand the importance of cost behavior. Cost behavior is the strike distin ction for decision making. Costs behave as either fixed or variable (Lanen, 2008). frozen(p) costs are fixed in issue forth, variable costs vary in total. On a per- unit basis, fixed costs vary inversely with bodily process and variable costs stay the same. Are you getting the idea? Cost behavior is critical for decision making. The formula that we function to estimate costs is interchangeable cost equation constitutional costs = fixed costs + variable cost per unit itemise of unitsT c = f + v x With a change in Activity In upshot Per Unit unflinching Cost Fixed Vary Variable Vary Fixed What Methods are use to Estimate Cost Behavior? troika general methods used to estimate the race surrounded by cost behavior and activity levels that are commonly used in practice Engineering estimates, Account analysis & statistical methods (Such as regression analysis) (Lanen, 2008). Results are likely to differ from method to method. Consequently, its a good idea to use to a greate r extent than one method so that results can be compared. These methods, therefore, should be go steadyn as ways to help management arrive at the trounce estimates possible.Their weakness and strengths require attention. Learning Objective Two Estimate costs using engineering estimates. Engineering Estimates Cost estimates are based on measuring and then pricing the work involved in a task. This method based on detailed plans and is frequently used for large projects or new products. This method often omits inefficiencies, such as down sequence for unscheduled maintenance, absenteeism and other miscellaneous random events that affect the entire firm (Lanen, 2008). Identify the activities involved aim Rent Insurance cartridge clip Cost Advantages of engineering estimates Details each step required to perform an operation Permits comparison of other centers with similar operations Identifies strengths and weaknesses. Disadvantages of engineering estimates 1. Can be quite cost ly to use.Read also Recording General Fund Operating Budget and Operating TransactionsLearning Objective Three Estimate costs using account analysis. Account Analysis Estimating costs using account analysis involves a review of each account making up the total costs being analyze and identifying each cost as either fixed or variable, depending on the relation between the cost and some activity. Account analysis relies heavily on personal judgment. This method is often based on decision periods cost along and is subject to managers focusing on specific issues of the old period even though these might be unusual and infrequent(Lanen, 2008) .Example Account Analysis ( show up 5. 1) 3C Cost inclination Using Account Analysis Costs for 360 liven Hours Account Total Variable Cost Fixed Cost Office Rent $3,375 $1,375 $2,000 Utilities 310 100 210 Administration 3,386 186 3,200 Supplies 2,276 2,176 100 Training 666 316 350 Other 613 257 356 Total $10,626 $4,410 $6,216 Per Repai r Hour $12. 25 ($4,410 divided by 360 repair-hours) 3C Cost Estimation Using Account Analysis (Costs at 360 Repair-Hours. A unit is a repair- hour) Total costs = fixed costs + variable cost per unit number of unitsT c = f + v x $10,626 = $6,216 + $12. 25 (360) $10,626 = $6,216 + $$4,410 Costs at 520 Repair-Hours Total costs = fixed costs + variable cost per unit number of units Tc = $6,216 + $12. 25 520 Total costs = $6,216 + $ $6,370 $12,586 = $6,216 + $ $6,370 Advantage of Account Analysis 1. Managers and accountants are familiar with company operations and the way costs answer to changes in activity levels. Disadvantages of Account Analysis 1. Managers and accountants may be biased. 2.Decisions often have major stinting consequences for managers and accountants. Learning Objective Four Estimate costs using statistical analysis. The statistical analysis deals with some(prenominal) random and unusual events is to use several periods of operation or several locations as the bas is for estimating cost relations . We can do this by applying statistical theory, which allows for random events to be separated from the underlying relation between costs and activities. A statistical cost analysis analyzes costs within the pertinent say using statistics. Do you remember how we defined relevant run for? A relevant range is the range of activity where a cost estimate is valid.The relevant range for cost estimation is usually between the upper and lower limits of past activity levels for which data is available (Lanen, 2008). Example Overhead Costs for 3C ( Exhibit 5. 2) The following information is used byout this chapter Here we have the bang costs data for 3C for the last 15 months. Lets use this data to estimate costs using a statistical analysis. Month Overhead Costs Repair-Hours Month Overhead Costs Repair-Hours 1 $9,891 248 8 $10,345 344 2 $9,244 248 9 $11,217 448 3 $13,200 480 10 $13,269 544 4 $10,555 284 11 $10,830 340 5 $9,054 200 12 $12,607 412 6 $10,662 380 13 $10,871 384 7 $12,883 568 14 $12,816 404 15 $8,464 212 A. Scattergraph Plot of cost and activity levelsDoes it look like a descent exists between repair-hours and overhead costs? We will start with a scatter graph. A scatter graph is a plot of cost and activity levels. This gives us a visual representation of costs. Does it look like a birth exists between repair-hours and overhead cost? We use eye judgment to determine the intercept and careen of the byplay. Now we eyeball the scatter graph to determine the intercept and the slope of a line through the data points. Do you remember graphing our total cost in Chapter 3? Where the total cost line intercepts the horizontal or Y axis represents fixed cost. What we are saying is the intercept equals fixed costs. in any case read Current Liabilities and Payroll AccountingThe slope of the line represents the variable cost per unit. So we use eyeball judgment to determine fixed cost and variable cost per unit t o arrive at total cost for a given level of activity. As you can imagine, preparing an estimate on the basis of a scatter graph is subject to a high level of error. Consequently, scatter graphs are usually non used as the sole basis for cost estimates but to illustrate the relations between costs and activity and to point out any past data items that might be solidly out of line. B. High-Low Cost Estimation A method to estimate costs based on two cost observations, usually at the highest and lowest activity level.Although the high-low method allows a computation of estimates of the fixed and variable costs, it ignores near of the information available to the analyst. The high-low method uses two data points to estimate costs (Lanen, 2008). Another approach Equations V = Cost at highest activity Cost at lowest activity Highest activity Lowest activity F = Total cost at highest activity level V (Highest activity) Or F = Total cost at lowest activity level V (Lowest activity) Le ts put the be in the equations V = $12,883 $9,054 V = $10. 0/RH 568 200 F = Total cost at highest activity level V (Highest activity) F = $12,883 $10. 40 (568), F= $6,976 Or F = Total cost at lowest activity level V (Lowest activity) F = $9,054 $10. 40 (200) Rounding Difference C. Statistical Cost Estimation Using Regression Analysis Statistical procedure to determine the blood between variables High-Low Method Uses two data points. Regression analysis Regression is a statistical procedure that uses all the data points to estimate costs. pic Regression AnalysisRegression statistically measures the relationship between two variables, activities and costs. Regression techniques are intentional to baffle a line that best fits a set of data points. In addition, regression techniques generate information that helps a manager determine how well the estimated regression equation describes the relations between costs and activities (Lanen, 2008). We recommend that users of r egression (1) fully understand the method and its limitations (2) specify the model, that is the hypothesized relation between costs and cost predictors (3) know the characteristics of the data being tested (4) examine a plot of the data .For 3C, repair-hours are the activities, the main(a) variable or predictor variable. In regression, the independent variable or predictor variable is set as the X term. An overhead cost is the dependent variable or Y term. What we are saying is overhead costs are dependent on repair-hours, or predicted by repair-hours. The Regression Equation Y = a + bX Y = Intercept + (Slope) X OH = Fixed costs + (V) Repair-hours You already know that an estimate for the costs at any given activity level can be computed using the equation TC = F + VX. The regression equation, Y= a + bX represents the cost equation.Y equals the intercept plus the slope times the number of units. When estimating overhead costs for 3C, total overhead costs equals fixed costs plus the variable cost per unit of repair-hours times the number of repair-hours. We leave the description of the computational details and theory to computer and statistics ladder we will focus on the use and interpretation of regression estimates. We describe the steps required to moderate regression estimates using Microsoft Excel in Appendix A to this chapter. Learning Objective Five Interpret the results of regression yield. Interpreting Regression pic Interpreting regression output allows us to estimate total overhead costs.The intercept of 6,472 is total fixed costs and the coefficient, 12. 52, is the variable cost per repair-hours. Correlation coefficient R measures the linear relationship between variables. The closer R is to 1. 0 the closer the points are to the regression line. The closer R is to zero, the poorer the regression line (Lanen, 2008). Coefficient of determination R2 The square of the correlation coefficient. The proportion of the variation in the dependent varia ble (Y) explained by the independent variable(s)(X). T-Statistic The t-statistic is the value of the estimated coefficient, b, divided by its standard error. Generally, if it is over 2, then it is considered significant.If significant, the cost is NOT exclusively fixed. The significant level of the t-statistics is called the p-value. Continuing to interpret the regression output, the aggregate R is called the correlation coefficient and measures the linear relationship between the independent and dependent variables. R Square, the square of the correlation cost efficient, determines and identifies the proportion of the variation in the dependent variable, in this case, overhead costs, that is explained by the independent variable, in this case, repair-hours. The Multiple R, the correlation coefficient, of . 91 tells us that a linear relationship does exist between repair-hours and overhead costs.The R Square, or coefficient of determination, tells us that 82. 8% of the changes in overhead costs can be explained by changes in repair-hours. Can you use this regression output to estimate overhead costs for 3C at 520 repair-hours? Multiple Regressions Multiple regressions are used when more than than one predictor (x) is needed to adequately predict the value (Lanen, 2008). For example, it might lead to more distinct results if 3C uses both repair hours and the cost of parts in hostel to predict the total cost. Lets look at this example. Predictors X1 Repair-hours X2 separate Cost 3C Cost Information Month Overhead Costs Repair-Hours ( X1) Parts ( X2) 1 $9,891 248 $1,065 2 $9,244 248 $1,452 3 $13,200 480 $3,500 4 $10,555 284 $1,568 5 $9,054 200 $1,544 6 $10,662 380 $1,222 7 $12,883 568 $2,986 8 $10,345 344 $1,841 9 $11,217 448 $1,654 10 $13,269 544 $2,100 11 $10,830 340 $1,245 12 $12,607 412 $2,700 13 $10,871 384 $2,200 14 $12,816 404 $3,110 15 $8,464 212 $ 752 In multiple regressions, the Adjusted R Square is the correlation coefficient squ ared and adjusted for the number of independent variables used to make the estimate. Reading this output tells us that 89% of the changes in overhead costs can be explained by changes in repair-hours and the cost of parts. Remember 82. % of the changes in overhead costs were explained when one independent variable, repair-hours, was used to estimate the costs. Can you use this regression output to estimate overhead costs for 520 repair-hours and $3,500 cost of parts? Learning Objective sixer Identify potential problems with regression data. Implementation Problems Its easy to be over confident when construe regression output. It all looks so official. But beware of some potential problems with regression data. We already discussed in earlier chapters that costs are curvilinear and cost estimations are only valid within the relevant range. Data may also include outliers and the relationships may be spurious. Lets talk a bit about each. Curvilinear costs Outliers Spurious relations Assumptions 1. Curvilinear costs Problem Attempting to fit a linear model to nonlinear data. Likely to occur near full-capacity. Solution Define a more limited relevant range (example from 25 75% capacity) or design a nonlinear model. If the cost function is curvilinear, then a linear model contains weaknesses. This generally occurs when the firm is at or near capacity. The leaner cost estimate understates the slope of the cost line in the ranges close capacity. This bunk is shown in exhibit 5. 5. 2. Outliers Problem Outlier moves the regression line.Solution Prepare a scatter-graph, analyze the graph and eliminate super unusual observations before running the regression. Because regression calculates the line that best fits the data points, observations that lie a significant distance away from the line could have an overwhelming effect on the regression estimate. Here we see the effect of one significant outlier. The computed regression line is a substantial distance from most of the points. The outlier moves the regression line. Please refer exhibit 5. 6. 3. Spurious or false relations Problem Using excessively many variables in the regression. For example, using direct labor to explain materials costs.Although the association is very high, actually both are driven by output. Solution Carefully analyze each variable and determine the relationship among all elements before using in the regression. 4. Assumptions Problem If the assumptions in the regression are not conform to then the regression is not reliable. Solution No clear solution. Limit time to help operate costs behavior remains constant, yet this causes the model to be weaker due to less data. Learning Objective Seven Evaluate the advantages and disadvantages of alternative cost estimation methods. Statistical Cost Estimation Advantages 1. Reliance on historical data is relatively inexpensive. 2.Computational tools allow for more data to be used than for non-statistical methods. Disadvantage s 1. Reliance on historical data may be the only readily available, cost-effective basis for estimating costs. 2. Analysts must be alert to cost-activity changes. Choosing an Estimation Method Each cost estimation method can yield a different estimate of the costs that are likely to result from a particular management decision. This underscores the advantage of using more than one method to arrive at a final estimate. Which method is the best? Management must weigh the cost-benefit related to each method (Lanen, 2008). Estimated manufacturing overhead with 520 repair-hours and $3,500 parts costs *.The more sophisticated methods yield more accurate cost estimates than the simple methods. Account Analysis = $12,586 High-Low = $12,384 Regression= $12,982 Multiple Regression= $13,588* Data Problems Missing data Outliers Allocated and discretionary costs Inflation Mismatched time periods No matter what method is used to estimate costs, the results are only as good as the data used. Coll ecting appropriate data is complicated by missing data, outliers, allocated and discretionary costs, inflation and mismatched time periods. Learning Objective Eight (Appendix A) Use Microsoft Excel to perform a regression analysis. Appendix A Microsoft as a ToolMany software programs exist to aid in do regression analysis. In order to use Microsoft Excel, the Analysis Tool Pak must be installed. There are software packages that allow users to slow generate a regression analysis. The analyst must be well schooled in regression in order to determine the meaning of the output Learning Objective Nine (Appendix B) Understand the mathematical relationship describing the learning phenomenon. Learning Phenomenon Leaning phenomenon refers to the systematic relationship between the amount of experience in performing a task and the time required to perform it. The learning phenomenon means that the variable costs tend to decrease per unit as the volume increase. Example Unit Time to Produce Calculation of Time First Unit 100 hours (assumed) Second Unit 80 hours (80 percent x 100 hours fourthly Unit 64 hours (80 percent x 80 hours Eighth Unit 51. hours (80 percent x 64 hours Impact Causes the unit price to decrease as production increases. This implies a nonlinear model. Another element that can change the shape of the total cost curve is the notion of a learning phenomenon. As workers become more skilled they are able to produce more output per hour. This will uphold the total cost curve since it leads to a lower per unit cost, the higher the output. Chapter 5 END crease WORK EXERCISE 5-25 A& B PROBLEM 5-47 -A& B REFERENCES Lanen , N. W. , Anderson ,W. Sh. & Maher ,W. M. ( 2008). Fundamentals of cost accounting. New York McGraw-Hill Irwin. pic