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Chapter 6

Chapter 6. Cost Estimation. Learning Objectives. Understand the strategic role of cost estimation Apply the six steps of cost estimation Use each of the cost estimation methods: the high-low method, work measurement, and regression analysis. Learning Objectives.

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Chapter 6

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  1. Chapter 6 Cost Estimation

  2. Learning Objectives • Understand the strategic role of cost estimation • Apply the six steps of cost estimation • Use each of the cost estimation methods: the high-low method, work measurement, and regression analysis

  3. Learning Objectives • Explain the data requirements and implementation problems of the cost estimation methods • Use learning curves in cost estimation when learning is present • Use statistical measures to evaluate a regression analysis

  4. Cost Estimation • A critical starting point for strategic cost management is having accurate cost estimates • Cost estimation is the development of a well-defined relationship between a cost object and its cost drivers for the purpose of producing the cost

  5. Learning Objective One Understand the strategic role of cost estimation

  6. Cost Estimation • Cost estimation facilitates strategic management in two important ways: • It helps predict future costs using previously identified activity-based, volume-based, structural, or executional cost drivers • Cost estimation helps identify the key cost drivers for a cost object and which of these costs drivers are most useful in predicting cost

  7. Using Cost Estimation to Predict Future Costs • Strategic management requires accurate cost estimates form many applications, including to facilitate: • strategic positioning analysis • value-chain analysis • target costing and life-cycle costing

  8. Using Cost Estimation to IdentifyCost Drivers • Often the most practical way to identify cost drivers is to rely on the judgment of product designers, engineers, and manufacturing personnel • Cost estimation sometimes plays a discovery role and at other times a collaborative role to validate and confirm the judgments of the designers and engineers

  9. Learning Objective Two Apply the six steps of cost estimation

  10. Six Steps of Cost Estimation • The six steps of cost estimation are to: • Define the cost object for which the related costs are to be estimated • Determine the cost drivers • Collect consistent and accurate data on the cost object and the cost drivers • Graph the data • Select and employ an appropriate estimation method • Evaluate the accuracy of the cost estimate

  11. Define the Cost Object • Defining the particular cost to be estimated requires great care • For example, if the goal is to estimate product costs to improve product pricing, then the relevant cost objects are the products manufactured in the plant • If the goal is to reward the managers most effective at reducing cost, then the most appropriate cost objects are the individual manufacturing departments in the plant

  12. Determine the Cost Drivers • Cost drivers are the causal factors used in the estimation of the cost • Identifying cost drivers is the most important step in developing the cost estimate • A number of relevant drivers might exists and some might not be immediately obvious

  13. Collect Consistent and Accurate Data • Once cost drivers have been selected, the next step is to collect data on the cost object and cost drivers • The data should be both consistent and accurate • The choice of cost drivers requires trade-offs between the relevance of the drivers and the consistency and accuracy of the data

  14. Graph the Data • The objective of graphing data is to identify unusual patterns • Any shift or nonlinearity in the data must be given special attention in developing the estimate • Any unusual occurrences can often be detected rather easily by studying a graph

  15. Select and Employ theEstimation Method • The various estimation methods that are available differ in their ability to provide accuracy in cost estimation relative to the cost of the expertise and resources required • A management accountant must choose the method with the best precision/cost trade-off for the estimation objectives

  16. Assess the Accuracy of theCost Estimate • The final step in cost estimation is to consider the potential for error when the estimate is prepared • A common approach for assessing the accuracy of an estimation method is to compare the estimates to the actual results over time • Errors can be evaluated using the mean absolute percentage error (MAPE), which is calculated by taking the absolute value of each error, and averaging these errors and converting the result to a percentage of the actual values of overhead

  17. Learning Objective Three Use each of the cost estimation methods: the high-low method, work measurement, and regression analysis

  18. Cost Estimation Methods • The three estimation methods are: • The high-low method • Work measurement • Regression analysis • The high-low method is the easiest and least costly, and the regression analysis method is both the most accurate and most costly • In choosing the best estimation method, management accountants must consider the level of accuracy desired and any limitations on cost, time, and effort

  19. Cost Estimation Methods Insert Exhibit 6.1 (Estimate Methods) Here

  20. Data on Maintenance Costs and Hours Insert Exhibit 6.3 (Data on Maintenance Costs and Hours) Here

  21. The High-Low Method The high-low method uses two points to estimate the general cost equation Y = a  bH Y= the value of the estimated maintenance cost a= a fixed quantity that represents the value of Y when H = zero b= the slope of the line, the unit variable cost for maintenance. H = the cost driver, the number of hours of operation for the plant

  22. The High-Low Method Ben used these two levels of activity to compute: • the variable cost per unit; • the fixed cost; and then • express the costs in equation form Y = a + bH.

  23. The High-Low Method • Unit variable cost = $520 ÷ 289 hours = $1.80 per hour

  24. The High-Low Method • Unit variable cost = $520 ÷ 289 hours = $1.80 per hour • Fixed cost = Total cost – Total variable cost Fixed cost = $23,030 – ($1.80 per hour × 3,614 hours) • Fixed cost = $23,030 – $6,505 = $16,525

  25. The High-Low Method • Unit variable cost = $520 ÷ 289 hours = $1.80 per hour • Fixed cost = Total cost – Total variable cost • Fixed cost = $23,030 – ($1.80 per hour × 3,614 hours) • Fixed cost = $23,030 – $6,505 = $16,525 • Total cost = Fixed cost + Variable cost (Y = a + bH) Y = $16,525 + $1.80H

  26. Work Measurement • Work measurement is a statistical cost estimation method that makes a detailed study of some production or service activity to measure the time or input required per unit of output • Although a variety of work measurement methods is used in practice, the most common is work sampling • Work sampling is a statistical method that makes a series of measurements about the activity under study

  27. Regression Analysis • Regression analysis is a statistical method for obtaining the unique cost estimating equation that best fits a set of data points • Least squares regression, which minimizes the sum of the squares of the estimation errors, is widely viewed as one of the most effective methods for estimating costs • A regression analysis has two types of variables: • The dependent variable is the cost to be estimated • The independent variable is the cost driver used to estimate the value of the dependent variable

  28. Regression Analysis The objective of the regression method is still a linear equation to estimate costs Y = a + bX + e Y= value of the dependent variable, estimated cost a= a fixed quantity, the intercept, that represents the value of Y when X = 0 b= the unit variable cost, the coefficient of the independent variable measuring the increase in Y for each unit increase in X X = value of the independent variable, the cost driver e = the regression error, the distance from the regression line to the data point

  29. Regression Analysis MonthSupplies Expense (Y)Production Level (X) 1 $250 50 units 2 310 100 units 3 325 150 units 4 ? 125 units Regression analysis willenable us to predict the amount of supplies expense for month four.

  30. Regression Analysis 400 350 300 250 200 Supplies Expense Regression for the data isdetermined by a statistical procedurethat finds the unique line throughthe data points that minimizesthe sum of squared error distances. 50 100 150 Units of Output

  31. Regression Analysis MonthSupplies Expense (Y)Production Level (X) 1 $250 50 units 2 310 100 units 3 325 150 units 4 ? 125 units Y = a + bX + e Y = $220 + $.75 per unit  125 units Y = $313.75 Expense estimate for month 4

  32. Regression Analysis 400 350 300 250 200 b = the slope of the regression line or the coefficient of the independent variableb = $.75 per unit e = 15 e = 7.5 Supplies Expense e = 7.5 220 Fixed Cost = $220 50 100 150 Units of Output

  33. Regression Analysis proper line, excluding the outlier improper line, influenced by outlier 400 350 300 250 200 Supplies Expense Outlier Outliers may be discarded toobtain a regression that is morerepresentative of the data. 50 100 150 200 Units

  34. Regression Analysis • Regression analysis also provides quantitative measures of its precision and reliability • Precision refers to the accuracy of the estimates form the regression such as the standard error of the estimate • Reliability indicates whether the regression reflects actual relations among the variables such as the coefficient of determination and the t-value • These measures can aid management accountants in assessing the usefulness of the regression

  35. Regression Analysis Evaluating a Regression Analysis R2, the coefficient of determination, is a measure of the explanatory powerof the regression, the degree thatchanges in the dependentvariable can be predicted by changesin the independent variable. The t-value is a measureof the reliability of each ofthe independent variables. The standard error of the estimate (SE) is a measureof the accuracy of the regression’s estimates.

  36. Regression Analysis 400 350 300 250 200 Dependent Variable Regression withhigh R2 (close to 1.0) 50 100 150 200 Independent Variable

  37. Regression Analysis 400 350 300 250 200 Dependent Variable Regression withlow R2 (close to 0) 50 100 150 200 Independent Variable

  38. Regression Analysis 400 350 300 250 200 standard error Dependent Variable Standard error is a range around the regression estimate in which we can be reasonably sure that theunknown value will fall. 50 100 150 200 Independent Variable

  39. Slide 7-34 Regression Analysis 400 350 300 250 200 standard error Dependent Variable Regression with Wide(Poor) Standard Error 50 100 150 200 Independent Variable

  40. Regression Analysis 400 350 300 250 200 standard error Dependent Variable Regression with Narrow(Good) Standard Error 50 100 150 200 Independent Variable

  41. Choosing the Independent Variable • Management accountants should consider all financial, operating, and other economic data that might be relevant for estimating the dependent variable • The goal is to choose variables that: • Are relevant (they change when the dependent variable changes) • Do not duplicate other independent variables

  42. Independent Variables for Selected Dependent Variables Insert Exhibit 6.6 (Independent Variables ) Here

  43. Regression Analysis • Most often the data in a regression analysis are numerical amounts in dollars or units • A dummy variable is another type of variable that is used to represent the presence or absence of a condition • For example, if production is always high in March, a dummy variable with a value of 1 for March and 0 for the other months could be used

  44. Regression Analysis • Other issues in regression analysis include: • Multicollinearity means that two or more independent variables are highly correlated with each other • Correlation means that a given variable tends to changes predictably in the same (or opposite) direction for a given change in the other, correlated variable

  45. Learning Objective Four Explain the data requirements and implementation problems of the cost estimation methods

  46. Data Requirements and Implementation Problems Data and Implementation Problems • Data Accuracy • Selecting the time period • Mismatched time period • Length of the time period • Nonlinearity problems • Trend and/or seasonality • Outliers • Data shift

  47. Data Accuracy • Management must carefully consider the source of the data and its reliability • If the source is inside the firm, management can develop reporting requirements to ensure the accuracy of the data • For external economic data, management determines the reliability of data by considering the source

  48. Selecting the Time Period • Mismatched Time Periods – data for each variable must be from the same time period • Length of Time Period – if the period is too short then the chance of mismatch increases because of recording lags; if the period is too long then important relationships in the data might be averaged out and not have explanatory power

  49. Nonlinearity Problems • Trend and/or Seasonality – When trend or seasonality is present, a linear regression is not a good fit to the data, and the management accountant should use a method to deseasonalize or to detrend a variable • The most common methods to accomplish this are: • Use of a price change index • Use of a decomposition technique • Use of a trend variable • Replacement of the original values of each of the variables with the first differences

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