Case 2 is quantitative and involves the analysis of selected financial data for a firm we’ll call “Motomart.” Motomart was a retail automobile dealership seeking permission to relocate in an area already served by a similar automobile dealership. The existing dealership brought a lawsuit to block the relocation. Although the data provided by the company is flawed, you’ll prepare a summary of your observations and answer several questions about problems with the case. Case 2: Motomart INTRODUCTION The Motomart case is designed to supplement your Managerial/ Cost Accounting textbook coverage of cost behavior and variable costing using real-world cost data and an auto-industry accepted cost driver. Unlike textbook problems, this data is real. It won’t necessarily produce a clear solution when you attempt to analyze cost behavior and apply scatter-plot, high-low, and regression methods to separate mixed costs into their fixed and variable components. This case also illustrates that Financial Accounting decisions and methods can have an influence on Cost Accounting and Managerial applications and decisions. OBJECTIVES When you complete this case, you’ll be able to • Explain the importance of accrual accounting and proper application of the matching principle for the computation of contribution margins and break-even points • Apply knowledge of generally accepted accounting principles (GAAP) to a specific real-world example • Integrate statistical analyses and scatter plots, line graphs, and regression to determine the reliability of financial information prepared for external use • Use analytical review procedures to examine a firm’s financial statements • Apply critical-thinking skills to real-world business circumstances CASE BACKGROUND This case is based on real financial data provided by a retail automobile dealership (Motomart) seeking to relocate closer to an existing retail dealership. You’ll examine the mixed cost data from Motomart and apply both high-low and regression to attempt to separate mixed costs into their fixed and variable components for break-even and contribution margin computations. You’ll find that the data is flawed because Motomart was a single observation in a larger database. Don’t attempt to correct the data (e.g., remove outliers or influential outliers). You’ll be producing a scatterplot and apply high-low and regression methods to the extent practicable and writing a summary report of the findings. Motomart operates a retail automobile dealership. The manufacturer of Motomart products, like all automobile manufacturers, produces forecasts. It has long been an industry practice to use variable costing-based/break-even analyses as the foundation for these forecasts, to examine their cost behavior as it relates to the new retail vehicles sold (NRVS) cost driver. In preparing this financial information, a common financial statement format and accounting procedures manual is provided to each retail auto dealership. The dealership is required to produce monthly financial statements using the guidelines provided by this common accounting procedures manual, and then furnish these financial statements to the manufacturer. General Motors, Ford, Nissan, and all other automobile manufacturers employ similar procedures manuals. The use of a common format facilitates the development of composite financial statements that can be used to estimate costs and produce financial forecasts for future or proposed retail dealership sites (Cataldo and Kruck 1998). Zimmerman (2003) suggests that as many as 77 percent of manufacturers divide costs into variable and fixed components, and that managers arrive at these estimates by classifying individual accounts as being primarily fixed or primarily variable (67). For this case, you’ll examine mixed costs as defined by the manufacturer. Using the scatterplot, high-low, and regression methods, separate these mixed costs into their fixed and variable components. The data is problematic, and a clear solution won’t exist. Don’t attempt to correct the data by removing outliers, but make observations based on any patterns you observe. The case will expose you to actual data and require you to summarize your findings, including any conclusions you’re able to reach and why the financial data makes it impossible to separate the mixed costs into their fixed and variable components. Motomart: A Litigation Support Engagement The Motomart case evolved from a litigation support engagement. The lead author of this case was hired to analyze the data and provide expert testimony. His report and testimony was made available to the public (for a fee to cover reproduction costs). A broad description of the relevant points for the Motomart case follows. Motomart wanted to move their retail automobile dealership, blaming their location for declining profits and increasing losses. They provided financial projections, using variable costing, to show that after relocation both Motomart and the existing dealership would be profitable. They created these financial projections using a database provided by the manufacturer, which included all North American retail automobile dealerships. Motomart was one of the observations or retail automobile dealerships included in the database used to create these financial projections. You’ll be examining portions of Motomart’s historical financial data. The relocation site was quite close to the existing dealership (which we’ll refer to as Existing Dealer), and Existing Dealer felt that, if the relocation was permitted, one or both of the dealerships would fail to break even and eventually go bankrupt, leading to poor service, or what the industry refers to as “orphaned” owners of these automobiles. Antitrust laws provided Existing Dealer with the means to block the relocation requested by Motomart, but only if it could prove that the relocation wasn’t in the best interest of the consuming public. Generally, the only way to prove this is to prove that there’s simply not enough business for both retail automobile dealerships to break even (or generate a reasonable return on investment, given the risks associated with the industry). Again, the manufacturer, in support of the proposed Motomart relocation, supplied financial projections showing that both retail automobile dealerships would be profitable after the relocation. The expert witness hired to investigate the merits of the relocation was given the Motomart data, but not the entire database that included the Motomart data. The Motomart data was in such poor form that it wasn’t possible to produce a financial forecast. An alternative forecast, not included in this case, was produced. This alternative forecast did not support the relocation of Motomart to a site closer to Existing Dealer. The alternative forecast showed that the market simply couldn’t support two retail automobile dealerships. The implication was that, as the weaker of the two dealerships, Motomart was losing business to Existing Dealer. In conclusion, the relocation request by Motomart was denied. Income and Expense Data The following tables give you information such as income statements, semi-fixed expenses, and salaries for Motomart. Look for unusual entries or discrepancies in their records and, where you can, note the cause of the problems. Table 3 summarizes financial and cost driver information produced by Motomart, where new retail vehicles sold (NRVS) is the cost driver. The account classification method has resulted in three cost behavior classifications: variable, semi-fixed, and fixed costs. Semi-fixed is the automobile industry-specific term used for mixed costs. We’ll assume that Motomart’s classifications of variable costs (VCs) and fixed costs (FCs) are correct, and focus our analysis on Motomart’s semi-fixed or mixed costs. Table 2 SELECTED HISTORICAL INCOME STATEMENT AND RELATED MEASURES 1984 1985 1986 1987 1988 Net Variable Revenues* 2,885,969 3,828,255 4,086,667 3,940,799 4,298,748 Semi-Fixed (S-F) Expenses: Salaries 613,006 968,789 1,211,464 1,289,758 1,360,489 Vacation 600 26,705 19,468 19,059 18,268 Advertising & Training 210,226 288,347 281,219 309,608 371,314 Supplies/Tools/Laundry 31,473 46,141 75,468 65,935 81,252 Freight 5,719 5,987 6,528 5,731 4,663 Vehicle 22,913 23,718 23,664 20,370 19,483 Demonstrators 10,465 4,969 –1,513 4,192 707 Floor-Planning 278,531 301,113 276,201 156,129 305,044 Total S-F Expenses 1,172,933 1,665,769 1,892,499 1,870,782 2,161,220 Fixed Expenses: Total Fixed Expenses 1,449,208 2,050,172 2,290,867 2,164,362 2,653,620 Operating Profit/(Loss)** 263,828 112,314 -96,699 -94,345 -516,092 New Retail Vehicles Sold 1,798 1,977 1,674 1,450 1,897 Notes: * Revenues less variable costs equal Net Variable Revenues (or Contribution Margin, in aggregate). ** Net Variable Revenue less Total S-F Expenses less Total Fixed Expenses equals Operating Profit/(Loss). Table 3 provides five years of monthly data (N=60) for NRVS and the related semi-fixed or mixed cost measures. Semifixed costs were significant. Recall that they ranged from nearly $1.2 million for calendar and fiscal year (FY) 1984 to almost $2.2 million for FY 1988 (see Table 2). Recall the cost function applying to the high-low and regression methods, which are provided in a variety of forms, depending on the texts you used in your previous math, economics, or accounting courses. Figure 3 is a brief outline of the high-low and regression methods. FIGURE 3—For the high-low method to work, the $H and #H and the $L and #L measures must be from the same accounting period Preparing Graphs The single cost driver and nonfinancial measure in Table 3 is new retail vehicles sold (NRVS or X in the above cost function). There are eight financial measures (salary; vacation; advertising and training; supplies, tools, and laundry; freight; vehicles; demonstrators; and floor-planning [also known in the automobile retail industry as interest expense relating to new car inventory]), as well as a total (aggregate measure) provided for all eight financial measures (or the Y in the above cost function). Using NRVS, the only available cost-driver, use Excel to prepare nine separate scatter plots and cost function-based trend lines and nine separate line graphs for each of the financial measures provided in Table 3. See Figure 4 and Figure 5 for a examples of completed graphs for salaries. FIGURE 4—A Scatterplot Graph for Motomart Salaries Now examine, on a preliminary basis, the pattern or trend (or lack thereof) for each of the “X” (NRVS) and “Y” (financial measure) data pairs and consider the following questions: • You’re observing these data pairs for a 60-month period (i.e., five years); are any annual or other seasonal patterns or trends immediately apparent? • Do the slopes of the trend lines (i.e., variable costs) make sense? FIGURE 5—A Line Graph for Motomart Salaries In the case of salaries (see Figures 4 and 5), there’s no apparent trend or pattern. It’s odd that salaries decrease as NRVS increases—in fact, this doesn’t make any sense. However, it’s consistent with the high-low results, which also didn’t make sense. But remember, since this data came from Motomart, the firm attempting to relocate, it’s real and from an actual litigation support engagement (not a textbook problem), so it won’t necessarily work out perfectly. The cost equation in Table 4 shows fixed costs (FC) at $106,866.00 and variable costs to be used to “reduce” total costs (TC) by $110.10 per NRVS. Compare the salary figures and coefficients (in bold type) to Figure 4. Notice that if you extended the trend line in Figure 4, it would hit the y-axis intercept at $106,866.00 (the fixed cost). Also notice that the R-squared (R-sq) measure in Table 4 equals 4.1 percent. Table 4 SALARY = $106,866.00 – $110.10 NRVS Predictor Coefficient Std Deviation t-statistic p-value Constant 106,866.00 10,793.00 9.90 0.000 NRVS 110.10 70.17 –1.57 0.122 s = 25300 R-sq = 4.1% Analysis of Variance SOURCE DF SS MS F-statistic p-value Regression 1 261,795 261,795 0.10 0.754 Error 58 152,801,120 2,634,502 Total 59 153,062,912 Your math and statistics courses probably reviewed the use of the t-statistic, overall F-statistic, and related p-values, as well as some of the other measures presented here. Our application is a very simple one, so we’ll focus on only the R-squared measure. The other measures are provided in this example only for completeness. Because the high-low technique didn’t work, it makes sense that the regression technique wouldn’t work well, either. Therefore, the results for high-low and regression are consistent. The advantage of the regression technique is that it mathematically quantifies the level of the problem or difficulty with the data. In this case, one of simple regression, the R-squared measure tells the story. Still focusing on the salaries example in Figure 5, the R-squared measure tells us that only 4.1 percent of the total or mixed or semi-fixed cost is explained by NRVS. This means that that cost equation developed from this historical data isn’t helpful in predicting future costs, as nearly 96 percent of the cost behavior, through use of this equation, remains unexplained. REQUIREMENTS The project requires five steps to be presented. Step 1 – Provide comments on a 5 year Income Statement. Step 2 – Discuss patterns in expense items. Step 3 – Identify High/Low activity levels. Step 4 – Compute cost equations. Step 5 – Summarize your findings. In one Word document, provide individual sections for each Step. This Word document along with the Excel file (described below) will be uploaded when you click on the Take Exam button on your Student Portal to submit your project (described under “Submitting Your Assignment” later in the instructions). This Senior Capstone project highlights your knowledge and the skills you have developed over the course of your education. There is nothing “new” to be learned here. The knowledge and skills required for this project include English Composition, Financial Accounting, Managerial Accounting, Business Statistics and the abilities to think critically and to present your work in a professional manner. If you are unsure or don’t understand something about the project, then go back to your previous subjects to review. For example, if you don’t remember how to use the High/Low Method, the revisit your Managerial Accounting to refresh your memory on how to use the High/Low Method Remember, there is nothing “new” here. Everything about this project you should already know how to do. On your Student Portal, under the Supplements section of the Senior Capstone subject is a downloadable Excel file titled “Exam 500896 – Motomart Excel Spreadsheet”. • The Excel file provides a detailed example of what needs to be done for one of the expenses in order to fill out the figures required in Steps 3 & 4. You will include this Excel file as part of your project submission along with the Word document you create to present this project. o There is a “60 Months” worksheet that has the 60 months of data already entered. There is also a “Sample” worksheet that an example of how to calculate the R-sq. o There is a “PLOT – SALARY” worksheet that shows how the FC, VC and R-sq figures are calculated for Salary. o There is also a “high&low” worksheet for help with the high/low method in Step 3. o Complete and include the Excel spreadsheet. You will need to create new worksheets for each of the other expenses following the example to calculate the figures needed for Table 5. Operating Profits and Semi-Fixed Expenses Step 1 First, using Tables 2–4, note the pattern of operating profits (or losses) over the five-year period. Then focus only on the semi-fixed expenses contained in Table 2. Do any amounts appear to be odd? (Think about whether the figures are right or wrong. What is it about the individual numbers that is not “right”?) Next, briefly comment on the five-year pattern or trend for operating profit/loss measures. You should be able to respond to this step in a few well-written sentences. Step 2 Focus only on the detailed semi-fixed expense contained in Table 3. Are there any unusual or odd patterns you might note in this detailed financial data? There are 5 expenses that have an oddity about them which doesn’t make sense. Similar to Step 1, what is it about the individual numbers that is not “right”? There are 4 expenses that “stick out” as not being correct and one that has an unusual pattern. attention. You should be able to respond to this requirement in a few wellwritten sentences. Briefly comment on only the most obvious or apparent measures or patterns, by expense item. Step 3 Identify the high and low measures in each column, just as you would in preparation for application of the high-low method or technique. For example, in Table 3 the high measure for the cost driver (NRVS) is 280 NRVS in month 13 and the low measure is 31 NRVS in month 12. Repeat this process for each of the eight separate semi-fixed expense columns and also for the total expense column. Insert a table for Step 3 to present your findings. The table should have three columns; 1. Expense 2. High Figure 3. Low Figure After the high and low measures have been identified in each column, try to match each expense column’s high and low measure, separately, to the highs and lows identified in the NRVS column. They won’t match. Don’t try to correct the data, but comment on the potential for application of the high-low technique. What happens when the high and low activity level doesn’t match the high and low expense measure? Does this prevent you from correctly applying the high-low technique? Don’t overanalyze this data, because there’s a problem with it and you don’t have sufficient information to correct it. Merely summarize your observations and unsuccessful attempts to match the high and low NRVS months (identified above), separately, with each of the high and low expense measure months. You should be able to do this in a very few well written sentences. Step 4 Using the Excel file "Exam 500896 – Motomart Excel Spreadsheet" as per the instructions found above under the "Project Requirements", reproduce and complete the following Table 5 and answer the four questions. The Excel file provides an example of how to arrive at the figures that need to be entered into the Table. You will create new worksheets for each of the remaining expenses. Do the work to arrive at the figures for each expense. Be sure to include the Excel file as part of your submission to "backup" the data presented in the Table in the Word document being submitted. The Excel spread sheet, while it will be included in your submission for the project, will not be graded. It is supporting documentation for what is being presented in the Word document. Only the information that is in the Word document will be graded. The FC and VC should be rounded to the nearest dollar. The R-sq is a percentage figure carried out to 2 decimal places. Table 5 Column Expense FC VC R-sq 1. Salaries $106,866 –$110 4.10% 2. Vacation 3. Advertising and training 4. Supplies/tools/laundry 5. Freight 6. Vehicles 7. Demonstrators 8. Floor planning Computed total 9. Complete the cost equations for the table. Use the R-squared as the single measure of “goodness of fit.” Don’t attempt to improve your results with the elimination of “outliers” or “influential outliers.” As you complete Table 5, answer the following questions: 1. What problems did you encounter? 2. Are the R-squared measures high or low? 3. Are the slopes negative or positive? 4. Are your conclusions consistent with those from the high-low effort? Step 5 Summarize your findings by answering the following questions: 1. Can the Motomart data be used to prepare a reliable financial forecast? Why or why not? 2. If Motomart is included in the very large database used to prepare the financial forecast that supports the relocation of Motomart closer to Existing Dealer, what concerns might present themselves with respect to the remainder of the database used for this forecast? 3. Would you rely on this forecast? Writing Guidelines Refer to the “Submitting Your Work” section at the end of this book for details on submission requirements for the Motomart Case assignment. Grading Criteria Your assignment will be evaluated according to the following criteria: Content 80 percent Written Communication 10 percent Format 10 percent Criteria Content 80 pts • Step 1 – Provides comments on 5 year income statement (worth 10 points) • Step 2 – Discuss patterns in expense items (worth 10 points) • Step 3 – Identify high and low activity levels (worth 10 points) • Step 4 – Compute cost equations (worth 30 points) • Step 5 – Summarize your findings (worth 20 points) Written Communication 10 pts • Answers each question in complete sentences leading to well-structured responses to each Step listed above. • Uses correct grammar, spelling, punctuation, and sentence structure • Provides clear organization by using words like first, however, on the other hand, and so on, consequently, since, next, and when • Makes sure the paper contains no typographical errors Format 10 pts The paper is double-spaced, typed in font size 12. It includes the student’s • Name and address • Student number, Course title and number, and project number SUBMITTING YOUR WORK Writing Guidelines 1. Type your submission, double-spaced, in a standard print font, size 12. Use a standard document format with 1-inch margins. (Do not use any fancy or cursive fonts.) 2. Include the following information at the top of your paper: a. Name and complete mailing address b. Student number c. Course title and number (Senior Capstone: Business, BUS 450) d. Project number (see Format instructions) e. Project title (Professional Development Activity, Case 1, etc.) 3. Read the assignments carefully and complete each one in the order given. 4. Be specific. Limit your submission to the questions asked and issues mentioned. 5. If you include quotes or ideas from textbooks or other sources, provide a reference page in either APA or MLA style. On this page, list books, Web sites, journals, or any other references used in preparing the paper. 6. Proofread your work carefully. Check for correct spelling, grammar, punctuation, and capitalization.
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