Motomart

Motomart

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-industryaccepted 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 Senior Capstone: Business 27 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 Senior Capstone: Business 28 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 Senior Capstone: Business 29 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. Senior Capstone: Business 30 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 4. 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? Senior Capstone: Business 34
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