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Project Senior Capstone Business case 2 motor

Case 2: Motomart
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 ;B@?>:A9<7C=>@B5A68C?@BCC 0 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 0- ;B@?>:A9<7C=>@B5A68C?@BCC 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 ;B@?>:A9<7C=>@B5A68C?@BCC 0% 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. /" ;B@?




Case 2: Motomart


Step 1 First, using Tables 3-5, note the pattern of operating profits (or losses) over the five-year period. Then focus only on the semi-fixed expenses contained in Table 3. Do any amounts appear to be odd? 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 4. Are there any unusual or odd patterns you might note in this detailed financial data? There are eight expense items. About five of the eight should immediately catch your attention. You should be able to respond to this requirement in a few well-written 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 4 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. (You could transfer the figures to Excel to use the maximum and minimum functions to assist you in identifying the high and low measures [N=60] for each of the ten columns.) Business 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 over analyze 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. Finally, summarize your findings with respect to the application of the high-low method to separate mixed costs into their fixed and variable components or the development of a cost equation.



Step 4 Use Table 6 to compute the cost equations and R-squared measures for each of the remaining eight expenses and total expenses. Notice that there’s a computed total requirement in the table. This just means that you must total these two columns and compare the computed totals to the Excel generated measures in the row below. In effect, you’re being asked to comment on whether the separate cost formulas are “additive.”

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