Chapter 6
Cost Estimation
Teaching Notes for Cases
6-1. High-Low Method and Regression Analysis
1, 2. The spreadsheet below shows the analysis of the Brenham Hospital data using both regression and
high-low methods. Before either method is applied, a scattergraph is prepared, as shown in the middle of
the spreadsheet; there are no apparent outliers or nonlinear patterns. The unit variable cost and fixed cost
for the high-low method are calculated, in the cells immediately below the figure, from the data in the
upper right hand corner of the spreadsheetunit variable cost is $9.73 and fixed cost is $5,264. The
regression analysis is shown at the bottom of the spreadsheetunit variable cost is similar to the highlow results - $9.35; fixed cost is also similar to that of the high-low method, $5,400. We can be relatively
confident that these figures are at least approximately correct.
To evaluate the two methods, we examine the square error terms for each method. The error
terms for the high low method (squared) are shown in the top right portion of the spreadsheetthe total is
2,426,417. The total squared error for the regression is shown in the ANOVA table - 1,484,453. Since the
regression results are better, we would choose the regression model for the most accurate predictions.
3. Since unit variable cost is apparently less than $10, from both the regression and high-low results, and
since the fixed costs are unlikely to change (in fact the hospital says it plans to keep the dietician and
equipment), the best plan would be to keep the kitchen open, since the variable cost of the kitchen
(approximately $9.50) is less than the outside price of $11.50.
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-1 ©The McGraw-Hill Companies, Inc., 2008
Brenham General Hospital
Patient Days
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Other
Dietician Staff
2,875
2,875
2,875
2,875
2,875
2,875
2,875
2,875
2,875
2,875
2,875
2,875
34,500
3,122
2,908
2,655
2,600
2,433
2,083
1,809
2,322
1,434
2,700
2,798
2,600
29,464
High Low
Food
Total
Patient
Prediction
Costs
Maint.
Equip.
Cost
Days
Error
9,674
1,401
1,649
18,721
1,382
9,184
1,322
1,415
17,704
1,312
112,513
8,302
1,322
1,313
16,467
1,186
119,441
7,084
1,288
1,105
14,952
1,012
27,693
6,398
1,200
1,089
13,995
914
28,634
4,338
1,133
1,011
11,440
604
86,535
3,612
1,093
900
10,289
516
6,275
1,122
1,112
13,706
896
80,063
6,734
1,235
1,103
13,381
962
1,563,944
9,002
1,302
1,300
17,179
1,286
368,783
8,456
1,300
1,442
16,871
1,208
24,277
7,798
1,322
1,396
15,991
1,114
14,534
86,857
15,040
14,835 180,696
12,392
2,426,417
1,400
1,200
1,000
800
600
400
200
-
Series1
-
5,000
10,000
15,000
20,000
Cost
Regression
SUMMARY OUTPUT
High Low:
Unit Variable cost
Fixed Cost
9.736721
5264.852
Regression Statistics
Multiple R
0.9897344
R Square
0.9795741
Adjusted R Square 0.9775315
Standard Error
385.28595
Observations
12
ANOVA
df
Regression
Residual
Total
Intercept
X Variable 1
1
10
11
SS
MS
F
Significance F
71,190,535 71190535 479.5743 8.83E-10
1,484,453 148445.3
72,674,988
Coefficients Standard Error t Stat
P-value
Lower 95%Upper 95%
5400.5461 454.8053919 11.87441 3.23E-07 4387.176 6413.91586
9.3519566 0.427045873 21.89919 8.83E-10 8.400439 10.3034743
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-2 ©The McGraw-Hill Companies, Inc., 2008
6-2. Cost Estimation; Implementation
Background for the Case Answers:
Students will find it helpful if you go through the computations in the example in the case for job
1. This will also assure that they understand the nature of the adjustment that is being made. As you go
through the exhibit, emphasize that these adjustments are made only once a year even though many of the
jobs are in process for two and three years.
In the example in the case, the original cost estimate given on the first line is for reference only. It
is not used in the adjustment calculation. The second section calculates the costs incurred to date plus the
estimated costs to complete the job, yielding a new estimate for the total cost that will be required to
complete the job.
The third portion of the exhibit is the market value computation using the net realizable value less
a normal profit margin approach. The last line is the amount of the adjustment required to bring the
inventory carrying value (cost) down to lower of cost of market (LCM) .
For job 1, the LCM value of inventory will be ($2,100 + $373) - $572 = $1,901, while for job 2
the inventory value is $100 - $800 = $(700). Although this latter value may seem strange, the firm is
properly recognizing its loss on the job as soon as they become aware of it.
Next, you may wish to begin the discussion of the case by asking students to identify the major
problem areas that affect profitability. Then proceed to a discussion of the two specific questions.
The class discussion of the problem areas should reveal:
1. It appears that some jobs are accepted at unprofitable prices due to faulty cost-estimate
analysis (e.g., job 2 in the exhibit).
2. There is likely to be pressure put on the cost-estimate analysts by sales to keep estimates
low so as to generate additional sales. The analysts report to sales.
3. Feedback is slow. Problems are identified only when a job is complete or at the year-end
review of inventory. This makes it difficult to pinpoint responsibility and makes the taking
of timely corrective action nearly impossible.
4. The long production cycle exposes the firm to considerable risk due to inflation.
5. The large overhead rates suggest that the firm is not doing a very good job of tracing direct
costs to products. This, in turn, makes it difficult to have much confidence in the
cost/profitability figures.
Answers to Questions
1. (a) The cost-estimate analysts should not report to sales. It is likely that more realistic cost estimates
would result if this unit reported to a production manager.
In addition, a performance report should be devised for each cost-estimate analyst. This report
should routinely compare original cost estimates to actual costs incurred. If an analyst consistently over or
under estimates costs, corrective action can be taken. To be useful, such a report must be generated on a
timely basis. A monthly report comparing actual costs incurred-to-date with the estimate should provide
the needed information.
The accounting system should be adjusted to treat more costs as direct costs. This should help the
analyst make more accurate estimates. If it is not possible to increase the proportion of costs treated as
direct costs, the controller should at least investigate whether overhead could be segregated into
additional cost pools and allocated to jobs on a more appropriate basis than direct labor costs. Again, such
an approach should help the analyst better estimate the actual costs that will be incurred to complete a job.
The controller should also undertake a study to determine the profits earned in the aftermarket
business relative to specific original equipment orders. Such a study would provide guidance on the
amount of the loss that can be justified on an original equipment order by the expected value of
aftermarket profits.
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-3 ©The McGraw-Hill Companies, Inc., 2008
(b) Currently, the LCM review comes too late for effective control. The analysis should be made
much more frequently. A monthly analysis would provide the basis for the cost analyst's performance
report and would also reveal any production problems on a more timely basis.
Although the firm does a very large dollar volume of business, the actual number of jobs is
relatively small. Consequently, a monthly evaluation of each job is not an overly onerous task.
(c) Physical control over inventory has not been a problem. The concern is solely with cost
overruns. The reports outlined in 1(a) and 1(b) should greatly increase control over costs.
2. Because of the very large dollar investment in these products and the long in-process production times,
the carrying costs for inventory are substantial. Progress payments and advance payments shift a good
portion of the carrying costs (the cost of capital) to the customer.
With a fixed price contract the inflation risk is borne entirely by the company. Inserting estimates
of inflation in the contract puts risk on both the company and the customer for errors in the inflation
estimate. The use of escalation clauses is preferable because they hold the customer responsible for only
the actual inflation relative to the original cost estimate. It does not subject the customer to responsibility
for errors in cost or inflation estimates.
What the Firm Actually Did
The firm recognized the critical role played by the cost analyst. Errors here could doom the firm
to unprofitable contract even before work had begun. The firm changed the reporting relationship of the
analysts to report directly to the plant manager. In addition, the firm upgraded the quality of the analysts
by requiring more professional training.
The original cost analysis for each job became a standard. Monthly reports of actual costs
compared to this standard are now prepared for each job. Thus production and estimation problems can
now be more quickly identified. To assure that personnel do not attempt to "manage" earnings through the
LCM adjustments, these adjustments are reviewed by the internal audit staff. The firm initiated a policy
that any negotiating sessions with customers involving contracts in excess of $500,000 must be attended
by either the plant controller or the cost-estimate manager. These individuals are responsible for seeing to
it that minimum margins are maintained. Any contract for less than the minimum margin must be
approved by the plant manager.
The firm now attempts to include progress billings in all contracts. If the customer refuses, a
charge for the cost of capital is now included in the original cost estimate. Coupled with the minimum
margin requirement, the cost of capital is now effectively included in each contract.
Escalation clauses are now required in all contracts which will require six or more months to
complete. Escalation clauses may be eliminated from a contract only with the plant manager's approval.
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-4 ©The McGraw-Hill Companies, Inc., 2008
6-3. Regression Analysis; Activity Based Costing; Strategy; International
The firm is a large manufacturer of packaging products. While the data is real, the locations and
descriptions are disguised. The firm has a goal of growth in sales, and has adopted a marketing strategy of
differentiation to achieve this. The result is a significant growth in the number and variety of
productssizes, colors, designs, both-side printing.
The firm operates in two marketing segmentsthe large grocery chain market, and the small
independent grocery market (who purchase from wholesalers). Laurent’s success has been in the large
chain market, where they have introduced an innovative, labor-saving bagging system. At the time of the
case, Laurent is contemplating a significant expansion into the wholesale segment. A consequence of this
strategy will be an increased variety of products.
Teaching Objectives:
The case is very useful for an illustration of the potential application of ABC costing, because of
the firm’s diverse product line. A key result of the strategy conflict identified above is that product
diversity increases rapidly (as a result of the marketing strategy) while the manufacturing strategy has
been to invest in plants oriented to high-volume, low-cost production. ABC can play a key role in
developing a better understanding of the cost implications of the marketing strategy.
The regression exercise is useful in three ways. First, it shows the students that a regression tool
within EXCEL can help provide useful information about cost drivers. Second, it gives the students an
opportunity to work with actual data in identifying cost driver relationships. Third, it illustrates the
complex inter-relationships among cost drivers.
The case was originally use in the manufacturing strategy course to illustrate the conflict between
manufacturing strategy (cost leadership) and marketing strategy (differentiation) in the case.
Main Points:
 Role of ABC Costing in a large packaging firm which is experiencing increased product
variety
 Use of Regression Analysis to Identify Cost Drivers
 Conflict of Manufacturing and Marketing Strategies
Additional Assigned Reading: To facilitate the regression exercise, the additional reading can be
assigned: R.J. Campbell, M. Janson, and J. Bush, “Developing Strategic Cost Standards in a MachinePaced Environment,” Cost Management, Winter 1991.
Discussion Questions:
1. What is Laurent’s competitive strategy?
Laurent has built its business on differentiation, in the large grocery chain market. The laborsaving bagging they have developed has differentiated them and given them a competitive advantage. As
other manufacturers are catching up in this segment, Laurent is moving to the wholesale segment of the
market, also with a differentiation strategybased on product variety. The wholesale segment is expected
to require higher product variety and smaller lot sizes.
The manufacturing operations of Laurent follow a cost leadership strategy, to keep costs down
because of the falling margins associated with increasing competition in Laurent’s major segment, the
large grocery segment, and because of the related increase in excess manufacturing capacity.
2. What are the implications of the marketing and manufacturing initiatives undertaken by Laurent?
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-5 ©The McGraw-Hill Companies, Inc., 2008
The point should be made that the new marketing focus, involving increased product variety (and
smaller customer order sizes), is not well supported by the manufacturing capabilities, which have been
built upon a cost leadership (large order sizes, low variety).
3. How does Laurent deal effectively with global competition in its business? How should it?
An important feature of competition in the packaging business is cost leadership, as noted above.
By locating its plants near to its major markets and customers, Laurent has located plants and schedules
production so as to minimize transportation costs. There are a large number of global issues facing a firm
such as Laurent. For example, sourcing of raw materials will consider the purchase cost and
transportation cost of the materials world-wide. Also, sales within the EC. and internationally will
consider the effects of potential currency fluctuations, custom charges and restrictions, and differences in
income and sales taxes. Laurent’s excess capacity situation currently points to the need to either close
some plants, or to find new markets to keep current plants busy. This is in line with the firm’s current
marketing initiatives.
4. Using the data in Table 1 and appropriate methods of analysis such as regression, analyze the effect of
order size and product variety on the productivity and cost structure of the Paris plant.
The case is a good illustration of the value of ABC costing, to help measure product profitability
in a firm experiencing increased product variety. This is well illustrated in the computer exercise (see
below).
There are two regressions. Each of the two have the same three independent variables:
a) machine type
b) order size
c) a code for number of colors on the package (“print type complexity”)
The two regression each have a different dependent variable:
Regression One: per unit setup time + downtime
Regression Two: per unit runtime
The students will likely make a variety of inferences from their individual analyses of the data.
After some discussion, make sure that the following points are made:
a. Assess the statistical measures for the regressions the students present, and/or for the regressions
attached. Make sure that the students understand the importance of evaluating the regressions:
a) the R-squared
b) the standard error
c) the direction of the coefficients and the related t-values
Generally, the regressions have mediocre statistical measures. The R-squares are relatively poor, and one
of the variables in regression one and two of the variables in regression two have non-significant t-value.
b. The regression results show clearly that:
a) larger orders have lower per unit set up time and per unit runtime (i.e., better productivity)
per both Regressions, the sign of the quantity independent variable is negative and significant (p<.05)
(Note however, that the these regressions are weak with low R-squares. However, since our focus
is on the relationships between the dependent variable and each independent variable, we focus primarily
on the t-values)
b) Product complexity affects set-up time. Print type complexity has a positive and significant
relationship with per unit setup time, as expected - regression one. There is no significant effect for
regression two.
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-6 ©The McGraw-Hill Companies, Inc., 2008
Dependent Variable: Per unit set-up time plus downtime
Regression Statistics
Multiple R
0.603189
R Square
0.363837
Adjusted R Square
0.327828
Standard Error
0.014407
Observations
57
ANOVA
df
Regression
Residual
Total
Intercept
Number
Quantity
Complexity
SS
MS
F
Significance F
3 0.006291226 0.002097 10.10399 2.28E-05
53 0.011000104 0.000208
56 0.01729133
CoefficientsStandard Error
-0.0032 0.007375574
0.000667 0.000671082
-8.7E-06 3.27751E-06
0.010325 0.002377343
Blocher, Stout, Cokins, Chen: Cost Management 4e
t Stat
-0.43452
0.993887
-2.65008
4.343194
P-value Lower 95%
0.665672
-0.018
0.324794 -0.00068
0.010585 -1.5E-05
6.37E-05 0.005557
6-7 ©The McGraw-Hill Companies, Inc., 2008
Dependent Variable: Per unit runtime
Regression Statistics
Multiple R
0.58985361
R Square
0.34792728
Adjusted R Square0.31101751
Standard Error
0.0034273
Observations
57
ANOVA
df
Regression
Residual
Total
Intercept
Number
Quantity
Complexity
3
53
56
SS
0.00033218
0.00062256
0.00095474
MS
0.00011073
1.1746E-05
Coefficients Standard Error
t Stat
0.04555712 0.00175464 25.9638504
2.3785E-05 0.00015965 0.14898304
-4.096E-06 7.7971E-07 -5.25336231
-0.0008911 0.00056557 -1.5755149
Blocher, Stout, Cokins, Chen: Cost Management 4e
F
Significance F
9.4264263 4.304E-05
P-value
8.209E-32
0.8821325
2.705E-06
0.1210886
Lower 95%
0.0420378
-0.0002964
-5.66E-06
-0.0020254
6-8 ©The McGraw-Hill Companies, Inc., 2008
6-4 Custom Photography
1. The Excel regression analysis for Custom Photography is shown below. Note that all the statistical
measures are excellent. The R-squared is very high, the standard error of the estimate is relatively low at
431/6,046 = 7% of the mean of the dependent variable and the t value of the independent variable is very
significant at t = 27.5.
The regression equation to predict payroll expense is:
Payroll Expense = $34.82 + $34.12 x hours
Note: While the regression is a very good fit, the management accountant should also check to see if
there are any potential outliers or non-linearity to the data, by studying the graph of residuals, checking
the Durbin-Watson statistics, and similar statistical measures. Graphs shown below of the residuals and
of the data do not appear to show any signs of outliers or non-linearity, so that the management
accountant can reasonably rely on the results.
Regression Statistics
Multiple R
0.988
R Square
0.977
Adjusted R
0.975
Square
Standard Error
431.538
Observations
20
ANOVA
df
Regression
Residual
Total
Intercept
Hours
SS
MS
1 140804738.725 140804738.725
18
3352047.475
186224.860
19 144156786.200
Coefficients
Standard Error
34.822
239.415
34.116
1.241
Blocher, Stout, Cokins, Chen: Cost Management 4e
t Stat
0.145
27.497
F
Significance F
756.101
0.000
P-value
0.886
0.000
Lower 95%
-468.171
31.509
6-9 ©The McGraw-Hill Companies, Inc., 2008
Residuals
Hours Residual Plot
2000
1000
0
-1000 0
100
200
300
400
Hours
Payroll Expense
Data Plot
15,000
10,000
5,000
0
100
200
300
400
Hours
2.
Using the equation above, Payroll Expense = $34.82 + $34.12 x hours, predicted payroll is obtained as
follows:
Qtr No.
1
2
3
4
Hours per
Qtr, 2002
188
233
145
298
Predicted Payroll
Expense
$
6,449
$
7,984
$
4,982
$
10,201
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-10
©The McGraw-Hill Companies, Inc., 2008
Teaching Strategies for Articles
“How to Find the Right Bases and Rates”
This article shows an actual application of regression analysis for determining multiple overhead
rates using the spreadsheet software. The article explains the interpretation of the R-squared and t-values
and provides a good discussion of when regression analysis is useful.
Discussion Questions:
1. What is regression analysis used to accomplish in this article?
The regression analysis is used to determine the best cost drivers to use when using multiple overhead
rates and when using activity-based costing. The article also notes that regression can be used to help
classify a large group of activities into a smaller group, based upon the degree of correlation with selected
cost drivers.
2. What are the steps to perform a simple regression analysis?
The use of regression, as explained in this article, requires a spreadsheet program such as EXCEL, and the
understanding of how to calculate the regression and to interpret the results, as explained in the article.
The main point of the article is that regression can be a simple and effective way to use available
computing applications (spreadsheets) to assist accounting tasks such as the choice of cost drivers.
3. What does Table 4 tell you? Which cost driver would you pick for each cost typemaintenance,
packaging, materials handling, storage, and production scheduling?
Table 4 provides the information we need to determine which single cost driver provides the best
fit for each cost type:
 maintenance: machine hours
 packaging: pounds of material
 materials handling: pounds of material
 storage: pounds of material
 production scheduling: machine hours.
The article notes that the management accountant should consider other statistical measures in
addition to the R-squared value, for example, the standard error of the estimate and the t-values.
Moreover, the article notes that a relatively small number of data points were used in finding these
statistical measures, and that in a practical application, a larger number of values would provide
additional statistical reliability.
Blocher, Stout, Cokins, Chen: Cost Management 4e
6-11
©The McGraw-Hill Companies, Inc., 2008