Operations Management in the Supply Chain Decisions and Cases 7th Edition Schroeder Solutions Manual

1
Best Homes, Inc.: Forecasting
Teaching Note
Synopsis and Purpose
Best Homes is one the largest builders of new residential homes in U.S. with 20,040 new
homes built in 2015. The case presents monthly sales data from 2011 to 2015. This data
is representative of home builders since we estimated the sales of Best Homes based on a
4% market share of the total sales of new homes in the U.S. from the U.S. Census web
site. Thus the trend and seasonality are in line with U.S. home sales in total.
The case explains the problem facing Best Homes in terms of annual planning and the
S&OP process. Forecasting is put in the context of how the forecast will be used. Also,
sales projections are being gathered from the field, and the case asks students to reconcile
those with the forecasts based on historical data.
Discussion Questions
1. What forecasting methods should the company consider? Please justify.
2. Use the classical decomposition method to forecast average demand for 2016 by
month. What is your forecast of monthly average demand for 2016?
3. Best Homes is also collecting sales projections from each of its regions for 2016?
What role should these additional sales projections play, along with the forecast from
question 2 in determining the final national forecast?
Analysis
Question 1:
Because of the seasonality and trend in the data, first order smoothing or an ordinary
moving average is not a good method for this case. Students could choose either
Winter’s Method of Exponential Smoothing or the Classical Decomposition Method from
the Supplement to Chapter 10 in order to forecast the trend and seasonal effects. We use
the Classical Decomposition Method for the next question because we think it gives a
better forecast and is more widely used in industry.
Question 2:
The data in the case is provided on the Instructor Resources Center in McGraw-Hill Connect
for the textbook. Only the data is provided on the Excel template. The user must enter the
formulas and analysis.
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2
Sixty months of data are provided on the template, see Exhibit 1. The first step in classical
decomposition is to develop a 12-month moving average which is done in the 3rd column
on the worksheet. Then a 2-month moving average is developed in the 4th column which is
centered on the original data. The 4th column contains data which is deseasonalized, since
12 months has been used as a base in the moving average. At this point the upward trend in
the moving average in column 4 is apparent.
In column 5 seasonal ratios are computed by dividing the sales data for each month by the
moving average in column 4. The data indicates seasonal fluctuations with April and May
being high months and November and December low months. This is not unexpected in the
home building industry for new home starts often follow this pattern. In column 6 average
seasonal ratios are computed. These ratios are obtained by averaging the seasonal ratios
from the same month in successive years. For example, the average July seasonal ratio is
obtained by averaging the July 2011, July 2012, July 2013, and July 2014 seasonal ratios.
When the resulting twelve seasonal ratios in column 6 are added the total is 12.011. The
sum of these ratios should be 12 in accordance with the 12-month seasonal period, because
the seasonal ratio is the percentage that a particular month is above or below the average. In
order to obtain a sum of 12, the seasonal ratios are normalized in column 7. This is done by
dividing each ratio by the sum 12.011 and multiplying by 12.
A regression analysis is now run to fit a straight line through the moving average data in
column 4. The purpose of this regression is to forecast the average level into 2016 on a
trend basis. The seasonal ratios will then be applied to this trend to arrive at a forecast. In
Excel a regression function is provided. In this case we have data from period 7 through
period 54. The formulas and procedure for calculating the regression equation are given in
the text. As a result of these calculations the following equation is obtained.
Y = 996.6 + 12.368 t
Where Y is the moving average and t is the time period.
To obtain the forecast of interest we calculate Y from the above equation for the twelve
months of 2016, which is t = 61 through t = 72. The results are shown at the bottom of
column 8. These Y values are multiplied by the monthly average seasonal ratios in column 9
(also transposed from Column 7) to arrive at the forecast for each month shown in Exhibit
1at the bottom of column 10. Note that the total of this forecast is 21,794 homes sold in
2016.
Question 3:
The forecast obtained is a bit concerning. Looking at the historical annual totals from the
case we see.
2011 2012 2013 2014 2015
Total 12200 14760 17160 17560 20040

3
Note, that the total sales leveled out in 2013 and 2014, but exhibited a substantial jump in
2015. The increase from 2014 to 2015 was 2,480 homes. But our forecast from the time
series decomposition is only (21,794 – 20,040) = 1,754.
The question is why the large increase from 2014 to 2015? Can this be expected to
continue, or will sales revert to the more normal increase of approximately 1754? This may
be where the regional sales forecasts from the field might be helpful. What trends are they
seeing and can we assume similar increases or more of the normal increase based on past
data. The final forecast will probably be somewhat of a combination of what we obtained
from the data and what the sales regions are seeing. This could lead to a lively discussion
about whether sales people are overly optimistic in setting sales forecasts, assuming they are
larger, or whether Best Homes should rely more on the data. Of course, this is also critical
in setting the company’s overall sales, net income and earnings projections for the coming
year. Forecasting will affect not only Operations, but Financial, Marketing, Sales and HR
planning.

4
Exhibit 1 New Residential houses sold (units)
1 2 3 4 5 6 7 8 9 10
Twelve Two Average Normalize
period period Seasonal Seasonal Seasonal
Sales M.A. M.S. Ratios Ratios Ratios
1 Jan 11 840
2 Feb 11 880
3 Mar 11 1120 regressionseasonal forecast
4 Apr 11 1200 predicted factors 2016
5 May 11 1120 y
6 Jun 11 1120 1016.7
7 July 11 1080 1023.3 1020.0 1.059 0.998 0.997 1083.2
8 Aug 11 1000 1050.0 1036.7 0.965 0.942 0.941 1095.5
9 Sept 11 960 1070.0 1060.0 0.906 0.918 0.917 1107.9
10 Oct 11 1000 1083.3 1076.7 0.929 0.951 0.950 1120.3
11 Nov 11 920 1106.7 1095.0 0.840 0.842 0.841 1132.6
12 Dec 11 960 1126.7 1116.7 0.860 0.859 0.859 1145.0
13 Jan 12 920 1146.7 1136.7 0.809 0.910 0.909 1157.4
14 Feb 12 1200 1166.7 1156.7 1.037 1.042 1.041 1169.7
15 Mar 12 1360 1186.7 1176.7 1.156 1.137 1.136 1182.1
16 Apr 12 1360 1200.0 1193.3 1.140 1.155 1.153 1194.5
17 May 12 1400 1216.7 1208.3 1.159 1.153 1.152 1206.8
18 Jun 12 1360 1230.0 1223.3 1.112 1.105 1.104 1219.2
19 July 12 1320 1260.0 1245.0 1.060 12.011 12.000 1231.6
20 Aug 12 1240 1280.0 1270.0 0.976 1243.9
21 Sept 12 1200 1303.3 1291.7 0.929 1256.3
22 Oct 12 1160 1333.3 1318.3 0.880 1268.7
23 Nov 12 1120 1350.0 1341.7 0.835 1281.0
24 Dec 12 1120 1380.0 1365.0 0.821 1293.4
25 Jan 13 1280 1380.0 1380.0 0.928 1305.8
26 Feb 13 1440 1380.0 1380.0 1.043 1318.1
27 Mar 13 1640 1383.3 1381.7 1.187 1330.5
28 Apr 13 1720 1406.7 1395.0 1.233 1342.9
29 May 13 1600 1420.0 1413.3 1.132 1355.2
30 Jun 13 1720 1430.0 1425.0 1.207 1367.6
31 July 13 1320 1433.3 1431.7 0.922 1380.0
32 Aug 13 1240 1430.0 1431.7 0.866 1392.3
33 Sept 13 1240 1423.3 1426.7 0.869 1404.7
34 Oct 13 1440 1410.0 1416.7 1.016 1417.1
35 Nov 13 1280 1420.0 1415.0 0.905 1429.4
36 Dec 13 1240 1403.3 1411.7 0.878 1441.8
37 Jan 14 1320 1410.0 1406.7 0.938 1454.2
38 Feb 14 1400 1426.7 1418.3 0.987 1466.5
39 Mar 14 1560 1446.7 1436.7 1.086 1478.9
40 Apr 14 1560 1453.3 1450.0 1.076 1491.3
41 May 14 1720 1450.0 1451.7 1.185 1503.6
42 Jun 14 1520 1463.3 1456.7 1.043 1516.0
43 July 14 1400 1483.3 1473.3 0.950 1528.4
44 Aug 14 1440 1516.7 1500.0 0.960 1540.7
45 Sept 14 1480 1540.0 1528.3 0.968 1553.1

5
1 2 3 4 5 6 7 8 9 10
Twelve Two Average Normalize
period period Seasonal Seasonal Seasonal
Sales M.A. M.S. Ratios Ratios Ratios
46 Oct 14 1520 1570.0 1555.0 0.977 1565.5
47 Nov 14 1240 1583.3 1576.7 0.786 1577.8
48 Dec 14 1400 1603.3 1593.3 0.879 1590.2
49 Jan 15 1560 1630.0 1616.7 0.965 1602.6
50 Feb 15 1800 1646.7 1638.3 1.099 1615.0
51 Mar 15 1840 1640.0 1643.3 1.120 1627.3
52 Apr 15 1920 1643.3 1641.7 1.170 1639.7
53 May 15 1880 1660.0 1651.7 1.138 1652.1
54 Jun 15 1760 1670.0 1665.0 1.057 1664.4
55 July 15 1720 1680.0 1676.8
56 Aug 15 1640 1689.2
57 Sept 15 1400 1701.5
58 Oct 15 1560 1713.9
59 Nov 15 1440 1726.3
60 Dec 15 1520 1738.6
61 Jan 16 1751.0 0.909 1592
62 Feb 16 1763.4 1.041 1835
63 Mar 16 Regression Slope 12.36756 1775.7 1.136 2017
64 Apr 16 y= 996.6 + 12.368 t 1788.1 1.153 2063
65 May 16 Y intercept 996.6087 1800.5 1.152 2075
66 Jun 16 1812.8 1.104 2001
67 July 16 1825.2 0.997 1820
68 Aug 16 1837.6 0.941 1729
69 Sept 16 1849.9 0.917 1697
70 Oct 16 1862.3 0.950 1769
71 Nov 16 1874.7 0.841 1576
72 Dec 16 1887.0 0.859 1620
TOTAL 21794

LAWN KING, INC.:Sales and Operations Planning
Teaching Notes
1
Synopsis and Purpose
Lawn King is a manufacturer of lawn mowers facing a highly seasonal demand for its products.
At the present time the demand forecast for the coming year has just been increased. This is
causing management to evaluate the accuracy of the forecast, and to construct several different
production strategies (level, chase, second shift) for meeting demand.
The purpose of this case is to illustrate the issues typically encountered in Sales and Operations
Planning (S&OP). The student is asked to make a demand forecast, to construct alternative
production strategies and to recommend a particular strategy. A substantial amount of "pencil
pushing" and "computer pushing" is required in this case to develop and evaluate the various
strategies. The case illustrates the tradeoffs involved in S&OP.
Discussion Questions
1. Develop a forecast to use as a basis for S&OP.
2. Develop an S&OP plan by month for fiscal 2015. Consider the use of several different
production strategies. Which strategy do you recommend? Hint: Use of Excel will
greatly save time in making these plans.
Analysis
The first step in analysis of this case is to evaluate the demand forecast. This can be done by
calculating the actual increase in total demand over the past year. The increase from FY13 to
FY14 was:
The projected increase from FY14 to FY15 is:
Thus a larger increase is being projected than was experienced last year.
We also observe that forecasts in the past have been very accurate (e.g., FY13 actual
compared to forecast and FY14 actual compared to forecast). But, the forecasts by model type
have not been nearly as accurate as total demand forecasts. Furthermore, the case states that
demand is highly influenced by the economy and the weather. In view of these insights, we can
conclude that past forecasts have been remarkably accurate.

Teaching Notes
2
For purposes of analysis we will accept the new forecast of 110,000 units. Although the
projected demand increase is larger than last year's actual increase, the forecast still appears
reasonable. It may be, however, that marketing is attempting to drive production through a
higher forecast to avoid stockouts. Therefore, we may wish to evaluate a somewhat lower
forecast, as well as the one given in the case.
To construct an S&OP plan we need to forecast aggregate demand by month. This can be
done by assuming the same monthly pattern as last year. From exhibit 4 in the case, the
percentage of annual sales by month can be calculated. These percentages are then multiplied
by the total forecast (110,000) to arrive at monthly demand forecasts. (See Exhibit 1 of the
teaching note.)
Next, we must decide on the inventory level needed at the end of the year and the stockout
policy desired. The current inventory is 16,460 units. On an annual basis this inventory level
represents a turnover of:
While a turnover of 6.7 might be considered good, the inventory level should ideally be
compared to the demand at the end of the year. Since the demand is seasonal, our goal should
be to have 1 or 2 months of inventory at year-end as a safety stock. More inventory is not
necessary, since all models are still in production and we can respond to changing demand
conditions. One month of inventory would amount to 1216 units (the projected demand for
September). Two months of inventory would be 3698 units, the demand for September and
October. By this criterion, a great deal of excess inventory exists. Therefore, we will assume
an 8/31/11 goal of 3700 units (2 months supply) of inventory for the remainder of this analysis.
Adjusting for the inventory change, we have a production requirement of 97,240 units.
Forecast 110,000
Beginning Inventory – 16,460
Ending Inventory + 3,700
Production Required 97,240
There are many alternative strategies to consider. For the sake of simplicity we shall consider
four strategies.
1. Level production
2. Level production with overtime
3. Chase demand
4. Two shifts

Teaching Notes
3
The level production strategy is shown in Exhibit 1 attached. A level of 100 workers is used for
September through February. This level is then phased down to 85 and then to 49 workers at
the end of the year in order to reach an ending inventory of about 3700 units. It is not possible
to use a completely level strategy in this case without significant stockouts or a larger ending
inventory than desired. Thus an arbitrary initial level of 100 workers is selected with a reduction
in work force later in the year. Other levels could also be selected.
The second strategy, shown in Exhibit 2, is a level strategy with overtime. In this case we
choose a level of 85 regular workers through May, and then phased down to 52 workers at the
end of the year to achieve an ending inventory level of 3700 units. Overtime is used in the
months of December through May to meet the peak demands. Other profiles of level production
and overtime could be selected.
Note, the ending inventory in all strategies should be the same in order to insure a comparable
basis of costing. Students often overlook this point and, as a result, arrive at very different cost
estimates.
The third strategy is to chase demand as shown in Exhibit 3. The chase strategy matches
demand in Sept through Feb. Then a maximum of 200 workers in used in March and April while
inventory is worked off and the level of workers is phased down to chase demand and end the
year with 3700 units.
The fourth strategy is a two-shift strategy, shown in Exhibit 4. This strategy starts with a level of
60 workers in Sept through Dec (first shift) and then doubles the level of workers to 120 (second
shift) from Jan through May. The second shift is phased down to arrive at the same ending
inventory as the rest of the strategies.
In order to evaluate these four strategies we will need to make various assumptions about costs
and resources. The first assumption is the nominal production rate of a worker in a month.
Using the data from Exhibit 4 in the case, an average daily production of 373 units is computed
as the following weighted average:
Since there are 260 production days in a year (52 weeks x 5 days per week), the average
monthly production is 8,082 units:
The initial variable work force level is 85 workers (excludes 10 maintenance and 5 office
workers). The production per direct (or variable) worker is therefore:

Teaching Notes
4
The hiring cost per worker is $800 and the layoff cost is $1500 per worker as given in the case.
To calculate inventory carrying cost, we need to know the cost of producing a unit. Using labor
and material costs for FY10, we arrive at a unit cost of:
This unit cost is multiplied by 2.5% a month carrying cost (30% a year) to arrive at
$3.125 per unit per month
This unit carrying cost is multiplied by the total inventory carried to arrive at the inventory
carrying cost for each month.
The direct labor cost per hour is obtained by taking the direct labor costs from the Profit and
Loss Statement (Exhibit 1 in the case) and dividing by the number of direct workers (85) times
2000 hours per year as follows:
The overtime rate is 150% of direct labor and thus the overtime rate is 150% times $15.30 =
$23.00 per hour.
In order to calculate the costs of each strategy we use the spreadsheet (file named LAWNKING)
for this case on the website for the textbook. The above cost numbers are input into the
spreadsheet for all of the strategies. Then each strategy is evaluated, one at a time, as shown
in Exhibits 1 to 4.
The result of these cost evaluations is as follows:
Strategy 1: Level $3,414,411
Strategy 2: Level with Overtime $3,360,109
Strategy 3: Chase $3,425,407
Strategy 4: Two-Shift $3,290,880
As noted above, the two-shift strategy has the lowest cost, by about $70,000 per year, over the
level with overtime strategy. The two-shift strategy is the cheapest, because overtime is more
expensive and it is relatively expensive to hire and lay off workers. In a sense the two-shift
strategy does the best job of fitting the demand profile by using regular workers.
The two-shift strategy not only offers cost advantages, but more flexibility and less inventory risk
than the level strategy. The two-shift strategy is therefore preferred to the level strategy,
provided employees can be found to work a second shift for only part of the year.
While the use of level strategy with overtime is more attractive than a pure level strategy, the
flexibility to meet further demand increases is no longer available once overtime is built into the
S&OP. Especially in view of the fact that a large amount of overtime is needed for strategy 2.

Teaching Notes
5
The two-shift strategy is also preferred to the chase strategy because it not only costs less but
requires less personnel turmoil. Hiring and layoff is only done once for the two-shift strategy
rather than frequently throughout the year. The chase strategy also implies that the production
line can be easily speeded up and slowed down, while the two shift strategy provides for a
constant line speed.
I think the above arguments provide a compelling case for the two-shift strategy. Lively
arguments can be constructed, however, because the company can "squeak by" without using a
second shift. Also, the two-shift strategy might not be entirely obvious to some students as an
option that should be evaluated.
Teaching Strategy
This case can be taught using the same order of discussion as the above analysis: forecast,
alternative strategies, costing, and recommendation. The case will take one hour or more to
teach depending on how much analysis is put on the board and how much discussion is
encouraged.
This case provides practice in formulating and evaluating S&OP strategies. Management is
facing a dilemma because the sales manager has pushed the forecast a little beyond a one-shift
operation, unless large amounts of overtime or hiring and layoff are used. The demand here is
also very seasonal so flexibility in addition to cost is an important issue in S&OP planning.
There is also the question of how much inventory to carry at the end of the year.
When assigning the case, the students should be warned not to engage in excessive number
crunching. Students can easily become bogged down in developing one schedule after
another, while not reaching a sound conclusion.

Teaching Notes
6
EXHIBIT 1 (LEVEL STRATEGY)
FILENAME: LAWNKING NAME: KEY
*
SECTION: *
BASIC INPUT DATA DATE: 03-Apr
=
MONTHLY PROD. RATE (UNITS/WORKER/MONTH): 95 UNITS
BEGINNING INVENTORY (UNITS): 16460 UNITS
BEGINNING NUMBER OF WORKERS: 85 WORKERS
HIRING COST PER WORKER: $800
LAYOFF COST PER WORKER: $1,500
INVENTORY HOLDING COST ($ PER UNIT PER MONTH): $3.13
INVENTORY SHORTAGE COST (COST/UNIT SHORT) : $0.00
REGULAR HOURLY WAGE RATE: $15.30
OVERTIME HOURLY WAGE RATE: $23.00
HOURS/MONTH: 160
MONTH SEPT. OCT. NOV. DEC. JAN.
- - - - -
Sales Forecast: 1,216 2,482 4,677 5,970 6,950
Units Produced: 9500 9500 9500 9500 9500
Ending Inventory: 24744 31762 36585 40115 42665
Number of Workers: 100 100 100 100 100
Overtime Percent: 0% 0% 0% 0% 0%
Total Equivalent
number of workers: 100 100 100 100 100
(# workers + O.T.)
Reg. Labor Costs: $244,800 $244,800 $244,800 $244,800 $244,800
O.T. Labor Costs: $0 $0 $0 $0 $0
Change in # workers
from last month: 15 0 0 0 0
Hiring Cost: $12,000 $0 $0 $0 $0
Layoff Cost: $0 $0 $0 $0 $0
End-Inv. Hold. Cost: $77,325 $99,256 $114,328 $125,359 $133,328
End-Inv. Short. Cost: $0 $0 $0 $0 $0
- - - - -
MONTHLY TOTAL COST: $334,125 $344,056 $359,128 $370,159 $378,128

Teaching Notes
7
EXHIBIT 1 (continued)
FEB. MAR. APR. MAY JUNE JULY AUG.
- - - - - - -
10,877 23,185 24,642 15,743 6,598 4,299 3,352
9500 8075 8075 8075 5700 5700 4655
41288 26178 9611 1943 1045 2446 3749
100 85 85 85 60 60 49
0% 0% 0% 0% 0% 0% 0%
100 85 85 85 60 60 49
$244,800 $208,080 $208,080 $208,080 $146,880 $146,880 $119,952
$0 $0 $0 $0 $0 $0 $0
0 -15 0 0 -25 0 -11
$0 $0 $0 $0 $0 $0 $0
$0 $22,500 $0 $0 $37,500 $0 $16,500
$129,025 $81,806 $30,034 $6,072 $3,266 $7,644 $11,716
$0 $0 $0 $0 $0 $0 $0
- - - - - - -
$373,825 $312,386 $238,114 $214,152 $187,646 $154,524 $148,168
STRATEGY DESCRIPTION & ASSUMPTIONS
===================== ========= =========
LEVEL STRATEGY
THIS IS A CLASSICAL LEVEL STRATEGY WITH A REDUCTION IN THE LEVEL
TOWARD THE END OF THE PLANNING PERIOD IN ORDER TO ARRIVE AT AN
ENDING INVENTORY OF 3700 UNITS. THE INITIAL WORK FORCE IS
ARBITRARILY SELECTED TO BE 100 WORKERS.
$3,414,411 TOTAL COST OF THIS STRATEGY

Teaching Notes
8
EXHIBIT 2 (OVERTIME)
MONTHS: SEPT. OCT. NOV. DEC. JAN.
Sales Forecast: 1,216 2,482 4,677 5,970 6,950
Units Produced: 8075 8075 8075 9206 9206
Total Equivalent
number of workers: 85 85 85 96.9 96.9
(# workers + O.T.)
Reg. Labor Costs: $208,080 $208,080 $208,080 $208,080 $208,080
O.T. Labor Costs: $0 $0 $0 $43,792 $43,792
Change in # workers
from last month: 0 0 0 0 0
Hiring Cost: $0 $0 $0 $0 $0
Layoff Cost: $0 $0 $0 $0 $0
End-Inv. Hold. Cost: $72,872 $90,350 $100,969 $111,080 $118,128
- - - - -
- - - - - - -
10,877 23,185 24,642 15,743 6,598 4,299 3,352
9206 9206 9206 9206 7125 5700 4940
36130 22150 6714 176 703 2104 3692
85 85 85 85 75 60 52
14% 14% 14% 14% 0% 0% 0%
96.9 96.9 96.9 96.9 75 60 52
$208,080 $208,080 $208,080 $208,080 $183,600 $146,880 $127,296
$43,792 $43,792 $43,792 $43,792 $0 $0 $0
0 0 0 0 -10 -15 -8
$0 $0 $0 $0 $0 $0 $0
$0 $0 $0 $0 $15,000 $22,500 $12,000
$112,905 $69,219 $20,980 $550 $2,197 $6,575 $11,538
$0 $0 $0 $0 $0 $0 $0
- - - - - - -
$364,777 $321,091 $272,852 $252,422 $200,797 $175,955 $150,834
LEVEL WITH OVERTIME
THIS IS A LEVEL STRATEGY WITH OVERTIME USED TO AVOID STOCKOUTS.
SOME WORKERS ARE LAID OFF AT THE END OF THE PLANNING PERIOD TO
ACHIEVE AN ENDING INVENTORY OF 3700 UNITS.

Teaching Notes
9
EXHIBIT 3 (CHASE STRATEGY)
SEPT. OCT. NOV. DEC. JAN.
Sales Forecast: 1,216 2,482 4,677 5,970 6,950
Units Produced: 1235 2470 4655 5985 6935
Total Equivalent
(# workers + O.T.)
Reg. Labor Costs: $31,824 $63,648 $119,952 $154,224 $178,704
O.T. Labor Costs: $0 $0 $0 $0 $0
Change in # workers
from last month: -72 13 23 14 10
Hiring Cost: $0 $10,400 $18,400 $11,200 $8,000
Layoff Cost: $108,000 $0 $0 $0 $0
End-Inv. Hold. Cost: $51,497 $51,459 $51,391 $51,438 $51,391
- - - - -
- - - - - - -
10,877 23,185 24,642 15,743 6,598 4,299 3,352
10830 19000 19000 13300 6650 3800 3325
16398 12213 6571 4128 4180 3681 3654
114 200 200 140 70 40 35
0% 0% 0% 0% 0% 0% 0%
114 200 200 140 70 40 35
$279,072 $489,600 $489,600 $342,720 $171,360 $97,920 $85,680
$0 $0 $0 $0 $0 $0 $0
41 86 0 -60 -70 -30 -5
$32,800 $68,800 $0 $0 $0 $0 $0
$0 $0 $0 $90,000 $105,000 $45,000 $7,500
$51,244 $38,166 $20,534 $12,900 $13,063 $11,503 $11,419
$0 $0 $0 $0 $0 $0 $0
- - - - - - -
$363,116 $596,566 $510,134 $445,620 $289,423 $154,423 $104,599
CHASE STRATEGY
THIS IS A CLASSIC CHASE STRATEGY. MAXIMUM WORK FORCE IS LIMITED
ARBITRARILY TO 200 WORKERS WHICH REDUCES THE INVENTORY IN PEAK
PERIODS TO ACHIEVE AN ENDING INVENTORY OF 3700 UNITS.

Teaching Notes
10
EXHIBIT 4 (TWO-SHIFT STRATEGY)
SEPT. OCT. NOV. DEC. JAN.
- - - - -
Sales Forecast: 1,216 2,482 4,677 5,970 6,950
Units Produced: 5700 5700 5700 5700 11400
Total Equivalent
(# workers + O.T.)
Reg. Labor Costs: $146,880 $146,880 $146,880 $146,880 $293,760
O.T. Labor Costs: $0 $0 $0 $0 $0
Change in # workers
from last month: -25 0 0 0 60
Hiring Cost: $0 $0 $0 $0 $48,000
Layoff Cost: $37,500 $0 $0 $0 $0
End-Inv. Hold. Cost: $65,450 $75,506 $78,703 $77,859 $91,766
- - - - -
- - - - - - -
10,877 23,185 24,642 15,743 6,598 4,299 3,352
11400 11400 11400 11400 8550 4750 4180
29888 18103 4861 518 2470 2921 3749
120 120 120 120 90 50 44
0% 0% 0% 0% 0% 0% 0%
120 120 120 120 90 50 44
$293,760 $293,760 $293,760 $293,760 $220,320 $122,400 $107,712
$0 $0 $0 $0 $0 $0 $0
0 0 0 0 -30 -40 -6
$0 $0 $0 $0 $0 $0 $0
$0 $0 $0 $0 $45,000 $60,000 $9,000
$93,400 $56,572 $15,191 $1,619 $7,719 $9,128 $11,716
$0 $0 $0 $0 $0 $0 $0
- - - - - - -
$387,160 $350,332 $308,951 $295,379 $273,039 $191,528 $128,428
A TWO-SHIFT STRATEGY IS USED TO CLOSELY MATCH DEMAND. THE FIRST
SHIFT PROVIDES A TOTAL OF 120 WORKERS. SOME WORKERS ARE LAID-OFF
AT THE END OF THE PLANNING PERIOD TO ARRIVE AT 3700 UNITS IN
INVENTORY.

©2013 by the Kellogg School of Management at Northwestern University. This teaching note was prepared by Professor Sunil
Chopra. Teaching notes are developed solely to help academic faculty teach specific cases. Cases and teaching notes are not intended
to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. To order copies or request
permission to reproduce materials, call 847.491.5400 or e-mail cases@kellogg.northwestern.edu. No part of this publication may be
reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical,
photocopying, recording, or otherwise—without the permission of Kellogg Case Publishing.
SUNIL CHOPRA 5-112-003TN
Polaris Industries Inc.
Case Synopsis
In September 2010 Suresh Krishna, vice president of operations and integration at Polaris
Industries Inc., a manufacturer of all-terrain vehicles, Side-by-Sides, and snowmobiles, needed to
recommend a location for a new plant to manufacture the company’s Side-by-Side vehicles.
The economic slowdown in the United States had put considerable pressure on Polaris’s
profits, so the company was considering whether it should follow the lead of other manufacturers
and open a facility in a country with lower labor costs. China and Mexico were shortlisted as
possible locations for the new factory, which would be the first Polaris manufacturing facility
located outside the Midwestern United States. By the end of the year Krishna needed to
recommend to the board whether Polaris should build a new plant abroad (near-shored in Mexico
or off-shored in China) or continue to manufacture in its American facilities.
Learning Objectives
After analyzing and discussing the case, students should be able to:
 Evaluate tradeoffs between different geographic locations when establishing a
manufacturing facility (off-shoring, near-shoring, and on-shoring)
 Run a sensitivity analysis on total cost
 Assess the impact of transportation costs, exchange rates, labor cost rates, lead times, and
other assumptions on total costs
 Identify qualitative factors to be considered when deciding between non-U.S. facility
locations, including transportation time variability, consumer perceptions, and cultural
differences
Suggested Uses
The case is appropriate for undergraduate, graduate, and executive education students. It will
be effective in operations management or supply chain management courses that discuss
sourcing.
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TEACHING NOTE: POLARIS INDUSTRIES 5-112-003TN
2 KELLOGG SCHOOL OF MANAGEMENT
Discussion Questions/Student Assignment
1. Why does Polaris outsource the manufacture of most components but in-source final
assembly?
2. Which manufacturing location provides Polaris with the greatest cost savings?
3. Would your recommendation change if foreign exchange rates increased or decreased by 15
percent?
4. Assuming all else is constant, would your recommendation change if labor rates in Mexico
increased by 20 percent annually instead of 7.1 percent?
5. What other factors should Suresh Krishna and his team consider when making the
manufacturing location recommendation?
Case Analysis
1. Why does Polaris outsource the manufacture of most components but in-source final
assembly?
The decision to outsource or in-source a particular activity depends upon a variety of factors
that include the scale and uncertainty of demand for the activity and whether the resources used
for the activity can be used for other activities/customers. It is also affected to some extent by the
relative transportation cost of performing an activity at a third party’s location and then bringing
it back within the company for assembly. In this case, a major reason for outsourcing components
is that machines required for component manufacture are expensive and would not be fully
utilized if dedicated to produce only for Polaris (as they would be if this activity is in-sourced).
These machines can be used by third parties to produce components for other customers besides
Polaris. Components can be packed with high density for shipping (or at least higher density than
an assembled Side-by-Side), making it feasible for components to be manufactured in China and
shipped to a Polaris assembly facility in the United States.
In contrast, assembly has a high enough scale (all versions of the Side-by-Side can be
assembled on the same line) that in-sourcing provides sufficient economies of scale.
Instructors may refer to a more detailed discussion of outsourcing in Chapter 15 of Sunil
Chopra and Peter Meindl, Supply Chain Management, 5th ed. (Upper Saddle River, NJ: Prentice
Hall, 2012).
2. Which manufacturing location provides Polaris with the greatest cost savings?
On a total cost basis, Polaris is better off moving its manufacturing facility to Monterrey,
Mexico. Our analysis evaluates the NPV over a six-year period with a discount rate of 10 percent.
United States
(US$)
Mexico
(US$)
China
(US$)
Net present cost 43,012,777 40,015,174 41,413,953
Savings vs. United States 0 2,997,603 1,868,824

5-112-003TN TEACHING NOTE: POLARIS INDUSTRIES
KELLOGG SCHOOL OF MANAGEMENT 3
Monterrey offers the most savings compared to the base case in addition to the highest return on
investment.
To establish total cost, a model has been built in Excel. It details the different costs as
follows:
 One-time expenses, including:
o Capital investment for Mexico ($9.5 million) and China ($10 million)
o Severance costs for Mexico and China: the company needs to lay off sixty American
workers at a cost of $20,000 each
 Yearly costs (for 2011 to 2015)
o Production costs:

Unit production costs: 400 USD (U.S.); 4,560 MXN (Mexico); 1,950 CNY (China)
o Labor costs:

# #

Hourly salary (2008): 26 USD (U.S.); 25.3 MXN (Mexico); 11.6 CNY (China)
Annual wage growth: 7.1 percent in Mexico; 13.4 percent in China
o Transportation costs:

To compute the unit transportation cost:
 For China, the unit transportation cost is given as $190 per unit
 For the United States and Mexico, calculations must be done as for a gravity
model:
Side-by-Sides Markets
Distribution
Center Location Units Demanded
Trucks Needed
for Transportation
Roseau Total
Transport Cost
($)
Monterrey Total
Transport Cost
($)
Tacoma, WA 3,650 140 528,239 730,042
Los Angeles 7,050 271 1,347,716 938,599
Irving, TX 3,800 146 425,907 146,899
14,500 558 2,301,862 1,815,540
Cost per unit 158.75 125.21

o Tariffs: if production is off-shored to China, the tariffs cost is:
5%
In this case, transportation cost represents a high percentage of total cost (excluding capital
investment and severance) compared to labor cost (as shown in the table below).
United States Mexico China
Labor cost as percentage of total cost 35.7% 4.9% 5.4%
Transport cost as percentage of total cost 25.4% 23.5% 34.8%
This was expected for low value-to-volume products such as Side-by-Sides. In this case,
reducing transportation cost is critical. It can be achieved by moving production closer to
consumption. Mexico turns out to have the lowest transportation cost because of its proximity to
the largest market in southern United States.
3. Would your recommendation change if foreign exchange rates increased or decreased by 15
percent?
The goal of this question is to help students understand how sensitive the total cost is to
changes in exchange rates. Future exchange rates cannot be predicted and could vary by more
than 15 percent; the number was chosen to reflect the impact on cost of variations in the exchange
rate. The exchange rates can be altered in the spreadsheet by altering the multipliers in Cells E28
and E29.
When the peso is devalued from the base case of 11.92 MXN/USD in 2008, it becomes even
cheaper to operate in Mexico. Thus, it is the strengthening of the peso and the yuan that make
Mexico and China potentially less attractive. If the peso strengthens compared to the dollar and
the yuan, Mexican costs increase relative to the other currencies. If the peso strengthens by 5
percent (change Cell E28 in the spreadsheet to 0.95) to 11.324 MXN/USD in 2010 (same growth
in following years), the savings from moving to Mexico become the same as the savings from
moving to China as shown in the tables below.
Expected Exchange Rates
Multiplier
Pesos 11.324 Pesos/USD 0.95
Yuan 6.47 Yuan/USD 1
Projected Annual Wage Growth
Multiplier
Mexico 7.1% 1
China 13.4% 1
United States
(US$)
Mexico
(US$)
China
(US$)
Net present cost 43,012,777 41,195,850 41,143,953

5-112-003TN TEACHING NOTE: POLARIS INDUSTRIES
KELLOGG SCHOOL OF MANAGEMENT 5
The peso has to strengthen by about 12 percent (change Cell E28 to 0.88) in 2010 (and
continue all other trends) to make Mexico more expensive than the United States. Meanwhile, the
yuan has to strengthen by about 9 percent (change Cell E29 to 0.91) to make China less attractive
than the United States.
From our analysis it is clear that if the peso and the yuan strengthen by 15 percent, the United
States actually becomes the lowest-cost production option. Thus, a key question that the company
must consider is whether they expect the peso or the yuan to have a higher chance of
strengthening in the future relative to the dollar.
4. Assuming all else is constant, would your recommendation change if labor rates in Mexico
increased by 20 percent annually instead of 7.1 percent?
In its calculations, Polaris management is assuming a 7.1 percent annual increase in labor
costs in Mexico. The goal of this question is to evaluate whether moving operations to Mexico
would still show a cost advantage if labor costs were to increase significantly. The annual
increase in labor costs can be changed by altering the multipliers in Cells D32 and D33. The
analysis below shows that even if labor costs in Mexico increased by 20 percent (change Cell
D32 to 2.8) a year over the five-year forecast period, Mexico still offers greater cost savings than
China. This result further demonstrates the effect that transportation cost has on the total
delivered cost of a product manufactured in a foreign facility.
Projected Annual Wage Growth
Multiplier
Mexico 20.0% 2.8
China 13.4% 1
United States
(US$)
Mexico
(US$)
China
(US$)
Net present cost 43,012,777 41,103,661 41,143,953
5. What other factors should Suresh Krishna and his team consider when making the
manufacturing location recommendation?
Besides the quantitative reasons for choosing to manufacture Side-by-Sides in Mexico,
several qualitative parameters were also considered.
Transportation lead times: Management was concerned with both the long lead time and
variability in transportation time associated with manufacturing in China. Due to the distance,
shipping time—and therefore order lead time—was expected to be much longer when sourcing
from China compared to Mexico. The variability in transportation time was also higher when
sourcing from China. Higher transportation time variability would lead to greater unpredictability
in delivery dates, meaning that Polaris would have to increase its investment in safety inventory
to maintain current service levels.
Culture: Polaris management reported that it felt more comfortable working with people in
Mexico because they were schooled in the Western way of doing business. Management expected
this to result in fewer communication issues between headquarters and the manufacturing facility.

Time-zone and physical proximity: There is only a one-hour time difference between Polaris
headquarters in the United States and Monterrey, Mexico, compared to a twelve-hour difference
with China. In addition, management could quickly travel to Mexico if required to, whereas travel
to China takes much longer and requires a visa.
Future sales growth: Polaris expected that much of its future sales growth would come from
emerging markets. In order to facilitate this demand, management is pursuing expansion into
these markets. In terms of domestic sales growth, the majority of consumer demand for Side-by-
Sides is expected to remain in the southern half of the United States, which would be better
served from Mexico.
Consumer perception of quality: Management was concerned that products would be
perceived as being of lower quality if they were manufactured in either Mexico or China.
Employee perception: Polaris management was concerned about a risk of backlash from
American employees and a reduction in productivity if some manufacturing was moved out of the
United States. Polaris traditionally manufactured exclusively in the United States and was a major
employer in several small Midwestern towns.
Talent pool: Polaris believed the United States would have a limited pool of skilled trade
labor in the future. By contrast, Polaris reported that Mexico and China had plenty of skilled trade
labor.
Supplementary Materials
An Excel spreadsheet model is available for the instructor’s use with the case.
Epilogue
Given the significant total cost savings, Polaris decided to build its next manufacturing
facility in Monterrey, Mexico. The plant opened in 2011 ahead of schedule. Although there were
still consumer concerns regarding sustained quality in manufacturing after the first year of
operation, Monterrey was performing well by Polaris’s quality metrics, possibly due to the
significant amount of management attention on the new facility. One factor management did not
consider at the time the decision was made was the security situation in Mexico. Drug trafficking
activity resulted in violence and the situation continued to deteriorate as of 2012. This has led to
high security costs and reduced Polaris management’s travel to Monterrey.
Furthermore, as part of an overall restructuring of its supply chain to drive more cost savings,
Polaris originally planned to close the Osceola plant. It was the only facility in its network that
did not construct vehicles; it supplied engines and other components for the other plants. The plan
was to move Osceola’s existing factory operations into the respective counterpart plants, that is,
snowmobile engine manufacturing into the snowmobile plant in Roseau, and so on. However,
with the strong demand across all divisions over the past two years, Polaris’s domestic plants
were running near capacity. Instead of closing down Osceola, Polaris decided to reduce its
footprint and keep it open for the interim to help provide additional capacity.
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