BMGT 311: Exam 1 Study Guide
Discussion questions: Review all the assigned discussion questions from the back of the chapters.
1.
List five important differences between goods production and service production; then list five important
similarities.
2.
Name three major contributors to Operations Management and describe their contribution.
3.
What are the basic functions of all firms?
4.
What are the ways in which productivity can be improved?
5.
List the qualitative forecasting methods and describe each.
6.
What are advantages and disadvantages of associative forecasting methods?
7.
List and describe the five components of a time series.
8.
Briefly describe the Delphi technique. What are the main benefits and weaknesses?
9.
Under what condition would exponential smoothing forecast be the same as a naive forecast?
10. How does the number of periods in a moving average affect the responsiveness of the forecast?
11. What is the primary purpose of the mean absolute deviation (MAD) in forecasting?
12. What is the difference between MAD and MAPE?
13. List and briefly explain a. The dimensions of service quality, b. The determinants of quality.
14. Define the terms quality of design and quality of conformance.
15. What are some possible consequences of poor quality?
16. Describe the quality ethics connection.
17. What is ISO 9000, and why is it important for global businesses to have lS0 9000 certification?
18. Briefly explain how a company can achieve lower production costs and increase productivity by
improving the quality of its products or services.
19. What are the key elements of the TQM approach?
20. Briefly describe each of the seven quality tools.
21. Describe the four Ms of Cause-and-effect diagram.
22. What are the values plotted on the two y-axes of a Pareto diagram?
23. List the steps in the control process.
24. What is the purpose of a control chart?
25. What is a run? How are run charts useful in process control?
26. If all observations are within control limits, does that guarantee that the process is random? Explain.
27. Why is it usually desirable to use both a median run test and an up/down run test on the same data?
28. If both run tests are used, and neither reveals non randomness, does that prove that the process is
29. random? Explain.
30. Define and contrast control limits, specifications, and process variability.
31. Describe Common Causes and Assignable Causes.
32. When using SPC charts, what constitutes Type I error and what is the consequence of it?
33. When using SPC charts, what constitutes Type II error and what is the consequence of it?
34. Describe the difference between a p-chart and a c-chart.
35. If a process is capable, does it also mean the process is “in-control”? Explain
36. Interpret a case where Cpk < 1.333, but Cp > 1.333.
PROBLEMS
1.
Mabel's Ceramics spent $3000 on a new kiln last year, in the belief that it would cut energy usage 25%
over the old kiln. This kiln is an oven that turns "greenware" into finished pottery. Mabel is concerned
that the new kiln requires extra labor hours for its operation. Mabel wants to check the energy savings of
the new oven, and also to look over other measures of their productivity to see if the change really was
beneficial. Mabel has the following data to work with:
The year before
4000
350
15000
3000
Production (finished units)
Labor (hrs)
Capital ($)
Energy (kWh)
Year just ended
4100
375
18000
2600
Also, suppose that the average labor cost is $12 per hour and cost of energy is $0.40 per kwh.
a. Were the modifications beneficial? (Compute labor, energy, and capital productivity for the two
years and compare.)
b. Compute percentage change in multi-factor productivity of the year just ended from that of year
before.
c. If the multifactor productivity for next year must be restored to what it was the year before, assuming
the same output next year as the year just ended, by how the input must be reduced from what it is
this year?
2.
An Appliance Service company made house calls and repaired 10 lawn-mowers, 2 refrigerators, and 3
washers in an 8-hour day with his standard crew of 3 workers. The retail price for each respective service
is $50, $200, and $120. The average wage for the workers is $12 per hour. The materials cost for a day
was $200 while the overhead cost was $50.
a. What is the company’s labor productivity?
b. What is the multifactor productivity?
c. How much of a reduction in input is necessary for a 5% increase in multifactor productivity?
3.
What is the forecast for May based on a 3-period MA and a weighted 3-period moving average
applied to the following past demand data? Let the weights be, 3, 3, and 4 (last weight is for
most recent data). Compute MAD and MAPE for both cases and compare.
Nov.
37
4.
Dec.
36
Jan.
40
Feb.
42
Mar.
47
April
43
Sales of music stands at the local music store over the past ten days are shown in the table below.
Forecast demand using exponential smoothing with an  of .6 and an initial forecast = 16.
a) Compute the forecast for period 6 and the MAD.
b) Compute the tracking signal for periods 1 to 5. What do you recommend for this forecasting process?
t
Demand
1
13
2
21
3
28
4
37
5
25
5.
Weekly sales of copy paper at Cubicle Suppliers are in the table below.
Week
Sales (cases)
1
17
2
22
3
27
4
32
5
35
6
37
7
41
a.
Find a Naïve forecast adjusted for trend for week 8.
b.
Find a linear trend forecast for week 8.
6.
The quarterly sales (1000) for specific educational software over the past three years are given in the
following table.
YEAR 1
YEAR 2
YEAR 3
17
7
25
20
18
9
24
26
16
11
26
23
Quarter 1
Quarter 2
Quarter 3
Quarter 4
a.
b.
c.
d.
7.
Compute the four seasonal relatives/indices.
Calculate depersonalized data.
Plot the depersonalized data and explain whether trend is present or not.
Use a MA3 for the depersonalized forecast and find a seasonally adjusted forecast.
Arnold Tofu owns and operates a chain of 6 vegetable protein "hamburger" restaurants in northern
Louisiana. Sales figures and profits for the stores are in the table below. Sales are given in millions of
dollars; profits are in hundred thousand dollars. Calculate a regression line for the data. What is your
forecast of profit for a store with sales of $24 million? $30 million?
Store
1
2
3
4
5
6
8.
Sales
7
2
6
4
14
15
Profits
15
10
13
15
25
27
A restaurant manager tracks complaints from the diner satisfaction cards that are turned in at each table.
Prepare a Pareto chart. To cover 80% of problems which complaints must be address first?
Complaint
Food taste
Food temperature
Order mistake
Slow service
Table/utensils dirty
Too expensive
Frequency
80
9
2
16
47
4
9.
Cartons of Plaster of Paris are supposed to weigh exactly 32 oz. Inspectors want to develop process
control charts. They take five samples of six boxes and weigh them. Based on the following data,
compute the lower and upper control limits and determine whether the process is in control.
Sample
1
2
3
4
5
Mean
33.8
34.4
34.5
34.1
34.2
Range
1
0.3
0.5
0.7
0.2
10.
McDaniel Shipyards wants to develop control charts to assess the quality of its steel plate. They take ten
sheets of 1" steel plate and compute the number of cosmetic flaws on each roll. Each sheet is 20' by 100'.
Based on the following data, develop limits for the control chart and determine whether the process is in
control.
Sheet
Number of flaws
Sheet
Number of flaws
1
6
6
2
2
1
7
1
3
3
8
0
4
2
9
0
5
1
10
2
11.
Rancho No Tengo Orchards wants to establish control limits for its mangos before they are sent to the
retailers. They randomly take six containers (assume it is enough) of one hundred mangos in an attribute
testing plan and find some mangos with blemishes. What should be the limits on the control chart? Is the
process in control?
Container
Number of mangos with blemishes
1
5
2
3
3
1
4
3
5
4
6
2
12.
A woodworker is concerned about the quality of the finished appearance of her work. In sampling units
of a split-willow hand-woven basket, she has found the following number of finish defects in ten units
sampled: 4, 0, 3, 0, 1, 0, 1, 1, 0, 2.
a. Calculate the average number of defects per basket
b. If 3-sigma control limits are used, calculate the lower control limit, centerline, and upper
control limit.
13.
For the following control chart using both median and up/down run tests with z = ±1.96 limits. Are
nonrandom variations present? Assume the center line is the long-term median.
14.
The specifications for a plastic liner for concrete highway projects call for a thickness of 6.0 mm ± 0.1
mm. The standard deviation of the process is estimated to be 0.02 mm. What are the upper and lower
specification limits for this product? The process is known to operate at a mean thickness of 6.04 mm.
Determine the values of Cpk and Cp for this process. Is the process capable? Explain.
Answers:
1.
The energy modifications did not generate the expected savings; labor and capital productivity
decreased.
Given data
Production
Labor
Capital =
Energy =
Last year
4000
350
15000
3000
Now
4100
375
18000
2600
Labor productivity (Units/hr) =
11.4286
10.9333
Change
-0.4952
Change %
-4.33%
Capital productivity (units/$) =
0.2667
0.2278
-0.0389
-14.58%
Energy productivity (Units/KWH) =
1.3333
1.5769
0.2436
18.27%
Labor cost = Hours x $12 =
4200
4500
Capital $ =
15000
18000
Energy $ = $0.40 x Energy =
1200
1040
Total input $ =
20400
23540
Multifactor productivity (Units/$) =
0.1961
0.1742
Target productivity =
0.1961
Target input = 4100/0.1961 =
20910
Reduction in input needed = 23540 – 20910 =
2630
-0.0219
-11.17%
#2
Number serviced
Dollar value/unit
Production in $
Labor hours = 3 workers x 8 hrs. =
Labor productivity = 1260/24 =
LM
10
50
500
24
52.50
$
Multifactor productivity
Labor cost = 3x8x$12 =
Material =
Overhead =
Total input cost =
Productivity = 1260/538 =
5% improvement in MF productivity =
Target productivity after 5% improvement =
Input for improved productivity =
Reduction in input needed =
$
$
$
R
W
2
3
200
120
400
360
per day
per hour of labor
288
200
50
538
2.3420
0.1171
2.4591
512.38
25.62
1260
<-- Total $
= 288 + 200 + 50
per $ input
<-- Output/Productivity = 1260/2.4591
<-- 538 – 512.38
3.
Month
Demand
(At)
Nov.
Dec.
Jan.
Feb.
Mar.
April
37
36
40
42
47
43
3-MA
Forecast
|Et|
|Et|/At
Weight
Weighted
3-MA
|Et|
|Et|/At
3
3
4
Forecast =
37.67
4.33
0.1031
37.90
4.1
0.0976
39.33
7.67
0.1632
39.60
7.4
0.1574
43.00
0
MAD =
4
43.40
0.4
MAD
= 3.97
0.0093
MAPE =
8.81%
44.00
0.0000
MAPE =
8.88%
Forecast
=
43.90
4.
Period
1
2
3
4
5
Demand
13
21
28
37
25
F11 =
Ft
Et
16.00
-3.00
14.20
6.80
|Et|
CFEt
CAEt
MADt
TS
3.00
-3.00
3.00
3
-1
6.80
3.80
9.80
4.9
0.78
18.28
9.72
9.72
13.52
19.52
6.51
2.08
24.11
12.89
12.89
26.41
32.41
8.1
3.26
31.84
-6.84
6.84
19.57
39.25
7.85
2.49
27.74
5.
XY
X2
1
17
17
1
2
22
44
4
3
27
81
9
n=
7
X2 =
140
4
32
128
16
X =
28
954
5
35
175
25
211
6
37
222
36
Y =
=
XY = 
b=
4.0000
41
211
287
954
49
140
=
30.14
7
28
b
a. Naïve forecast adjusted for trend =
i.e. = 41 + (41 – 37) = 45
Sales
Week
 XY  n X Y
 X  nX
b
2
2
a=
3.9286
14.4286
954  7(4)(30.14)
 3.9286
140  7(4) 2
a  30.14  3.9286(4)  14.4286
a  Y  bX
Regression equation: Ŷ = 14.4286 + 3.9286t
F8 = 14.4286 + 3.9286(8) =
45.8571
#6
Quarter
1
2
3
4
Year 1
17
7
25
20
Demand
Year 2
18
9
24
26
Year 3
16
11
26
23
Average
17.0
9.0
25.0
23.0
Overall
average =
Demand
17
7
25
20
18
9
24
Index
0.9189
0.4865
1.3514
1.2432
0.9189
0.4865
1.3514
Deseasonalized
18.50
14.39
18.50
16.09
19.59
18.50
17.76
26
16
11
26
1.2432
0.9189
0.4865
1.3514
20.91
17.41
22.61
19.24
23
1.2432
18.50
Index
0.9189
0.4865
1.3514
1.2432
18.5
Deseasonlized Dmeand
30.00
20.00
10.00
0.00
MA3 =
20.12
Forecast for Q=1, year 4 = 20.12 x 0.9189 = 18.5
1
2
3
4
5
6
7
8
9
Quarters (Year 1 to Year 3)
10
11
12
7.
Store
1
2
3
4
5
6
Sum =
X
24
30
Sales (X)
7
2
6
4
14
15
48
Profits (Y)
15
10
13
15
25
27
105
Y
37.55634
45.07746
Estimated
profit
$ 3,755,634
$ 4,507,746
8.
Complaint
Food taste
Table/utensils dirty
Slow service
Food temperature
Too expensive
Order mistake
XY
105
20
78
60
350
405
1018
Frequency
80
47
16
9
4
2
158
X2
49
4
36
16
196
225
526
n=
X =
Y =
6
48
105
X2 =
XY = 
b=
a=
Ft = 7.472 + 1.254 X

%
Cum %
50.6%
29.7%
10.1%
5.7%
2.5%
1.3%
100.0%
50.6%
80.4%
90.5%
96.2%
98.7%
100.0%
Frequency
Pareto Chart: Complaints
90
80
70
60
50
40
30
20
10
0
To cover 80% of complaints, Food Taste and dirty utensils must be addressed first.
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
526
1018
1.254
7.472
9.
Sample
1
2
3
4
5
R
1.0
0.3
0.5
0.7
0.2
33.8
34.4
34.5
34.1
34.2
𝑋̿ = 34.2
n=6
A2 =
A2 =
LCL = 𝑋̿ - A2 =
UCL = 𝑋̿ + A2 =
0.48
0.26
33.94
D2 =
D3 =
LCLR =
0
2.0
0
34.46
UCLR =
1.08
= 0.54
The process is not in control, since the values for samples 1, 2, 3, and 9 fall outside the control
limits. Although all the sample ranges fall within 0 and 1.0, the assignable causes should be
investigated and eliminated.
10.
= total defects/number of sheets = 1.8
Use c-chart
UCLc = 1.8 + 3 √1.8 = 1.8 + 4.02 = 5.825
LCLc = 1.8 - 3 √1.8 = 1.8-4.02 = converts to zero
Sheet number 1 has too many flaws; investigate the cause.
11.
UCLp =
0.03 (1−0.03)
100
0. 03 + 3√
0.03 (1−0.03)
100
LCLp = 0. 03 − 3√
= 0.03 + (3 * 0.017) = .081
= 0.03 - (3 * 0.017) = -0.021 converts to 0
Limits are LCL = 0 and UCL = 0.081. All six points are in control; there is no pattern or trend in
the data.
12.
(a)
= 1.2; (b) LCLc = 1.2 – 3 √1.2 = -2.0862, or zero
UCLc = 1.2 + 3 √1.2 = 4.49.
13.
Median run test
N=
26
r=
8
Up/Down run test
N=
r=
26
22
E(r)med =
E(r)u/d =
17
med =
Z=
14.
14
2.5
-2.4
u/d =
Z=
2.07
2.415
Lower Specification = 5.9 mm, Upper speification = 6.1 mm.
Cpk = min{(6.1-6.04)/(3*0.02), (6.04 - 5.9)/(3*0.02) = min{1.00, 2.33} = 1.
Cp = (6.1 – 5.9)/(6*.02) = 1.67
Cpk is < 1.333 -- the process is not capable. Since Cp = 1.67, the process variability is small enough to be within
the desired specification range. Therefore, the process needs to be centered to achieve a Cpk of at least 1.33.