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 explaina. 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 byimproving 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 ofa 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. Whyisitusuallydesirabletousebotha median run testandanup/downruntestonthesamedata?
  28. If both run tests are used, and neither reveals nonrandomness, 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 / Year just ended
Production (finished units) / 4000 / 4100
Labor (hrs) / 350 / 375
Capital ($) / 15000 / 18000
Energy (kWh) / 3000 / 2600

Also, suppose that the average labor cost is $12 per hour and cost of energy is $0.40 per kwh.

  1. Were the modifications beneficial? (Compute labor, energy, and capital productivity for the two years and compare.)
  2. Compute percentage change in multi-factor productivity of the year just ended from that of year before.
  3. 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?
  1. 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.
  2. What is the company’s labor productivity?
  3. What is the multifactor productivity?
  4. How much of a reduction in input is necessary for a 5% increase in multifactor productivity?
  1. 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. / Dec. / Jan. / Feb. / Mar. / April
37 / 36 / 40 / 42 / 47 / 43
  1. 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 .6and 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 / 1 / 2 / 3 / 4 / 5
Demand / 13 / 21 / 28 / 37 / 25
  1. Weekly sales of copy paper at Cubicle Suppliers are in the table below.

WeekSales (cases)

117

222

327

432

535

637

741

a.Find a Naïve forecast adjusted for trend for week 8.

b.Find a linear trend forecast for week 8.

  1. 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
Quarter 1 / 17 / 18 / 16
Quarter 2 / 7 / 9 / 11
Quarter 3 / 25 / 24 / 26
Quarter 4 / 20 / 26 / 23

a. Compute the four seasonal relatives/indices.

b. Calculatedepersonalized data.

c. Plot the depersonalized data and explain whether trend is present or not.

d. Use a MA3 for the depersonalized forecast and find a seasonally adjusted forecast.

  1. 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 / Sales /

Profits

1 / 7 / 15
2 / 2 / 10
3 / 6 / 13
4 / 4 / 15
5 / 14 / 25
6 / 15 / 27
  1. 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 / Frequency
Food taste / 80
Food temperature / 9
Order mistake / 2
Slow service / 16
Table/utensils dirty / 47
Too expensive / 4
  1. 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 / Mean / Range
1 / 33.8 / 1
2 / 34.4 / 0.3
3 / 34.5 / 0.5
4 / 34.1 / 0.7
5 / 34.2 / 0.2
  1. 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
  1. 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?

ContainerNumber of mangos with blemishes

15

23

31

43

54

62

  1. 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.
  2. Calculate the average number of defects per basket
  3. If 3-sigma control limits are used, calculate the lower control limit, centerline, and upper control limit.
  1. 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.
  1. 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 Cpkand 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 / Last year / Now
Production / 4000 / 4100
Labor / 350 / 375
Capital = / 15000 / 18000
Energy = / 3000 / 2600
Change / Change %
Labor productivity (Units/hr) = / 11.4286 / 10.9333 / -0.4952 / -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 / -0.0219 / -11.17%
Target productivity = / 0.1961
Target input = 4100/0.1961 = / 20910
Reduction in input needed = 23540 – 20910 = / 2630
#2 / LM / R / W
Number serviced / 10 / 2 / 3
Dollar value/unit / 50 / 200 / 120
Production in $ / 500 / 400 / 360 / 1260 / <-- Total $
Labor hours = 3 workers x 8 hrs. = / 24 / per day
Labor productivity = 1260/24 = / $ 52.50 / per hour of labor
Multifactor productivity
Labor cost = 3x8x$12 = / 288
Material = / 200
Overhead = / 50
Total input cost = / 538 / = 288 + 200 + 50
Productivity = 1260/538 = / $ 2.3420 / per $ input
5% improvement in MF productivity = / 0.1171
Target productivity after 5% improvement = / 2.4591
Input for improved productivity = / $ 512.38 / <-- Output/Productivity = 1260/2.4591
Reduction in input needed = / $ 25.62 / <-- 538 –512.38

3.

Month / Demand (At) / 3-MA Forecast / |Et| / |Et|/At / Weight / Weighted 3-MA / |Et| / |Et|/At
Nov. / 37 / 3
Dec. / 36 / 3
Jan. / 40 / 4
Feb. / 42 / 37.67 / 4.33 / 0.1031 / 37.90 / 4.1 / 0.0976
Mar. / 47 / 39.33 / 7.67 / 0.1632 / 39.60 / 7.4 / 0.1574
April / 43 / 43.00 / 0 / 0.0000 / 43.40 / 0.4 / 0.0093
Forecast = / 44.00 / MAD = 4 / MAPE = 8.88% / Forecast = / 43.90 / MAD = 3.97 / MAPE = 8.81%

4.

Period / Demand / Ft / Et / |Et| / CFEt / CAEt / MADt / TS
1 / 13 / 16.00 / -3.00 / 3.00 / -3.00 / 3.00 / 3 / -1
2 / 21 / 14.20 / 6.80 / 6.80 / 3.80 / 9.80 / 4.9 / 0.78
3 / 28 / 18.28 / 9.72 / 9.72 / 13.52 / 19.52 / 6.51 / 2.08
4 / 37 / 24.11 / 12.89 / 12.89 / 26.41 / 32.41 / 8.1 / 3.26
5 / 25 / 31.84 / -6.84 / 6.84 / 19.57 / 39.25 / 7.85 / 2.49
F11 = / 27.74

5.

Week / Sales / XY / X2 / a. Naïve forecast adjusted for trend =
i.e. = 41 + (41 – 37) = 45
1 / 17 / 17 / 1
2 / 22 / 44 / 4
3 / 27 / 81 / 9 / n = / 7 / X2 = / 140
4 / 32 / 128 / 16 / X = / 28 / XY = / 954
5 / 35 / 175 / 25 / Y = / 211 / b = / 3.9286
6 / 37 / 222 / 36 /  = / 4.0000 / a = / 14.4286
7 / 41 / 287 / 49 /  = / 30.14
28 / 211 / 954 / 140
Regression equation: Ŷ = 14.4286 + 3.9286t
F8 = 14.4286 + 3.9286(8) = / 45.8571
#6
Demand
Quarter / Year 1 / Year 2 / Year 3 / Average / Index
1 / 17 / 18 / 16 / 17.0 / 0.9189
2 / 7 / 9 / 11 / 9.0 / 0.4865
3 / 25 / 24 / 26 / 25.0 / 1.3514
4 / 20 / 26 / 23 / 23.0 / 1.2432
Overall average = / 18.5
Demand / Index / De-seasonalized
17 / 0.9189 / 18.50 /
7 / 0.4865 / 14.39
25 / 1.3514 / 18.50
20 / 1.2432 / 16.09
18 / 0.9189 / 19.59
9 / 0.4865 / 18.50
24 / 1.3514 / 17.76
26 / 1.2432 / 20.91
16 / 0.9189 / 17.41
11 / 0.4865 / 22.61
26 / 1.3514 / 19.24
23 / 1.2432 / 18.50
MA3 = / 20.12
Forecast for Q=1, year 4 = 20.12 x 0.9189 = 18.5

7.

Store / Sales (X) / Profits (Y) / XY / X2 / n = / 6 / X2 = / 526
1 / 7 / 15 / 105 / 49 / X = / 48 / XY = / 1018
2 / 2 / 10 / 20 / 4 / Y = / 105 / b = / 1.254
3 / 6 / 13 / 78 / 36 / a = / 7.472
4 / 4 / 15 / 60 / 16
5 / 14 / 25 / 350 / 196 / Ft = 7.472 + 1.254 X
6 / 15 / 27 / 405 / 225
Sum = / 48 / 105 / 1018 / 526
X / Y / Estimated profit
24 / 37.55634 / $ 3,755,634
30 / 45.07746 / $ 4,507,746

8.

Complaint / Frequency / % / Cum %
Food taste / 80 / 50.6% / 50.6%
Table/utensils dirty / 47 / 29.7% / 80.4%
Slow service / 16 / 10.1% / 90.5%
Food temperature / 9 / 5.7% / 96.2%
Too expensive / 4 / 2.5% / 98.7%
Order mistake / 2 / 1.3% / 100.0%
158 / 100.0%

To cover 80% of complaints, Food Taste and dirty utensils must be addressed first.

9.
Sample /  / R
1 / 33.8 / 1.0 / n = 6
A2 = / 0.48 / D2 = / 0
A2 = / 0.26 / D3 = / 2.0
LCL = - A2 = / 33.94 / LCLR = / 0
UCL = + A2 = / 34.46 / UCLR = / 1.08
2 / 34.4 / 0.3
3 / 34.5 / 0.5
4 / 34.1 / 0.7
5 / 34.2 / 0.2
= 34.2 /  = 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.Use c-chart= total defects/number of sheets = 1.8

UCLc = 1.8 + 3= 1.8 + 4.02= 5.825

LCLc = 1.8 - 3= 1.8-4.02 = converts to zero

Sheet number 1 has too many flaws; investigate the cause.

11.UCLp = 0 = 0.03 + (3 * 0.017) = .081

LCLp = 0 = 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 = -2.0862, or zero

UCLc= 1.2 + 3 = 4.49.

13.

Median run test / Up/Down run test
N = / 26 / N = / 26
r = / 8 / r = / 22
E(r)med = / 14 / E(r)u/d = / 17
med = / 2.5 / u/d = / 2.07
Z = / -2.4 / Z = / 2.415

14.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

Cpkis < 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.