CHAPTER 3

STATISTICAL PROCESS CONTROL

CHAPTER LEARNING OBJECTIVES

1. Explain the basics of applying statistical process control (SPC) in production and services. SPC is one of the main quantitative tools used in most quality management systems. Process control is achieved by taking period samples from the process and plotting these sample points on a chart to see if the process is within statistical control limits. Quality-focused companies provide extensive training in SPC methods for all employees at all levels. In this environment, employees have more responsibility for their own operation or process. Employees recognize the need for SPC for accomplishing a major part of their job: product quality. When employees are provided with adequate training and understand what is expected of them, they have little difficulty using statistical process control methods.

2. Discuss the rationale and procedure for the initial construction of a control chart. Control charts are graphs that visually show whether a sample is within statistical control limits. The four commonly used control charts include: p-charts and c-charts for attributes; and mean () and range (R) control charts for variables. Even though these control charts differ in how they measure process control, they all have certain similar characteristics. They look alike, with a line through the centre of a graph that indicates the process average and lines above and below the centre line that represent the upper and lower limits of the process.

3. Use attribute control charts. The quality measures used in attribute control charts are discrete values reflecting a simple decision criterion such as good or bad. A p-chart uses the proportion of defective items in a sample as the sample statistic; a c-chart uses the actual number of defects per item in a sample. A p-chart can be used when it is possible to distinguish between defective and non-defective items and to state the number of defectives as a percentage of the whole. A c-chart is used when it is not possible to compute a proportion defective and the actual number of defective items must be used.

4. Use variable control charts Variable control charts are used for continuous variables that can be measured, such as weight or volume. Two commonly used variable control charts are the range chart, or R-chart, and the mean chart, or -chart. A range (R-) chart reflects the amount of dispersion present in each sample; a mean () chart indicates how sample results relate to the process average or mean. Companies normally use these charts together to determine whether a process is in control.

5. Identify control chart patterns and describe appropriate data collection. If the sample values display a consistent pattern, even within the control limits, it suggests that this pattern has a non-random cause that might warrant investigation. We expect the sample values to “bounce around” above and below the centre line, reflecting the natural, random variation in the process that will be present. However, if the sample values are consistently above (or below) the centre line for an extended number of samples or if they move consistently up or down, there is probably a reason for this behaviour; that is, it is not random. Pattern tests determine if the observations within the limits of a control chart display a non-random pattern.

6. Evaluate the process capability of a process. Process capability refers to the natural variation of a process relative to the variation allowed by the design specifications. Process control charts are used for process capability to determine if an existing process is capable of meeting design specifications. The three main elements associated with process capability are process variability (the natural range of variation of the process), the process centre (mean), and the design specifications. One measure of the capability of a process to meet design specifications is the process capability ratio (Cp). It is defined as the ratio of the range of the design specifications (the tolerance range) to the range of the process variation. A second measure of process capability is the process capability index (Cpk). The process capability index specifically measures the capability of the process relative to the upper and lower specifications.

TRUE-FALSE STATEMENTS

1. Statistical process control involves monitoring and controlling a process to prevent poor quality.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

2. Unique or special cause variation reflects the random variation associated with the output of a process.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

3. Process control charts are rarely useful for monitoring and controlling the output of service processes.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

4. Statistical process control is a subset of statistical quality control.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

5. Statistical process control is based on a philosophy of inspection as opposed to prevention.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

6. One reason some companies fail in their attempt to apply statistical process control is lack of training.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

7. One goal of statistical process control is to prevent a process from producing items that have to be scrapped or reworked.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

8. Two types of variation associated with the output of a process are common (random) cause and special cause.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

9. Control limits are based on the special cause variation inherent in a process.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

10. A process that is determined to be in control contains no variation.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

11. Common cause (random) variation provides evidence that the process is not in control.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

12. Employee training in statistical process control is a fundamental principle in total quality management programs.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

13. Statistical process control is a tool used to monitor and improve quality.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

14. Statistical process control is only effective for service processes.

Difficulty: Easy

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

15. After special cause variation is detected, the focus changes to identifying the root cause of the variation and eliminating it.

Difficulty: Hard

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

16. Process control is achieved by taking periodic samples from a process and plotting the sample points on a chart to determine if the process is within control limits.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

17. All processes contain a certain amount of variation in their output.

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

18. Special cause variation can be identified using statistical process control.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

19. Process control charts are often used at a critical point after which it is difficult to correct or rework the process output.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

20. Control charts visually show when a process is not within statistical control limits.

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

21. The individual that detects special cause variation in a process is not allowed to diagnose the root cause and correct it.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

22. A quantitative variable classifies while a qualitative variable (attribute) measures.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

23. The formula used to determine the upper and lower control limits is based on specification limits.

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

24. When calculating control limits for a process, the number of standard deviations (z value) is typically six.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

25. When constructing a control chart for the first time, all points should be within the control limits indicating the process is in control.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

26. Control charts are never implemented until special cause variation has been detected in a process.

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

27. It is sometimes not necessary to determine new control limits after special cause variation has been identified if the source has been eliminated without changing the process.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

28. When a control chart detects no special cause variation in a process, the upper and lower control limits are the same value.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

29. When special cause variation is detected, it is normally eliminated by increasing the number of standard deviations (z value) used to calculate the control limits.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

30. A c-chart monitors the number of defects in small samples.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

31. A p-chart is used to monitor the proportion defective in the output of a process.

Difficulty: Easy

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

32. Attribute (qualitative) control charts are used to monitor descriptive characteristics of the output of a process.

Difficulty: Easy

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

33. Defect and defective mean the same thing for attribute (qualitative) control charts.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

34. With a c-chart, the sample size is small and often contains only one item.

Difficulty: Easy

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

35. The smaller the historical proportion defective reported for a process, the larger the sample size required to detect special cause variation with a p-chart.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

36. Variable control charts are used for quantitative measures such as weight or time.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

37. When monitoring a process’s output with a quantitative variable either an R-chart or an -chart is used, but never both.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

38. Construction and use of an -chart is based on an assumption that the sample points are normally distributed around the centre line.

Difficulty: Hard

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

39. The range is the difference between the smallest and largest values in a sample.

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

40. The range measures the variation within samples versus the variation between samples.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

41. The R-chart is used for monitoring and controlling variation within samples.

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

42. It is possible to have low variation within samples while at the same time having high variation between sample means.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

43. Variable (quantitative) control charts are used to monitor measurable characteristics of a process’s outputs.

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

44. Information from an R-chart can be used to construct the control limits for an -chart.

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

45. An and R-chart constructed to monitor and control a process use same raw data.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

46. An R-chart monitors the robustness of a process.

Difficulty: Hard

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

47. In some cases, the -chart is used without the R-chart because the within sample variation is not of concern.

Difficulty: Hard

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

48. The process centre line for both and R-charts are both the same value because they are based on the same raw data.

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

49. In some cases, the -chart is used without an R-chart because there is no variation between the samples.

Difficulty: Hard

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

50. Statistical process control can prevent poor quality before it occurs if a pattern is evident in the plotted points.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

51. A sequence of sample points that display a pattern is known as a run.

Difficulty: Easy

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

52. A pattern test can identify an out-of-control process even if all sample points are within control limits.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

53. If the points plotted on a control chart display a pattern, it is called a run.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

54. A control chart is in control when the plot of the sample points exhibits a pattern.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

55. If a pattern is evident in the points plotted on a control chart, the points are always considered evidence that the process is in control.

Difficulty: Easy

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

56. One advantage of using a pattern test is that special cause variations may be identified before any points are plotted outside the control limits.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

57. Control chart sample sizes are becoming smaller because it is easier to detect a pattern with Excel or other data analysis software.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

58. Using Excel to construct control charts should be avoided because most people believe using software results in too many errors.

Difficulty: Easy

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

59. Only certified statisticians should use Excel to construct a control chart for a process.

Difficulty: Easy

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

60. The popularity of Excel and other data analysis software has been a major factor in the increased use of statistical process control.

Difficulty: Medium

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

61. Tolerances and specification limits report similar information for a given product design.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

62. Tolerances reflect the amount of common cause variation allowed in a process.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

63. Design specification limits should always be wider than the control limits for a given process.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

64. The goal of statistical process control is to ensure that the control limits and specifications limits for a process always remain the same.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

65. For a given process, the process capability ratio is not related to its control limits.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

66. A process capability ratio greater than one shows that a process is capable of producing output within its specification limits.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

67. A process capability ratio reflects the number of (Z) sigmas included in the range between the upper and lower control limits.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

68. Some companies that strive to provide extremely high quality use Z=6 (six sigma) vs. Z=3 (three sigma) when constructing control limits.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

69. The process capability ratio measures the specification limits over the control limits.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

70. The process capability index is used only for p and c charts while the process capability ratio is used only for and R charts.

Difficulty: Easy

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

71. The process capability index indicates how much a process mean differs from the target specification value.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

72. Excel can be used to increase the efficiency and accuracy of determining the process capability ratio and the process capability index.

Difficulty: Easy

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

MULTIPLE CHOICE QUESTIONS

73. Possible root causes of special cause variation include all of the following except

a) operator error.

b) process out of adjustments.

c) over producing.

d) defective materials.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

74. Which of the following is not a primary purpose of statistical process control?

a) to establish control limits

b) to detect special cause variations

c) to identify specification limits

d) to determine when a process is not in control

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

75. Four common types of control charts include all of the following except

a) -chart

b) t-chart

c) p-chart

d) c-chart

Difficulty: Easy

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

76. Which of the following is not a characteristic of a control chart?

a) The centre line is determined using special cause variations.

b) The upper and lower control limits are based on special cause variation.

c) The centre line is determined by using the target value.

d) None of the above are true.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

77. Which of the following statements is true?

a) With wider control limits (larger Z), the process is more likely to be in control.

b) With narrower control limits, (smaller Z), the process is more likely to be in control.

c) With wider control limits (larger Z), the process is less likely to be in control.

d) None of the above are true.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

78. Special cause variation in a process is more likely to be detected with

a) wider control limits.

b) narrow control limits.

c) wider specification limits.

d) narrow specification limits.

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

79. Which of the following statements concerning control chart limits is true?

a) The smaller the value of z, the more narrow the control limits are and the more sensitive the chart is to changes in the production process.

b) The larger the value of z, the more narrow the control limits are and the more sensitive the chart is to changes in the production process.

c) The smaller the value of z, the wider the control limits are and the less sensitive the chart is to changes in the production process.

d) The larger the value of z, the more narrow the control limits are and the less sensitive the chart is to changes in the production process.

Difficulty: Hard

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

80. A control chart that uses the actual number of defects per item to monitor a process is known as a

a) p-chart.

b) c-chart.

c) R-chart.

d) -chart.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

81. If a sample of 40 units of output found 500 defects, then the centre line for monitoring the average number of defects per unit of output would be

a) = 40.

b) = 0.08.

c) = 12.5.

d) = 20,000.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

82. If a sample of 40 units of output found 500 defects, then the 3-sigma upper control limit for the chart would be

a) 12.5.

b) 23.11.

c) 37.5.

d) 75.0.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

83. A company randomly selects 100 light bulbs every day for 40 days from its production process. If 600 defective light bulbs are found in the sampled bulbs then the estimate for the process average defective would be

a) 6.667.

b) 0.167.

c) 0.150.

d) 0.250.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

84. A company randomly selects 100 light bulbs every day for 40 days from its production process. If 600 defective light bulbs are found in the sampled bulbs then the 3-sigma lower control limit would be

a) 0.0357.

b) 0.0429.

c) 0.15.

d) 0.1857.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

85. To monitor the number of blemishes on a polished surface, a company randomly selects 10 units of output from its process and counts the number of blemishes on each unit. The sample results are shown below:

Given the sample information above, the average number of defects per unit for this process would be

a) 160.

b) 80.

c) 16.

d) 10.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

86. To monitor the number of blemishes on a polished surface, a company randomly selects 10 units of output from its process and counts the number of blemishes on each unit. The sample results are shown below:

Given the sample information above, the standard deviation of the number of defects for this process would be

a) 16.

b) 10.

c) 4.

d) 0.

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

87. Which of the following control charts is based on the number of defects within a sample?

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

88. Which of the following control charts is used to monitor the number of defective items within a sample?

a)

b) R

c) c

d) p

Difficulty: Easy

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

89. If the quality of a process’s output is determined by the number of defects within a small sample, use a(n) ___ control chart.

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

90. If the quality of a process’s output is determined by classifying the output as being defective or not defective, use a(n) ___ control chart.

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

91. Which of the following control charts are based on sample sizes as small as one?

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

92. Which of the following control charts are often based on sample sizes larger than one hundred?

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

93. A control chart that reflects the amount of dispersion, or spread, present within each sample is known as a(n)

a) p-chart

b) c-chart

c) R-chart

d) -chart

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

94. If the quality of a process’s output is determined by the difference between the largest and smallest values in a sample, use a(n) ___ control chart.

a)

b) R

c) c

d) p

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

95. If the quality of a process’s output is determined by the average value of a sample, use a(n) ___ control chart.

a)

b) R

c) c

d) p

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

96. Which of the following control charts is used to control the variation within samples?

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

97. Which of the following control charts is used to control the variation between samples?

a)

b) R

c) c

d) p

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

98. Which of the following charts are frequently used together to monitor and control quality?

a) p/

b) R/p

c) c/R

d) R/

Difficulty: Easy

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

99. In general, a process is considered to be in control for all the following conditions except

a) no points are outside the control limits.

b) all points are above the centre line.

c) the points are randomly distributed following a normal population.

d) no pattern exists in the plotted points (no evidence of a run).

Difficulty: Easy

Learning Objective: Identify control chart patterns and describe appropriate data collection.

Section Reference: 3.5 Control Chart Patterns

100. A process is generally considered to be in control when

a) there are no sample points outside the control limits.

b) most points are near the centre line, without many being close to the control limits.

c) sample points are randomly distributed equally above and below the centre line.

d) all of the above are true.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

101. For the process to be capable of meeting design specification, the process capability index must be

a) less than one (1.0).

b) equal to or greater than one (1.0).

c) smaller than Six Sigma.

d) larger than Six Sigma.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

102. A company produces a product which is designed to weigh 10 oz., with a tolerance of + 0.5 oz. The process produces products with an average weight of 9.95 oz. and a standard deviation of 0.10 oz. The process capability ratio for this process, with z = 3, is

a) 1.67.

b) 0.

c) 0.8333.

d) –1.67.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

103. A company produces a product which is designed to weigh 10 oz., with a tolerance of + 0.5 oz. The process produces products with an average weight of 9.95 oz. and a standard deviation of 0.10 oz. According to the process capability ratio, is the process capable of meeting design specifications?

a) No, the process capability ratio is less than 1.0.

b) Yes, the process capability ratio is less than 1.0.

c) No, the process capability ratio is greater than 1.0.

d) Yes, the process capability ratio is greater than 1.0.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

104. A company produces a product which is designed to weigh 10 oz., with a tolerance of + 0.5 oz. The process produces products with an average weight of 9.95 oz. and a standard deviation of 0.10 oz. The process capability index for this process, with z = 3, is

a) 1.50.

b) –1.50.

c) 1.83.

d) –1.83.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

105. A company produces a product which is designed to weigh 10 oz., with a tolerance of + 0.5 oz. The process produces products with an average weight of 9.95 oz. and a standard deviation of 0.10 oz. According to the process capability index

a) the process mean is off centre and most of the items are defective.

b) the process is capable of meeting design specifications.

c) the process mean is centred on the design target.

d) none of the above.

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

106. A company manufactures a product that has a design (nominal) target width of 5 inches with tolerances of + .05 inch. The process that produces the product has a mean of 4.995 inches and a standard deviation of 0.01 inch. The process capability ratio for this process is

a) –1.67.

b) –1.5.

c) 1.5.

d) 1.67.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

107. A company manufactures a product that has a design (nominal) target width of 5 inches with tolerances of + .05 inch. The process that produces the product has a mean of 4.995 inches and a standard deviation of 0.01 inch. The process capability index for this process is

a) 1.67.

b) 1.5.

c) –1.5.

d) –1.67.

Difficulty: Hard

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

SHORT-ANSWER ESSAY QUESTIONS

108. Briefly discuss attribute and qualitative variable quality measures.

Difficulty: Medium

Learning Objective: Explain the basics of applying statistical process control (SPC) in production and services.

Section Reference: 3.1 The Basics of Statistical Process Control

109. Using control charts, how do we evaluate whether a process is in control?

Difficulty: Medium

Learning Objective: Discuss the rationale and procedure for the initial construction of a control chart.

Section Reference: 3.2 Control Charts

110. What is a c-chart and when is it used?

Difficulty: Medium

Learning Objective: Use attribute control charts.

Section Reference: 3.3 Control Charts for Attributes

111. Why are and R-charts used together?

Difficulty: Medium

Learning Objective: Use variable control charts.

Section Reference: 3.4 Control Charts for Variables

112. What is the process capability ratio and how is it calculated?

Difficulty: Medium

Learning Objective: Evaluate the process capability of a process.

Section Reference: 3.6 Process Capability

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