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Quality Management > Module 2 (Statistical Process Control) > Flashcards

Module 2 (Statistical Process Control) Flashcards

1
Q

Is a powerful collection of problem-solving tools useful in achieving process stability and improving capability through reduction of variability.

A

SPC

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2
Q

Magnificent 7

A

A. Histogram or stem-and-leaf plot
B. Check sheet
C. Pareto chart
D. Cause-and-effect diagram
E. Defect Concentration Diagram
F. Scatter Diagram
G. Control Chart

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3
Q

Is the cumulative effect of many small, essentially unavoidable causes.

Often called the “Stable system of chance causes”

A

Natural Variability or Background Noise

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4
Q

Possible sources of variation of natural variability:

A
  1. Environmental Factors (Temperature, humidity, air quality fluctuations)
  2. Characteristics of Raw Materials
  3. Machine equipment Precision (Machine tolerance, wear and tear)
  4. Operator Influence (reaction time, force application, skills levels)
  5. External Disturbances (Vibrations and Mechanical disturbances)
  6. Process Aging
  7. Measurement and Sampling Variability
  8. Supply Chain Variability
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5
Q

Such variability is generally large when compared to the background noise

Usually represents an unacceptable level of process performance.

A

Assignable Causes of Variation

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6
Q

unacceptable level of process performance

A

Out-of-control process

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7
Q

Major sources of variation of Assignable Causes of Variation

A
  1. Improperly adjusted or controlled machines
  2. Operator errors
  3. Defective raw material
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8
Q

What is control chart really for

A

Elimination of variability in the process

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9
Q

Crucial to verify certain assumptions, necesarry for the validity of the test results

A

Hypothesis Tests

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10
Q

primarily used for detecting deviations

A

Control Charts

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11
Q

refers to a situation where the process ‘mean ’ has shifted to a new value and remains consistently at that level

A

Sustained Shift

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12
Q

The mean could also shift suddenly, but the assignable cause might be short-lived, and the process could return to its original
state.

A

Abrupt Shift

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13
Q

An assignable cause might lead to a gradual
continuous change in the process mean over time.

A

Steady Drift or Trend

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14
Q

This occurs when the control chart wrongly signals that the process is out of control, when in fact it is in control. FALSE Positive.

A

Type 1 Error

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15
Q

This occurs when the control chart fails to signal that the process is out of control when it is actually not in control. FALSE Negative.

A

Type 2 Error

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16
Q

a detailed set of instructions, presented in the form of a flowchart or text, that outlines the specific steps that need to be taken in response to an activating event.

A

Out of Control Plan (OCAP)

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17
Q

These are potential assignable causes for the observed out-of-control signal. Serves as reference points for investigation.

A

Checkpoints

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18
Q

These are the actions taken to rectify the
out-of-control condition.

A

Terminators

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19
Q

3 types of Variability

A
  1. Stationary Behavior
  2. Uncorrelated
  3. Autocorrelated
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20
Q

the process data vary around a fixed mean in a stable and predictable manner.

A

Stationary Behavior

21
Q

the data gives the appearance of having been drawn at random from a stable population, perhaps normal distribution.

A

Uncorrelated

22
Q

data are dependent; that is a value above the mean tends to be followed by another such value.

A

Autocorrelated

23
Q

CC reason: Reduces scrap and rework

A

Control charts are a proven technique for improving productivity.

24
Q

CC reason: Consistent with the “Do it right the first time” philosophy.

A

Control charts are effective in defect prevention.

25
Q

CC reason: Consistent with the “If it isn’t broken, don’t fix it” philosophy

A

Control charts prevent unnecessary process adjustments.

26
Q

CC reason: Allows the implementation of a change in the process.

A

Control charts provide diagnostic information

27
Q

CC reason: Useful for product and process designers.

A

Control charts provide information about process capability.

28
Q

the outer limits, usually at 3 sigma. A point plotted outside of these limits, a search for assignable cause is made and corrective action is taken if necessary.

A

Action Limits

29
Q

the inner limits, usually at 2 sigma, Points plotted between warning and control limits shall be suspicious that the process may not be operating properly.

A

Warning limits

30
Q

Process control schemes that change the sample size and or the sampling frequency depending on the position of the current sample value.

A

Adaptive or variable sampling interval (or
variable sample size)

31
Q

Refers to how often samples are taken from the process. It determines the rate at which data points are collected and plotted on the control chart.

A

Sampling Frequency

32
Q

T or F: Ideally, frequent sampling, where samples are taken at short intervals, enhances the ability to detect shifts quickly.

A

T

33
Q

T or F: A larger sample size detects small shifts more
easily

A

T

34
Q

A key metric used to evaluate the performance of a control chart.
It represents the average number of data points that need to be plotted on the control chart before an out-of-control condition is detected

A

Average Run Length (ARL)

35
Q

to enhance the sensitivity of control charts in detecting meaningful changes or shifts in the process.

A

Goal of Rational Subgroup

36
Q

This means if an assignable cause is present it is more likely to significantly manifest.

A

Maximizing Inter-Subgroup Differences

37
Q

Differences within a subgroup are most likely due to random or common causes

A

Minimizing Intra-Subgroup Differences

38
Q

This basis for subgrouping allows us to capture changes or trends in the process over time.

A

Time Order of Production

39
Q

Each sample consists of units that were produced at the same time (or as closely together as possible)
Used to detect process shifts

A

Snapshot Approach

40
Q

All subgroup is a random sample of all process output over the sampling interval.
Used to make decisions about the acceptance of all units of products produced within the interval.

A

Random Sample Approach

41
Q

Points in a row increase in magnitude

A

Run, Run Up

42
Q

Points in a row decrease in magnitude

A

Run down

43
Q

Run lenght of (blank) or more is often taken as a signal of an out-of-control condition

A

8

44
Q

Sensitizing rules for shewhart control charts

A
  1. One or more points outside the control limit
  2. Two or three consecutive points outside the two-sigma warning limits but still indife the control limits
  3. Four or five consecutive points beyond the one-sigma limit
  4. A run of eight consecutive points on one side of the center line
  5. Six points in a row steadily increasing or decreasing
  6. Fifteen points in a row in zone C (both above and below the center line)
  7. Fourteen points in a row alternating up and down
  8. Eight points in a row on both sides of the center line with none in zone C
  9. An unusual or nonrandom pattern in the data
  10. One or more points near a warning or control limit
45
Q

Commonly used in “Measure” step of DMAIC
Purpose: Collecting historical or current operating data for process investigation.

A

Check Sheet

46
Q

A visual tool used in the Measure and Analysis steps of DMAIC.
It represents a frequency distribution of attribute data arranged by category.
Helps identify the most frequently occurring types of defects or issues.
Does not automatically prioritize by importance, only by frequency.
Consider using a weighing scheme for defects with varying consequences.

A

Pareto Chart

47
Q

Is a formal tool frequently useful in un-layering potential causes.
This diagram is useful in the Analyze and Improve steps of DMAIC.
Constructed by a quality improvement team assigned to identify problem areas.

A

Cause-and-Effect Diagram

48
Q

Used in the analyze step of DMAIC.
Provides a visual representation of the unit with all relevant views.
Defect types are drawn on the diagram to analyze their location for potential causes.

A

Defect Concentration Diagram

49
Q

A useful plot for identifying a potential relationship between two variables.
Then y is plotted against the corresponding x.
The shape of the (blank) often indicates what type of relationship may exist between two variables – regression modeling.
This is useful in the analyze step of DMAIC.

A

Scatter Diagram

Quality Management (3 decks)

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