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Statistical Process Control > Statistical Process Control (SPC) and Control Charts in Six Sigma > Flashcards

Statistical Process Control (SPC) and Control Charts in Six Sigma Flashcards

1
Q

Are all processes subject to variation?

A

Yes.

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2
Q

Define Statistical Process Control

A

A way to measure, monitor, and control processes.

SPC is a methodology that uses control charts to determine when a process is out of control.

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3
Q

Describe the larger “continuous improvement” philosophy undergirding SPC

A

Continuous improvement

Inputs > Process/System > Outputs

SPC analyzes the “Process/System” part

Inputs are:
People
Materials
Methods
Equipment
Environment
Measurement

Output = Product/Service

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4
Q

Define common cause variation

A

Those causes that are inherent to the process and not controllable by process operators.

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5
Q

Define special cause variation

A

AKA “assignable causes”, include unusual events that the operator can usually remove.

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6
Q

Do the effects of common cause vs special cause variation need to be similar?

A

No. They often are not.

It is the CAUSES that vary.

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7
Q

What are the three objectives of SPC?

A
  1. Monitor the performance of a process
  2. Identify special and common cause variation
  3. Control and improve process performance
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8
Q

Describe (1) “monitor the performance of a process” objective of SPC

A
  • Determine process capability and the natural range of the process
  • How the process measures up to specifications
  • Charts: control, histogram, run charts, check sheets
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9
Q

Describe the difference between “defect” and “control.”

A

A defect is determined by the process violating the specification limits.

A process would be call unstable/out of control when it violates the control limits/exhibits abnormal behavior.

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10
Q

Describe control limits vs specification limits.

A

Control limits look inward and are calculated from process data itself.

Specification limits are usually determined by factors external to the process (customer requirements, industry standards, etc).

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11
Q

List the considerations for implementing SPC

A
  • Team members’ buy-in
  • Avoiding overanalysis
  • Focus on process issues (not human ones)
  • Feedback on process behavior
  • Control charts
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12
Q

Rational Subgrouping

A

A small homogenous sample from the process taken in a short space or time, such that every item in the subgroup is produced under similar conditions.

Done so that just normal/random effects are within that group.

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13
Q

Two types of variation

A

Within subgroup and between subgroup

Minimize within subgroup variation, maximize between subgroup variation.

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14
Q

Rational subgrouping principles

A
  • Observations must be independent
  • Obersvations are from a single, stable process
  • Observations taken in a time-ordered sequence
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15
Q

Variation of interest

A

Choose rational subgrouping in a manner that isolates your variation of interest. Ex:

  • Shift to shift
  • Day to day
  • Hour to hour
  • Batch to batch
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16
Q

Selecting variables for SPC

A

Use your judgement, but can generally choose variables that:

  • Most difficult to hold
  • Tied to customer, organization, or regulatory imperatives
  • Represent a critical dimension of the product or process
  • Salient or known
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17
Q

Selecting variables for SPC

A

Use your judgement, but can generally choose variables that:

  • Most difficult to hold
  • Tied to customer, organization, or regulatory imperatives
  • Represent a critical dimension of the product or process
  • Salient or known
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18
Q

Most difficult to hold

A

Directly associated with high defect rates

Known to exhibit a lot of variation

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19
Q

Tied to customer/organizational/regulatory imperatives

A
  • Customer complaints
  • Key customer requests
  • Standards
20
Q

Variables representing a critical dimension of the product/process

A
  • Those that effect human safety, environment, or the community
  • KPIVs, KPOV (key process input/output variables)
  • Variables that have caused processing difficulties
  • Variables that help control the process
  • Variables that contribute to high internal cost

The Ideal: one variable that’s so critical, you HAVE to do it

If you CAN’T tell, use other tools to narrow down

  • analysis of variance
  • Pareto analysis
21
Q

Salient/known variables

A
  • Variables with special cause variation
  • Variables that can be measured
  • Items that can be counted by the person counting
  • A leading indicator
22
Q

How to use these categories in your own project?

A

Narrow down to those with biggest effect on:

  • Cost
  • Customer satisfaction
  • Key process variable
23
Q

Avoid overcharting

A

When you use more than a few charts, the benefits increase costs, not decrease.

24
Q

How to apply rational subgrouping

A

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25
Q

Final considerations of rational subgrouping

A

Size

  • If you have a large subgroup taken over a short period of time, it may contain dependent data
  • If you take it over a longer period of time, it may contain special cause variation.
26
Q

Subgroup size

A

A sample size of 2-3 items

  • Are more economical and assumed to be rational
  • Can detect large shifts, but not small shifts as easily
  • You need a sufficient history of the process in order to create meaningful control chart
  • Need enough subgroups
  • Large subgroups may contain dependent data or special cause variation
  • Service processes typically have a sample of one
27
Q

SELECTING CONTROL CHARTS AND DETERMINING LIMITS

A

SELECTING CONTROL CHARTS AND DETERMINING LIMITS

28
Q

Controls charts are used for

A

Analyzing variation in almost any process to
- Control ongoing processes
- Predict range of outcomes expected
- Determine whether process stable/in stat control
- Analyze process variation patterns from special causes
- Determine if goal of QI project should involve making changes to the process
-

29
Q

Anatomy

A

Center Line CL
UCL
LCL

Individual data points = measurements/observations

30
Q

6 steps to create

A
  1. Choose appropriate chart for data
  2. Determine CL
  3. Determine UCL and LCL
  4. Draw those lines
  5. Plot data points
  6. Analyze & interpret
31
Q

How to choose the appropriate chart and how to determine CL, UCL, LCL

A

Important to remember dif between types

32
Q

Variable data

A

When quality characteristic can be expressed as numerical data (time, length, degrees, money)
- Can be expressed as decimals

33
Q

Attribute data

A

For product characteristics with a discrete response (yes/no, pass fail, good bad)

  • Usually for defects
  • Ex: off production line as acceptable or not
34
Q

Visual difference

A

Control charts for variable data is displayed in pairs
- Control charts for attribute data is displayed singly

Sometimes you’ll have a choice

  • Ex: you haven’t collected yet and
  • Variable charts are good but also intensive and expensive
35
Q

Control charts for variable data when subgroup size is greater than one

A
  • X-bar and R chart (or simply R chart or X-R chart)

- X-bar and s chart, aka s chart or X-S chart

36
Q

Control charts for variable data when subgroup size is one

A
  • Individual/moving range charts AKA ImR charts

- ImR chart is a pair of control charts

37
Q

Control charts for attributable data

A
  • C & U charts, which follow the Poisson distribution

- Np and p charts for binomial distribution

38
Q

Control charts for variables data

A

X-bar and R chart

Typically used when subgroups 2-10 measurements

X-bar and s chart

  • For subgroups greater than 10
  • Because s (standard deviation) is more accurate depiction of dispersion for large groups vs. R (range)
39
Q

Individual & moving range chart

A

ImR chart
Used for variable data that uses individual characteristics
- Plots both individual measurements and moving range between subgroups

Used when sample data isn’t in subgroups, but rather individual observations
- AKA use when the subgroup size is one

40
Q

For attributes data

A
  • C and u charts follow poisson distribution (can count the number of defects)
  • – Ex: defects by day, batch, or machine (use when sample size constant)
  • U chart when sample size not constant

NP & P charts (binomial distribution)
- NP -

P
- Most effective when sample size 50 or more

41
Q

Control chart data point abbreviatiosn

A 
I - individual values
X-bar - subgroup averages
R - subgroup ranges
p & u - proportions or averages
np & c- numbers per unit
42
Q

Variable control limits

A

SEtting them proportionally to subgroup sizes

- control limits are not straight lines

43
Q

Constants are involved in the calculation of variable charts

A

Remember dis

44
Q

Is mortgage processing time in control?

A

Won’t be homogenous
- Msut consider each case individually and have subgroup of 1
- So use ImR / moving range chart
-

45
Q

Invoicing process: counting the number of errors that occur

A

U chart

Statistical Process Control (2 decks)

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