Statistical Process Control - Definition

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Statistical Process Control - Definition The
application of statistical techniques is to
understand and analyze the variation in a
process. - Joseph Juran Quality Control
Handbook
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Statistical Process Control History 1920s Origin
al techniques developed by Bell Telephone Labs
Dr. Walter Shewhart - Statistical control -
Control charts - Random (common) versus
special (assignable) causes of
variation 1930s While Dr. W. Edwards Deming is
at Department of Agriculture, he meets and
studies with Shewhart 1940s Deming tapped to
perform census, the first using sampling
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Statistical Process Control 1942 At request of
Stanford University professor, outlines
proposal for teaching statistical quality
control to engineers, inspectors, and others at
companies in wartime production - Taught to
over 31,000 people - Led to formation of
American Society for Quality Control 1946
to Unparalleled demand for goods 1949 No
competition
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Statistical Process Control 1946 to Scientific
Management in full bloom 1949 - Developed by
Frederick Winslow Taylor - Minimize complexity
to maximize efficiency (idiot proof -
Resulted in removing power from lower levels
(workers, supervisors) - Led to top heavy,
overly-powerful management and modern
corporate structure.
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Statistical Process Control 1946 to Quality took
back seat to production - get the numbers
out 1949 - Shifted to end-of-the-line
inspection, rework, etc. By 1949 Deming
notes No control charts left, not even
smoke. Management didnt want workers to
apply the techniques, so they
didnt. 1947 Deming recruited by MacArthur to
do 1951 Japanese census
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Statistical Process Control 1947 Deming works
and teaches eager Japanese managers and
workers Shewharts methods 1951 Japanese had
established Deming Prize 1980 America
rediscovers quality
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• Statistical Process Control (SPC)
• SPC is a powerful collection of problem-solving
• tools useful in achieving process stability and
• improving capability through the reduction of
• variability.
• SPC can be applied to any process
• Seven major tools
• 1. Histogram or stem and leaf display
• 2. Check sheet
• 3. Pareto chart
• 4. Cause and effect diagram
• 5. Defect concentration diagram
• 6. Scatter diagram
• 7. Control chart

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• Statistical Process Control Tools
• Control charts
• Histograms
• Process capability indices
• Process capability studies
• Process flow diagram
• Cause and effect diagram
• Pareto diagram
• Scatter diagram

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Statistical Process Control Causes of
Variation Assignable (special) - Intermittent
sources of variation that are unpredictable.
Signaled by violation of Western Electric
rules Common (natural) - Sources of variation
always present affecting all output from
a process Only management can affect common
causes of variation
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Statistical Process Control - Control
Charts Interpretation based on Western Electric
rules 1. Analyze the chart by separating it into
equal zones above and below the centerline
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Statistical Process Control- Control Charts 2.
A process is out of statistical control if (a)
any point is above or below the control
limits (b) two out of three points in a row in
zone A or above (c) four out of five
points in a row in zone B or above (d)
eight in a row in zone C or above
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• Statistical Process Control- Control Charts
• In general specification limits should not be on
• control charts
• Data must be displayed in time sequence
• Management controls the natural variation
  between
• the control limits
• Do not tweak the process

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• Statistical Process Control - Control Charts
• Questions to Ask
• Is variables data on the product or process?
• Are the operators seeing this data?
• How long has control chart had this appearance?
• Do the operators know what to do when
  out-of- control conditions occur?

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Statistical Process Control - Control Charts If
out-of-control Are there differences in the
measurement accuracy of instruments used? Are
there differences in the methods used by
different operators? Is process affected by
environment? Is process affected by tool wear?
machine calibration? Has there been a change in
raw materials used?
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Statistical Process Control - Control Charts If
out-of-control Did data come from different
machines? shifts? operators? Are operators
afraid to report bad news?
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• Statistical Process Control - Histograms
• Histograms
• Used to display data to discover distribution
• Used with variables data
• Data are grouped into cells for display
• Reveals amount of variation in measurements
• (product/process)
• Reveals centering of measurements
• Include specification limits to check for
  capability
• Include process (production) limits

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• Statistical Process Control - Histograms
• Histograms - Questions to ask
• What is the shape of distribution?
• What would you expect shape to be?
• If computer generated, is data really normal?
• Is variation acceptable?
• Is the centering acceptable?
• Did you generate a histogram with and without
• outlier points?
• Did you include specification limits and process
• limits on the histogram?

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• Statistical Process Control - Histograms
• Possible answers for a Cliff-like histogram
• Hiding data that should be outside the
  specification
• Supplier is screening the product before
  shipment
• Lower specification is a physical limit like
  zero
• thickness, but this is not normally the case

upper spec
lower spec
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• Statistical Process Control - Histograms
• Possible answers for a Bimodal histogram
• Two primary sources of process variation
• The process is stable, but it has experienced
• a large shift during the time the data were
  collected

upper spec
lower spec
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• Statistical Process Control - Histograms
• Possible answers for a Comb-like histogram
• Insufficient data collected
• Too many classes displayed
• Process is unstable
• Process is stable but is multimodal

upper spec
lower spec
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• Statistical Process Control - Histograms
• Possible answers for a Skewed histogram
• May be the natural result of the process
• For a machined part, the equipment may be losing
• tolerance or tools may be wearing out
• The process is shifting slowly to the side with
  the
• long tail

upper spec
lower spec
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Statistical Process Control - Histograms By
including specification limits on a histogram,
the amount of data that falls outside of the
specification limits can be easily seen
specification
frequency
upper spec
lower spec
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Statistical Process Control ppm parts per
million Interpretation CpK lt 1 process not
capable 1 ? CpK lt 1.5 process capable,
monitor frequently CpK ? 1.5 process
capable, monitor infrequently Pareto CpKs to
attack worst problems Can only convert CpK, Cp
to ppm if distribution normal
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Statistical Process Control Use Z-scores and
standard normal table for this calculation Must
be based on first pass data collected
over normal operating cycle of process
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• Statistical Process Control
• Questions to ask
• Was this data collected over a short or long
  period
• of time?
• Was the collection of data structured?
• Did you construct a histogram?
• Is your data normal?
• If repeating the calculation, did your CpK
  improve?
• What is Cp compared to CpK?

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• Statistical Process Control
• Process capability studies
• Determines the centering of the process
• Determines the variation of the process
• Puts stake in the ground to measure future
• improvement
• Short term study provides snapshot of capability
• It is not the true process capability
• Long term study (over normal operating cycle of
• the process) provides true process capability CpK

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Statistical Process Control Impact of special
causes on process capability
process stable
process unstable
time
time
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Statistical Process Control Difference between
process capability and process control
process control
time
out of control
size
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Statistical Process Control Difference between
process capability and process control
process capability
time
in control but not capable
size
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• Statistical Process Control
• Process Capability Studies - Questions to ask
• Were adequate records maintained?
• Is this data a result of a short or long study?
• What is the centering of the process?
• How does it relate to the center of the spec?
• What is the variation of the process?
• How does the process spread compare to the spec?
• What actions have been taken as a result of this
• study?
• When will another study by conducted to verify
  that
• improvements have been made?

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• Statistical Process Flow Diagram
• Process Flow Diagram
• Used to detail the actual steps of a process
• Allows understanding of points where problems
  arise
• Ensures feedback mechanisms in place
• Shows relationship between process steps
• Must be designed by those involved in process,
  not
• by outsiders

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• Statistical Process Flow Diagram
• Questions to ask
• Is this the correct level of detail?
• Do we agree on all blocks?
• Is process unnecessarily complicated?
• Do all loops have an exit?
• Have we captured every step?

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• Statistical Process Control - Cause Effect
  Diagram
• Cause and effect diagram
• Used to identify and explore all possible causes
  for
• a problem
• Also called fishbone or Ishikawa diagram
• Should be generated by team
• Use as many categories as needed for causes
• Once generated must discover which cause
  impacts
• the effect
• Combine with process flow diagram to form cause
• and effect flow diagram
• Best used early in problem solving success

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Statistical Process Control - Cause Effect
(Fishbone) Diagram

• All contributing factors and their relationship
  are displayed
• Identifies problem area where data can be
  collected and analyzed

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• Statistical Process Control
• Questions to ask
• Have you at least covered the 6 Ms
• materials
• manpower
• machines
• measurements
• methodology
• mother nature
• Has ever one who impacts the process had input?
• How did you prioritize causes to begin to
  attack?
• What have you done to mistake proof the process?

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• Statistical Process Control - Pareto Diagram
• Used to display relative importance of problems
• Pareto principle 80 of costs are associated
  with
• 20 of defects
• Prioritize problems to direct resources
• Attack tall bars first
• Use check sheets or collected data to build
• Provide to those involved in the process
• Do before and after snapshots to check for
• improvement
• Generally used for attribute data
• Can use time rollups to see trends

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• Statistical Process Control - Pareto Diagram
• Questions to ask
• Has the data been sanitized
• Have people who do the work see the information?
• What action has been taken to prevent tall bar
• recurrence?
• Are the operators collecting this data?

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• Statistical Process Control - Scatter Plot
• Scatter Plot
• Used to display relationship between two
  variables
• Tests for cause and effect
• Doesnt prove that one variable causes the other
• Does provide for existence and strength of
• relationship
• Horizontal cause
• Vertical effect
• Interpretation based on picture if relationship
  is
• linear

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• Statistical Process Control - Scatter Plot
• Questions to ask
• Are you sure the relationship is linear?
• Have you chose the most relevant data?
• Did you gather enough data?
• Was relationship negative or positive? How
  strong?

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Correlation
Possible Relationship Between X and Y as
Indicated by Scatter Diagrams
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• Statistical Process Control
• How to implement
• Must have a model to work from
• Must have discipline to follow model
• Cannot only be quality championed
• Needs to be team driven
• Must not chase charts for charts sake
• Management must understand, believe, and expect
• results
• Start small
• Focus on process
• Get operators involved in the process
• Must provide right training to right people at
  right
• time
• Do not need fancy computers
• Dont take capability for granted

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• Statistical Process Control
• Recommendations
• Establish steering team to implement SPC
• Establish SPC methodology
• Choose pilot processes to study
• Train practitioners with detailed understanding
  of
• SPC
• Put stake in ground on chosen processes
• Follow and document your chosen SPC plan
• Understand the process!

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• Background of Six Sigma
• Six Sigma is a business initiative first
  espoused by
• Motorola in the early 1990s.
• Six Sigma strategy involves the use of
  statistical
• tools within a structured methodology for
  gaining
• the knowledge needed to achieve better, faster,
  and less expensive products and services than
  the
• competition.
• A Six Sigma initiative in a company is designed
• to change the culture through breakthrough
• improvement by focusing on out-of-the-box
• thinking in order to achieve aggressive, stretch
  goals

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Motorolas Six Sigma Ten Steps 1. Prioritize
opportunities for improvement 2. Select the
appropriate team 3. Describe the total
process 4. Perform measurement system
analysis 5. Identify and describe the potential
critical process 6. Isolate and verify the
critical processes 7. Perform process and
measurement system capability
studies 8. Implement optimum operating conditions
and control methodology 9. Monitor processes
over time/continuous improvement 10. Reduce
common cause variation toward achieving six
sigma