The document outlines key components for evaluating and monitoring processes using measurement system analysis (MSA) and statistical process control (SPC). It details principles of measurement system evaluation, steps for conducting gauge repeatability and reproducibility studies, and methods for monitoring processes with control charts. The content is focused on utilizing JMP software for data analysis and practical applications in process improvement.

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EVALUATING & MONITORING
YOUR PROCESS USING
MSA AND SPC
Peter Bartell
JMP Systems Engineer
peter.bartell@jmp.com

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OBJECTIVES
• At the end of this presentation you will be able to
• List the key components of a sound and effective measurement system evaluation
process
• Using JMP, list the key steps for Gauge Repeatability and Reproducibility studies.
• Using JMP, list the key steps for performing measurement system evaluation using select
methods articulated by Don Wheeler in “EMP III – Using Imperfect Data” (2006).
• List the key components of a sound and effective process monitoring approach
• Using JMP, list the key steps for creating and analyzing select Shewhart, Rare Event, and
multivariate control charts.

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BASIC MEASUREMENT SYSTEM
EVALUATION PRINCIPLES
• The three principles of Statistical Thinking
• All work is a process.
• All processes are variable.
• Use data to make decisions and guide actions.
• “Measure something once, you know what you’ve got. Measure it
again…you’ve got no clue.”
Which process
do you want?

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BASIC MEASUREMENT SYSTEM
EVALUATION PRINCIPLES
Start
Define
Process
Monitor
Process
Stable ?
Identify,
Remove
Causes
Estimate
Bias,
Precision
Adequate ?
Establish
Control
Charts
Stop
Improve
ProcessNo
Yes
Yes
No
My focus today…

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AN EXAMPLE OF GAUGE R & R
• Essentially a designed experiment coupled with appropriate analysis.
• Continuous and attribute responses.
• Focuses on repeatability and reproducibility of the system.
• Fixed, random effects, nesting vs. crossed designs, all considerations.
• Measuring multiple items, by multiple operators, across instruments, sites,
etc.
Cindy Tom GeorgeOperator (i)
Part (j) 1….10
Measurement (k)
y111… y3,10,3
Example: Using 2 Factors Crossed.jmp

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AN EXAMPLE OF THE “EMP” METHOD
• Described by Don Wheeler in “EMP III – Using Imperfect Data” (2006)
• A novel use of xbar and R charts and other graphical approaches.
A B COperator (i)
Part (j) 1….5
y111… y3,5,2
Example: Using gasket.jmp
Measurement (k)

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PROCESS MONITORING
• “Is the process behaving in a way that suggests there are no assignable
causes of variation occurring?”
• “If assignable causes rear their heads or occur, how will I know?”
AND
• “How can I balance the risk of making either of these two mistakes?”
• Going to look for assignable causes when they aren’t present.
• Failing to look for assignable causes when they are present.
• As Dr. Deming often asked, “By what method?”

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PROCESS MONITORING APPROACH
• Phase I
• Process stabilization, characterizing common cause variability.
• Phase II
• Process monitoring, ongoing characterizationto identify assignable cause variability
in a timely fashion, with acceptable risk.

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STATISTICAL PROCESS CONTROL
CHARTS
• Shewhart variables and attribute.
• AnnualSnowfall.jmp, (Individuals, Moving Range charts),
• SocketThickness.jmp (xbar, r charts), BottleTops.jmp (np chart)
• Rare event.
• FanBurnout.jmp (t chart)
• Multivariate charts (Hotelling’s T2)
• ThicknessPhaseI.jmp
• ThicknessPhaseII.jmp
• ThicknessTargets.jmp
• Via either the Control Chart Builder or Analyze -> Quality and Process ->
Control Chart path.