statistical process control using colormetrix (.mdb) and minitab for windows (.mtw) presented by...
TRANSCRIPT
![Page 1: Statistical Process Control Using ColorMetrix (.mdb) and Minitab for Windows (.mtw) Presented by Howard Nelson, Ed.D ColorMetrix 4th Annual User Group](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649c775503460f9492bdc5/html5/thumbnails/1.jpg)
Statistical Process Control Using ColorMetrix (.mdb) and Minitab for Windows (.mtw)
Presented by Howard Nelson, Ed.D
ColorMetrix 4th Annual User Group Meeting
August 8-10, 2004 • Las Vegas, Nevada
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Minitab Build 13(MTBWIN)
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The Printing IndustryCycle of Despair
• What do you see as the biggest problem with consistency in your plants???
• Pressrun high and low data points fall outside of aimpoints
• Makeovers and reruns
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Numerical Naiveté
• We’ve all been taught that 2+2 = 4• Nature is a process that has a cycle• Temperature and Precipitation• 2+2 = 4 in pure math, but in nature,• 2 +2 = x4
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Everything is a Process
• Printing job cycle– Process the order info
• Estimate• Production schedule
– Prepress• Images from many sources• Composite layout image• RIP /proof• Plates
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• Pressrun– Material logistics
•Ink, Paper•Plates, blankets, information
– Makeready•Register•Run-up to density•Approval
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– The pressrun•Count•Quality
– Finishing•Folding•Binding or Filling•Traffic and shipping
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Processes Can Be Measured
• Usually, any process can be measured at many points or stages of input
• If you can make a measurement, you can turn that measurement into data
• The difficulty is in turning that data into information you can understand and act on– Critical task: separating info from noise
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Any Process That Can Be Measured Can Be Improved
• There are three ways we can make our process look more consistent– Shift the aim-point– Reduce variation in the process
• Special-cause variation• Common-cause variation
– Change the mathematically-determined specifications
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Step 1: Workflow Analysis
• Do a three-level workflow analysis to understand where to start looking for trouble
• Steering committee?– Made up from all levels of stakeholders at the
company
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3-level Workflow Analysis
• Level 1: Count failure occurrences(by cost centers?)
– “Where is the problem and what does it seem due to?”
• Level 2: Determine if there are any “Ripe Fruit” problems by collecting enough data to identify it
• Level 3: Address chronic process inconsistency
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Step 2: Process Mapping
• Chart your workflow (Plan for several drafts)
– Identify each process input point– Describe the kind of data that can be
collected at each of these points– List suppliers and customers for each point– Map backwards from the error point
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Generic Process Map
• Map the Process– Define the Scope– Understand the Process Steps
• Measure Process Output– Create a Sampling Plan– Collect Data on the Process
• Describe the Process– Numerically and Graphically Summarize
the Process• Assess the Measurement Process
– Determine Stability, Precision, Bias and Linearity
– Take Required Improvement Action• Assess Process Stability
– Create a Process Behavior Chart– Identify and Remove Special Causes of
Variation if Needed• Assess and Understand Process Capability
– Determine the Ability of the Process to Meet Requirements
– Understand the How the Inputs Effect the Outputs
– Take Required Improvement Action• Establish and Implement Process Controls
The Improvement Process
Map theProcess
Measure &Describe the
Process
Assess theMeasurement
System
Improve theMeasurement
System
IsMeasurement
SystemCapable?
AssessProcessStability
IsProcessStable?
Identify &Remove
Special Causes
Assess &Understand
ProcessCapability
ReduceCommonCauses
IsProcess
Capable?
Create & Implement
Control Plan($$$$)
No
Yes
No
Yes
No
Yes
Strategy: Assess then Improve
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Collect the Data
• Example: on a hand-operated press, how do we get consistent product?
• Factors– Pantone 032– Coated paper– Spectrophotometer
• Number of Remakes (caused by the failures)– Remake % Rate?
• Try to collect at least 30 data points
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ColorMetrix Density Graph
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ColorMetrix Trending
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Transfer the Data to Minitab
• Step-by-step procedure to export data base to MTW– Step 1: Quit all ColorMetrix Applications– Step 2: Locate the ColorMetrix.mdb database,
usually found in C:\program files\colormetrix\colormetrix.mdb
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Step by Step, Continued
– Step 3: Open the database using Microsoft Access
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Step by Step, Continued
– Step 4: Locate the table that contains the data needed. (In our example, the magenta data base is the file that contains the data we need for Minitab)
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Step by Step, Continued
– Step 5: Scroll to, select and copy the data needed for Minitab
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Step by Step, Continued
– Step 6: Open Minitab and paste the data into a new Minitab worksheet
– Step 7: Clean up the worksheet by deleting the unnecessary columns from Colormetrix database
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To Baseline Your Process…
MeasurePerformance
AnalyzePerformance
ImprovePerformance
ControlPerformance
120 140 160 180 200 220
LSL USL
Process Capability Analysis for Viscosity
USL
Target
LSL
Mean
Sample N
StDev (ST)
StDev (LT)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
190
*
150
168
32
15.0423
14.6132
0.44
0.49
0.40
0.40
*
0.46
0.50
0.41
0.41
93750.00
93750.00
187500.00
115726.66
71796.65
187523.31
109018.53
66099.06
175117.59
Process Data
Potential (ST) Capability
Overall (LT) Capability Observed Performance Expected ST Performance Expected LT Performance
STLT
Is theProcess
Predictable?
Can WeMeasure the
Process?
Is theProcess
Capable?
0 10 20 30
120
170
220
Observation Number
I Chart for Viscosit
X=168.0
3.0SL=213.1
-3.0SL=122.9
Source %Study Var
Total Gage R&R 3.72
Repeatability 2.62
Reproducibility 2.64
Part-To-Part 99.93
Total Variation 100.00
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Step 3: Conduct aProcess Description
• Collect data to baseline or benchmark your process
• Find placement of the median, • Find the spread of the data,
x
o
Mean X
Sigma X1
Sigma X2
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Run Basic Statistics
• Display the Minitab worksheet• Path = Stat > Basic Statistics > Display
Descriptive Statistics
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Basic Statistics
• Data needed: Mean & standard deviation for use in later functions
x
6
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Step 4: Search for Special Cause Variation
• Run data in Minitab’s I-MR function• Path = Stat > Control Charts > I-MR
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I-MR Chart
• Count the number of violations of the natural process limits
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Step 5: Conduct the CAPA (Process Capability Analysis)
• Run data using the Process Capability tool• Path = Stat > Quality Tools > Capability
Analysis (Normal)
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Process Stability Analysis
• The Cp index– Ratio of the spec limits to the width of the process– Cp > 2 means the process is stable– Cp = 1 or less means the process is unstable
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Process Stability Analysis
• The Cpk index– Ratio of the process width to the spec width including centering of the spec on the process– Cpk > 1 means the process is capable of meeting spec– Cpk = 1 or less means the process is incapable of meeting spec
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Step 6: Address Common Cause Variation
• There are 2 ways to reduce variation using process experiments
• OVAT (One Variable At a Time)– Move one variable at a time and collect data – Keep that up until all variables are exhausted– Good for problems that have 2 factors at 2 levels– Too slow if trying to integrate more factors
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Design of Experiment-Factorial
• Analyzes up to 32 factors simultaneously• Example: Compare the performances of
Pantone 032 printing ink with variables in printing pressure, printing speed and ink roller loading
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DOE-F
Fast Speed
Slow Speed
Low Pressure
High pressure
High ink loading
Low ink loading
Record % Dot Gain at each factorial point
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• In this plan, the key is in the structure of columns 5, 6, and 7.
DOE-F Control Plan
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DOE-F Control Plan
•Printing pressure is changed randomly
•Press speed alternated every other run
•Ink roller loading is alternated every 4th run
•This combination assures that all combinations of factors are tested and data from each factorial is compared
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DOE-F MTB Chart
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• Repeatability– Variation in the Measurement
Instrument
• Reproducibility– Variation Under Different
Conditions
• Stability– Total Variation over Time
• Accuracy (Bias)– Difference Between the Average of the
Observed and the “True” Value
• Linearity– Difference in Bias in the Operating Range
Reproducibility
Condition A
Condition C
Condition B
Reproducibility
Condition A
Condition C
Condition B
RepeatabilityRepeatability
Stability
Time 1
Time 2
Stability
Time 1
Time 2
True Value
Average
Bias
True Value
Average
Bias
True Value
Average
True Value
Average
Smaller Bias Larger Bias
Lower Values Higher Values
True Value
Average
True Value
Average
Smaller Bias Larger Bias
Lower Values Higher Values
Definitions
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The Measurement Systems Analysis
• We can’t assume that our measurement devices are accurate– If not accurate, that must be known– if uncorrectable, they must be improved
• The Measurement System is a Process– As a Process it Has Variation in its Output
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The MSA
• Treats the measurement system itself as a source of variation in the process
• Test to determine the repeatability and reproducibility of measurement– Seeks to define variation in the process– Separates those variables from other variation in the
process
• Defines the Difference Between the terms Accuracy and Precision
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The Gage R&R
• The measurement system is comprised of:– The units being measured– The gauge or measuring instrument itself– Operators in the measurement process– The measuring methods
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Accuracy vs Precision• Precision (RR)
– Describes Variation and Spread– The Extent to Which the Instrument Repeats its results when Making Repeat Measurements on the Same Unit of Output
• Accuracy (Bias)– Describes Average and Location
• Closeness to the True Value– The Extent to Which the Average of a Long Series of Repeat Measurements Made by the Instrument on a Single Unit of Output Differs from the True Value
– Systematic Error: Contribution to the Total Error Comprised of all Sources of Variation that Tends to Offset Consistently the Results
• Precision and Accuracy are Independent of Each Other– Generally, Separate Actions are Required for Improvement
True Value
Average
Bias
RR
True Value
Average
Bias
RR
True ValueAverage
Bias
RR
Precise, but not Accurate
Accurate, but not Precise
Accurate, and Precise
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Bias and Linearity
• Perform Bias and Linearity Calculations– Bias, or accuracy, is the difference between the
average value of the measurements compared to a known standard
– Linearity, or offset, determines if that bias exists to the same amount or value over the entire operating scale-range of the instrument
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MSA Stability Analysis
• Determine whether the measurement system’s bias drifts over time
• Monitor the sample average and the average moving range over time
• Uses I-MR charts to monitor the stability of your measurement process
• Specifies the accuracy, repeatability and reproducibility of your measurement system
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Ideas to Get You Started
• List what measures you routinely see• Identify the measures you use from the list• Pick three measures you actually use and plot
them on a process behavior chart• Ask yourself if you are collecting the right data• Insist upon analyzing data within their context
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• Filter out the noise of routine variation before analyzing variation– Don’t try to explain noise in the system
• A process that is predictable is performing as consistently as possible right now
• Distinguish between the voice of the process and the voice of the customer
• Take action on assignable causes
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Thanks
• ColorMetrix Technologies LLC– Jim Raffel– Mike Litscher– Mike Woods
• Flint Ink, Inc.– Jeff Gilbert– Craig Stone
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At the End of Today
• ColorMetrix / Minitab Data Breakout Session– Interested in some hands-on??– Mike Woods and Howard Nelson will host a
breakout session for those who would like more info about data analysis