Transcript Statistical Process Control

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
Minitab Build 13
(MTBWIN)
The Printing Industry
Cycle 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
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
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
• Pressrun
– Material logistics
• Ink, Paper
• Plates, blankets, information
– Makeready
• Register
• Run-up to density
• Approval
– The pressrun
• Count
• Quality
– Finishing
• Folding
• Binding or Filling
• Traffic and shipping
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
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
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
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
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
Generic Process Map
The Improvement Process
•
Map the Process
–
–
•
–
Determine Stability, Precision, Bias
and Linearity
Take Required Improvement Action
Create a Process Behavior Chart
Identify and Remove Special Causes
of Variation if Needed
Assess and Understand Process
Capability
–
–
–
•
Numerically and Graphically
Summarize the Process
Is
Process
Stable?
No
Identify &
Remove
Special Causes
Yes
Assess the
Measurement
System
Assess &
Understand
Process
Capability
Is
Measurement
System
Capable?
Is
Process
Capable?
Assess Process Stability
–
–
•
Create a Sampling Plan
Collect Data on the Process
Assess the Measurement Process
–
•
Measure &
Describe the
Process
Describe the Process
–
•
Define the Scope
Understand the Process Steps
Measure Process Output
–
–
•
Map the
Process
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
Improve the
Measurement
System
No
Yes
Assess
Process
Stability
No
Yes
Create &
Implement
Control Plan
($$$$)
Strategy: Assess then Improve
Reduce
Common
Causes
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
ColorMetrix Density Graph
ColorMetrix Trending
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
Step by Step, Continued
– Step 3: Open the database using
Microsoft Access
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)
Step by Step, Continued
– Step 5: Scroll to, select and copy the data
needed for Minitab
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
To Baseline Your Process…
Measure
Performance
Analyze
Performance
Improve
Performance
Control
Performance
Process Capability Analysis for Viscosity
I Chart for Viscosit
Source
%Study Var
Total Gage R&R
3.72
Repeatability
2.62
Reproducibility
2.64
Part-To-Part
99.93
Total Variation
100.00
Individual Value
220
USL
Target
LSL
Mean
Sample N
StDev (ST)
StDev (LT)
170
190
*
150
168
32
15.0423
14.6132
USL
3.0SL=213.1
ST
LT
X=168.0
Potential (ST) Capability
Cp
0.44
CPU
CPL
Cpk
Cpm
120
0
Can We
Measure the
Process?
LSL
Process Data
10
20
Is the
Process
Predictable?
0.49
0.40
0.40
*
120
140
160
180
200
220
30
Overall (LT) Capability
0.46
PPU
0.50
PPL
0.41
Ppk
0.41
Observation Number
Pp
-3.0SL=122.9
Is the
Process
Capable?
Observed Performance
PPM < LSL
93750.00
PPM > USL
93750.00
PPM Total
187500.00
Expected ST Performance
PPM < LSL
115726.66
PPM > USL
71796.65
PPM Total
187523.31
Expected LT Performance
PPM < LSL
109018.53
PPM > USL
66099.06
PPM Total
175117.59
Step 3: Conduct a
Process Description
• Collect data to baseline or benchmark
your process
• Find placement of the median,
x
• Find the spread of the data,
o
Sigma X1
Sigma X2
Mean X
Run Basic Statistics
• Display the Minitab worksheet
• Path = Stat > Basic Statistics >
Display Descriptive Statistics
Basic Statistics
• Data needed: Mean & standard
deviation for use in later functions
x
6
Step 4: Search for Special
Cause Variation
• Run data in Minitab’s I-MR function
• Path = Stat > Control Charts > I-MR
I-MR Chart
• Count the number of violations of the
natural process limits
Step 5: Conduct the CAPA
(Process Capability Analysis)
• Run data using the Process Capability tool
• Path = Stat > Quality Tools > Capability
Analysis (Normal)
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
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
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
Design of ExperimentFactorial
• 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
DOE-F
Record % Dot
Gain at each
factorial point
High ink loading
Low ink loading
High pressure
Low Pressure
Fast Speed
Slow Speed
DOE-F Control Plan
• In this plan, the key is in the structure of
columns 5, 6, and 7.
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
DOE-F MTB Chart
Definitions
• Stability
– Total Variation over Time
•
Repeatability
– Variation in the Measurement
Instrument
Time 2
Time 1
• Accuracy (Bias)
Stability
– Difference Between the Average of
the Observed and the “True” Value
Repeatability
• Reproducibility
Bias
– Variation Under Different
Conditions
• Linearity
True Value
– Difference in Bias in the Operating
Range
Average
Condition A
True Value
Condition B
True Value
Smaller Bias
Larger Bias
Condition C
Average
Average
Lower Values
Higher Values
Reproducibility
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
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
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
Accuracy vs Precision
RR
•
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
Average
Bias
True Value
Precise, but not Accurate
RR
Average
Bias
True Value
•
Accurate, but not Precise
Bias
RR
Average
True Value
Accurate, and Precise
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
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
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
• 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
Thanks
• ColorMetrix Technologies LLC
– Jim Raffel
– Mike Litscher
– Mike Woods
• Flint Ink, Inc.
– Jeff Gilbert
– Craig Stone
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