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 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

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

• 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

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…

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

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

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 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

DOE-F

Fast Speed

Slow Speed

Low Pressure

High pressure

High ink loading

Low ink loading

Record % Dot Gain at each factorial point

• In this plan, the key is in the structure of columns 5, 6, and 7.

DOE-F Control Plan

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

• 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

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• 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

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