GE/Tarpley

Dec 1, 2010

Tools For Recognizing

And Quantifying Process Drift

Statistical Process Control

(SPC)

J. Scott TarpleyGE Intelligent Platforms, Inc.

December 1, 2010

GE/Tarpley

Dec 1, 2010

Timely Data Analyses

Improved Measurement Capability

- Precision & Accuracy

Increased Sample Sizes

Process Analytical Technology (PAT) brings us…

More data, but what do we do with it??

Significant mistakes without proper understanding!

GE/Tarpley

Dec 1, 2010

SPC Basics and Special Cause Tests

Common Application Mistakes

Time-Ordered and Global Data Analyses

Topics of Discussion

Summary – Q&A

GE/Tarpley

Dec 1, 2010

The components of a Quincunx:Hopper of beads which allows a “drop”

of one bead at a time with the target

determined by the “operator”

4 5 6 7 8 9 10 11 12 13 14 15

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Dec 1, 2010

The process can shift by

moving the hopper as needed

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The components of a Quincunx:

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Dec 1, 2010

The pins represent common

causes of variation!

4 5 6 7 8 9 10 11 12 13 14 15

The components of a Quincunx:

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Dec 1, 2010

The bins represent the

measurement or result.

4 5 6 7 8 9 10 11 12 13 14 15

The components of a Quincunx:

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Dec 1, 2010

4 5 6 7 8 9 10 11 12 13 14 15

What if one pin looked like this –

Is this one a “common” or “special” cause?

(i.e., does it have a “special” influence on the process?)

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Dec 1, 2010

What other ways could special causes be created

(or identified) on this quincunx?

4 5 6 7 8 9 10 11 12 13 14 15

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Dec 1, 2010

Global Analysis versus Time-Order Analysis

A global analysis of the data does not take time ordering into account.

For example, the resulting histogram or dot plot of data from the results of

1,000 dropped beads in the quincunx would be a “global” analysis of data.

Let’s take a look at the potential risk of missing important process behavior

or process performance information by only looking at the data from a global

viewpoint – as opposed to considering the analysis in time order.

In 1924, Dr. Walter Shewhart drew his first control chart to study this process

behavior considering time-ordering instead of a global analysis.

* data on following 3 slides from the book, “Normality and the Process Behavior Chart”, written by Dr. Donald J. Wheeler

GE/Tarpley

Dec 1, 2010

This is an example of “global analysis” – not in time order!

What did we learn about process behavior?

Median

Mean

10.610.410.210.0

A nderson-Darling N ormality Test

V ariance 6.076

Skew ness 0.029882

Kurtosis -0.529051

N 192

M inimum 5.000

A -Squared

1st Q uartile 8.250

M edian 10.000

3rd Q uartile 12.000

M aximum 15.000

95% C onfidence Interv al for M ean

9.883

1.66

10.585

95% C onfidence Interv al for M edian

10.000 10.069

95% C onfidence Interv al for S tDev

2.241 2.740

P -V alue < 0.005

M ean 10.234

S tDev 2.465

9 5 % C onfidence Inter vals

14121086

Summary for Wheeler Quincunx Example

GE/Tarpley

Dec 1, 2010

The same data in time order – what do we learn?

Sample

Sa

mp

leM

ea

n

464136312621161161

15.0

12.5

10.0

7.5

5.0

__X=10.23

U C L=13.03

LC L=7.44

Sample

Sa

mp

leR

an

ge

464136312621161161

8

6

4

2

0

_R=3.839

U C L=8.757

LC L=0

Xbar-R Chart of Wheeler Quincunx Example

GE/Tarpley

Dec 1, 2010

SPC Basics and Special Cause Tests

Common Application Mistakes

Time-Ordered and Global Data Analyses

Topics of Discussion

Summary – Q&A

GE/Tarpley

Dec 1, 2010

Elements of a Control Chart…

The Mean is in the middle and the Upper (UCL) and Lower (LCL)

Control Limits are defined by going up and down (+/-)

3 standard deviations from the mean as the general formula for CL’s.

Control limits are the VOICE OF THE PROCESS (VOP)!!!

Mean

UCL

LCL

+3s

-3s

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Dec 1, 2010

Traditional Tests for Special Causes:

Rule 1

Any data point outside the Control Limits

Mean

UCL

LCL

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Dec 1, 2010

Traditional Tests for Special Causes:

Rule 2

6 Consecutive Data Points Increasing or Decreasing (“Trend”)

Mean

UCL

LCL

*Notice how none of the data points are outside the CL’s!!!

GE/Tarpley

Dec 1, 2010

Traditional Tests for Special Causes:

Rule 3

8 Consecutive Data Points On One Side of the Centerline (“Shift”)

Mean

UCL

LCL

A shift is often the result of a “SHOCK” to the system – new batch of materials,

maintenance, shift change, process improvement implementation, etc.

GE/Tarpley

Dec 1, 2010

Traditional Tests for Special Causes:

Rule 4

14 Consecutive Data Points Alternating Up and Down (“Sawtooth”)

Mean

UCL

LCL

A sawtooth is often the result of a constant “tweaking” of the process –

material usage shift-to-shift, overreaction to natural variability, etc.

GE/Tarpley

Dec 1, 2010

Additional Tests for Special Causes:

5. 2 out of 3 data points more than 2 standard deviations from the centerline (same side)

6. 4 out of 5 data points more than 1 standard deviation from the centerline (same side)

7. 15 data points consecutively within +/- 1 standard deviation of the centerline (either side)

8. 8 data points consecutively outside +/- 1 standard deviation of the centerline (either side)

Mean

+1s

+2s

-1s

-2s

UCL

LCL

These tests are typically used for more advanced control

GE/Tarpley

Dec 1, 2010

SPC Basics and Special Cause Tests

Common Application Mistakes

Time-Ordered and Global Data Analyses

Topics of Discussion

Summary – Q&A

GE/Tarpley

Dec 1, 2010

Control charting “myths”

A. “Data must be normally distributed before a control

chart can be used”

B. “Individual data must be independent and have no

auto-correlation with other collected data”

C. “Data must be in control before a control chart

can be utilized”

GE/Tarpley

Dec 1, 2010

Common mistakes

1. Treating a common cause of variation as if it is special

(Deming called this “tampering”)

2. Ignoring a special cause of variation

(Treating as if common)

3. Using specification limits as control limits

(Spec limits are the VOC – voice of the customer)

4. Do not refresh control limits often enough

(Data from many years ago affecting current process)

Design the control charts to “scream” at

you whenever and however you desire

GE/Tarpley

Dec 1, 2010

Common mistakes

5. Too much data

0 1000 2000

30

40

50

Observation Number

Indiv

idual V

alu

e

I Chart for Dissolution

11 1

1

44

4

4

4

1

X=40.04

3.0SL=48.94

-3.0SL=31.14

GE/Tarpley

Dec 1, 2010

Common mistakes

6. Focusing on select few data points in time

(Same month year before and previous month)

Nov 2009 Oct 2010 Nov 2010

Potential for being fooled into believing no change

and missing out on important process information

GE/Tarpley

Dec 1, 2010

Common mistakes

7. Measurement system not granular enough

Ind

ivid

ua

l Va

lue

10997857361493725131

2.0

1.8

1.6

UCL=1.9573

LCL=1.6289

_X=1.7931

Mo

vin

g R

an

ge

10997857361493725131

0.2

0.1

0.0

__MR=0.0617

UCL=0.2017

LCL=0

Observation

Va

lue

s

11511010510095

1.9

1.8

1.7

2.12.01.91.81.71.6

LSL USL

LSL 1.6

USL 2.1

Specifications

1.951.801.651.50

Within

O v erall

Specs

StDev 0.0547333

C p 1.52

C pk 1.18

Within

StDev 0.0670318

Pp 1.24

Ppk 0.96

C pm *

O v erall

Process Capability Sixpack of SPECIFIC VOLUME (AS IS)

I Chart

Moving Range Chart

Last 25 Observations

Capability Histogram

Normal Prob Plot

A D: 10.660, P: < 0.005

Capability Plot

GE/Tarpley

Dec 1, 2010

SPC Basics and Special Cause Tests

Common Application Mistakes

Time-Ordered and Global Data Analyses

Topics of Discussion

Summary – Q&A

GE/Tarpley

Dec 1, 2010

Suggestions

1. Develop a rational subgrouping strategy

2. Invest in shop floor SPC software

3. Institute a broad QC Plan

4. Deploy the correct control charts (I/MR, X-Bar/R, p, etc.)

5. Avoid mass deployment overnight

6. Proper training is critical

7. Like all initiatives, leadership is required!!!

In

div

idu

al V

alu

e

1009080706050403020101

300.5

299.5

298.5

_X=299.603

UCL=301.054

LCL=298.153

Mo

vin

g R

an

ge

1009080706050403020101

2

1

0

__MR=0.545

UCL=1.782

LCL=0

Observation

Va

lue

s

10095908580

300.0

299.6

299.2

300.60300.15299.70299.25298.80

301300299298

Within

Overall

Specs

Within

StDev 0.48356

C p 10.34

C pk 10.07

C C pk 10.34

O v erall

StDev 0.47714

Pp 10.48

Ppk 10.20

C pm *

1

Process Capability Sixpack of Core Weight

I Chart

Moving Range Chart

Last 25 Observations

Capability Histogram

Normal Prob Plot

A D: 0.310, P: 0.550

Capability Plot

Example

In

div

idu

al V

alu

e

464136312621161161

104

96

88

_X=93.51

UCL=101.98

LCL=85.04

Mo

vin

g R

an

ge

464136312621161161

10

5

0

__MR=3.18

UCL=10.40

LCL=0

Observation

Va

lue

s

5045403530

100

95

90

10096928884

1009080

Within

O v erall

Specs

Within

StDev 2.82259

C p *

C pk 1

C C pk 1

O v erall

StDev 4.07004

Pp *

Ppk 0.7

C pm *

1

1

2

222

222

1

Process Capability Sixpack of Dissolution 20 min Low

I Chart

Moving Range Chart

Last 25 Observations

Capability Histogram

Normal Prob Plot

A D: 0.390, P: 0.369

Capability Plot

Example

GE/Tarpley

Dec 1, 2010

Contact Info

Thank you for your attention! I welcome your feedback!

Scott Tarpley

Product Quality Manager

GE Intelligent Platforms, Inc.

Embedded Systems Business

(Phone) 01-434-249-6653

(email) james.tarpley@ge.com

Visit GEIP on the net at:

www.ge-ip.com