SPC
description
Transcript of SPC
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”
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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!
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The components of a Quincunx:
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Dec 1, 2010
The bins represent the
measurement or result.
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The components of a Quincunx:
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Dec 1, 2010
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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?
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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
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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
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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!!!
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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.
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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.
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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”
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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
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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
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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
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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) [email protected]
Visit GEIP on the net at:
www.ge-ip.com