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7/28/2019 -- Basic SPC Tools
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Basic SPC Tools
Presented by
Russell A. Boyles, PhD
Six Sigma Master Black Belt
SPC
Statistical Process Control
Statistical ProcessMonitoring
SPM?
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Mistake proofing
Kanban
Warning systems
Prevent problemsfrom occurring
Visual controls
Periodic audits
Warning systems
Monitor key variables
using statistical controlcharts and documented
response plans
Standardization
Training
Documentation
Visual controls
Periodic audits
Identify and remove causesof problems
Reduce the chance thatproblems will occur
Process Control Strategies
Key Concept in Statistical Monitoring
Common-cause variation:
Assignable-cause variation:
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Systematic
Mistakes, malfunctions, external factors
Occasional large fluctuations
Causes can be determined
Outcomes are not predictable
Random
Inherent in the process
Many small fluctuations
Causes cannot be determined
Outcomes are predictable
Assignable causesCommon causes
Two Kinds of Variation
170
171
172
173
174
175
176
177
Baseline phase
Monitoring phase
Com
moncauses
Assignable cause
Two Kinds of Variation
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5
10
15
20
Baseline phase
Assignable cause
Monitoring phase
Commoncauses
Two Kinds of Variation
5
10
15
20
25
30
35
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Month
No assignable causes!
New manager hasspecial meeting
with CEO!
Manager gets bonus!Manager is reassigned!
New manager makes big improvement!
Customer
complaints
Two Kinds of Variation
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Response Plan Example 1
Assignable cause?
Verify the data
Document
problem
and
solution
Able to fix?
Continue
Verify the gage
Collect and
enter data
Able to diagnose?
Fix the problemEscalate
N
Y
N
N
Y
Y
Assignable
cause?
Take another
sample
Assignable
cause?
Do operator
checklist
Enter into
process log
Call
Technician
Do technician
checklist
Problem
solved?
Start new lot
Call
Engineer
N
Y
N
Y
Y
N
Take sample fromcurrent lot
Problem
solved?YN
Response Plan Example 2
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Most often we use three-sigma limits to distinguish
operationally between assignable causesand common causes
3 + 3
Commoncauses
Baseline distribution of quantity to be monitored
Assignable
causes
Assignable
causes
Calculating Control Limits
If the quantity to be monitored follows a Normal
distribution, there is only a 0.3%
chance of a false alarm
3 + 3
99.7%
Baseline distribution of quantity to be monitored
Calculating Control Limits
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0 2 4 6 8 10 12 14 16 18 20 22
99.4%
Dont need a Normal distribution Three-sigma limits are an economic
compromise between false alarms
and missed signals
0 2 4 6 8 10 12 14
99.0%
0 1 2 3 4 5 6
98.1%
Calculating Control Limits
Control Chart
Upper Control Limit (UCL)
Average
Lower Control Limit (LCL)
+ 3
3
Baseline
distribution of
quantity to be
monitored
Time
Evidence of assignable causes
Evidence of assignable causes
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Regular sigma calculation
Based on deviations from the data average
Time0
5
10
15
20
25
30
35
40
Control limits based on regular sigma
0
5
10
15
20
25
30
35
40
Average = 24.7Standard deviation = 7.1
45
50
Time
Y
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Often there are assignable causes in the baselinedata (trends, outliers, . . . )
In this case, regular sigma is inflated by assignable
causes, and is not an accurate estimate of common-
cause variation
Control limits based on regular sigma are too wide
to detect assignable causes if and when they occur
in the future
Problem with using regular sigma
0
5
10
15
20
25
30
35
40
Based on deviations from the previous data point
Time
Y
Calculating short-term sigma
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Data Avg.
Regular
sigma
Moving
ranges
Avg.
moving
range
Short-term
sigma
15.10 24.7 7.1 -- 3.73 3.30
14.30 0.80
16.70 2.40
23.10 6.40
25.50 2.40
23.00 2.50
28.70 5.70
29.90 1.20
33.90 4.00
32.10 1.80
28.90 3.20
33.40 4.50
29.70 3.70
28.50 1.20
19.90 8.60
12.40 7.50
Calculatingshort-term
sigma
=STDEV() = Avg. moving range / 1.128
0
5
10
15
20
25
30
35
40
Average = 24.7Short- term sigma 3.3
45
50
Time
Y
Control limits based on short-term sigma
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Often there are assignable causes in the baselinedata (trends, outliers, . . . )
Short-term sigma is not inflated by assignable
causes, so it is still an accurate estimate of
common-cause variation
Control limits based on short-term sigma will
detect assignable causes if and when they occur in
the future
Rationale for using short-term sigma
What about specification limits?
Lower
specification
limit
(LSL)
Customers
expectationis met
Upper
specification
limit
(USL)
Customers
expectationis not met
Customers
expectationis not met
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Out-of-specification event
USL
LSL
What do we do?
Well, that depends on
Process Capability
If our process has good capability, it will virtually never produce a
defective outcome, except by assignable cause
Therefore, any defective outcome should trigger the response plan
Of course, we also need to disposition the affected material (scrap,
rework, . . .)
LSL USL
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LSL USL
If our process has bad capability, there will be defective outcomes
that are notassignable causes
Therefore, not all defect outcomes should trigger the response plan
Of course, we still need to disposition the affected material (scrap,
rework, . . .)
Process Capability
Exercise
LSL USL
Indicate in the table below which of the suggested actions are appropriate
for process outcomes in each of the 4 zones shown above.
1 3
3
Do nothing
1
4
2
Scrap, rework or
other disposition ofaffected material
Initiate responseplanZone
LSL USLLCL UCL LCL UCL
2 2 1 1 34 4 1