8 ssgb amity bsi capability
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Module-8
Process Capability
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At the end of this section delegates will be able to:
• Recognise the difference between process controland process capability
• Carry out capability studies on variable and
attribute data• Assess and interpret the data from capability
studies
• Convert data from capability studies into DPM orCpk values
Process Capability Learning Objectives
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1. Process Control v Process Capability
2. Process Capability – Variable Data3. Capability Study – 6 steps
4. Capability Study – Minitab
5. Workshop6. Process Capability – Non-Normal Data
7. Process Capability – Attribute Data
8. Summary
Process Capability Agenda
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Process Control
• Process control refers tothe evaluation of processstability over time
• Process Capability refersto the evaluation of how
well a process meetsspecifications
LSL USL
0 5 10 15 20 25
Time
UCL
LCL
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15 20 25 30 35
LSL USL
Define Measures (y’s)
Check Data Integrity
Determine ProcessStability
Determine ProcessCapability
Set Targets forMeasures
Phase Review
Control Critical x’s
Monitor y’s
Validate ControlPlan
Identify furtheropportunities
Close Project
1 5 10 15 20
10.2
10.0
9.8
9.6
Upper Control Limit
Lower Control Limit
y
Phase Review
Develop DetailedProcess Maps
Identify CriticalProcess Steps (x’s)by looking for:
– Process Bottlenecks– Rework / Repetition
– Non-value AddedSteps
– Sources of Error /Mistake
Map the Ideal
Process
Identify gapsbetween current andideal
START
PROCESS
STEPS
DECISION
STOP
Phase Review
Brainstorm PotentialImprovement Strategies
Select ImprovementStrategy
Plan and ImplementPilot
Verify Improvement
ImplementCountermeasures
Criteria A B C D
Time + s - +
Cost + - + s
Service - + - +
Etc s s - +
15 20 25 30 35
LSL USL
Phase Review
Analyse Improve ControlMeasureDefine Select Project
Define Project
Objective
Form the Team
Map the Process
Identify CustomerRequirements
Identify Priorities
Update Project File
Phase Review
Define
Six Sigma Transactional Improvement Process
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Process Capability – Variable Data
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“Special” causesof variation arepresent
T i m e
TotalVariation
Target
Unstable Process
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T i m e
Target
TotalVariation
Only “Common”causes of variationare present
Stable Process
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T i m
e
Spec Limits
CAPABLE
NOTCAPABLE
Process is Stable butProcess is not Capable
Process is Stable andProcess is Capable
Capable Process
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• Defined as:
• USL-LSL represents the width of the spec
• 6σσσσWithin represents width of process (±3σσσσ)
−−−−≈≈≈≈
−−−−====
2
p
d
R6
LSLUSL
6σσσσWithin
LSLUSLC
Process Capability, Cp Index
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LSL
USL
Cp = 0.5 Cp = 1 Cp = 2Cp = 1.5
Process Capability, Cp Index
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• Defined as:
• Numerator represents the distance
to the nearest spec
• Denominator represents the amount of this distance
consumed by variation
(((( ))))
3σσσσWithin
LSLX,XUSLminCpk
−−−−−−−−====
d2
RσσσσWithin ≈≈≈≈
Process Capability, Cpk Index
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LSLUSL
Numerator
Target
Denominator
Process Capability, Cpk Index
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LSL
USL
Cpk
= 2
Cpk = 0.5
Cpk = 0.5Cpk = 1
Cpk = 1
Cpk = 1.5
Cpk = 1.5
Cpk When Cp=2
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LSL
USL
Cp= 1Cp= 2
Cp= 2
Cp= 4
Cpk = 1
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• Cpk ≤≤≤≤ Cp
• Cpk = Cp only when the process is perfectly centred
• Cp represents the highest possible value for Cpk
Cpk and Cp
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• Cp is not defined – requires 2 spec limits
• Cpk can still be calculated – requires only one speclimit
One Spec Limit
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• Determines if process is stable and capable, i.e. can
it consistently produce good outputs.
• Measures success and progress achieved using the
other tools.
20
10
0
74.8 74.9 75.0 75.1 75.2 75.3
Response Time
30
NUMBE
R
USLLSL
Capability Study
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Capability Study – 6 Steps
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1. Collect data
2. Draw Control Charts
3. Interpret charts and draw conclusions
4. Draw histogram5. Test for Normality
6. Calculate Cp and Cpk and draw conclusions
Capability Study
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(1) Collect Data
Month No. of Month No. of
number absences number absences
1 48 21 51
2 56 22 59
3 63 23 42
4 51 24 47
5 39 25 60
6 45 26 41
7 56 27 52
8 38 28 45
9 45 29 61
10 41 30 6911 57 31 53
12 53 32 60
13 64 33 57
14 54 34 51
15 58 35 55
16 65 36 42
17 42 37 37
18 48 38 48
19 57 39 46
20 66 40 38
• Collect data over sufficient
period to capture knownsources of variation
• In this example, 40 months
of data on absences has
been captured
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• Open Worksheet Absences.MTW
• Stat> Control Charts> Variables Charts for
Individuals>I-MR
(2) Draw Control Charts
Enter absence as
variable
Select I-MR
Options
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(2) Draw Control Charts
Select Median
Moving Range
Select Estimate
Select Moving
Range of 2
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(2) Draw Control Charts
Observation
I n d i v i d u a
l V a
l u e
403632282420161284
70
60
50
40
30
_ X=51.5
UCL=76.66
LCL=26.34
Observation
M o v
i n g
R a n g e
403632282420161284
30
20
10
0
__ MR=9.46
UCL=30.91
LCL=0
I-MR Chart of absence
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(3) Interpret Control Charts and Draw Conclusions
• The control limits representthe range of valuesexpected for the individual
values and the movingrange, if the process isstable
• Points outside the control
limits indicate the processis possibly unstable
Conclusions• The process is stable
Observation
I n d i v i d u a
l V a
l u e
403632282420161284
70
60
50
40
30
_ X=51.5
UCL=76.66
LCL=26.34
Observation
M o v
i n g R a n g e
403632282420161284
30
20
10
0
__ MR=9.46
UCL=30.91
LCL=0
I-MR Chart of absence
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(4) Draw Histogram
absence
F r e q u e n c y
72666054484236
10
8
6
4
2
0
Histogram of absence
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(5) Normal Probability Plot
absence
P
e r c e n t
7060504030
99
95
90
80
70
60
50
40
30
20
10
5
1
Mean
0.533
51.5
StDev 8.641
N 40
A D 0.314
P-Value
Probability Plot of absenceNormal
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1. A shift occurred in the middle of the data
2. Mixed populations3. Truncated data
4. Rounding to a small number of values
5. Outliers6. Too much data
7. The underlying distribution is not normal
Reasons for Failing a Normality Test
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Open Worksheet: Absences
(6) Calculate Cp and Cpk
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1. Enter absence
4. Select Estimate
3. Enter Specs:
LSL = 0 (Boundary)
USL = 50
(6) Calculate Cp and Cpk
2. Enter Subgroup
Size (1)
5. Select Options
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Select:
Median moving range
(6) Calculate Cp and Cpk
Select:
Moving range of length 2
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(6) Calculate Cp and Cpk
Select “Include
confidence intervals”
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(6) Calculate Cp and Cpk
7260483624120
LB USL
LB 0
Target *
USL 50
Sample Mean 51.5
S ample N 40
StDev (Within) 8.38574
StDev (Overall) 8.69655
Process Data
Low er C L *
U pper C L *
PPL *
PPU -0.06
Ppk -0.06
Lower CL -0.16
Upper CL 0.05
Cpm *
Low er C L *
Cp *
Low er C L *
U pper C L *
CPL *
C PU -0.06
C pk -0.06
Lower CL -0.16
Upper CL 0.04
Pp *
Ov erall Capability
Potential (Within) Capability
PP M < LB 0.00
PPM > USL 575000.00
PPM Total 575000.00
Observed Performance
PPM < LB *
PPM > USL 570982.07
PPM Total 570982.07
Exp. Within Performance
PPM < LB *
PPM > USL 568470.77
PPM Total 568470.77
Exp. O verall P erformance
Within
Overall
Process Capability of absence(using 95.0% confidence)
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Capability Analysis Individuals – Sixpack
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1. Enter absence
4. Select Estimate
2. Enter Subgroup Size
3. Enter Specs
Capability Analysis Individuals – Sixpack
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Capability Analysis Individuals – Sixpack
Select:
Median moving range
Select:
Moving range of length 2
C bilit Si k Ab
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Capability Sixpack – Absences
I n d i v i d u a
l V a
l u e
37332925211713951
80
60
40
_ X=51.5
UCL=76.66
LCL=26.34
M o
v i n g
R a n g e
37332925211713951
30
15
0
__ MR=9.46
UCL=30.91
LCL=0
Observation
V a
l u e s
4035302520
70
55
40
7260483624120
LSL USL
LS L 0
USL 5 0
Specifications
80604020
Within
Overall
Specs
StDev 8.38574
C p 0.99
Cpk -0 .0 6
Within
StDev 8.69655
P p 0.96
Ppk -0.06
C pm *
Overall
Process Capability Sixpack of absenceI Chart
Moving Range Chart
Last 25 Observations
Capability Histogram
Normal Prob Plot AD : 0.314, P: 0.533
Capability Plot
D C l i
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Draw Conclusions
• This process is stable, but more than 50% of
months have absences in excess of the target of no
more than 50• To improve this process will require detailed
investigation of all of the reasons for absences
I di id l D t W k h
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Individuals Data Workshop
• Open the Payment Times Worksheet
• Perform a Stability and Capability study on the
process assuming that the specification is for
payments to be made within 72 hours
• Draw conclusions
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Process Capability - Non-normal Data
R f F ili N lit T t
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1. A shift occurred in the middle of the data
2. Mixed populations3. Truncated data
4. Rounding to a small number of values
5. Outliers6. Too much data
7. The underlying distribution is not normal
Reasons for Failing a Normality Test
Non Normal or Unstable Data Sets
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Non - Normal or Unstable Data Sets
• Some data sets are non-normal because the
underlying distribution is non-normal
• If the process is non-normal or unstable, we canestimate the process capability by calculating the
percentage of occurrences which fall outside of the
specification• This can be shown graphically in a histogram with the
specification drawn on
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Process Capability – Attribute Data
Target 3 4 dpm
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3.4 defects per million(99.9997% Good Quality)
Target – 3.4 dpm
C / DPM Conversion Table
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• We need a minimum Cpk value of 1.5 to achieve 3.4 DPM
when we have a stable process• We can test for stability and calculate Cpk for variable
data using the Capability Study described earlier
Cpk / DPM Conversion Table
Capability Study – Binomial Data
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• Over 10 working days a random sample of orders
were checked for accuracy
• A pass / fail criteria was used for assessment
Capability Study – Binomial Data
Order Accuracy Data
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Day Sample Size Defectives
1 87 0
2 95 13 76 0
4 124 1
5 69 2
6 86 0
7 77 1
8 98 0
9 87 010 96 1
Order Accuracy Data
Minitab – Binomial Capability
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Open Worksheet: “TSS Binomial Capability”Select: Stat> Quality Tools>Capability Analysis>Binomial
Minitab Binomial Capability
Minitab Output
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Minitab Output
Sample
P r o p o r t i o n
10987654321
0.03
0.02
0.01
0.00
_ P=0.00670
UC L=0.03169
LC L=0
Sample
% D e f e c t i v e
108642
1.6
1.2
0.8
0.4
0.0
Summary Stats
0.00
PPM Def: 6704Low er C I: 2464
Upper C I : 14534
Process Z: 2.4727
Lower CI:
(using 95.0% confidence)
2.1826
Upper CI : 2.8117
%Defective: 0.67
Low er C I: 0.25
Upper C I: 1.45
Target:
Sample Size
% D e f e c t i v e
12010080
6
4
2
0
3.02.52.01.51.00.50.0
4.8
3.6
2.4
1.2
0.0
Tar
Binomial Process Capability Analysis of DefectivesP Chart
Tests performed with unequal sample sizes
Cumulative %Defective
Rate of Defectives
Dist of %Defective
P – Chart
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The P-Chart indicates that the process is stable
(no points outside the control limits)
P Chart
Summary Statistics
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This gives the actual PPM Defective (DPM) = 6704This value can be converted to an approximate Cpk
Value using the Cpk /DPM conversion table
A 95% confidence interval is returned for DPM(2464 to 14534)
The confidence interval assumes the process is stable
Su a y Stat st cs
Capability Study – Poisson Data
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• The data on the next page was collected from 20
consecutive working days
• The data refers to the number of errors found indaily transactions
p y y
Transactional Errors Data
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Day Trans Errors
1 326 3
2 543 13 456 2
4 301 1
5 297 1
6 420 07 421 1
8 564 3
9 289 0
10 378 3
Day Trans Errors
11 345 5
12 432 413 573 4
14 387 2
15 361 1
16 489 217 317 0
18 379 1
19 423 2
20 499 4
Minitab – Poisson Capability
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Open Worksheet: “TSS Poisson Capability”Select: Stat> Quality Tools>Capability Analysis>Poisson
p y
Minitab Output
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p
Sample
S a m p l e
C o u n
t P e r
U n
i t
191715131197531
0.015
0.010
0.005
0.000
_
U=0.00488
UC L=0.01426
LCL=0
Sample
D P U
2015105
0.010
0.008
0.006
0.004
Summary Stats
0.0000
Max DPU: 0.0145Targ DPU: 0.0000
(using 95.0% confidence)
Mean DPU: 0.0049
Lower CI: 0.0035
Upper CI: 0.0066
Min DPU :
Sample Size
D P U
500400300
0.015
0.010
0.005
0.000
0 . 0 1 4
0 . 0 1 2
0 . 0 1 0
0 . 0 0 8
0 . 0 0 6
0 . 0 0 4
0 . 0 0 2
0 . 0 0 0
4.8
3.6
2.4
1.2
0.0
Tar
Poisson Capability Analysis of ErrorsU Chart
Tests performed with unequal sample sizes
Cumulative DPU
Defect Rate
Dist of DPU
U – Chart
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The U-Chart indicates that the process is stable
(no points outside the control limits)
Summary Statistics
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Yield = e–DPU = e–0.00488 = 0.995131
This is equivalent to a DPM of 4869
Our DPM of 4869 can be converted to anapproximate Cpk Value using the Cpk /DPM
conversion table
A 95% confidence interval is returned for DPU
(0.0035 to 0.0066)
The confidence interval assumes the process is stable
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Process Capability – Summary
Variable v Attribute Data
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• Variable Data allows us to calculate capability indices(Cp, Cpk) directly
• Achieving a stable process with a Cpk value of 1.5 or
more at the end of our project means that we haveachieved 6 Sigma
• Achieving a Cpk value of 1.5 for a variable measure (y)would mean that we would have 3.4 DPM or less
• The Six Sigma target for each attribute measure is thesame – 3.4 DPM or less
Capability Metrics
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Process
Output
yAttribute
Data
Variable
Data
Defective Items
Defects per Unit
DPM
Cp
Cpk
Role of Capability Studies
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• Capability studies are the primary tool for managing
variation reduction. They act as the scorecard.
• Capability studies help characterise the type of problem
(unstable, off-target, excessive variation) guiding which
tools and strategies to apply.
Six Sigma Transactional Improvement Process
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15 20 25 30 35
LSL USL
Define Measures (y’s)
Check Data Integrity
Determine ProcessStability
Determine Process
Capability
Set Targets forMeasures
Phase Review
Control Critical x’s
Monitor y’s
Validate ControlPlan
Identify furtheropportunities
Close Project
1 5 10 15 20
10.2
10.0
9.8
9.6
Upper Control Limit
Lower Control Limit
y
Phase Review
Develop DetailedProcess Maps
Identify CriticalProcess Steps (x’s)by looking for:
– Process Bottlenecks
– Rework / Repetition– Non-value Added
Steps
– Sources of Error /Mistake
Map the IdealProcess
Identify gapsbetween current andideal
START
PROCESSSTEPS
DECISION
STOP
Phase Review
Brainstorm PotentialImprovement Strategies
Select ImprovementStrategy
Plan and ImplementPilot
Verify Improvement
ImplementCountermeasures
Criteria A B C DTime + s - +
Cost + - + s
Service - + - +
Etc s s - +
15 20 25 30 35
LSL USL
Phase Review
Analyse Improve ControlMeasureDefine Select Project
Define Project
Objective
Form the Team
Map the Process
Identify Customer
Requirements
Identify Priorities Update Project File
Phase Review
Define
DMAIC – Measure Phase
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• Capability studies can be used to determine:
• process stability
• process capability• the nature of the problem, e.g. unstable process,
off-target, excessive variation, outliers
DMAIC – Improve Phase
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• Capability Studies can be used to determine the
stability and capability of critical x’s
• Capability Studies can be used to verify processimprovement
• Capability studies measure the current state of the
processes, track progress and are used todemonstrate improvements
• They measure the success of the other variation
reduction tools and processes
DMAIC – Control Phase
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• In conjunction with the use of control charts,
process capability can be used during the
control phase to:
• Control critical x’s
• Confirm process capability during Process
Validation
• Ensure effective long-term monitoring of the y’s
(measures)
Process Capability – Summary
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• Long-term studies are more reliable than short-term
studies. We must ensure that a sufficient time period is
sampled.
• Always check whether the process is stable before or
during a capability analysis. Special causes should be
removed, or at least understood, before continuing.
• Test for normality. Some types of data will requiredifferent capability analyses.
• If the process is stable and a sufficient time period is taken,
then achieving a Cpk of 1.5 at the end of your projectmeans that you have achieved the Six Sigma Target!