Post on 08-Apr-2018
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MINITAB Applications in Six
Sigma
AgendaAgenda
13:0013:00--13:30 Registration13:30 Registration
13:3013:30--14:20 MSA14:20 MSA--MINITABMINITAB
14:2014:20--14:30 Break14:30 Break
14:3014:30--15:20 SPC15:20 SPC--MINITABMINITAB
15:2015:20
--15:30 Break15:30 Break
15:3015:30--16:20 Case study (Healthcare & Banking)16:20 Case study (Healthcare & Banking)
16:2016:20--16:30 Q&A16:30 Q&A
July 14, 2006 in TaipeiJuly 14, 2006 in Taipei
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MSA-MINITAB
Application inApplication in
(Measurement Systems Analysis, MSA)(Measurement Systems Analysis, MSA)
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Measuring System.
What is a Measuring System?What is a Measuring System?
The measuring device (Repeatability)The measuring device (Repeatability)
The person who is taking the measurementThe person who is taking the measurement
(Reproducibility)(Reproducibility)
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Measurement Process
The ideal measurement system produceThe ideal measurement system produce truetruemeasurements every timemeasurements every time
Quality of the measurement system is characterized byQuality of the measurement system is characterized by
statistical propertiesstatistical properties
PropertiesProperties
Must be inMust be in Statistical ControlStatistical Control Variability must be smallVariability must be small compared to productcompared to product
specificationsspecifications
Variability must be smallVariability must be small comparedcomparedprocess variationprocess variation
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Basic Model
2 2 2Total Product Measurement System = +
The Total Variation is equal to the real productvariation plus the variation due to the
measurement system
Mi i E i
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Minita Exercise
Capability and Measurement
Error Assume we have a process that has anAssume we have a process that has an
actualactual sigma of 5 unitssigma of 5 units and aand a mean of 70mean of 70
unitsunits
Also assume we have a measurementAlso assume we have a measurement
system that has the same measurement errorsystem that has the same measurement error
as the processas the process -- sigma of 5 unitssigma of 5 units
LetLets use Minitab to simulate the effect ofs use Minitab to simulate the effect of
measurement error on process capabilitymeasurement error on process capability
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Exercise - Continued
Follow these methodsFollow these methodsto create data to fit ourto create data to fit our
process model:process model:Creates a randomnormal distribution
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Exercise - Continued
What value should we usefor the mean of the
measurement error?
Calculate observed
process behavior.
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11010090807060504030
15
10
5
0
Observed
Frequency
LSL USL
ActualActualprocess variation -NoNomeasurement error
ObservedObserved processvariation -
WithWithmeasurement error
11010090807060504030
15
10
5
0
Process
Frequency
LSL USL
Results of Simulations
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Effects of Measurement Error
AveragesAverages
VariabilityVariability
total product measurement 2 2 2
= +
total product measurement= +
Measurement SystemVariability - Determinedthrough R&R Study
AccuracyAccuracy
PrecisionPrecision
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Sources of VariationProduct Variability
(Actual variability)
Product Variability
(Actual variability)
Measurement
Variability
Measurement
Variability
Total Variability
(Observedvariability)
Total Variability
(Observedvariability)
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Two General Kinds of Data
ATTRIBUTE - Discrete, Counted Data
Ex: 1, 2, 3, 4 etcGood/Bad, Go/NoGo, Pass/Fail
Machine 1 , 2 , 3 ...
VARIABLES - Continuous, Measured Data
Ex: Weight = 10.2 Lbs
Thickness = 11.211 inches
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10%10%
30%30% 30%30%
P/T
Ratio
P/T
Ratio %R&R%R&R
Red
Ye
llow
Green
15%15%
Main Question - Is my measurement system okay to use on my project?
GR&R Metrics
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Part Operator Response
1 1 0.65
1 1 0.60
2 1 1.002 1 1.00
3 1 0.85
3 1 0.80 ...
MSA Exercise
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Minitab - Gage R&R Studies
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Minitab - Gage R&R Studies
GAGE Options
If There Is A
Tolerance For
This Part -
Put It Here
General Info
On Your Study
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Misc:
Tolerance:
Reported by :
Date of study:Gage name:
0
1.1
1.0
0.9
0.8
0.70.6
0.5
0.4
0.3
321
Xbar Chart by Operator
Sample
Mean
Mean=0.8075UCL=0.8796
LCL=0.7354
0
0.15
0.10
0.05
0.00
321
R Chart by Operator
Sample
Range
R=0.03833
UCL=0.1252
LCL=0
10987654321
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
Part
OperatorOperator*Part Interaction
Average
1
2
3
321
1.1
1.0
0.9
0.8
0.70.6
0.5
0.4
Operator
By Operator
10987654321
1.1
1.0
0.9
0.8
0.70.6
0.5
0.4
Part
By Part
%Contribution
%Study Var
%Tolerance
Part-to-PartReprodRepeatGage R&R
200
100
0
Components of Variation
Percent
Gage R&R (ANOVA) for Response
Minitab - Output
Expected To
Be Out of Control
%R&R &
P/T Ratios
Gives Us The 1st
Look At
Discrimination
Graphical View
Of Results
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What decision do we make?
Minitab - Output
%P/TBest case: 10% Acceptable: 30%
% R&R
As a target, look for %R&R < 30%
Gage R&RStdDev Study Var %Study Var %Tolerance
Source (SD) (5.15*SD) (%SV) (SV/Toler)
Total Gage R&R 0.066615 0.34306 32.66 68.61Repeatability 0.035940 0.18509 17.62 37.02
Reproducibility 0.056088 0.28885 27.50 57.77
Operator 0.030200 0.15553 14.81 31.11
Operator*Part 0.047263 0.24340 23.17 48.68
Part-To-Part 0.192781 0.99282 94.52 198.56
Total Variation 0.203965 1.05042 100.00 210.08
Number of Distinct Categories = 4
%P/T% R&R
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Reducing recruiting costs Due to high costs and significantDue to high costs and significant
time needed to screen resumestime needed to screen resumes
submitted electronically, the teamsubmitted electronically, the team
decides to evaluate the screeningdecides to evaluate the screeningprocess for resumes submitted viaprocess for resumes submitted via
the web.the web. Think about the response. ResumesThink about the response. Resumes
are evaluated by team membersare evaluated by team members--what type of data is this?what type of data is this?
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Attribute Agreement Analysis
Binary responseBinary response-- Pass/Fail.Pass/Fail. A resume either proceeds to theA resume either proceeds to the
next level or not.next level or not. 4 people are responsible for4 people are responsible for
screening resumes. Each isscreening resumes. Each ispresented with 50 resumes,presented with 50 resumes,
randomly selected and alreadyrandomly selected and alreadyjudged to be either a pass or fail.judged to be either a pass or fail.
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Attribute Agreement Analysis
Appraiser
Percent
WendyNinaDavidAlex
100
95
90
85
80
95.0% C I
Percent
Appraiser
Percent
WendyNinaDavidAlex
100
95
90
85
80
95.0% C I
Percent
Assessment Agreement
Within Appraisers Appraiser vs Standard
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MSA - Summary
IntroduceIntroduceMeasurement Systems AnalysisMeasurement Systems Analysis
Define basic measurement termsDefine basic measurement terms
Outline procedure for performing a GageOutline procedure for performing a Gage
Study (Measurement Systems Analysis)Study (Measurement Systems Analysis)
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SPC-MINITAB
Application inApplication in
(Statistical Process Control, SPC)(Statistical Process Control, SPC)
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S = Statistical techniques used to examine process variation
C = Controlling the process through active management
P = Process, ANY Process
The Way We Manage Data - Today
SPC
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Control Chart Methods
Where Did It Come From ?
19201920ss -- Western Electric / Dr. Walter ShewhartWestern Electric / Dr. Walter Shewhart
Used to identify Controlled & Uncontrolled VariationUsed to identify Controlled & Uncontrolled Variation
Controlled:Controlled: Common Cause or InherentCommon Cause or InherentVariationVariation
Uncontrolled: Special Cause or AssignableUncontrolled: Special Cause or AssignableVariationVariation
Uses Control Charts as main toolUses Control Charts as main tool
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Types of Variation
Common vs. Special
COMMON CAUSECOMMON CAUSE Is present in every processIs present in every process
Is produced by the process itself (the way we doIs produced by the process itself (the way we do
business)business) Can be removed and/or lessened but requires aCan be removed and/or lessened but requires a
fundamental change in the processfundamental change in the process
A process isA process is Stable, PredictableStable, Predictable, and, andInIn--ControlControl
when only Common Cause Variation exists in thewhen only Common Cause Variation exists in the
processprocess
Types of Variation
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SPECIAL CAUSESPECIAL CAUSE
UnpredictableUnpredictable
Typically large in comparison to Common CauseTypically large in comparison to Common Causevariationvariation
Caused by unique disturbances or a series of themCaused by unique disturbances or a series of them
Can be removed/lessened by basic process controlCan be removed/lessened by basic process control
and monitoringand monitoring
A process exhibiting Special Cause variation is saidA process exhibiting Special Cause variation is said
to beto be OutOut--ofof--ControlControl andandUnstableUnstable
Types of Variation
Common vs. Special
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Minitab - Control Charts
]
]
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DATA PLOTTED OVER TIME
M
ONITORED
CHA
RACTERI
STIC
UCL
Center Line
LCL
UCL = Upper Control Limit / LCL = Lower Control Limit
Plotted Data
Key Component - Control
Charts
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A set of standard tests have been created to help identifySPECIAL CAUSE events in our processes
We use the phrase Out of Control when a test (or rule)has been broken.
The tests we suggest:Pattern rule: If you see a pattern, the process is out of control
The tests we suggest:Pattern rule: If you see a pattern, the process is out of control
Process Control Tests
This means something unusual has happened -
Go check it out!!Go check it out!!
R l f St d d D i ti
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1 Sigma
2 Sigma
3 Sigma
1 Sigma
2 Sigma
3 Sigma
60-75%
90-98%
99-99.9%
% of Data PointsUCL
LCL
TIMETIME
The ItemWe Are
Measuring
Rules of Standard Deviation
Where should the data lie?
Minitab Tests
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Minitab Tests
Test #1
Test #2 .
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In Control or Out of Control ? ___________________
If out of Control, which rule(s) is broken or condition(s) is present?_______________________________________________________
3020100
10
5
0
-5
Observation Number
IndividualValue
I Chart for C1
X=0.2800
3.0SL=5.416
-3.0SL=-4.856
UCL
LCL
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In Control or Out of Control ? ___________________
If out of Control, which rule(s) is broken or condition(s) is present?_______________________________________________________
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If a point falls beyond the upper or lower controlcontrol limit
does this mean we are making a defect for the customer?
Control Limits vs.
Specification Limits
UCL
LCL
TIMETIME
Control Limits vs.
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Specification Limits
Process Control Limits are calculated based on dataProcess Control Limits are calculated based on data
from the process itselffrom the process itself They are based on +/They are based on +/-- 33 (99.73% of the process(99.73% of the process
variation is expected to fall between these limits)variation is expected to fall between these limits)
Product Specification LimitsProduct Specification LimitsARE NOTARE NOTfound on thefound on thecontrol chartcontrol chart
Understanding how the process matches up againstUnderstanding how the process matches up againstcustomer requirementscustomer requirementsISIS important to knowimportant to know
To determine how the process performs to CustomerTo determine how the process performs to CustomerExpectations, aExpectations, a Process Capability StudyProcess Capability Study is requiredis required
Different Variables Control Charts
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I-MR Individuals - Moving Range
X-Bar-R Average -Range Chart
Different Variables Control Charts
0Subgroup 10 2021
222324252627
282930
IndividualValue
X=25.42
3.0SL=29.00
-3.0SL=21.83
0
1
2
3
4
5
MovingRange
1
R=1.348
3.0SL=4.404
-3.0SL=0.00E
I and MR Chart for C1
0Subgroup 10 20 30
65
70
75
80
SampleMean
1 1
X=72.81
3.0SL=80.71
-3.0SL=64.90
0
10
20
30
SampleRange
1
R=13.70
3.0SL=28.97
-3.0SL=0.00E
Xbar/R Chart for Output
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Different Attribute Control Charts : Defects
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C Number of defectsU Number of defects per unit
Different Attribute Control Charts : Defects
Exercise: What Type of Control Chart?
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1. # of typos per sales contract (C,U)
2. Number of notebooks with defects in monthly production (P)
3. % of defective vehicles in monthly production (I-MR,P)
4. Per accounts receivable, amount of time it takes to close it (I-MR_
5. Number of transmissions with defects per 100 built (NP)
Exercise: What Type of Control Chart?
Requirements for Effective Use
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q
of Control Charts
ManagementManagement
MUSTMUST
establish and support anestablish and support an
environment that promotes proper action andenvironment that promotes proper action andsupport to the information collected on the controlsupport to the information collected on the controlchartscharts
Control Charts are implementedControl Charts are implemented ONLYONLYon Keyon KeyProcesses on which improvement will bringProcesses on which improvement will bringbenefit to the organization and/or the customerbenefit to the organization and/or the customer
Data collected from the process is validatedData collected from the process is validatedthrough the use of athrough the use of a CAPABLECAPABLEmeasurementmeasurement
systemsystem
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SPC - Summary
Linked Control Chart Methods to theLinked Control Chart Methods to theDMAIC roadmapDMAIC roadmap
Discussed different types of variationDiscussed different types of variation
Introduced various Control Chart typesIntroduced various Control Chart types
Discussed the interpretation of ControlDiscussed the interpretation of Control
ChartsCharts
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Case study (Healthcare & Banking)
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Healthcare industry
MD Anderson Cancer Center (TX, USA)MD Anderson Cancer Center (TX, USA)
19981998--20002000
CT process reCT process re--arrangedarranged
Service capacity increased 28%Service capacity increased 28%
H l h i d
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Healthcare industry _
City hospital of Taipei
XX--ray transparency delivery time< 30ray transparency delivery time< 30 minsmins
KPIVsKPIVs
No dedicated delivery personnelNo dedicated delivery personnel
No specific processNo specific process
MSAMSA
Attribute dataAttribute data SPCSPC
Control chartsControl charts
H l h i d
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Healthcare industry_
Local hospital in Taichung
TQIPTQIP
Unexpected return rate within 15 daysUnexpected return rate within 15 days--
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Banking
1.1. Customer waiting time
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Thank you
Q& AQ& A
Control charts For data in subgroups:For data in subgroups:
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For data in subgroups:For data in subgroups:
subgroup means,subgroup means, XbarXbar XbarXbar
subgroup ranges, rsubgroup ranges, r R ChartR Chart subgroup standard deviations, ssubgroup standard deviations, s S ChartS Chart
XbarXbar and r on same screenand r on same screen XbarXbar and Rand R
XbarXbar and s on same screenand s on same screen XbarXbar and Sand S
I chart, MR chart, and R chart for subgroups on the same screenI chart, MR chart, and R chart for subgroups on the same screen II--MRMR--R/SR/S
(Between/Within)(Between/Within) For individual observations:For individual observations:
individual observationsindividual observations IndividualsIndividuals
moving rangesmoving ranges Moving RangeMoving Range
individual observations and moving ranges on same screenindividual observations and moving ranges on same screen II--MRMR
For subgroup combinations:For subgroup combinations: exponentially weighted moving averagesexponentially weighted moving averages EWMAEWMA
moving averagesmoving averages Moving AverageMoving Average
cumulative sumscumulative sums CUSUMCUSUM
individual observations or subgroup means according to their disindividual observations or subgroup means according to their distance from thetance from the
center linecenter line ZoneZone For short runs:For short runs:
standardized individual observations and moving ranges from shorstandardized individual observations and moving ranges from short run processest run processesZZ--MRMR
All Rights Reserved. 2000 Minitab, Inc.All Rights Reserved. 2000 Minitab, Inc.