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Transcript of Six_Sigma_Introduction.ppt
Introduction to Six Sigma
Topics (Session 1) Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOU can use Six Sigma
Six Sigma is. . . A performance goal, representing 3.4 defects for every million
opportunities to make one.
A series of tools and methods used to improve or design products, processes, and/or services.
A statistical measure indicating the number of standard deviations within customer expectations.
A disciplined, fact-based approach to managing a business and its processes.
A means to promote greater awareness of customer needs, performance measurement, and business improvement.
μ
σ
What’s in a name? Sigma is the Greek letter representing the standard
deviation of a population of data.
Sigma is a measureof variation
(the data spread)
What does variation mean? Variation means that a
process does not produce the same result (the “Y”)
every time.
Some variation will exist in all processes.
Variation directly affects customer experiences.
Customers do Customers do notnot feel averages! feel averages!
-10
-5
0
5
10
15
20
Measuring Process PerformanceThe pizza delivery example. . .
Customers want their pizza delivered fast!
Guarantee = “30 minutes or less”
What if we measured performance and found an average delivery time of 23.5 minutes?– On-time performance is great, right?– Our customers must be happy with us, right?
How often are we delivering on time?Answer: Look at the variation!
Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.
s
x
30 min. or less
0 10 20 30 40 50
Reduce Variation to Improve PerformanceHow many standard deviations can you “fit” within customer expectations?
Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).
s
x
30 min. or less
0 10 20 30 40 50
Managing Up the Sigma Scale
Sigma % Good % Bad DPMO
1 30.9% 69.1% 691,462
2 69.1% 30.9% 308,538
3 93.3% 6.7% 66,807
4 99.38% 0.62% 6,210
5 99.977% 0.023% 233
6 99.9997% 0.00034% 3.4
Examples of the Sigma ScaleIn a world at 3 sigma. . .
There are 964 U.S. flight cancellations per day.
The police make 7 false arrests every 4 minutes.
In MA, 5,390 newborns are dropped each year.
In one hour, 47,283 international long distance calls are accidentally disconnected.
In a world at 6 sigma. . .
1 U.S. flight is cancelled every 3 weeks.
There are fewer than 4 false arrests per month.
1 newborn is dropped every 4 years in MA.
It would take more than 2 years to see the same number of dropped international calls.
Topics Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOU can use Six Sigma
The Six Sigma Evolutionary Timeline
1736: French mathematician Abraham de Moivre publishes an article introducing the normal curve.
1896: Italian sociologist Vilfredo Alfredo Pareto introduces the 80/20 rule and the Pareto distribution in Cours d’Economie Politique.
1924: Walter A. Shewhart introduces the control chart and the distinction of special vs. common cause variation as contributors to process problems.
1941: Alex Osborn, head of BBDO Advertising, fathers a widely-adopted set of rules for “brainstorming”.
1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures for Performing a Failure Mode Effects and Criticality Analysis.
1960: Kaoru Ishikawa introduces his now famous cause-and-effect diagram.
1818: Gauss uses the normal curve to explore the mathematics of error analysis for measurement, probability analysis, and hypothesis testing.
1970s: Dr. Noriaki Kano introduces his two-dimensional quality model and the three types of quality.
1986: Bill Smith, a senior engineer and scientist introduces the concept of Six Sigma at Motorola
1994: Larry Bossidy launches Six Sigma at Allied Signal.
1995: Jack Welch launches Six Sigma at GE.
Six Sigma Companies
Six Sigma and Financial Services
Topics Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOU can use Six Sigma
DMAIC – The Improvement MethodologyDefine Measure Analyze Improve Control
Objective:
DEFINE the opportunity
Objective:
MEASURE current performance
Objective:
ANALYZE the root causes of problems
Objective:
IMPROVE the process to eliminate root causes
Objective:
CONTROL the process to sustain the gains.
Key Define Tools:• Cost of Poor
Quality (COPQ)• Voice of the
Stakeholder (VOS)
• Project Charter• As-Is Process
Map(s)• Primary Metric
(Y)
Key Measure Tools:
• Critical to Quality Requirements (CTQs)
• Sample Plan• Capability
Analysis• Failure Modes
and Effect Analysis (FMEA)
Key Analyze Tools:
• Histograms, Boxplots, Multi-Vari Charts, etc.
• Hypothesis Tests• Regression
Analysis
Key Improve Tools:
• Solution Selection Matrix
• To-Be Process Map(s)
Key Control Tools:
• Control Charts• Contingency
and/or Action Plan(s)
What is the problem? The “problem” is the Output (a “Y” in a math equation Y=f(x1,x2,x3) etc).
What is the cost of this problem Who are the stake holders / decision makers Align resources and expectations
Define – DMAIC ProjectWhat is the project?
Six SigmaSix Sigma
Project Project CharterCharter
Voice of the
Stakeholder
S takeho lders
$
Cost of Poor
Quality
Define – As-Is ProcessHow does our existing process work?Move-It! Courier Package HandlingProcess
Acc
ou
ntin
gF
ina
lizin
gD
eliv
ery
Out-Sort SupervisorOut-Sort ClerkAccounts
SupervisorAccounts
Receivable ClerkWeight Fee ClerkDistance Fee ClerkIn-Sort SupervisorIn-Sort ClerkMail ClerkCourier
Observ e packageweight (1 or 2) onback of package
Look upappropriate
Weight Fee andwrite in top middle
box on packageback
Take packagesf rom Weight FeeClerk Outbox toA/R Clerk Inbox.
Add Distance &Weight Fees
together and writein top right box on
package back
Circle Total Feeand Draw Arrow
f rom total tosender code
Take packagesf rom A/R Clerk
Outbox toAccounts
Superv isor Inbox.
Write Total Feef rom package in
appropriateSender column onAccts. Supv .’s log
Add up Total # ofPackages and
Total Fees f romlog and createclient inv oice
Deliv er inv oice toclient
Submit log toGeneral Managerat conclusion of
round.
Take packagesf rom Accounts
Superv isorOutbox to Out-
Sort Clerk Inbox.
Draw 5-point Starin upper right
corner of packagef ront
Sort packages inorder of Sender
Code bef oreplacing in outbox
Take packagesf rom Out-Sort
Clerk Outbox toOut-Sort
Superv isor Inbox.
Observ e senderand receiv er
codes and makeentry in Out-SortSuperv isor’s log
Deliv er Packagesto customers
according to N, S,E, W route
Submit log toGeneral Managerat end of round
Submit log toGeneral Managerat end of round
Does EVERYONE agree how the current process works?
Define the Non Value Add steps
Define – Customer RequirementsWhat are the CTQs? What motivates the customer?
Voice of the CustomerVoice of the Customer Key Customer IssueKey Customer Issue Critical to QualityCritical to QualitySECONDARY RESEARCH
PRIMARY RESEARCH
Surveys
Surveys
OTM
Market DataIn
du
stry
In
tel
List
en
ing
Post
s
Industry Benchmarking
Focus Groups
Customer Service
Customer Correspondence
Obser-vations
Measure – Baselines and CapabilityWhat is our current level of performance?
Sample some data / not all data Current Process actuals measured against
the Customer expectation What is the chance that we will succeed
at this level every time?50403020100
95% Confidence Interval for Mu
26.525.524.523.522.521.520.519.5
95% Confidence Interval for Median
Variable: 2003 Output
19.7313
8.9690
21.1423
Maximum3rd QuartileMedian1st QuartileMinimum
NKurtosisSkewnessVarianceStDevMean
P-Value:A-Squared:
26.0572
11.8667
25.1961
55.290729.610023.147516.4134 0.2156
1000.2407710.238483
104.34910.215223.1692
0.8540.211
95% Confidence Interval for Median
95% Confidence Interval for Sigma
95% Confidence Interval for Mu
Anderson-Darling Normality Test
Descriptive Statistics
OthersAmount
Late
41779 4.017.079.0
100.0 96.0 79.0
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Pe
rce
nt
Co
unt
Pareto Chart for Txfr Defects
Measure – Failures and RisksWhere does our process fail and why? Subjective opinion mapped into an “objective” risk profile number
Failure Modes and Effects Analysis (FMEA)
Process or Product Name:
Prepared by: Page ____ of ____
Responsible: FMEA Date (Orig) ______________ (Rev) _____________
Process Step/Part Number Potential Failure Mode Potential Failure Effects
SEV Potential Causes
OCC Current Controls
DET
RPN
Actions Recommended Resp. Actions Taken
SEV
OCC
DET
RPN
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Process/Product
X1
X2
X4
X3
etc
Six SigmaSix Sigma
Analyze – Potential Root CausesWhat affects our process?
y = f (xy = f (x11, x, x22, x, x33 . . . x . . . xnn))
Ishikawa Diagram
(Fishbone)
Analyze – Validated Root CausesWhat are the key root causes?
41779 4.017.079.0
100.0 96.0 79.0
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Perc
ent
Coun
t
Pareto Chart for Txfr Defects
Six SigmaSix Sigma
y = f (xy = f (x11, x, x22, x, x33 . . . x . . . xnn))Critical Xs
2 31211.817.670.6
100.0 88.2 70.6
15
10
5
0
100
80
60
40
20
0
Defect
CountPercentCum %
Perc
ent
Coun
t
Pareto Chart for Amt Defects
Process Simulatio
n
Data Stratificatio
n
Regression Analysis
Experim ental Design
Improve – Potential SolutionsHow can we address the root causes we identified?
Address the causes, not the symptoms.
y = f (xy = f (x11, x, x22, x, x33 . . . x . . . xnn))
Critical Xs
Decision
Evaluat
e
Clarify
Generat
e
Divergent | ConvergentDivergent | Convergent
Improve – Solution SelectionHow do we choose the best solution?
Time
Quality
Cost
Solution Sigma Time CBA Other Score
Six SigmaSix Sigma
Solution Solution ImplementatioImplementatio
n Plann Plan
Solution Selection Matrix
☺ Nice Try
Nice Idea X
Solution Right Wrong
Imp
lem
enta
tio
n
Bad
G
ood
Control – Sustainable BenefitsHow do we ”hold the gains” of our new process?
Some variation is normal and OK How High and Low can an “X” go yet not materially impact the “Y” Pre-plan approach for control exceptions
0 10 20 30
15
25
35
Observation NumberIn
divi
dual
Val
ue
Mean=24.35
UCL=33.48
LCL=15.21
Process Owner: Date:Process Description: CCR:
Measuring and Monitoring
Key Measurements
Specs &/or
Targets
Measures (Tools)
Where & Frequency
Responsibility (Who)
Contingency (Quick Fix)
Remarks
P1 - activity duration, min.
P2 - # of incomplete loan applications
Process Control System (Business Process Framework)
Direct Process Customer:
Flowchart
Custom er Sales Branch ManagerProcessingLoan Service
Manager
1.1
Ap
plic
atio
n &
Re
vie
w1
.2P
roce
ssin
g1
.3C
red
it re
vie
w1
.4R
evi
ew
1.5
Dis
clo
sure
Apply forloan
Reviewappliation for
com pleteness
ApplicationCom plete?
Com pletem eeting
inform ationNo
DFSS – The Design MethodologyDesign for Six Sigma
Uses– Design new processes, products, and/or services from scratch– Replace old processes where improvement will not suffice
Differences between DFSS and DMAIC– Projects typically longer than 4-6 months– Extensive definition of Customer Requirements (CTQs)– Heavy emphasis on benchmarking and simulation; less emphasis on
baselining Key Tools
– Multi-Generational Planning (MGP)– Quality Function Deployment (QFD)
Define Measure Analyze Develop Verify
Topics Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOU can use Six Sigma
Champions Promote awareness and execution of Six Sigma
within lines of business and/or functions
Identify potential Six Sigma projects to be executed by Black Belts and Green Belts
Identify, select, and support Black Belt and Green Belt candidates
Participate in 2-3 days of workshop training
Black Belts
Use Six Sigma methodologies and advanced tools (to execute business improvement projects
Are dedicated full-time (100%) to Six Sigma
Serve as Six Sigma knowledge leaders within Business Unit(s)
Undergo 5 weeks of training over 5-10 months
Green Belts Use Six Sigma DMAIC methodology and basic tools
to execute improvements within their existing job function(s)
May lead smaller improvement projects within Business Unit(s)
Bring knowledge of Six Sigma concepts & tools to their respective job function(s)
Undergo 8-11 days of training over 3-6 months
Subject Matter Experts– Provide specific process knowledge to Six Sigma teams– Ad hoc members of Six Sigma project teams
Financial Controllers– Ensure validity and reliability of financial figures used
by Six Sigma project teams– Assist in development of financial components of
initial business case and final cost-benefit analysis
Other Roles
Topics Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOU can use Six Sigma
Questions?
Topics for Detailed Discussion Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
Problem Identification
“If it ain’t broke, why fix it
“This is the way we’ve always done it…”
Problem Identification
• First Pass Yield
• Roll Throughput Yield
• Histogram
• Pareto
Problem IdentificationFirst Pass Yield (FPY):
The probability that any given unit can go through a system defect-free without rework.
Step 1
Step 2
Step 3
Step 4
Scrap 10 Units
100 Units
100
90
87
Scrap 3 Units
Scrap 2 Units
85
Outputs / Inputs
100 / 100 = 1
90 / 100 = .90
87 / 90 = .96
85 / 87 = .97
At first glance, the yield would seem to be 85% (85/100 but….)
When in fact the FPY is (1 x .90 x .96 x .97 = .838)
Problem Identification
Step 1
Step 2
Step 3
Step 4
Re-Work 10 Units
100 Units
Re-Work 3 Units
Re-Work 2 Units
Rolled Throughput Yield (RTY):
The yield of individual process steps multiplied together. Reflects the hidden factory rework issues associated with a process.
Outputs / Inputs
90 / 100 = .90
97 / 100 = .97
98 / 100 = .98
.90 x .97 x .98 = .855
100 Units
100 Units
100 Units
100 Units
Problem IdentificationRTY Examples - Widgets
Function 1
Function 2
Function 3
Function 4
50
5
10
5
50
50
50
50
Roll Throughput Yield
50/50 = 1
(50-5)/50 = .90
(50-10)/50 = .80
(50-5)/50 = .90
1 x .90 x .80 x .90 = .65
Put another way, this process is operating a 65% efficiency
RTY Example - Loan Underwriting
Roll Throughput Yield
50/50 = 1
(50-7-2)/50 = .82
(43-6)/43 = .86
(43-1-2)/43 = .93
1 x .82 x .86 x .93 = .66
Put another way, this process is operating a 66% efficiency
Application
Underwrite
Complete Full Paperwork
Close
50
Fails Underwriting
Decide not to borrow
2
6
2
7
1
42
50
43
43
Problem Identification
Histogram – A histogram is a basic graphing tool that displays the relative frequency or occurrence of continuous data values showing which values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whether there are any outliers.
Problem Identification
5004003002001000
40
30
20
10
0
C8
Fre
que
ncy
Histogram of Cycle Time
Histogram – Can also help us graphically understand the data
Problem Identification
40032525017510025
95% Confidence Interval for Mu
9484746454
95% Confidence Interval for Median
Variable: CT
55.753
61.098
69.947
Maximum3rd QuartileMedian1st QuartileMinimum
NKurtosisSkewnessVarianceStDevMean
P-Value:A-Squared:
84.494
75.664
90.417
444.000105.000 66.000 31.000 1.000
1708.263562.317124569.8167.600380.1824
0.0006.261
95% Confidence Interval for Median
95% Confidence Interval for Sigma
95% Confidence Interval for Mu
Anderson-Darling Normality Test
Descriptive Statistics
Pareto – The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects.
Problem Identification
159613.586.5
100.0 86.5
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Per
cen
t
Cou
nt
Pareto Chart for WEB
Topics (Session 2) Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
Cost of Poor QualityCOPQ - The cost involved in fulfilling the gap between the desired and actual product/service quality. It also includes the cost of lost opportunity due to the loss of resources used in rectifying the defect.
Examples / Buckets–
Roll Throughput Yield Inefficiencies (GAP between desired result and current result multiplied by direct costs AND indirect costs in the process).
Cycle Time GAP (stated as a percentage between current results and desired results) multiplied by direct and indirect costs in the process.
Square Footage opportunity cost, advertising costs, overhead costs, etc…
Hard Savings - Six Sigma project benefits that allow you to do the same amount of business with less employees (cost savings) or handle more business without adding people (cost avoidance).
Soft Savings - Six Sigma project benefits such as reduced time to market, cost avoidance, lost profit avoidance, improved employee morale, enhanced image for the organization and other intangibles may result in additional savings to your organization, but are harder to quantify.
Topics (Session 2) Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
Multi Level Pareto – Logically Break down initial Pareto data into sub-sets (to help refine area of focus)
Problem Refinement
159613.586.5
100.0 86.5
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Per
cen
t
Cou
nt
Pareto Chart for WEB
1613354514.711.932.141.3
100.0 85.3 73.4 41.3
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Per
cen
t
Cou
nt
Pareto Chart for Type
Problem Statement – A crisp description of what we are trying to solve.
Primary Metric – An objective measurement of what we are attempting to solve (the “y” in the y = f(x1, x2, x3….) calculation).
Secondary Metric – An objective measurement that ensures that a Six Sigma Project does not create a new problem as it fixes the primary problem. For example, a quality metric would be a good secondary metric for an improve cycle time primary metric.
Problem Refinement
Fish Bone Diagram - A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. Also called the Cause & Effect diagram and Ishikawa diagram
Problem Refinement
Provides tool for exploring cause / effect and 5 whys
Topics (Session 2) Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
SIPOC – Suppliers, Inputs, Process, Outputs, Customers
You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.
Process Understanding
Process Map – should allow people unfamiliar with the process to understand the interaction of causes during the work-flow. Should outline Value Added (VA) steps and non-value add (NVA) steps.
Process Understanding
Receipt / Extract
Requal Group
Remit
Data Cap
I nventory
Start Size Sorts Control Docs
Open Pull & Sort
Verif y
Pass 1
Key f rom image Balance
Pass 2Rulrs
Perfection
No
Prep cks Ship to I P
Full Form QCReview
Ship to Cust
Vouch OK
Prep Folders /
Box
Yes
No
Vouchers
Full Form
Ck / Vouch
Yes Prep cks, route vouch
Operations
HR / Recruit
Training
Start
Manually Update HR
Billet Request
Create Staff Billet
Review Staff Billet
Check off desired returnee
staff & "need to retrain"
list
Send Letters to desired
staff
Do they respond?
Call (3x)
No
Have we hired
enough?
Stop!
Yes
Rev original billet &
call uncheck
ed
I nterview / pre-hire
Meet Fleet hiring
criteria
Stop!
No
Place into dept
Yes
show up orienta
tion
Call3X
No
To Floor
schedule for
training
Show up?
Call1X
No
Train
Pass?
Need OJ T Re-Tng
No
HR sends req for staffi ng
nos.
Create daily peak staff need plan
Add 30% to the required
no.
What if the returnee is
already working here on another program? Currently
send the ltr anyways
Do they want to
work this peak?
Do they want to
stay on the list
No
Take off I PS
system
No
Set 14 month
flag (on I PS?)
Yes
Yes
Add 40% to staff needed
Yes
New & Other People call in
Wait List
No Rank as "1 2 3"
New
Update I PS
Compare to original Billet rpt
Call employee(3x)
Can they make it?
Action Plan
No
To FloorYes
ReachYes
Update I PS
Gen rpt f or Ops Kronos
Recruit
Gen Event Roster rpt in I PS
No
NoYes
OJ TMake it?
Yes
Yes
No
Yes
Hire in 1-2 order (3's are
not placed)
Notif y HR
Need re-train
No Yes
Do they want to
work this peak?
Do they want to
stay on the list
No
Yes
Set 14 month
flag (on I PS?)
Take off I PS
system
Have we hired
enough?
Call Wait List
NoYes
Stop!
Yes
No
No
Yes
Yes
Process Understanding
Topics (Session 2) Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
Potential X to Critical X
“Y” is the dependent output of a variable process. In other words, output is a function of input variables (Y=f(x1, x2, x3…).
Through hypothesis testing, Six Sigma allows one to determine which attributes (basic descriptor (generally limited or binary in nature) for data we gather – ie. day of the week, shift, supervisor, site location, machine type, work type, affect the output. For example, statistically, does one shift make more errors or have a longer cycle time than another? Do we make more errors on Fridays than on Mondays? Is one site faster than another? Once we determine which attributes affect our output, we determine the degree of impact using Design of Experiment (DOE).
Potential X to Critical X
A Design of Experiment (DOE) is a structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Not only is the direct affect of an X1 gauged against Y but also the affect of X1 on X2 against Y is also gauged. In other words, DOE allows us to determine - does one input (x1) affect another input (x2) as well as Output (Y).
Potential X to Critical XDOE Example
P2JamSKDCDELJams
1.4
1.3
1.2
1.1
1.0
Ela
pse
d
Main Effects Plot (data means) for Elapsed
1.00
1.25
1.50
1.00
1.25
1.50
1.00
1.25
1.50
1.00
1.25
1.50Jams
DCDEL
SK
P2Jam
3
1
1
3
1
3
1
3
Interaction Plot (data means) for Elapsed
Main Effects Plot – Direct impact to Y
Interaction Plot – Impacts of X’s on each other
Potential X to Critical X
DOE Optimizer – Allows us to statistically predict the Output (Y) based on optimizing the inputs (X) from the Design of experiment data.
Topics (Session 2) Problem Identification Cost of Poor Quality Problem Refinement Process Understanding Potential X to Critical X Improvement
Improvement
Once we know the degree to which inputs (X) affect our output (Y), we can explore improvement ideas, focusing on the cost benefit of a given improvement as it relates to the degree it will affect the output. In other words, we generally will not attempt to fix every X, only those that give us the greatest impact and are financially or customer justified.
Control
Once improvements are made, the question becomes, are the improvement consistent with predicted Design of Experiment results (ie – are they what we expected) and, are they statistically different than pre-improvement results.
1.00.50.0-0.5-1.0
USLLSL
Process Capability Analysis for Sept
% Total
% > USL
% < LSL
% Total
% > USL
% < LSL
% Total
% > USL
% < LSL
Ppk
Z.LSL
Z.USL
Z.Bench
Cpm
Cpk
Z.LSL
Z.USL
Z.Bench
StDev (Overall)
StDev (Within)
Sample N
Mean
LSL
Target
USL
12.62
12.62
0.00
6.35
6.35
0.00
13.04
13.04
0.00
0.38
4.40
1.14
1.14
*
0.51
5.87
1.53
1.53
0.221880
0.166425
23
-0.02391
-1.00000
*
0.23000
Exp. "Overall" PerformanceExp. "Within" PerformanceObserved PerformanceOverall Capability
Potential (Within) Capability
Process Data
Within
Overall
Control
Control Chart - A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process(common cause) from variation that yields a change to the process(special cause). This change may be a single point or a series of points in time - each is a signal that something is different from what was previously observed and measured.
Sept 20Sept 13Subgroup
0.5
0.0
-0.5
Ind
ivid
ua
l Va
lue
9/259/13Date
2
1
Mean=0.03
UCL=0.5293
LCL=-0.4693
0.70.6
0.50.40.30.20.10.0
Mo
vin
g R
an
ge
1
R=0.1877
UCL=0.6134
LCL=0
I and MR Chart for Sept
Wrap Up