six sigma intro jan 2005
TRANSCRIPT
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Introduction to Six Sigma
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Topics (Session 1)
Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOUcan use Six Sigma
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Six Sigma is. . .
A performance goal, representing 3.4 defects forevery million opportunities to make one.
A series of tools and methods used to improve ordesign products, processes, and/or services.
A statistical measure indicating the number of
standard deviations within customer expectations.
A disciplined, fact-based approach to managing abusiness and its processes.
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Whats in a name?
Sigma is the Greek letter representing the standarddeviation of a population of data.
Sigma is a measure
ofvariation(the data spread)
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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 not feel averages!
-10
-5
0
5
10
15
20
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Measuring Process PerformanceThe pizza delivery example. . .
Customers want their pizzadelivered fast!
Guarantee = 30 minutes or less
What if we measured performance and found anaverage delivery time of 23.5 minutes?
On-time performance is great, right?
Our customers must be happy with us, right?
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How often are we delivering on
time?Answer: Look atthe variation!
Managing by the average doesnt tell the whole story. Theaverage andthe variation togethershow whats happening.
s
x
30 min. or less
0 10 20 30 40 50
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Reduce Variation to Improve
PerformanceHow many standard
deviations can you
fit within
customerexpectations?
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
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Managing Up the Sigma Scale
Sigma % Good % Bad DPMO
1 30.9% 69.1% 691,462
2 69.1% 30.9% 308,5383 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
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Examples of the Sigma Scale
In a world at 3 sigma. . .
There are 964 U.S. flightcancellations per day.
The police make 7 false arrestsevery 4 minutes.
In MA, 5,390 newborns aredropped each year.
In one hour, 47,283international long distance callsare accidentally disconnected.
In a world at 6 sigma. . .
1 U.S. flight is cancelled every3 weeks.
There are fewer than 4 falsearrests per month.
1 newborn is dropped every 4years in MA.
It would take more than2 years to see the same numberof dropped international calls.
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Topics
Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOUcan use Six Sigma
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The Six Sigma Evolutionary Timeline
1736: Frenchmathematician
Abraham deMoivre publishesan articleintroducing the
normal curve.
1896: Italian sociologist VilfredoAlfredo Pareto introduces the 80/20rule and the Pareto distribution inCours dEconomie Politique.
1924: Walter A. Shewhart introduces
the control chart and the distinction ofspecial vs. common cause variation ascontributors to process problems.
1941: Alex Osborn, head ofBBDO Advertising, fathers awidely-adopted set of rules for
brainstorming.
1949: U. S. DOD issues MilitaryProcedure MIL-P-1629, Proceduresfor Performing a Failure Mode Effectsand Criticality Analysis.
1960: Kaoru Ishikawaintroduces his now famouscause-and-effect diagram.
1818: Gauss uses the normal curve
to explore the mathematics of erroranalysis for measurement, probabilityanalysis, and hypothesis testing.
1970s: Dr. Noriaki Kanointroduces his two-dimensionalquality model and the three
types of quality.
1986: Bill Smith, a seniorengineer and scientist introducesthe concept of Six Sigma atMotorola
1994: Larry Bossidy launchesSix Sigma at Allied Signal.
1995: Jack Welchlaunches Six Sigma at GE.
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Six Sigma Companies
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Six Sigma and Financial Services
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Topics
Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOUcan use Six Sigma
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DMAICThe Improvement
Methodology
Define 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 rootcauses
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)
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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
DefineDMAIC ProjectWhat is the project?
Six Sigma
Project
Charter
Voice ofthe
Stakeholder
Stakeholders
$
Cost ofPoor
Quality
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DefineAs-Is ProcessHow does our existing process work?
Move-It! Courier Package Handling
Process
Accounting
Finalizing
Delivery
Out-SortSupervisorOut-SortClerkAccounts
Supervisor
Accounts
ReceivableClerkWeightFeeClerkDistanceFeeClerkIn-SortSupervisorIn-SortClerkMail ClerkCourier
Observe package
weight (1 or 2) on
back of package
Look up
appropriateWeight Fee and
write in top middle
box on package
back
Take packages
from W eightFee
Clerk Outbox to
A/RClerkInbox.
Add Distance &
WeightFees
together and writein top right box on
package back
Circle Total Fee
and Draw Arrow
from total to
sender code
Take packages
from A/RClerkOutbox to
Accounts
SupervisorInbox.
Write Total Fee
from package inappropriate
Sender column on
Accts . Supv.s log
Add up Total # ofPackages and
Total Fees from
log and create
clientinvoice
Deliver invoiceto
client
Submit log to
General Manager
at conclusion of
round.
Take packages
from AccountsSupervisor
Outbox to Out-
Sort ClerkInbox.
Draw 5-point Star
in upper right
corner of package
front
Sort packages in
order of Sender
Code beforeplacing in outbox
Take packagesfromOut-Sort
Clerk Outbox to
Out-Sort
SupervisorInbox.
Observe senderand receiv er
codes and make
entry in Out-Sort
Supervisors log
DeliverPackages
to customers
according to N, S,E, W route
Submit log to
General Managerat end of round
Submit log to
General Managerat end of round
Does EVERYONE
agree how the current
process works?
Define the Non Value
Add steps
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DefineCustomer RequirementsWhat are the CTQs? What motivates the customer?
Voice of the Customer Key Customer Issue Critical to QualitySECONDARY RESEARCH
PRIMARY RESEARCH
Surveys
OTM
MarketData
IndustryIntel
ListeningP
osts
IndustryBenchmarking
Focus Groups
CustomerService
CustomerCorrespondence
Obser-vations
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MeasureBaselines 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
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MeasureFailures 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 Fai lure Mode Potential Fai lure Effects
S
E
V Potential Causes
O
C
C Current Controls
D
E
T
R
P
N
Actions
Recommended Resp. Actions Taken
S
E
V
O
C
C
D
E
T
R
P
N
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
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Six Sigma
AnalyzePotential Root CausesWhat affects our process?
y = f (x1, x2, x3 . . . xn)
Ishikawa Diagram(Fishbone)
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AnalyzeValidated Root CausesWhat are the key root causes?
Six Sigma
y = f (x1, x2, x3 . . . xn)
Critical Xs
Process
Simulatio
n
DataStratification
RegressionAnalysis
Experimental Design
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ImprovePotential SolutionsHow can we address the root causes we identified?
Address the causes, not the symptoms.
y = f (x1, x2, x3 . . . xn)
Critical Xs
Decision
Evaluate
Clarify
Generate
Divergent | Convergent
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ImproveSolution SelectionHow do we choose the best solution?
Time
Qualit
y
Cost
Solution Sigma Time CBA Other Score
Six Sigma
Solution
Implementatio
n Plan
Solution Selection Matrix
Nice
Try
Nice
Idea X
SolutionRight Wrong
Implementation
Bad
Good
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ControlSustainable 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 Number
Individua
lValue
Mean=24.35
UCL=33.48
LCL=15.21
Process Owner: Date:
Process Description: CCR:
Measuring and Monitoring
Key
Measure
ments
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 ProcessCustomer:
Flowchart
Customer Sales Branch Manager ProcessingLoan Service
Manager
1.1
Application&
Review
1.2
Processing
1.3
Creditreview
1.4
Review
1.5
Disclosure
Apply for
loan
Review
appliation for
completeness
ApplicationComplete?
Complete
meeting
information
No
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DFSSThe Design MethodologyDesign forSix 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 emphasison baselining
Key Tools
Multi-Generational Planning (MGP)
Quality Function Deployment (QFD)
Define Measure Analyze Develop Verify
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Topics
Understanding Six Sigma
History of Six Sigma
Six Sigma Methodologies & Tools
Roles & Responsibilities
How YOUcan use Six Sigma
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Champions
Promote awareness and execution of Six Sigmawithin lines of business and/or functions
Identify potential Six Sigma projects to beexecuted by Black Belts and Green Belts
Identify, select, and support Black Belt and
Green Belt candidates
Participate in 2-3 days of workshop training
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Green Belts
Use Six Sigma DMAIC methodology and basictools to execute improvements within theirexisting job function(s)
May lead smaller improvement projects withinBusiness Unit(s)
Bring knowledge of Six Sigma concepts & tools totheir respective job function(s)
Undergo 8-11 days of training over 3-6 months
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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
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Questions?
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Topics for Detailed Discussion
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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Problem Identification
If it aint broke, why fix itThis is the way weve always done it
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Problem Identification
First Pass Yield Roll Throughput Yield
Histogram
Pareto
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Problem IdentificationFirst Pass Yield (FPY):
The probability that
any given unit can gothrough 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)
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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 thehidden 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
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Problem Identification
RTY 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
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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
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HistogramA 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 theshape, centering, and spread of data distribution and indicates whether
there are any outliers.
Problem Identification
5004003002001000
40
30
20
10
0
C8
Frequency
Histogram of Cycle Time
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HistogramCan 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
Maximum
3rd QuartileMedian1st QuartileMinimum
NKurtosisSkewnessVarianceStDevMean
P-Value:A-Squared:
84.494
75.664
90.417
444.000
105.00066.00031.000
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
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Topics (Session 2)
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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Cost of Poor Quality
COPQ - 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.
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Topics (Session 2)
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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Multi Level ParetoLogically Break down initial Pareto data into sub-
sets (to help refine area of focus)
Problem Refinement
(Web)Others
Non-WEB
1596
13.586.5
100.086.5
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Perc
ent
Cou
nt
Pareto Chart for WEB
Others
OneTimeandOnGoing
OneTime
Annual
16133545
14.711.932.141.3
100.085.373.441.3
100
50
0
100
80
60
40
20
0
Defect
Count
PercentCum %
Percent
Coun
t
Pareto Chart for Type
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Problem StatementA crisp description of what we are trying to solve.
Primary MetricAn objective measurement of what we are attempting
to solve (the y in the y = f(x1, x2, x3.) calculation).
Secondary MetricAn 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
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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
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Topics (Session 2)
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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SIPOCSuppliers, 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
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Process Mapshould 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
Inventory
Start Size SortsControl
DocsOpen Pull & Sort
Verify
Pass 1
Key fromimage Balance
Pass 2Rulrs
Perfection
No
Prep cks Ship to IP
Full Form
QCReviewShip to
Cust
Vouch
OK
Prep
Folders /
Box
Yes
No
Vouchers
Full Form
Ck / Vouch
Yes Prep cks,
route
vouch
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Topics (Session 2)
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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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 gatherie. day ofthe 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).
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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).
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Potential X to Critical XDOE Example
P2JamSKDCDELJams
High
LowHigh
LowHigh
LowHigh
Low
1.4
1.3
1.2
1.1
1.0
Elapsed
Main Effects Plot (data means) for Elapsed
1 3 1 3 1 3 1 3
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 Xs on
each other
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Potential X to Critical X
DOE Optimizer
Allows us tostatistically predict the
Output (Y) based on
optimizing the inputs
(X) from the Design of
experiment data.
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Topics (Session 2)
Problem Identification
Cost of Poor Quality
Problem Refinement
Process Understanding Potential X to Critical X
Improvement
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Improvement
Once we know the degree to which inputs (X) affect our
output (Y), we can explore improvement ideas, focusingon 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.
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Control
Once improvements are made, the question becomes, are the
improvement consistent with predicted Design of Experiment results
(ieare 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. "W ithin" PerformanceObserved PerformanceOverall Capability
Potential (W ithin) Capability
Process Data
Within
Overall
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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
-0.5IndividualValue
9/259/13Date
2
1
Mean=0.03
UCL=0.5293
LCL=-0.4693
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0MovingRange
1
R=0.1877
UCL=0.6134
LCL=0
I and MR Chart for Sept
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