dmaic lean six sigma
DESCRIPTION
A methodology of Business Process Improving, which is essential for organizations want to use Six SigmaTRANSCRIPT
© 2010 International Institute for Learning, Inc.SCS Singapore Version 1.0
CSICSI SingaporeSingaporeFollowing the Chain of Evidence (the Facts)
in Lean Six Sigma Process Improvement Projects (DMAIC)
Robert Johnston, Ph.D.Executive Director, Six Sigma
International Institute for Learning, Inc.
© 2010 International Institute for Learning, Inc.
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SCS Singapore Version 1.0
Who Am I?
Robert Johnston, Ph.D. Statistics, MBB
Philosophy: practicality trumps theory• Utility = (Perfection of idea) * (Probability people will use it)
ExperienceAnimal Feed Products, Pharmaceuticals, GE Capital
Allstate, Coca-Cola, Carlson (Radisson), Caterpillar, Deutsche Bank, DHL, FDMS, Intuit, TRW, Schreiber Foods, StarHub, U.S. Navy
Trained/Coached several hundred Lean Six Sigma practitioners/projects
© 2010 International Institute for Learning, Inc.
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What is Lean Six Sigma?
“SIX SIGMA: A comprehensive and flexible system for achieving, sustaining, and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes.”
- “The Six Sigma Way” – Pande p. xi
© 2010 International Institute for Learning, Inc.
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What is Lean Six Sigma?
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Lean Six Sigma Triad
MainFocus
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Process Design – DMADV?
DMADV is the recipe for designing new processes/products. Usually more complex/longer than DMAIC, so companies often implement DMADV after successfully completing some DMAIC projects.
D
M
A
D
V
Define the process/product and the
business case
Measure: Define the customer requirements
and prioritize them
Analyze functional requirements, create high-level design
Develop detailed design
Verifyprocess/product
performance
QFDFMEA
Drive Customer Requirements Through
Entire Design Cycle
ManageRisk
© 2010 International Institute for Learning, Inc.
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Measure Performance &
Focus on Critical Areas
What is DMAIC?
Where’s the PAIN to the Customer? The Business?
80% 20%
Drill Down for Root Cause
Pull It Out by the Roots
Monitor & Take Action If Root
Cause Re-appears
DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey.
Shortcu
t
© 2010 International Institute for Learning, Inc.
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Use of Data in DMAIC: “It’s all about the evidence”
Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction.
Voice of Customer, Financials
Baseline data, focusing data (Pareto Principle)
Cause & Effect Data
Before / After Data
Real-time Monitoring Data
Coun
t
Perc
ent
LocationCount
7.2 4.3 1.4Cum % 72.5 87.0 94.2 98.6 100.0
50 10 5 3 1Percent 72.5 14.5
OtherMWSWNW
70
60
50
40
30
20
10
0
100
80
60
40
20
0
Month
Erro
rs
DecNovOctSepAugJulJunMayAprMarFebJan
10
9
8
7
6
5
4
3
2
1
6
Experience
Cycl
e Ti
me
98765432
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14
12
10
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2Observation
Cycl
e Ti
me
2018161412108642
18
16
14
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8
6
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_X=4.50
UCL=7.71
LCL=1.29
Before After
Observation
Cost
24222018161412108642
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12
10
8
6
4
2
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_X=5.84
UCL=12.28
LCL=-0.61
© 2010 International Institute for Learning, Inc.
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Six Sigma & Lean (It’s like Chocolate and Peanut Butter)
Six Sigma Focus on QualityCustomer RequirementsVariation & Defect ReductionData BasedSupport Infrastructure
Lean Focus on SpeedCycle Time ReductionElimination of WasteRapid Project Execution
Six Sigma
Lean
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Why is it called Six Sigma? (optional)
Sigma (σ, standard deviation ) measures process variation (VOP)
Mean
σ
Customer Requirement
Customer Requirement
Good BadBad
σ σσ σ σ
Compared to Customer Requirements (VOC) shows the % Defects
© 2010 International Institute for Learning, Inc.
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Why is it called Six Sigma? (optional)
Reducing variation means reducing the number of defects
Mean
Six Sigma represents 6 standard deviations from the mean to the upper or lower specification limits of the customer
σ σ σ σ σ σ
CustomerRequirement
σ σ σ σ σ σ
CustomerRequirement
Good BadBad
3.4 Defects per Million
© 2010 International Institute for Learning, Inc.
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DMAIC: Following the Chain of Evidence
Improving Processes
© 2010 International Institute for Learning, Inc.
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Define: Houston, we have a problem!
ID the ProcessIncluding Supplier, Inputs, Outputs, Customer
ID the Customer ,his/her Requirements, and the Performance Gap
Critical To Quality (CTQ)
Make them MeasureableDefine a Defect
D M A I C
Input Output
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Define: CTQ Identification Example
You’ve just ordered a pizza from a local pizza delivery shop. What are your CTQs ?
D M A I C
Not very specific or measureable Not very specific or measureable ……
40-50oC on delivery
<30 min
4-5 oz cheese…
More specific and measureable More specific and measureable ……
© 2010 International Institute for Learning, Inc.
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Measure: So, how bad is it?
Map Process in detail
Establish data collection plan
Output data (y)Stratification data (x’s)
Check Measurement System
Collect Data
Baseline Process Performance
Focus- stratify
D M A I C
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Process Focus
What is supposed to happen…
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Process Focus
What really happens…
Rework … Inspection … Delays … Work-a-rounds …
“Hidden Factory”
© 2010 International Institute for Learning, Inc.
Impact of Hidden Factory on Cycle Time
Process Lead Time (PLT)From Customer request to customer receipt
Value Add Process Time (VAPT)Time spent on tasks customer is willing to pay for
Process Cycle Efficiency (PCE)PCE = VAPT / PLT
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What is a typical value for PCE?
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WIP & Little’s Law: What is WIP?
WIP stands for Work in Process (or Progress).
If we have too much WIP:Cycle times grow and are unpredictable.Resources are spent handling it.Processes are cluttered so it’s hard to expedite something if necessary.
© 2010 International Institute for Learning, Inc.
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Little’s Law
Little’s Law states:
Where…
PLT = Process Cycle TimeWIP = Work In ProcessExit Rate = Units/Time
Like the line at an amusement park:
RateExit WIP PLT =
IN
OUTExit Rate:2 people
minute
Minutes 6 2People 12 PLT
MinutePeople
=
=
© 2010 International Institute for Learning, Inc.
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Little’s Law: WIP (1 of 2)
If WIP is reduced, then Lead Time is reduced:
While this is common sense, it is not usually how processes are run. We keep throwing more “stuff” into the process (as fast as orders come) increasing WIP and Lead Time.
But if we don’t throw the orders into the process, what do we do with them and why?
IN
OUTExit Rate:2 people
minute Minutes 3 2People 6 PLT
MinutePeople
=
=
© 2010 International Institute for Learning, Inc.
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Little’s Law: WIP (2 of 2)
Have a “triage” or waiting area.
Waiting orders can be reprioritized (expedited).
Orders in the process can be found and expedited more easily.
We know exactly how long it will take an order to be processed once it enters the queue.
…but don’t forget, the Customer experiences Waiting Time + PLT
IN
OUTExit Rate:2 people
minute Minutes 3 2People 6 PLT
MinutePeople
=
=
This one can be expedited if necessary(can be done in 3 minutes instead of
the original 6 minutes).
Waiting Room
© 2010 International Institute for Learning, Inc.
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General Application of Little’s Law to Projects/Initiatives/Work
Project W1 W2 W3 W4 W5
A $ $
B $ $
C $ $
Project W1 W2 W3 W4 W5
A $ $ $ $
B $ $ $
C $ $
Work many things at once
Focus on a few things at a time
Increased Flexibility
D
Increased Value
© 2010 International Institute for Learning, Inc.
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A Word on Planning Data Collection: Avoid a Port-Mortem
1. What is the question?
3. Collect data to go from 1. to 2.
2. What Graph/Summary will answer it?
D M A I C
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Check the Measurement System –Is Our Data Any Good?
Process
Measurement System
XX
D M A I C
© 2010 International Institute for Learning, Inc.
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Measurement Systems Analysis (MSA) Exercise
M&M Company wants to improve the quality of their output.
It’s a Good M&M if…Clear/Legible Logo, andUniform/Consistent Color, andNo Cracks in Shell
Otherwise, it’s a Bad M&M.
D M A I C
© 2010 International Institute for Learning, Inc.
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Measurement Systems Analysis (MSA) Exercise
Teams of 5 or 6
Make a Team grid, 5x5, place 25 M&Ms in the grid (flip chart paper)
Each team member makes a 5x5 score sheet (8.5x11 or A4)
Independently grade each M&M as Good (G) or Bad (B). No talking, sounds of amazement, etc.
A B C D E1
2
3
4
5
A B C D E12345
G G B G BG B B G GB B G B GB B B G BB G G G B
D M A I C
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When done, choose a spokesperson to read through score sheet one item at a time.
If all Team Members agree, then they get a point.
Report Team Point Total.
A B C D E12345
G G B G BG B B G GB B G B GB B B G BB G G G B
D M A I C
Measurement Systems Analysis (MSA) Exercise Answers
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1 2 3 4 5 6 …Team
% A
gree
men
t 100
75
50
25
0
Desired Results
Typical Results!
D M A I C
Measurement Systems Analysis (MSA) Exercise Answers
© 2010 International Institute for Learning, Inc.
MSA Examples
Banking
IT
Manufacturing
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© 2010 International Institute for Learning, Inc.
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Existing Data Sources
There is a lot of data out there
Review whatever you can find
Guidelines for using existing dataHow was the data created?– Using which operational definition? (Yours?)– For which purpose/intention? – Under which circumstances? (Rush, end of the shift, …?)
If the data does not follow your operational definition can it be reformatted to fit your needs? (maybe they collected more data than they showed)
© 2010 International Institute for Learning, Inc.
Looking at DataGS-34
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Which Regions/Teams are better? Worse?
Fooled you! It’s all generated from an identical source … the differences are just random…not real. Summaries – like averages
or totals – may not tell the whole story
© 2010 International Institute for Learning, Inc.
Look at the DataGS-35
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Need to start looking at the raw data – not just summaries of the data – variation is important!
© 2010 International Institute for Learning, Inc.
Look at the Data: Another ExampleGS-36
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Company complaint resolution process:Goal: Resolution <50 daysActual: Average Resolution = 97 days!
CEO decides need major/fundamental process change
Requires fundamentalprocess change
Fundamentally process OK– it’s the exceptions
Which is it? Both have average of 97!
© 2010 International Institute for Learning, Inc.
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Analyze: Find the Root Cause: y=f(x)D M A I C
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Analyze: Verify Cause & Effect Relationship
Y: E
ffect
Dis
cret
e
Con
tinuo
us
Discrete ContinuousX: Potential Cause or
Stratification Factor
Loan $
Cyc
le T
ime
175000150000125000100000
65
55
45
35
Scatterplot of Cycle Time vs Loan $
Approval TimeLo
cati
on70605040
London
NY
Dotplot of Approval Time vs Location
Each symbol represents up to 2 observations.
Face Time
Sale
70605040
YES
NO
Dotplot of Face Time vs Sale
Each symbol represents up to 2 observations.Sale
Cou
nt
YESNO
25
20
15
10
5
0
YESNO
Region = E Region = W SaleNOYES
Pareto Chart of Sale by Region
ScatterplotStratified•Dotplot•Boxplot•Histogram
Stratified•Dotplot•Boxplot•Histogram
Stratified•Pareto
orTable
•Regression•Multiple Regression
•t-test•ANOVA / ANOM•Test of Equal Variance•DOE
•Test of Two Proportions•Chi-square
•Logistic Regression
Y: E
ffect
Dis
cret
e
Con
tinuo
us
Discrete ContinuousX: Potential Cause or
Stratification Factor
Loan $
Cyc
le T
ime
175000150000125000100000
65
55
45
35
Scatterplot of Cycle Time vs Loan $
Approval TimeLo
cati
on70605040
London
NY
Dotplot of Approval Time vs Location
Each symbol represents up to 2 observations.
Face Time
Sale
70605040
YES
NO
Dotplot of Face Time vs Sale
Each symbol represents up to 2 observations.Sale
Cou
nt
YESNO
25
20
15
10
5
0
YESNO
Region = E Region = W SaleNOYES
Pareto Chart of Sale by Region
ScatterplotStratified•Dotplot•Boxplot•Histogram
Stratified•Dotplot•Boxplot•Histogram
Stratified•Pareto
orTable
•Regression•Multiple Regression
•t-test•ANOVA / ANOM•Test of Equal Variance•DOE
•Test of Two Proportions•Chi-square
•Logistic Regression
D M A I C
© 2010 International Institute for Learning, Inc.
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N/N
Y/Y
Analyze: Verify Cause & Effect Relationship- YY/NN
NO YESPotential Cause (X) Present?
Effe
ct (Y
) Pre
sent
?N
O
YES
D M A I C
© 2010 International Institute for Learning, Inc.
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Causal Relationships- Lurking Variables
Lurking Variables are ones you did not measure, or even consider, that impact your process/data
What’s the Lurking Variable?
0 500 1000# Ice-cream Sales
# D
row
ning
s0
5
1
0
20
25
D M A I C
© 2010 International Institute for Learning, Inc.
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Causal Relationships- Lurking Variables
The number of people at the beach which is a function of Temperature!
50 70 90Temperature
# D
row
ning
s0
5
1
0
20
25
# Ic
e-C
ream
Sal
es0
50
0
1
000
50 70 90Temperature
D M A I C
© 2010 International Institute for Learning, Inc.
Examples of Lurking VariablesGS-42
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Num
ber o
f Dam
aged
Car
tons
pe
r shi
ft
Training didn’t solve the problem…
It was the fork-trucks! New employees got theold fork-trucks – they had a design flaw
© 2010 International Institute for Learning, Inc.
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Lurking Variables: Aggregated Data
Death Rates in Hospitals
What if account for Patient Condition?
Watch out for Lurking Variables in Causal Analysis!
A B
Deaths 450 (15%)
130 (11.8%)
Patients 3000 1100
A B
Deaths 50 (5%)
100 (10%)
Patients 1000 1000
A B
Deaths 400 (20%)
30 (30%)
Patients 2000 100
Good Condition Poor Condition
D M A I C
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Improve: Fix It!
Eliminate the Root Cause
Brainstorm solutions
Evaluate Solutions and Select best
Manage Risk
Pilot Solution
Verify Results
D M A I C
© 2010 International Institute for Learning, Inc.
Before & After
Many solutions don’t actually help
How will you know if yours did?
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Control: Make it Stay Fixed
Standardize Process
Train on the new Process
On-going Process Monitoring
D M A I C
© 2010 International Institute for Learning, Inc.
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Responding to Variation Inappropriately
Rule 1: Do Nothing– Start Funnel at 50– Drop 24 Balls
Rule 2: Compensate– Start Funnel at 50– Drop– Adjust: e.g., if ball drops 3
below target, adjust funnel 3 up, etc.
– Repeat Drop & Adjust cycle 24 times
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Responding to Variation Inappropriately
Rule 1: Do Nothing– Start Funnel at 50– Drop 24 Balls
Rule 2: Compensate– Start Funnel at 50– Drop– Adjust: e.g., if ball drops 3
below target, adjust funnel 3 up, etc.
– Repeat Drop & Adjust cycle 24 times
Rule 1 Results
Rule 2 Results
41% increase in variation!
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Control: Two Kinds of Variation
Common Cause – events happen sometimes to everyone
Special Cause – events only happen sometimes to some people/processes
D M A I C
© 2010 International Institute for Learning, Inc.
2-50
Exercise: Two Kinds of Variation
Sign your name 3 times
Now with other hand
Common Cause
Common Cause(just more of it than with the other hand)
SpecialCause
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Understanding Variation
Why it mattersVariation exists in all processes
There are two fundamental kinds of variation:Special Cause and Common Cause
The correct response depends on whether it is Special or Common Cause…
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Responding to Variation
Type ofVariation?
SpecialCommon
Yes
MeetsRequirements?
No
Do Nothing Use all the data to understand cause of variation. Make fundamental process change.
Respond to individual data points, determine cause, take corrective action
Common Cause VariationCustomer or Internal Requirement
1.2.
3.
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
Introduction to Control Charts
Distinguishing Common & Special Cause Variation
Example of Standard Business Reporting
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© 2010 International Institute for Learning, Inc.
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This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
Business Performance Report: Sales
Please assess our recent performance• Last month’s performance (108) is better than this month’s (101).
• This month’s performance (101) is about the same as YTD’s (102).
• But this month’s performance (101) is better than theperformance the same month last year (98).
Let’s see if our interpretation changes when we plot our data over time, where variation can be seen and taken into account…
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Month
Scen
ario
1
FebDecOctAugJunAprFebDecOctAugJun
110
105
100
95
90
97.6197.61
Time Series Plot of Scenario 1
This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
This chart supports an interpretation of a significant change last month – a special cause.
Scenario 1
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© 2010 International Institute for Learning, Inc.
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Month
Scen
ario
2
FebDecOctAugJunAprFebDecOctAugJun
110
105
100
95
90
97.6197.61
Time Series Plot of Scenario 2
Scenario 2
This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
Last month’s result doesn’t appear unusual – just common cause variation.
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
Control Chart for Scenario 1
Month
Indi
vidu
al V
alue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_X=97.61
UCL=104.96
LCL=90.26
1
I Chart of Scenario 1
Last month’s performance is Special Cause variationSCS Singapore Version 1.0
Control Charts are based on the data and show Common Cause variation
© 2010 International Institute for Learning, Inc.
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This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
Control Chart Scenario 2
Month
Indi
vidu
al V
alue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_X=97.61
UCL=114.49
LCL=80.73
I Chart of Scenario 2
Last month’s performance is Common Cause variationSCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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This Month
Last Month
Year-To-
Date
Same Month Last Year
101 108 102 98
Control Chart Scenario 2: Tampering
Month
Indi
vidu
al V
alue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_X=97.61
UCL=114.49
LCL=80.73
I Chart of Scenario 2
If a process with Common Cause variation is adjusted based on individual data points (tampering) then process variation will increase!
Minimum Requirement
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© 2010 International Institute for Learning, Inc.
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Conclusions: Standard Business Reporting
Two radically different processes, requiring different management approaches, both produce the same standard management report … this should concern you!
Charting data over time gives context.Can see patterns and variation in the data
Control Charts plot data over time and use Control Limits to detect Special Cause variation so appropriate action can be taken.
Do managers and workers in your company understand the difference between common and special cause variation? If not, then tampering is occurring.
Month
Indi
vidu
al V
alue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_X=97.61
UCL=114.49
LCL=80.73
I Chart of Scenario 2
Month
Indi
vidu
al V
alue
FebDecOctAugJunAprFebDecOctAugJun
115
110
105
100
95
90
85
80
_X=97.61
UCL=104.96
LCL=90.26
1
I Chart of Scenario 1
This Month
Last Month
Year-To-Date
Same Month Last Year
101 108 102 98
SCS Singapore Version 1.0
© 2010 International Institute for Learning, Inc.
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Two Kinds of Variation: Responding Appropriately
Management takes a big step forward when it stops asking
workers to explain randomness.
D M A I C
© 2010 International Institute for Learning, Inc.
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Measure Performance &
Focus on Critical Areas
Summary: What is DMAIC?
Where’s the PAIN to the Customer? The Business?
80% 20%
Drill Down for Root Cause
Pull It Out by the Roots
Monitor & Take Action If Root
Cause Re-appears
DMAIC is the recipe or methodology for improving existing processes; it is the backbone of Six Sigma and the starting point for most companies beginning the Six Sigma journey.
Shortcu
t
© 2010 International Institute for Learning, Inc.
GS-63
SCS Singapore Version 1.0
“It’s all about the evidence”
Data is the bedrock of Six Sigma & DMAIC; it helps separate fact from fiction.
Voice of Customer, Financials
Baseline data, focusing data (Pareto Principle)
Cause & Effect Data
Before / After Data
Real-time Monitoring Data
Coun
t
Perc
ent
LocationCount
7.2 4.3 1.4Cum % 72.5 87.0 94.2 98.6 100.0
50 10 5 3 1Percent 72.5 14.5
OtherMWSWNW
70
60
50
40
30
20
10
0
100
80
60
40
20
0
Month
Erro
rs
DecNovOctSepAugJulJunMayAprMarFebJan
10
9
8
7
6
5
4
3
2
1
6
Experience
Cycl
e Ti
me
98765432
16
14
12
10
8
6
4
2Observation
Cycl
e Ti
me
2018161412108642
18
16
14
12
10
8
6
4
2
0
_X=4.50
UCL=7.71
LCL=1.29
Before After
Observation
Cost
24222018161412108642
14
12
10
8
6
4
2
0
_X=5.84
UCL=12.28
LCL=-0.61
© 2010 International Institute for Learning, Inc.
Data Specific Concepts (“It’s all about the evidence”)
DefineScoping ProjectsUnderstanding Customer Requirements
Measure
Seeing the ProcessThe Devil’s in the Details (PCE<5%)Impact of Multitasking
The State of DataMSA
Look at the Data (not just summaries of the data)
Analyze
Causal Reasoning (YY/NN)Lurking Variables
Improve
Verify Solutions (Before/After)
Control
Responding to Variation (Special/Common Cause)
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© 2010 International Institute for Learning, Inc.
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SCS Singapore Version 1.0
The Games afoot!
If you love…a mystery, andthe thrill of discovery, andthe satisfaction of verifiable, positive, enduring change
ThenLean Six Sigma will add a powerful new dimension to your skills!
© 2010 International Institute for Learning, Inc.
GS-66
SCS Singapore Version 1.0
About IIL
IIL Worldwide LocationsIIL has regional offices throughout the US and in major cities in Europe, Canada, Latin America and Asia. We can deliver the corporate solution that’s just right for your global needs. Our training materials can be delivered to you in different languages, and the experience of our subject matter professionals is international in scope.
ACE College Credit Recommendations The American Council on Education (ACE) College Credit Recommendation Service (CREDIT) has recommended numerous IIL courses for undergraduate and graduate ACE credits.
Member of SCS's Corporate Council The Corporate Council is designed to provide corporations the opportunity to support and associate with SCS directly and to develop synergies between SCS and senior executives at leading corporations in the global community.
SCS Registered Education ProviderRegistered Education Providers (REPs) are organizations approved by SCS to offer project management training for Professional Development Units (PDU).
Certificate of Course Completion IIL is an authorized CEU sponsor member of the International Association for Continuing Education and Training.
Letter Grades and TranscriptsIIL has established cooperative agreements with universities, such as The University of Chicago.
The Kerzner Approach® to Best Practices (APMC™) Completion of this 64-hour advanced live eLearning curriculum extends beyond what is needed to complete individual projects on time and within budget. It focuses on providing you with advanced project management knowledge and integrating project management process improvement into an organization at every level--from individual projects up through enterprise-wide portfolio management.