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Dmaic Lean Six Sigma
- 1. CSI Singapore
Following 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.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 2. 2
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© 2010 International Institute for Learning, Inc.
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© 2010 International Institute for Learning, Inc.
- 4. GS-4
Who Am I?
Robert Johnston, Ph.D. Statistics, MBB
Philosophy: practicality trumps theory
• Utility = (Perfection of idea) * (Probability people will use it)
Experience
Animal 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
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 5. GS-5
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
SCS ingapore © 2010 International Institute for Learning, Inc. Version 1.0
- 6. GS-6
What is Lean Six Sigma?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 7. GS-7
Lean Six Sigma Triad
Main
Focus
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 8. GS-8
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.
Define the
process/product and the
business case
Verify D Drive Customer
Requirements Through
V
process/product
performance Entire Design Cycle
Measure: Define the
FMEA QFD M customer requirements
and prioritize them
Manage
Risk
Develop detailed
design
D
A
Analyze functional requirements,
create high-level design
© 2010 International Institute for Learning, Inc.
- 9. GS-9
What is DMAIC?
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.
Where’s the PAIN to the
Customer? The Business?
Monitor & Take
Action If Root
Cause Re-appears t
tcu Measure
or Performance &
Sh Focus on Critical
Areas
80% 20%
Pull It Out by
the Roots
Drill Down for
Root Cause
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 10. GS-10
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.
Real-time Voice of Customer,
Monitoring Data Financials
14
12 UCL=12.28
10
8
Cost
_
6 X=5.84
4
10
2
9
0
LCL=-0.61 8
2 4 6 8 10 12 14 16 18 20 22 24 7
Observation
Errors
6 6
5
4
3
2
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Baseline data, focusing data
Before / After (Pareto Principle)
Data Before After
70
60
50
100
80
18
60
Percent
40
Count
16
16
14 30
40
14
12 20
Cycle Time
12 20
10
10
8 UCL=7.71
Cycle Time
10 0 0
6 Location NW W S MW Other
_ Count 50 10 5 3 1
X=4.50 8
4 Percent 72.5 14.5 7.2 4.3 1.4
Cum % 72.5 87.0 94.2 98.6 100.0
2 6
LCL=1.29
0
2 4 6 8 10 12
Observation
14 16 18 20
Cause & Effect Data 4
2
2 3 4 5 6
Experience
7 8 9
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 11. GS-11
Six Sigma & Lean (It’s like Chocolate and Peanut Butter)
Six Sigma Focus on Quality
Customer Requirements
Variation & Defect Reduction
Six Sigma
Data Based
Support Infrastructure
Lean Focus on Speed Lean
Cycle Time Reduction
Elimination of Waste
Rapid Project Execution
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 12. GS-12
Why is it called Six Sigma? (optional)
Sigma (σ, standard deviation ) measures process variation (VOP)
Customer Customer
Requirement Requirement
σ σ σ σ σ σ
Mean
Bad Good Bad
Compared to Customer Requirements (VOC) shows the % Defects
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 13. GS-13
Why is it called Six Sigma? (optional)
Reducing variation means reducing the number of defects
3.4 Defects
per Million
Customer Customer
Requirement Requirement
σ σ σ σ σ σ σ σ σ σ σ σ
Mean
Bad Good Bad
Six Sigma represents 6 standard deviations from the mean
to the upper or lower specification limits of the customer
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 14. GS-14
DMAIC: Following the Chain of Evidence
Improving Processes
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 15. GS-15
Define: Houston, we have a problem!
D M A I C
ID the Process
Including Supplier, Inputs, Outputs, Customer
ID the Customer ,his/her Requirements, and
the Performance Gap
Critical To Quality (CTQ)
Make them Measureable
Define a Defect
Input Output
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 16. GS-16
Define: CTQ Identification Example
D M A I C
You’ve just ordered a pizza from a local pizza delivery
shop. What are your CTQs ?
4-5 oz cheese…
40-50oC on delivery
<30 min
More specific and measureable …
Not very specific or measureable …
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 17. GS-17
Measure: So, how bad is it?
D M A I C
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
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 18. 1-18
Process Focus
What is supposed to happen…
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 19. 1-19
Process Focus
What really happens… “Hidden
Factory”
Rework … Inspection … Delays … Work-a-rounds …
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 20. GS-20
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
What is a typical value for PCE?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 21. 2-21
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.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 22. 2-22
Little’s Law
Little’s Law states: Like the line at an
amusement park:
WIP
PLT =
Exit Rate
IN
Exit Rate:
Where… OUT 2 people
minute
PLT = Process Cycle Time
WIP = Work In Process
Exit Rate = Units/Time
12 People
PLT = People
2 Minute
= 6 Minutes
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 23. 2-23
Little’s Law: WIP (1 of 2)
If WIP is reduced, then Lead Time is reduced:
IN 6 People
PLT = People
Exit Rate: 2 Minute
OUT 2 people
minute = 3 Minutes
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?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 24. 2-24
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
Waiting Room IN 6 People
PLT = People
Exit Rate: 2 Minute
OUT 2 people
minute = 3 Minutes
This one can be expedited if necessary
(can be done in 3 minutes instead of
the original 6 minutes).
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 25. 2-25
General Application of Little’s Law to
Projects/Initiatives/Work
Work many things at once
Project W1 W2 W3 W4 W5
A $ $
B $ $
C $ $
Focus on a few things
at a time
Project W1 W2 W3 W4 W5
A $ $ $ $
B $ $ $
C D $ $
Increased Value
Increased Flexibility
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 26. GS-26
A Word on Planning Data Collection: Avoid a Port-Mortem
D M A I C
1. What is the question?
3. Collect data to go from 1. to 2.
2. What Graph/Summary will answer it?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 27. Check the Measurement System – 2-27
Is Our Data Any Good?
D M A I C
Measurement
System
X
X
Process
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 28. 2-28
Measurement Systems Analysis (MSA) Exercise
D M A I C
M&M Company wants to improve the quality of their
output.
It’s a Good M&M if…
Clear/Legible Logo, and
Uniform/Consistent Color, and
No Cracks in Shell
Otherwise, it’s a Bad M&M.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 29. 2-29
Measurement Systems Analysis (MSA) Exercise
D M A I C
A B C D E
Teams of 5 or 6
1
Make a Team grid, 5x5,
place 25 M&Ms in the 2
grid (flip chart paper)
3
Each team member
makes a 5x5 score sheet 4
(8.5x11 or A4)
Independently grade 5
each M&M as Good (G)
or Bad (B). No talking, 1
A B C
GG B
D E
G B
sounds of amazement, 2 G B B GG
etc.
3 B B G B G
4 B B B G B
5 B GG G B
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 30. 2-30
Measurement Systems Analysis (MSA)
Exercise Answers
D M A I C
When done, choose a
spokesperson to read
through score sheet one item 1
A B C
GG B
D E
G B
at a time. 2
3
G B B
B B G
GG
B G
4 B B B G B
If all Team Members agree, 5 B GG G B
then they get a point.
Report Team Point Total.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 31. 2-31
Measurement Systems Analysis (MSA)
Exercise Answers
D M A I C
100 Desired Results
% Agreement
75
50
Typical Results!
25
0
1 2 3 4 5 6…
Team
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 32. GS-32
MSA Examples
Banking
IT
Manufacturing
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 33. 2-33
Existing Data Sources
There is a lot of data out there
Review whatever you can find
Guidelines for using existing data
How 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)
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 34. GS-34
Looking at Data
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
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 35. GS-35
Look at the Data
Need to start looking at the raw data – not just
summaries of the data – variation is important!
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 36. GS-36
Look at the Data: Another Example
Company complaint resolution process:
Goal: Resolution <50 days
Actual: Average Resolution = 97 days!
CEO decides need major/fundamental process change
Requires fundamental
process change
Fundamentally process OK
– it’s the exceptions
Which is it? Both have average of 97!
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 37. GS-37
Analyze: Find the Root Cause: y=f(x)
D M A I C
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 38. GS-38
Analyze: Verify Cause & Effect Relationship
D M A I C
Dotplot of Approval Time vs Location Scatterplot of Cycle Time vs Loan $
Stratified 65 Scatterplot
Location
Cycle Time
London
•Dotplot 55
Continuous •Boxplot 45
NY
40 50
Approval Time
60 70
•Histogram 35
100000 125000 150000 175000
Each symbol represents up to 2 observations.
Loan $
•t-test •Regression
•ANOVA / ANOM •Multiple Regression
•Test of Equal Variance
Y: Effect
•DOE
Pareto Chart of Sale by Region Dotplot of Face Time vs Sale
25
Region = E
NO YES
Region = W Sale
NO
YES
Stratified Stratified
•Pareto •Dotplot
Sale
20
Count
YES
15
10 or •Boxplot
5
Table •Histogram
NO
40 50 60 70
Discrete
0
NO YES Face Time
Sale Each symbol represents up to 2 observations.
•Test of Two Proportions •Logistic Regression
•Chi-square
Discrete Continuous
X: Potential Cause or
Stratification Factor
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 39. GS-39
Analyze: Verify Cause & Effect Relationship- YY/NN
D M A I C
Effect (Y) Present?
YES
Y/Y
NO
N/N
NO YES
Potential Cause (X) Present?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 40. GS-40
Causal Relationships- Lurking Variables
D M A I C
Lurking Variables are ones you did not measure, or even
consider, that impact your process/data
0 5 10 20 25
# Drownings
0 500 1000
# Ice-cream Sales
What’s the Lurking Variable?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 41. GS-41
Causal Relationships- Lurking Variables
D M A I C
The number of people at the beach which is a function
of Temperature!
1000
0 5 10 20 25
# Ice-Cream Sales
# Drownings
0 500
50 70 90 50 70 90
Temperature Temperature
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 42. GS-42
Examples of Lurking Variables
Number of Damaged Cartons
per shift
Training didn’t solve the problem…
It was the fork-trucks! New employees got the
old fork-trucks – they had a design flaw
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 43. GS-43
Lurking Variables: Aggregated Data
D M A I C
Death Rates in Hospitals A B
Deaths 450 130
(15%) (11.8%)
Patients 3000 1100
What if account for Patient Condition?
Good Condition Poor Condition
A B A B
Deaths 50 100 Deaths 400 30
(5%) (10%) (20%) (30%)
Patients 1000 1000 Patients 2000 100
Watch out for Lurking Variables in Causal Analysis!
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 44. GS-44
Improve: Fix It!
D M A I C
Eliminate the Brainstorm solutions
Root Cause Evaluate Solutions and Select
best
Manage Risk
Pilot Solution
Verify Results
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 45. GS-45
Before & After
Many solutions don’t actually help
How will you know if yours did?
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 46. GS-46
Control: Make it Stay Fixed
D M A I C
Standardize Process
Train on the new Process
On-going Process
Monitoring
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 47. 2-47
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 © 2010 International Institute for Learning, Inc. Version 1.0
- 48. 2-48
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 2
Results
41% increase
Rule 1
in variation!
Results
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 49. GS-49
Control: Two Kinds of Variation
D M A I C
Special Cause – events Common Cause – events
only happen sometimes to happen sometimes to
some people/processes everyone
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 50. 2-50
Exercise: Two Kinds of Variation
Sign your name 3 times
Common Cause
Now with other hand Special
Cause
Common Cause
(just more of it than with the other hand)
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 51. 2-51
Understanding Variation
Why it matters
Variation 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 © 2010 International Institute for Learning, Inc. Version 1.0
- 52. 2-52
Responding to Variation
Type of
Variation?
Common Special
Meets Respond to individual data points,
determine cause, take corrective action
Requirements?
3.
Yes No
Use all the data to understand cause of
Do Nothing
variation. Make fundamental process change.
1.
2.
Common Cause Variation
Customer or Internal Requirement
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 53. Introduction to Control Charts
Distinguishing Common & Special Cause Variation
Example of Standard Business Reporting
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 54. 2-54
Business Performance Report: Sales
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
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 the
performance 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 © 2010 International Institute for Learning, Inc. Version 1.0
- 55. 2-55
Scenario 1
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
Time Series Plot of Scenario 1
110
105
Scenario 1
100
97.61
95
90
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Month
This chart supports an interpretation of a significant change last
month – a special cause.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 56. 2-56
Scenario 2
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
Time Series Plot of Scenario 2
110
105
Scenario 2
100
97.61
95
90
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Month
Last month’s result doesn’t appear unusual – just
common cause variation.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 57. 2-57
Control Chart for Scenario 1
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
I Chart of Scenario 1
115
110 1
105 UCL=104.96
Control Charts are based
Individual Value
100 _
95
X=97.61
on the data and show
90 LCL=90.26 Common Cause variation
85
80
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Month
Last month’s performance is Special Cause variation
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 58. 2-58
Control Chart Scenario 2
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
I Chart of Scenario 2
115 UCL=114.49
110
105
Individual Value
100 _
X=97.61
95
90
85
80 LCL=80.73
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Month
Last month’s performance is Common Cause variation
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 59. 2-59
Control Chart Scenario 2: Tampering
Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98
I Chart of Scenario 2
115 UCL=114.49
110
105
Individual Value
100 _
X=97.61
95
Minimum Requirement
90
85
80 LCL=80.73
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Month
If a process with Common Cause variation is adjusted based on
individual data points (tampering) then process variation will increase!
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 60. 2-60
Conclusions: Standard Business Reporting
Two radically different processes, requiring Year-
Same
different management approaches, both produce This
Month
Last
Month
To-
Month
Last
the same standard management report … this Date
Year
should concern you! 101 108 102 98
Charting data over time gives context.
Can see patterns and variation in the data
Control Charts plot data over time and use
I Chart of S cenario 1 I Chart of S cenario 2
115 115 UCL=114.49
110 110
1
Control Limits to detect Special Cause variation
105 UCL=104.96 105
Individual Value
Individual Value
100 100 _
_
X=97.61 X=97.61
95 95
so appropriate action can be taken.
90 LC L=90.26 90
85 85
80 LC L=80.73
80
Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb Jun Aug Oct Dec Feb Apr Jun Aug Oct Dec Feb
Mont h Mont h
Do managers and workers in your company
understand the difference between common and
special cause variation? If not, then tampering is
occurring.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 61. GS-61
Two Kinds of Variation: Responding Appropriately
D M A I C
Management takes a big step
forward when it stops asking
workers to explain randomness.
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 62. GS-62
Summary: What is DMAIC?
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.
Where’s the PAIN to the
Customer? The Business?
Monitor & Take
Action If Root
Cause Re-appears t
tcu Measure
or Performance &
Sh Focus on Critical
Areas
80% 20%
Pull It Out by
the Roots
Drill Down for
Root Cause
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 63. GS-63
“It’s all about the evidence”
Data is the bedrock of Six Sigma & DMAIC; it helps
separate fact from fiction.
Real-time Voice of Customer,
Monitoring Data Financials
14
12 UCL=12.28
10
8
Cost
_
6 X=5.84
4
10
2
9
0
LCL=-0.61 8
2 4 6 8 10 12 14 16 18 20 22 24 7
Observation
6 6
Errors
5
4
3
2
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Baseline data, focusing data
Before / After (Pareto Principle)
Data Before After
70
60
50
100
80
18
60
Percent
40
Count
16
16
14 30
40
14
12 20
Cycle Time
12 20
10
10
8 UCL=7.71
Cycle Time
10 0 0
6 Location NW W S MW Other
_ Count 50 10 5 3 1
X=4.50 8
4 Percent 72.5 14.5 7.2 4.3 1.4
Cum % 72.5 87.0 94.2 98.6 100.0
2 6
LCL=1.29
0
2 4 6 8 10 12
Observation
14 16 18 20
Cause & Effect Data 4
2
2 3 4 5 6
Experience
7 8 9
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 64. GS-64
Data Specific Concepts (“It’s all about the evidence”)
Define
Scoping Projects
Understanding Customer Requirements
Measure
Seeing the Process
The Devil’s in the Details (PCE<5%)
Impact of Multitasking
The State of Data
MSA
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)
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 65. GS-65
The Games afoot!
If you love…
a mystery, and
the thrill of discovery, and
the satisfaction of verifiable, positive, enduring change
Then
Lean Six Sigma will add a powerful new dimension to your
skills!
SCS Singapore © 2010 International Institute for Learning, Inc. Version 1.0
- 66. GS-66
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IIL has regional offices throughout the US and in major provide corporations the opportunity
cities in Europe, Canada, Latin America and Asia. We can to support and associate with SCS
deliver the corporate solution that’s just right for your directly and to develop synergies
global needs. Our training materials can be delivered to you between SCS and senior executives at
in different languages, and the experience of our subject leading corporations in the global
matter professionals is international in scope. community.
SCS Registered Education Provider
Registered Education Providers (REPs)
are organizations approved by SCS to
offer project management training for
Professional Development Units (PDU).
The Kerzner Approach® to Best Practices (APMC™)
Completion of this 64-hour advanced live eLearning curriculum Certificate of Course Completion
extends beyond what is needed to complete individual IIL is an authorized CEU sponsor
projects on time and within budget. It focuses on providing member of the International
you with advanced project management knowledge and Association for Continuing
integrating project management process improvement into an Education and Training.
organization at every level--from individual projects up
through enterprise-wide portfolio management.
ACE College Credit Recommendations
The American Council on Education
(ACE) College Credit Recommendation
Service (CREDIT) has recommended
Letter Grades and Transcripts numerous IIL courses for
IIL has established cooperative agreements with undergraduate and graduate ACE
universities, such as The University of Chicago. credits.
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