Dmaic Lean Six Sigma

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

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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

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

GS-6

What is Lean Six Sigma?


GS-7

Lean Six Sigma Triad

Main
Focus


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


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


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


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


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


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

GS-14

DMAIC: Following the Chain of Evidence

Improving Processes


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


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 …

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


1-18

Process Focus

What is supposed to happen…


1-19

Process Focus

What really happens… “Hidden
Factory”

Rework … Inspection … Delays … Work-a-rounds …

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?


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.


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


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?


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).


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


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?


Check the Measurement System – 2-27

Is Our Data Any Good?
D M A I C

Measurement
System
X
X

Process


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.


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


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.


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


GS-32

MSA Examples

Banking

IT

Manufacturing


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)


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

GS-35

Look at the Data

Need to start looking at the raw data – not just
summaries of the data – variation is important!


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!

GS-37

Analyze: Find the Root Cause: y=f(x)
D M A I C


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


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?


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?

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


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


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!


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


GS-45

Before & After

Many solutions don’t actually help
How will you know if yours did?


GS-46

Control: Make it Stay Fixed
D M A I C

Standardize Process
Train on the new Process
On-going Process
Monitoring


2-47

Responding to Variation Inappropriately

Rule 1: Do Nothing
– Start Funnel at 50
– Drop 24 Balls

Rule 2: Compensate
– Drop
– Adjust: e.g., if ball drops 3
below target, adjust funnel
3 up, etc.
– Repeat Drop & Adjust
cycle 24 times


2-48

Responding to Variation Inappropriately

Rule 1: Do Nothing
– Drop 24 Balls

Rule 2: Compensate
– 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


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


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)


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…


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


Introduction to Control Charts

Distinguishing Common & Special Cause Variation

Example of Standard Business Reporting


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…


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.

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

Month

Last month’s result doesn’t appear unusual – just
common cause variation.

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
Month

Last month’s performance is Special Cause variation

2-58

Control Chart Scenario 2

Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98

115 UCL=114.49

110

105
Individual Value

100 _
X=97.61
95

90

85

80 LCL=80.73

Month

Last month’s performance is Common Cause variation

2-59

Control Chart Scenario 2: Tampering

Same
Year-
This Last Month
To-
Month Month Last
Date
Year
101 108 102 98

115 UCL=114.49

110

105
Individual Value

100 _
X=97.61
95
Minimum Requirement
90

85

80 LCL=80.73

Month

If a process with Common Cause variation is adjusted based on
individual data points (tampering) then process variation will increase!

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.


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.


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


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


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)


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!


GS-66

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