1. 6σ Overview
Greenbelt Training
1

2. Concept of Competitive Quality
Historically, Quality has been defined as
“ Meeting the Specifications”
“ Fitness for Use”
Today the most acceptable definition of Quality is
“ Meeting the Customer’s Requirements”
2

3. Concept of Competitive Quality
Will just meeting the requirements
ensure future Market share ?
NO !
3

4. Concept of Competitive Quality
The definition of Competitive Quality is :
“ Product, Process or Service Uniformity
around a Target Value”
The most noticeable difference in this operational definition of Quality
The most noticeable difference in this operational definition of Quality
is that ititrequires
is that requires
CONTINUOUS IMPROVEMENT
CONTINUOUS IMPROVEMENT
4

5. Uniformity Around A Target Value
WHY CHANGE ?
The reason is simple, to remain or become competitive and thereby
The reason is simple, to remain or become competitive and thereby
increase Profitability and Market Share
increase Profitability and Market Share
6

6. The Impact of Added Inspection
If the likelihood of detecting the defect is 70% and
we have 10 consecutive inspectors with this level
of capability, we would expect about 6 escaping
1,000,000 ppm
defects out of every 1,000,000 defects produced.
6 ppm
3.4 ppm
You can save your self by producing quality
You can save your self by producing quality
7 not by Inspection
not by Inspection

7. Sigma - The Standard Deviation
1 Sigma - 68%
µ σ= Σ (X – X)2
2 Sigma - 95%
3 Sigma - 99.73 %
n
1σ p(d)
Upper Specification Limit (USL)
Target Specification (T)
T USL
Lower Specification Limit (LSL)
Mean of the distribution (m)
Standard Deviation of the distribution (s) 3σ
8

8. What is Six Sigma
A 3σ process because 3 standard deviations
fit between target and spec
Before
Target Customer
3σ Specification
1σ
0.27% Defects up-to 6.6 %
2σ
3σ
Customer
Target Specification
After
1σ
2σ 6σ !
3σ No Defects!
4σ
5σ
6σ
9 Reducing Variability Is The Key To Six Sigma

9. Philosophy of Six Sigma
Six Sigma focuses on :
♦ Continuous Improvement of
Processes
♦Defect Prevention through the
use of Statistical tools as
opposed to Defect Detection
through inspection.
10

10. The Many Facets of Six Sigma
• Focus and commitment to quality • Six Sigma provides classical
must be driven by top leadership problem solving tools enhanced
• Leadership must be fully with a fundamental knowledge of
engaged and accountable for statistics and variation
6σ
success
Leadership Tools
Process Metrics
• Focus is on statistical process • Goal is defect free products and
capability and process variation processes
analysis • Focus is on true capability
• Products must be designed to be (rolled throughput yield) rather
manufactured within process than end of line yield
capability • All decisions must be data
• Process capability must be driven
quantified
11

11. How Good is Six Sigma
99% Good (3.8 Sigma) 99.99966% Good (6 Sigma)
20,000 lost articles of mail per hour Seven articles lost per hour
Unsafe drinking water for almost One unsafe minute every seven months
15 minutes each day
5,000 incorrect surgical operations per 1.7 incorrect operations per week
week
Two short or long landings at most major One short or long landing every
airports each day five years
200,000 wrong drug prescriptions each 68 wrong prescriptions per year
year
No electricity for almost seven One hour without electricity every 34 years
hours each month
12

12. What does Six Sigma means to [Company
Name]……
Top Line Growth - satisfied customers are repeat customers
Bottom Line Growth - it costs less to do it right the first time
13

13. Industry Overview
The Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC)
model consistently yields rapid, measurable, benefits
Avery Dennison Results:
Avery Dennison Results: AlliedSignal Results:
AlliedSignal Results:
• •Implemented 6s in March, 1998.
Implemented 6s in March, 1998. • •Implemented 6 in 1994 for Operations.
Implemented 6 in 1994 for Operations.
• •52 completed projects
52 completed projects • •Initial 44months, 600 projects reduced
Initial months, 600 projects reduced
Forecast ‘99 savings == $18,500,000
Forecast ‘99 savings $18,500,000 defects by 68%.
defects by 68%.
Actual savings (through 2Q)=$5,800,000 • •Saved $175M at bottom line in ‘95;
Actual savings (through 2Q)=$5,800,000 Saved $175M at bottom line in ‘95;
$500M in ‘98 ! ! (not includingoverhead,
$500M in ‘98 (not including overhead,
• •130 projects planned or in progress
130 projects planned or in progress inventory, indirect charges, or avoidance.
inventory, indirect charges, or avoidance.
Annualized savings ==$35M
Annualized savings $35M • •Over $2B in savings realized since 1992.
Over $2B in savings realized since 1992.
‘99 impact ==$12.3M • •Fastest rate for implementing 6 yet!
‘99 impact $12.3M Fastest rate for implementing 6 yet!
GE Results: Motorola Results:
Motorola Results:
GE Results: • •Implemented 6 program in 1987 when
Implemented 6 program in 1987 when
Implemented  efforts in late 1995.
••Implemented 66 efforts inlate 1995. was performing at  level.
itit was performingat aa44 level.
• • Targets savings over$10 BB duringnext
Targets savings over $10 during next • •By 1992 it averaged a 5.21 level.
few years by reducing its current COPQ By 1992 it averaged a 5.21 level.
few years by reducing its current COPQ
($7B /yr.) to less than $1B annually by:
($7B /yr.) to less than $1B annually by: • •Sales productivity increased from
- - Reducingscrap parts.
Reducing scrap parts. Sales productivity increased from
$68.9K to $110.1K per employee
$68.9K to $110.1K per employee
- - Reducingreworked parts.
Reducing reworked parts. and savings due to US operations
- - Rectifyingtransaction mistakes.
Rectifying transaction mistakes. and savings due to US operations
14 improvements were over $2.2 Billion.
improvements were over $2.2 Billion.

14. Cost Opportunity
Cost of Failure (% Revenue)
40% If [Company Name] is a 3σ Company, Cost of
35% Failure
is Estimated to be at Least 15% of Revenue
30%
25%
20%
15%
10%
5%
Defects per Million
3.4 233 6210 66,807 308,537 691,462
Sigma 6 5 4 3 2 1
A $4.5 Million Cost Reduction Opportunity!
15

15. Six Sigma Saving in GE
renc e only
For Refe Six Sigma Cost and Benefits
2500
2000
2000 Cost
Benefits 1500
1450
1500 Net
$ Million
1050
1000
700
550
380 450
500 200 320
170 -30
0
1996 1997 1998 1999
-500
Mostly variable cost productivity and asset utilization
Up front investment and staying power
Up front investment and staying power
Significant impact on the bottom line
Significant impact on the bottom line
16

16. How much Cost Reduction is possible
Traditional Quality Costs
(Easily Identified) Inspection
Warranty
Scrap
1.5 % COQ
Rework
Rejects
(tangible)
Hidden Quality Costs Lost sales
(Difficult to measure)
Customer Sat
Long cycle times
Overtime 15 % COQ
Field Modifications (intangible) Late delivery
More Setups T&L
Excess inventory
Expediting costs Lost Opportunity
Lengthy Installs
Customer Productivity Loss
Lost Customer Loyalty
Engineering change orders
Employee Morale, Productivity, Turnover
. .. .. .Six Sigma Reveals hidden facts and capabilities
Six Sigma Reveals hidden facts and capabilities
17

17. Six Sigma as a Goal
Distribution Shifted ± 1.5s
σ PPM
2 308,537
3 66,807
4 6,210
5 233
6 3.4
Process Defects Per Million
Capability Opportunities
18

18. Harvesting the Fruit of Six Sigma
Sweet Fruit
Design for Manufacturability
5 5 s Wall Must Address Designs
s Wall - - Must Address Designs
Bulk of Fruit
Process Characterization and Optimization
----------------------------------
4 4 s Wall Must Improve Internally
s Wall - - Must Improve Internally
Low Hanging Fruit
Seven Basic Tools
----------------------------------
3 3 s Wall Demand improvement
s Wall - - Demand improvement
Ground Fruit
Logic and Intuition
The walls crumble faster when
addressing process issues
19

19. Attacking the Problem
Practical
Practical Rejects for bad estimation of cost =20% average
Rejects for bad estimation of cost =20% average
Problem
Problem
• •Process characterization data set is non-normal.
Process characterization data set is non-normal.
Statistical
Statistical • •After normalization: σST ==5.50
After normalization: σST 5.50
Problem
Problem σLT ==2.36
σLT 2.36
• •DOE Results:
DOE Results:
Statistical
Statistical - -Technology
Technology - -52%
52%
Solution - -Labour rate
Labour rate - -24%
24%
Solution - -Interaction - -19%
Interaction 19%
• •Install standard measurement system for each technology
Install standard measurement system for each technology
Practical
Practical • •Reward &&recognition policy to retain experienced labours in order to
Reward recognition policy to retain experienced labours in order to
Solution
Solution increase productivity
increase productivity
20

20. The Focus of Six Sigma
Y f(X)
• Y • X1 . . . Xn
• Dependent • Independent
• Output • Input-Process
• Effect • Cause
• Symptom • Problem
• Monitor • Control
Would you control shooter or target to get the Gold Medal at Olympics
21

21. Controlling the Output
Y = F (x)
OUTPUT SIGNAL IN-PROCESS PARAMETERS
RELATIONSHIP or EQUATION
THAT EXPLAINS Y IN TERMS OF X
Determined by
Distance traveled Car speed, traveling time
Determined by
Money to Spend Income, Commitments,
Credit Rating
OUTPUT (Y) IS DETERMINED BY THE VALUES
OF THE IN-PROCESS PARAMETERS (X’s)
22

22. Controlling the Output
Y = F (x)
OUTPUT SIGNAL IN-PROCESS PARAMETERS
RELATIONSHIP or EQUATION
THAT EXPLAINS Y IN TERMS OF X
Determined by
Distance traveled Car Speed traveling time
Car speed,
Amount of wear on brakes
Selection of CDs available
Amount of gas in the tank
Time since last service
Understanding theF Traveling time
Number of passengers
gives insight into the right (x)S Weather
23 Car inside temperature

23. Controlling the Output
Y = F (x)
UNDERSTANDING OF F
OUTPUT SIGNAL, Y VERIFY
UNDERSTANDING SELECT IMPORTANT (x)S
OF F
QUANTIFY WITH
CORRELATION
How we’ve been taught to search for F
24

24. Controlling the Output
Y = F (x)
POOR OR NO
UNDERSTANDING OF F
AIN
OUTPUT SIGNAL, Y TRY
AG
BRAINSTORM (x)S
NO CORRELATION
Thousands of(x)s to choose from.......
Without an understanding of F - it’s your opinion vs mine !
25

25. Model
Y = F (x)
Capturing the measurement
on a Customer unit basis
Understanding our output
Verify Correlation to find F
as the Customer sees it
Learning from variance
in performance on Customer Y
Unlocking the process keys
that control Customer impact
26

26. What are these opportunities…..
Cost of Quality
SPAN
Customer Satisfaction
27

27. Cost of Quality
Defects/Million
Sigma Level Cost of Quality
Opportunities
2 308,000 Not Competitive
3 66,800 ($12b) 25 - 40% of Sales
6210 ($7b)
4 15 - 25% of Sales
Industry Average
5 233 ($4.5b) 5 - 15% of Sales
3.4($0.3b)
6 <1% of Sales
World Class
28

28. SPAN
Order-to-Delivery Time vs Customer Want Date
40 Day Span
5% of orders
are >25 days
5% of orders
late to request
are >15 days
early to request
-15 days 0 +25 days
Early Late
Customer Want Date
29 On Time

29. Difference between Mean & variance
Average River
Depth - 4ft
Focus on Average can turn your business red
Focus on Average can turn your business red
30

30. Outside In Thinking
Delivery cycle time (days) Insight Through Variance
Baseline Improved? What WE see
12 27
24 7
13 15
7 4
16 18
8 6
20 23
25 6 11.2 15.8
14 2
10 24 What customers feel
11 2 • Using mean-based thinking, we improve
30 6 average performance by 29%, and we
16 5 break out the champagne ...
Mean 15.8 11.2 • But our customer only feels the variance
Std Dev 7.0 9.0 and cancels the next order!
Customers feel variance, not the mean
Customers feel variance, not the mean
31

31. The Eye of the Beholder
Customer’s How did [Company Name] influence my
View A→ C Performance?
Missing data during download
Customer
Process
A B C
[Company Name] Process
How did I do against my [Company
A→ B Obligations? Name]’s View
Missing data during logical execution
32

32. Where in [Company Name]…
... Can Be Applied To Every Business Function
Business Operations
Development
Training
HR
6 Sigma IT
CRT
Methods
Finance Quality
Admin /
Transport Projects
33

33. Why Now?
Customers & Competitors are adopting elements of
this business improvement process:
– Customers:
• HP
• Intuit
– Competitors:
• Wipro Spectramind
• EXL
• Hughes
Driven by Customer Excellence at Lowest Cost
Driven by Customer Excellence at Lowest Cost
34

34. Table of Contents
Six Sigma Overview Pre-Tea
What is Six Sigma
Overview of Scope
Linkage to Vision
Roles and Responsibilities as Leaders/ Sponsor Pre-Tea
Criteria for Project Selection Pre-Tea
Criteria for BB/GB Selection Post-Tea
Introduction to Process Post- Tea
35

35. Implementation Strategy
Train….
Apply…..
Review…..
Every
Every
Participant arrives
Participant arrives
to training with aa well
to training with well
defined project with
defined project with
measurable savings
measurable savings
opportunities!
opportunities!
Integrate training with metrics performance to maximize the bottom line impact.
Integrate training with metrics performance to maximize the bottom line impact.
36

36. Six Sigma Program Structure
Define
Define
Measure
Measure
Analyze
Analyze
Improve
Improve
Program Direction,
Management Process Control
Support, Control
Owner
and Marketing Leadership
Black Belt
Change Agents
and
Process Leaders
Green Belt
Organizational
“Buy-in”
Program is structured to build aaself-sustaining critical mass of
Program is structured to build self-sustaining critical mass of
process improvement competencies.
process improvement competencies.
37

37. Champion / Functional Leader Role
1. Lead the Six Sigma efforts overall in their BU
2. Provide Strategic Direction for Six Sigma Project teams
3. Track the Project’s Progress, Offer rewards as appropriate
4. Help the Black Belt / Green Belt overcome roadblocks, including seeking collaboration
5. Help find resources for the team as Needed, Allocate resources when authorized
6. Keep Black Belt / Green Belt focused on desired results
7. When immovable objects block the road, Redirect Project / Team activities
8. Serve as the Team’s Champion from Top-To-Bottom of Entire Business
9. Ensure that Project Solutions are well implemented, Gains are sustained and on-going
responsibility transfers to Process Owner
38

38. Six Sigma Roles
• Champion/Sponsor/Functional Leader
– The Champion Or Sponsor Is The Person(s) Who Is Accountable For And Sanctions A Six Sigma Project. The
Champion Or Sponsor Is Involved In Project Team Chartering, Reviews Progress, Helps Remove Organizational
Barriers To Project Success, And Is Often The Decision-Maker For Approval Of Final Recommendations
• Master Black Belt
– Full-Time Positions Dedicated To Supporting Six Sigma Efforts. “Expert” Resources To Black Belts And Teams On The
Six Sigma Tools And Techniques Coach And Assist Black Belts And Team Members. Train The Black Belts,
Champions, And Employees As Needed.
• Black Belt (Team Leader)
– Full-time position where the Black Belt Is Accountable, Usually To The Champion, For The Project / Team Results. The
Black Belt Is Responsible For The Project / Team’s Progress, Provides Leadership In Planning The Project / Team’s
Work, Applies Six Sigma Tools And Teaches Team Members How To Apply Them, Often Leads Team Meetings, And
Ensures That Decisions Are Made By The Team In A Timely Manner To Meet Its’ Goals
• Green Belt (Team Leader)
– Part-time position where the Green Belt Is Accountable, Usually To The Champion, For The Project / Team Results.
The Green Belt Is Responsible For The Project / Team’s Progress, Provides Leadership In Planning The Project /
Team’s Work, Applies Six Sigma Tools And Teaches Team Members How To Apply Them, Often Leads Team
Meetings, And Ensures That Decisions Are Made By The Team In A Timely Manner To Meet Its’ Goals
• Team Members
– Team Members Are The Individuals Who Comprise The Six Sigma Project Team. Team Members Are Individually And
Collectively Accountable For Specific Tasks That Will Result In The Team’s Final Recommendation. When Team
Members Are Responsible For A Particular Aspect Of A Project, They Often Will Make Their Own Decisions
39

39. BQC – Roles & Responsibility
Six Sigma Steering Committee - BQC
– Assures [Company Name]’s Six Sigma implementation plan:
• Has appropriate resources allocated
• Has appropriate scope and is involving all required elements of the organization
• Is consistent with [Company Name]’s culture of Exceeding customer expectation
– Develop a communication plan to energize the organization around the Six Sigma
implementation.
– Remove roadblocks to facilitate implementation.
– Review the status of training / project implementation on a regular basis.
– Establish [Company Name]’s business metrics and goals, assess progress towards
goals, analyze strengths and weaknesses of implementation, and provide strategic
direction as necessary.
– Verify the financial (as calculated by Finance) impact of projects implemented.
– Provide a forum to share best practices within the organization.
40

40. Table of Contents
Six Sigma Overview Pre-Tea
What is Six Sigma
Overview of Scope
Linkage to Vision
Roles and Responsibilities as Leaders/ Sponsor Pre-Tea
Criteria for Project Selection Pre-Tea
Criteria for BB/GB Selection Post-Tea
Introduction to Process Post- Tea
41

41. Customer Focus
Start With The Customer
1.
1. Measure the same as the customer does
Measure the same as the customer does
2.
2. Determine your capability as the customer sees it
Determine your capability as the customer sees it
3.
3. Understand the variance in the output signal
Understand the variance in the output signal
4.
4. Find the in-process keys to impact the customer
Find the in-process keys to impact the customer
42

42. Customer Focus
What I Want to
be
What the Exceeding
What I What I What I
Customer Customer
Am (?) Am(?) Am(?)
Wants Expectations
Competition ?
Unhappy Status Quo Delighted
Customers Customers
Performance Continuum
How Important
43 Is This GAP?

43. Project Selection
Objective:
1. What exactly is the problem being addressed in measurable terms?
Need:
2. Why is the project worth doing?
3. Is the project tied to a high importance customer CTQ?
4. What are the consequences of not doing this project?
5. How would it fit with business initiatives and targets?
Scope:
6. Is the scope reasonable? Can the problem be effectively broken apart into projects with
reasonable scope?
Expectations:
7. Is there a clear owner in the organization for the problem and for the benefits of improving?
8. What specific goals would project be trying to achieve? What would constitute stretch results?
44

44. Project Objectives
What exactly is the problem being addressed in measurable terms?
PROBLEM
The problem this project is going to solve is:
- Takes too long to submit the quotation to customer
- The productivity is very poor in our company
- There is too much documentation in our work
MEASURABLE
In measurable terms that means:
- Improve time of submission of quotation from the time enquiry comes from customer
- Improve productivity by 15 %
- Reduce the paperwork by 20%
45

45. Project CTQs (Critical to Quality)
Who are your customers?
What do you provide your customers?
What is critical to quality for your customers?
What are your internal processes for providing your product or
service to customers?
What CTQ is this project addressing?
46

46. Project Need
Why is this project worth doing? What activities have higher or
equal priority?
Customer [Company Name]
Why is it important to do now? How does it fit with the business
initiatives and targets?
Customer [Company Name]
What are the consequences of
not doing this project?
Customer [Company Name]
47

47. Project Need
Write down threat and opportunity for short term and long term for the
problem you are addressing in your project
Threat Opportunity
Short
Term
1 3
2 4
Long
Term
48

48. Expectations
What specific goals must be met? When must they be met?
For each goal, what milestones are critical and must be met?
What would constitute stretch results?
49

49. Project Scoping
50

50. Project Scoping
How far down should I scope my project?
Why?
High level Why?
problem Initial Why?
Contributor Secondary
Level A Why?
Contributor Project
Level B
Level
Level C ???
Level D
Project Level
When you can no longer answer the “Why?” with confidence,
you have arrived at the project level.
51

51. Project Scoping
Why?
AHT too Why?
high AHT for Pavilion
P.L. is too high
Why?
AHT for Team
Level A -11 is too high Why?
AHT for New
Level B Agents is too
high
Level C ???
Level D
Project Level
Our project then becomes in measurable terms:
Improving the AHT for New Batches from 30 min. to 20 min.
52

52. Project Scoping
What must this project What (if anything) is out of
accomplish? bounds for the team?
What resources are available to What (if any) constraints must
the team? the team work under?
53

53. Criteria for BB / GB Selection
 Business Acumen
 Project and Process Management
 Data Affinity
 Result Orientation
 Relationship Building and Influence
 Coaching and Mentoring
 Team Leadership
 Change Leadership
54

54. Curriculum for Green Belt
 Receive 5 days training
 Understand the statistical tools and practice them
 Work on the project
 Monthly presentation to the Project Sponsor / MBB
 Close the project
 Clear Green Belt Certification Test
55

55. Table of Contents
Six Sigma Overview Pre-Tea
What is Six Sigma
Overview of Scope
Linkage to Vision
Roles and Responsibilities as Leaders/ Sponsor Pre-Tea
Criteria for Project Selection Pre-Tea
Criteria for BB/GB Selection Post-Tea
Introduction to Process Post- Tea
56

56. DFSS (DMADOV) vs DMAIC
DEFINE
IDENTIFY NO PROCESS ? YES MEASURE
ANALYSE
DESIGN NO CAPABLE ?
YES
OPTIMISE IMPROVE
VALIDATE CONTROL
57

57. DMAIC Process
How much and
Define the Measure What’s wrong what I can
project (Y) & Y & X’s with X’s improve
make the team
& plan
Control
Sustain the
improvement
Improve
Define
Measure Analyze CHECK
&
PLAN DO ACT
58

58. Six Sigma Tools Used……….
• Project Scoping
• SIPOC
• Thought Process Mapping
• Quality Metrices
• Process Mapping
• C&E matrix
• FMEA
• MSA
• Concepts of DOE
• DOE Strategies & Analysis
• DOE
• Control Strategies
59

59. Measurement Purpose

Document Process Map

Begin To Link CTQs to Input Variables

Establish Measurement Capabilities
 Establish Baseline Process Capabilities
60

60. The Funnel Effect
Process Map
+30 Inputs All X’s
C&E Matrix and FMEA
10-15 1st “Hit List”
Multi-Vari Studies
8 - 10 Screened List
Experimentation
4-8 Found Critical X’s
Control Plans / SPC 3-6 Controlling Critical X’s
Optimized Process
61

61. Input, Output & Process Measures
Input Measures Process Measures Output Measures
Measures That Are Internal
To Your Process. They
Include Quality And Delivery Output Measures Are
The Key Quality And Measures Important To Your Measures Used To
Delivery Requirements Internal Customers As Well Determine How Well
Placed On Your Suppliers. As Waste And Cycle Time Customer Needs And
Measures. They Are Requirements Are Met.
Correlated To The Pertinent
Output Measures.
62

62. Steps to Business Process Mapping
Develop A Picture Of The Working Process As A Team
Process
Suppliers Inputs Outputs Customers Requirement
Start
64

63. Define the Boundaries of Business Process
START
Boundary Boundary
Input
What Must My Suppliers Provide
My Process To Meet My Needs?
Process How Can I Assure That
My Process Output
Output
Meets The Needs Of
My Customer?
65

64. Process Map
[Company Name]
What You Think It Is... What It Really Is... What It Should Be... What It Could Be...
66

65. Industry Overview
Traditional View Final Test
“The Hidden Factory”
RTY is the probability that a product will pass through the entire process without rework and without any
defects. It is the true yield for a product at the completion of all the individual processes.
Six sigma View
Value Stream optimization is enabled by elimination of the hidden factory.
Value Stream optimization is enabled by elimination of the hidden factory.
67

66. Rolled Throughput Yield
Develop A Better Understanding of Your Operations
To Know Where To Begin
If this is your process, where do you put your key resources ?
A B C D
RTY 0.80 0.90 0.90 0.90 0.583
COPQ $2 / Unit $10 / Unit $ 5 / Unit $2 /Unit $19
Capacity 700 un/dy 500 un/dy 400 un/dy 200 un/dy 200 un
68 –Rolled Throughput Yield (RTY) -- A true estimate of process yield

67. Project Prioritization
A B C D
RTY 0.80 0.90 0.90 0.90 0.583
COPQ $2 / Unit $10 / Unit $ 5 / Unit $2 /Unit $19
Capacity 700 un/dy 500 un/dy 400 un/dy 200 un/dy 200 un
Project # A Project # B Project # C
69

68. Role of Statistics
Can you always measure …100% or less
What is Population ……what is sample?
Roll of statistics in measurement (descriptive / Inference)
1. We only use experience, not data.
2. We collect data, but just look at the numbers.
3. We group the data so as to form charts and graphs.
4. We use census data with descriptive statistics.
5. We use sample data with descriptive statistics.
6. We use sample data with inferential statistics.
70

69. Basic Statistics
 Types of data
 Measures of the Center of the data
 Mean
 Median
 Mode
 Measures of the Spread of Data

Range

Variance
 Standard Deviation
 Normal Distribution and Normal Probabilities
71

70. Measures of Central Tendency
Mean: Arithmetic average of a set of values
n
− Reflects the influence of all values
x = ∑ xi n
− Strongly Influenced by extreme values i =1
Median: Reflects the 50% rank - the center number after a set of numbers
has been sorted from low to high.
− Does not include all values in calculation
− Is “robust” to extreme scores
The mean and median will be affected by the nature of the distribution of
numbers
Mode - Most Common Observation
Why would we use the mean instead of the median in process Improvement?
72

71. Different Distributions
Sketch in the Means and Medians on each Distribution.
Sketch in the Means and Medians on each Distribution.
Comparison of Distributions. Comparison of Distributions.
300 300
Frequency
200 200
Frequency
100
Tail 100 Tail
0
0
60 70 80 90 100 110 120 130
0 10 20 30 40 50 60 70 80
C2
C3
Negative Skew Positive Skew
Comparison of Distributions.
100
Frequency
50
0
20 30 40 50 60 70 80 90 100 110
C1
Symmetric Distribution
73

72. Population Parameters vs Sample Statistics
Examples of Examples of SAMPLE:
POPULATION:
500 people
Entire India randomly
Average Literacy rate selected
X = Sample Mean
µ = Population Mean ^
σ = Sample Standard Deviation
σ = Population Standard Deviation
74

73. Computational Equations
N
Population Mean ∑X i
µ = i =1
N
N
Population Standard
Deviation ∑ (X i − µ ) 2
σ =S= i=1
N
n
Sample Mean
^
∑x i
µ =x= i =1
n
N
∑ (X
2
Sample Standard i
-- X)
Deviation ^
σ = s= i =1
75
n -1

74. Measures of Variability
Range: the distance between the extreme values of
a data set. (Highest - Lowest)
Variance ( σ 2 ): the Average Squared Deviation of
each data point from the Mean.
Standard Deviation ( σ ): the Square Root of the
Variance.
The range is more sensitive to outliers than the
variance.
76

75. Calculating Standard Deviation
X X-X (X - X) 2
1 2
2 1
3 3 Variance
4 5 N
5
6
4 ∑ (X i − X ) 2
i=1
7 N -1
8
9 N
10 ∑ (X i − X )2
Sum Σ i=1
Mean
N -1
σ square Standard Deviation
σ 1.581139
77

76. Types of Data
Attribute / Discrete Data (Qualitative)
 Categories
 Yes, No
 Go, No go
 Operator 1, Operator 2, Operator 3
 Pass / Fail
Variable / Continuous Data (Quantitative)
 Decimal subdivisions are meaningful
 Time, Pressure, Conveyor Speed
78

77. Variation
“While every process displays Variation, some processes display controlled
variation, while other processes display uncontrolled variation (Walter Shewhart).
Controlled Variation is characterized by a stable and consistent pattern of
variation over time. Associated with Common Causes.
Uncontrolled Variation is characterized by variation that changes over time.
Associated with Special Causes.
Process A shows controlled variation.
Process B shows uncontrolled variation
X-Bar C hart for Proc es s A X-B ar C hart for P roc es s B
UCL=77.20 80
75 UCL=77.27
Sample Mean
Sample Mean
70 X =70.98
X =70.91
70 LCL=64.70
60
65 LCL=64.62
50
0 5 10 15 20 25 0 5 10 15 20 25
Sample Number Sample Number
Special Causes
79

78. Stratification
Customer Type
Geography
Company Process
Etc.
ALL DATA
n = 2000
0 (-11, 38) 49
Sample Size , Median (Min, Max) SPAN
The most powerful potential process labels are those
that are known at the beginning of the process.
80

79. Stratification
ALL DATA
n = 2000
0 (-11, 38) 49
Dashboard Non-Dashboard
n = 899 n = 1101
-2 (-9, 21) 30 1 (-12, 70) 82
North South East West
n = 261 n = 297 n = 103 n = 238
-2 (-8, 8) 16 -2 (-10, 24) 34 -1 (-8, 15) 23 -1 (-8, 23) 32
Commercial Government Industrial Even with small sets of
n = 119 n = 74 n = 68 Data, the median
-2 (-10, 5) 15 -2 (-8, 9) 17 -2 (-7, 40) 47
difference appears.
Credit A Credit B Credit C
n = 71 n = 41 n=7
-3 (-10, 0) 10 0 (-7, 6) 13 5 (-8, 31) 39
81

80. Stratification
Key Learning Points:
• The first thing you must do is Separate the Processes. We call this Stratification. If you don’t Stratify
(isolate) the processes, you will have more than one central tendency in the data set and you will never
figure out what drives variance.
• If you think you have found the right label to stratify the processes, make sure you double check it to
see if there is another label that is influencing the way the data appears. In this case, the real process
label was Credit Rating, but it appeared in the Dashboard/Non-Dashboard data. You can double check
by cross-cutting the data (look at Credit Rating and Dashboard at the same time in a tabular format), or
by continuing to segment to see if the central tendency indicator still moves even though we thought it
was an isolated process. In this case, if you continue with Dashboard as an isolated process, you will
see the median move for various segmentations (especially Credit Rating).
• Once you have Stratified (isolated the processes) and you have a segment that reflects several
different levels of Variance, you have the first clues to find the critical x’s that drive variance.
• When you find a critical x for one of the processes, check to see if it is also the critical x for the other
processes. Often the factors that drive variance in one of the processes, also drive it in another.
82

81. FMEA Model
Prevention Detection
What made failure mode
to take place. Ask 5
Why’s…. Detection
Cause
Cause
What manner my
Material or process process was not able to
input obey me
Failure Mode
Failure Mode
(Defect)
(Defect)
Process Step
Effect
Effect
External customer or
downstream process step.
Because of your process
what all I will not be able to do
Controls
Controls
83

82. Measurement System Analysis
A measurement system will not willingly disclose the type of distortion, inaccuracy or
A measurement system will not willingly disclose the type of distortion, inaccuracy or
imprecision ititis transmitting to our data. We must actively force ititto reveal its hidden effects.
imprecision is transmitting to our data. We must actively force to reveal its hidden effects.
CAUTION: Objects in mirror are closer
than they appear
84

83. Measurement System Analysis
Parts
(Example)
• Observations
Inputs Outputs Inputs Measurement Outputs • Measurements
Process Process • Data
Product Variability Measurement Total Variability
(Actual variability) Variability (Observed variability)
σ2 σMeas.System
2
σObserved
2
Measurement
+ =
System Variability
- Investigated Actual(Part)
through “R&R (Total)
Study”
The Measurement System will transmit variation to our data.
85

84. Establishing the Process Capability
LSL USL
Short-Term
Capability
Long-Term Capability
Over time, aaprocess tends to shift by approximately 1.5σ . .
Over time, process tends to shift by approximately 1.5σ
86

85. Visualizing the Causes
Within Group
• Called σ short term (σ st)
• Our potential - the best we can be
Time 1
• The σ reported by all 6 sigma
Time 2 companies
Time 3 • The trivial many
Time 4
σ st + σ shift = σ total Between Groups
• Called σ shift (truly a measurement in
sigma's of how far the mean has shifted)
• Indicates our process control
• The vital few
87

86. Analyse Purpose
To reduce the number of Process Input Variables to
a manageable number
To determine high risk inputs from Failure Modes
and Effects analysis
To determine the presence of and eliminate Noise
Variables through Multi-vari Studies
To plan the first improvement activities
88

87. Sources of Variation
A common method of analysis at this stage is the variables tree.
Try thinking about your process in this manner........
Customer Service Example
Not resolved the
call
Agent to Agent Customer to
Skill to Skill Call to Call Type of call
Customer
89

88. Tools Used……..
Time Series Plot Scatter Plot
15
15
10
H Vr
rs a
H Vr
10
rs a
5
5
0
Index 5 10 0
May Jun Jul
Date
ANOVA (Analysis of Variance)
Main Effects Plot - Means for HrsVar
13.0
10.5
H Vr
r a
8.0
s
5.5
3.0
Date Customer Salesman
Box Plot Pareto Chart
Pareto Chart for : Defects
15
1000 100
HrsVar
900
10 800 80
700
Percent
600
ount
60
500
C
400
5 300
40
200 20
100
0 0
0 ev
. le t io
n
t D bb l or ma
Defect
Water Util Mining Paper We
i gh
Ai
r Bu Co
De
f or
Count 431 293 132 120
C us tom er Percent
Cum %
44.2
44.2
30.0
74.2
13.5
87.7 1
12.3
00.0
90

89. Regression…..
Some examples:
Y=Gas Mileage Y=Son’s
(mpg) 30 Height 80
20 60
10 40
0 .5 1 1.5 2 60 70 80
X=Car Weight (tons) X=Dad’s Height (inches)
Y=Grades Y=Selling Price
(of 100%) 80 (Thousands) 35
60 25
40 5
0 .5 1 1.5 2 1 6 14 22 30
X=Study Time(Hours/Night) X=Age of Car
91

90. Regression…..
How do you find a line that “fits” the data?
How do you find a line that “fits” the data?
What we are looking for is a line which will minimize the
distances from the plotted points to the line....
Deviations (distances)
Deviations (distances)
“How much the line missed by”
“How much the line missed by” * Regression Line
Regression Line
*
Response *
Variable * *
(Y)
* Scatter Plot Points
Scatter Plot Points
(actual data values)
(actual data values)
*
* *
*
Input Variable (X)
92

91. Regression…..
The R2 Statistic is Y
defined as the sum of Measured
squares of errors
divided by sum of the Error
square of difference Measured
from average:
Predicted Y=a+bX
n
∑ (y − yi )
2
i
^
i =1
r = 1−
2
n
∑ (y − y )
2
i
i =1
X
93

92. Improve
What will you do for Improve
– Identify solutions.
– Develop change management plan.
– Conduct cost / benefit analysis.
– Create implementation plan.
94

93. Improve
What will you do for Control
– Define and implement ongoing measurement / monitoring plan.
– Document procedures.
95

94. Control
In the physical world, the law of entropy Target
explains the gradual loss of order in a
system. The same law applies to business
processes.
Unless we add “energy” (in the form of
documentation and ongoing process
controls); processes will tend to degrade
overtime, losing the gains achieved by
design and improvement activities.
The quality plan is the structure through
which we add this “energy” to business
processes.
g
96

95. Control
Three Main Control Mechanism……..
Avoid Potential Problems Control Potential Problems
Risk Management
SPC
Mistake Proofing
97

96. Project Sign-off
Answer the following questions before the project is signed off:
• What can go wrong and derail improvements ?
• What controls are in place ?
• Can you show me your closure plan ?
• What happen when the people change ?
• Are there any follow up on projects ?
• Is all documentation completed ?
• Is the savings verified by finance ?
• Is the audit plan in place ?
98

97. Project Sign-off
Finalize Financial Results
•Calculate tangible benefits
•Determine implementation costs
•Calculate net financial gain
•Calculate the intangible benefits e.g. cost avoidance, customer retention
Tangible Benefits - Implementation Costs = Net Financial gains (Over one financial
year)
Bank
99

98. Documentation
Complete Documentation Package
……..Compile and organize a record of the key aspects of your six sigma project
Typical Elements of the Documentation Package
•A description of the project
•Problem statement & business case
•A list of CTQs + Xs
•Hypothesis tests
•Process capability analysis
•Control parameters
•Audit Plan/ owner
•Financial results
•Operational metrics
•Lessons learned and best practices
Project to be signed off by GB/BB, MBB, Financial controller, Process Owner, Champion.
100

99. Reward & Recognition
?...
101