six based dmaic ppt

1. Submitted By:- Guided By:-
Aakif Rab (40102907418) Dr. Anil Kumar
Shubham Mishra (20320903617) (Associate Professor)
Abhishek (00120903617)
Akansha Singh (50120903617)
Six Sigma: Concepts, Methodology, Application
to Manufacturing and Service sector Including IT.

2. Sigma (σ) is a statistical concept that represents how much
variation there is in a process relative to
customer specifications.
Sigma Value is based on “defects per million
opportunities” (DPMO).
Six Sigma (6σ) is equivalent to 3.4 DPMO. The variation
in the process is so small that the resulting products
and services are 99.99966% defect free.

3. SIX SIGMA AS A
PHILOSOPHY
Internal &
External
Failure
Costs
Prevention &
Appraisal
Costs
Old Belief
4s
Costs
Internal &
External
Failure Costs
Prevention &
Appraisal
Costs
New Belief
Costs
4s
5s
6s
Quality
Quality
Old Belief
High Quality = High Cost
New Belief
High Quality = Low Cost
s is a measure of how much
variation exists in a process

4. SIX SIGMA AS A METRIC
1
)
( 2




n
x
x i

s
Sigma = s = Deviation
( Square root of variance )
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
Axis graduated in Sigma
68.27 %
95.45 %
99.73 %
99.9937 %
99.999943 %
99.9999998 %
result: 317300 ppm outside
(deviation)
45500 ppm
2700 ppm
63 ppm
0.57 ppm
0.002 ppm
between + / - 1s
between + / - 2s
between + / - 3s
between + / - 4s
between + / - 5s
between + / - 6s
s =

6.  Many familiar quality tools
applied in a structured
methodology
Process
Mapping
Tolerance
Analysis
Structure Tree Components
Search
Pareto Analysis Hypothesis
Testing
Gauge R & R Regression
Rational
Subgrouping
DOE
Baselining SPC

7. SIX SIGMA AS A METHOD
To get results, should we focus our behavior on the Y or X
•Y •X1…Xn
•Dependent •Independent
•Output •Input-Process
•Effect •Cause
•Symptom •Problem
•Monitor •Control

8. DISTINGUISH “VITAL FEW”
FROM “TRIVIAL MANY”
Y = Dependent Variable Output, Defect
x = Independent Variables Potential Cause
x* = Independent Variable Critical Cause
Define the Problem / Defect Statement
Y = f ( x1
*, x2, x3, x4
*, x5. . . Xn)
Process
(Parameters)
Material
Methods
People
Environment
Output
Machine
Measurements

9. METHODOLO
GIES OF SIX
SIGMA
 There are two major
methodologies used within Six
Sigma, both of which are
composed of five
sections, according to the 2005
book “JURAN Institute Six
Sigma Breakthrough and
Beyond” by Joseph A. De
Feo and William Barnard.

11. DMAIC
 .
DMAIC: The DMAIC
method is used primarily
for improving existing
business processes. The
letters stand for:
Define the problem and
the project goals
Measure in detail the
various aspects of the
current process
Analyze data to, among
other things, find the
root defects in a process
Improve the process
Control how the process
is done in the future

12. DMADV
 .
DMADV: The DMADV
method is typically used to
create new processes and
new products or services.
The letters stand for:
Define the project goals
Measure critical
components of the process
and the
product capabilities
Analyze the data and
develop various designs for
the process, eventually
picking the best one
Design and test details of
the process
Verify the design by
running simulations and a
pilot program, and then
handing over the process
to the client

13. STRATEGY BY PHASE -
Phase
Measure
(What)
Analyze
(Where, When, Why)
Improve
(How)
Control
(Sustain, Leverage)
Step
What is the frequency of Defects?
• Define the defect
• Define performance standards
• Validate measurement system
• Establish capability metric
Where, when and why do Defects occur?
• Identify sources of variation
• Determine the critical process parameters
How can we improve the process?
• Screen potential causes
• Discover relationships
• Establish operating tolerances
Were the improvements effective?
• Re-establish capability metric
How can we maintain the improvements?
• Implement process control mechanisms
• Leverage project learning's
• Document & Proceduralize
Focus
Y
Y
Y
Y
X
Vital X
X
Vital X
Vital X
Y, Vital X
Y, Vital X
Process Characterization
Process Optimization
Measure
Improve
Analyze
Control
Measure
Improve
Analyze
Control
Measure
Improve
Analyze
Control
Measure
Improve
Analyze
Control
Measure
Improvement

14. Measure
Characterize Process
Understand Process
Evaluate
Improve and Verify Process
Improve
Maintain New Process
Control

15. Define
Problem
Understand
Process
Collect
Data
Process
Performance
 Process Capability
- Cp/Cpk
- Run Charts
 Understand Problem
(Control or
Capability)
 Data Types
- Defectives
- Defects
- Continuous
 Measurement
Systems Evaluation
(MSE)
 Define Process-
Process Mapping
 Historical
Performance
 Brainstorm
Potential Defect
Causes
 Defect
Statement
 Project
Goals
Understand the Process and Potential Impact
Measure Phase

16. Reduce
Complaints
(int./ext.)
Reduce
Cost
Reduce
Defects
Problem Definitions need to be based on
quantitative facts supported by analytical data.
What are the Goals?
 What do you want to improve?
 What is your ‘Y’?
Problem Definition

17. How do we know our process?
Process Map
Historical Data
Fishbone

18. Assignable Cause
 Outside influences
 Potentially controllable
 How the process is actually
performing over time
Fishbone

19. Common Cause Variation
 Variation present in every process
 Not controllable
 The best the process can be within the present technology
Data within subgroups (Z.st) will contain only Common Cause
Variation

20. Repeatability (Equipment variation)
• Variation observed with one measurement
device when used several times by one
operator while measuring the identical
characteristic on the same part.
Reproducibility (Appraised variation)
• Variation Obtained from different operators
using the same device when measuring the
identical characteristic on the same part.
Stability or Drift
• Total variation in the measurement obtained
with a measurement obtained on the same
master or reference value when measuring the
same characteristic, over an extending time
period.

21. To understand where you want to be,
you need to know how to get there
Map the Process
Measure the Process
Identify the variables - ‘x’
Understand the Problem -
’Y’ = function of variables -’x’
Y=f(x)

22. Measure
Characterize Process
Understand Process
Evaluate
Improve and Verify Process
Improve
Maintain New Process
Control

23. In many cases, the data sample can be transformed so that it is approximately normal.
For example, square roots, logarithms, and reciprocals often take a positively skewed
distribution and convert it to something close to a bell-shaped curve

24. What do we Need?
LSL USL LSL USL
LSL USL
Off-Target, Low Variation
High Potential Defects
Good Cp but Bad Cpk
On Target
High Variation
High Potential Defects
No so good Cp and Cpk
On-Target, Low Variation
Low Potential Defects
Good Cp and Cpk
 Variation reduction and process
centering create processes with
less potential for defects.
 The concept of defect reduction
applies to ALL processes (not just
manufacturing)

25. Eliminate “Trivial Many”
 Qualitative Evaluation
 Technical Expertise
 Graphical Methods
 Screening Design of Experiments Identify “Vital Few”
 Pareto Analysis
 Hypothesis Testing
 Regression
 Design of Experiments
Quantify
Opportunity
 % Reduction in Variation
 Cost/ Benefit
Our Goal:
Identify the Key Factors (x’s)

26. Statistical Analysis
0.025
0.020
0.015
0.010
0.005
0.000
7
6
5
4
3
2
1
0
New Machine
Frequency
0.025
0.020
0.015
0.010
0.005
0.000
30
20
10
0
Machine 6 mths
Frequency
 Is the factor really important?
 Do we understand the impact for
the factor?
 Has our improvement made an
impact
 What is the true impact?
Hypothesis Testing
Regression Analysis
55
45
35
25
15
5
60
50
40
30
20
10
0
X
Y
R-Sq = 86.0 %
Y = 2.19469 + 0.918549X
95% PI
Regression
Regression Plot
Apply statistics to validate actions & improvements

27. A- Poor Control, Poor Process
B- Must control the Process better, Technology is fine
C- Process control is good, bad Process or technology
D- World Class
A B
C D
TECHNOLOGY
1 2 3 4 5 6
good
poor
2.5
2.0
1.5
1.0
0.5
CONTROL
Z
shift
ZSt
poor
good

28. Con-
sumer
Cue
Technical
Requirement
Preliminary
Drawing/Database
Identity
CTQs
Identify
Critical
Process
Obtain Data on
Similar Process
Calculate Z
values
Rev 0
Drawings
Stop
Adjust
process &
design
1st piece
inspection
Prepilot
Data
Pilot data
Obtain data
Recheck
‘Z’ levels
Stop
Fix process
& design
Z<3
Z>= Design Intent
M.A.I.C
M.A.D
Six Sigma Design Process

29. Measure
Characterize Process
Understand Process
Evaluate
Improve and Verify Process
Improve
Maintain New Process
Control

30. DESIGN OF EXPERIMENTS (DOE)
 To estimate the effects of independent Variables on
Responses.
 Terminology
Factor – An independent variable
Level – A value for the factor.
Response - Outcome
X Y
PROCESS

31.  Define Objective.
 Select the Response (Y)
 Select the factors (Xs)
 Choose the factor levels
 Select the Experimental Design
 Run Experiment and Collect the Data
 Analyze the data
 Conclusions
 Perform a confirmation run.

32. Measure
Characterize Process
Understand Process
Evaluate
Improve and Verify Process
Improve
Maintain New Process
Control

33.  Control Phase Activities:
 Confirmation of Improvement
 Confirmation you solved the practical problem
 Benefit validation
 Buy into the Control plan
 Quality plan implementation
 Procedural changes
 System changes
 Statistical process control implementation
 “Mistake-proofing” the process
 Closure documentation
 Audit process
 -Scoping next project

34. How to create a Control Plan:
1. Select Causal Variable(s). Proven vital few X(s)
2. Define Control Plan- 5Ws for optimal ranges of X(s)
3. Validate Control Plan- Observe Y
4. Implement/Document Control Plan
5. Audit Control Plan
6. Monitor Performance Metrics

35. CONTROL
PHASE -
SIX
SIGMA
 Control Plan Tools:
 1. Basic Six Sigma control methods.
 - 7M Tools: Affinity diagram, tree diagram,
process
 decision program charts, matrix
diagrams,
 interrelationship diagrams, prioritization
matrices,
 activity network diagram.
 2. Statistical Process Control (SPC)
 - Used with various types of distributions
 - Control Charts
 Attribute based (np, p, c, u). Variable
based (X-R, X)
 Additional Variable based tools
 -PRE-Control
 -Common Cause Chart
(Exponentially Balanced
 Moving Average (EWMA))

36. PRODUCT
MANAGEMENT
OVERALL
GOAL OF
SOFTWARE
KNOWLEDGE OF
COMPETITORS
SUPERVISION
PRODUCT
DESIGN
PRODUCT
MANAGEMENT
PRODUCT
DESIGN
PRODUCT
MANAGEMENT
INNOVATION
OUTPUT
DIRECTORY
ORGANIZATION
INTUITIVE
ANSWERS
SUPPORT
METHODS TO MAKE
EASIER FOR USERS
CHARACTERISTICS:
• Organizing ideas into meaningful
categories
• Data Reduction. Large numbers of qual.
Inputs into major dimensions or categories.
Affinity Diagram

37. How do we select the correct Control Chart:
Type
Data
Ind. Meas. or
subgroups
Normally dist.
data
Interest in
sudden mean
changes
Graph defects
of defectives
Oport. Area
constant from
sample to
sample
X, Rm
p, np
X - R
MA, EWMA or
CUSUM and
Rm
u
C, u
Size of the
subgroup
constant
p
If mean is big, X and
R are effective too
Ir neither n nor p are
small: X - R, X - Rm
are effective
More efective to
detect gradual
changes in long term
Use X - R chart with
modified rules
Variables
Attributes
Measurement
of subgroups
Individuals
Yes
No No
Yes
Yes
No
Yes
No
Defects Defectives

38. Improvement fully
implemented and
process re-baselined.
Quality Plan and
control procedures
institutionalized.
Owners of the
process: Fully
trained and running
the process.
Any required
documentation done.
History binder
completed. Closure
cover sheet signed.
Score card developed
on characteristics
improved and
reporting method
defined.

39. BLACK BELT TRAINING
Task
Time on
Consulting/
Training
Mentoring
Related
Projects
Green
Belt
Utilize
Statistical/
Quality
technique
2%~5%
Find one
new green
belt
2 / year
Black
Belt
Lead use
of
technique
and
communic-
ate new
ones
5%~10%
Two green
belts
4 / year
Master
Black
Belt
Consulting/
Mentoring/
Training
80~100%
Five Black
Belts
10 / year

40. Industry Examples of Six Sigma Applicability
Automotives
Few Examples of
Companies: Tata
Motors, Ford, GM,
Maruti, Telco,
Toyota
i. Enhancing Supplier Quality
ii. Improving Safety & Reliability of Finished Vehicles
iii. Reducing Manufacturing defects at each stage
iv. Using Design FMEA to understand and prevent any
possible design failures
v. Reducing variation in all the critical parameters that
impact the finished product
vi. Improving the overall Incoming Material Quality or parts
Quality
vii. Optimizing Inventory levels for all major parts
viii. Reducing time to manufacture
ix. Reducing Design defects
x. Reducing Supplier Lead time i.e the time take by each
supplier to deliver goods
xi. Improving First time yield and efficiency of each step in
the Manufacturing assembly line

41. Industry Examples of Six Sigma Applicability
Continuous Process
Plants
Few Examples of
Companies:
Reliance Industries
Limited, Asian
Paints, Ge Plastics,
Wipro Consumer
Products
i. Improving overall Yield of each shift
ii. Reduce scrap or spilled materials
iii. Reduce the Process failures or breakdowns
iv. Increase Plant capacity utilization
v. Improve Operator Productivity
vi. Reduce time to restart the process after failure
vii. Create mechanisms to prevent failures at each stage
viii.Improve overall process stability & control
Six Sigma : Industry Wide Application

42. Six Sigma : Results
Company Annual Savings
General Electric $2.0+ billion
JP Morgan Chase
*$1.5 billion (*since inception
in 1998)
Texas Instruments $600 million
Johnson & Johnson $500 million
Honeywell $600 million

43. "If you don’t measure work processes – You can't
possibly manage them"
Bill Hewlett