IBM-09: Six Sigma Tools and Techniques
A. Ramesh PhD
Department of Management Studies
Indian Institute of Technology Roorkee
Lecture 1: Introduction
Class Venue and Time
Room No.LHC204
Monday From 8 AM - 8.55 AM
Wednesday - From 8 AM - 8.55 AM
Friday - From 8 AM - 8.55 AM
Welcome!!!
About me!!!
Flow of Presentation
1. Course content
2. Evaluation scheme
3. Six Sigma
4. Definition of quality
5. Dimensions quality
6. History of quality
1. Course content
Module 1(10 Hours) - Introduction
History, definition, dimensions, responsibility for quality
Six Sigma Basics Overview & Implementation
Define phase, Measure phase, Process Flow Charting/Process Mapping
Basic Tools
Probability
Overview of Distributions and Statistical Process
Probability and Hazard Plotting
Six Sigma Measurements
Basic Control Charts
Process Capability and Process Performance Metrics
Module 2 (12 Hours) - Six Sigma Analysis Phase
Visualization of Data,
Confidence Intervals and Hypothesis Tests,
Inferences : Continuous Response,
Inference : Attribute (Pass/Fail) Response,
Comparison Tests : Continuous Response, Comparison Tests : Attribute (Pass/Fail) Response,
Bootstrapping,
Variance Components,
Correlation and Simple Linear Regression,
Single Factor (One Way) Analysis of Variance (ANOVA) and Analysis of Means (ANOM),
Two-Factor (Two-Way) Analysis of Variance,
Multiple Regression
Logistic Regression, and Indicator Variables.
Module 3 (10 Hours) - Six Sigma Improve Phase
Benefiting from Design of Experiments (DOE)
Understanding the Creation of Full and Fractional Factorial
2K DOEs
Planning 2K DOEs Design and
Analysis of 2K DOEs
Response Surface Methodology
Module 4 (10 hours) - Lean Six Sigma
Lean and its Integration with Six Sigma process,
Integrating of Theory of Constraints
Design for Six Sigma Manufacturing applications, Service/Transactional Applications
DFSS Overview and Tools
Product DFSS, Process DFSS
Management of Six Sigma
Change Management
Project Management and Financial Analysis, Team Effectiveness, Creativity
References
S.
No.
Name of Authors/Book/Publisher Year of
Publication
/ Reprint
1 Breyfogle, Forrest: Implementing Six Sigma : Smarter Solutions Using
Statistical Methods, New York John Wiley & Sons
1999
2 Harry, Mikel and Rich Schroeder, Six Sigma : The Breakthrough Management
Strategy Revolutionizing the Worlds Top Corporations, New York
Doubleday
2000
3 Besterfield, D C and Besterfield C, Total Quality Management, Pearson
Education Asia
1999
4 Montgomery, D.C, Statistical Quality Control- A modern introduction, 6th
Edition, Wiley India
2010
5 Feigenbaum, Total Quality Control, 3rd Edition, McGraw Hill 1991
6 Hansen B L, and Ghare P M, Quality Control and Application, Prentice Hall
India
1993
2. Evaluation Scheme
Evaluation Scheme
Midterm Evaluation : 35 %
End Term Evaluation : 50 %
Mini Project : 10 %
Surprise Quizzes : 05 %
What is six sigma?
Six Sigma is. . .
A performance goal, representing 3.4 defects for every million opportunities to make one
A series of tools and methods used to improve or design products, processes, and/or services
A statistical measure indicating the number of standard deviations within customer expectations
A disciplined, fact-based approach to managing a business and its processes
A means to promote greater awareness of customer needs, performance measurement, and business improvement
Whats in a name?
Sigma is the Greek letter representing the standard deviation of a population of data.
Sigma is a measureof variation
(the data spread)
What does variation mean?
Variation means that a process does not produce
the same result (the Y)
every time.
Some variation will exist in all processes.
Variation directly affects customer experiences.
Customers do not feel averages!
-10
-5
0
5
10
15
20
Measuring Process PerformanceThe pizza delivery example. . .
Customers want their pizza delivered fast!
Guarantee = 30 minutes or less
What if we measured performance and found an average delivery time of 23.5 minutes? On-time performance is great, right? Our customers must be happy with us, right?
How often are we delivering on time?Answer: Look at
the variation!
Managing by the average doesnt tell the whole story. The average and the variation together show whats happening.
s
x
30 min. or less
0 10 20 30 40 50
Reduce Variation to Improve Performance
Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).
s
x
30 min. or less
0 10 20 30 40 50
How many standard
deviations can you
fit within
customer
expectations?
4.20
The Empirical Rule If the histogram is bell shaped
Approximately 68% of all observations fall
within one standard deviation of the mean.
Approximately 95% of all observations fall
within two standard deviations of the mean.
Approximately 99.7% of all observations fall
within three standard deviations of the mean.
21
Empirical Rule
Data are normally distributed (or approximately normal)
1 2
395
99.7
68
Distance from
the Mean
Percentage of Values
Falling Within Distance
4.22
Chebysheffs TheoremNot often used because interval is very wide.
A more general interpretation of the standard deviation is derived from
Chebysheffs Theorem, which applies to all
shapes of histograms (not just bell shaped).
The proportion of observations in any sample that lie within k standard deviations
of the mean is at least: For k=2 (say), the theorem states that at least 3/4 of all observations lie within 2 standard deviations of the mean. This is a lower bound compared to Empirical Rules approximation (95%).
Centered normal distribution between Six Sigma limits
23
Effects of a 1.5 shift
24
The Six Sigma Evolutionary Timeline
1736: French mathematician Abraham de Moivre publishes an article introducing the normal curve.
1896: Italian sociologist Vilfredo Alfredo Pareto introduces the 80/20 rule and the Pareto distribution in Cours dEconomie Politique.
1924: Walter A. Shewhart introduces the control chart and the distinction of special vs. common cause variation as contributors to process problems.
1941: Alex Osborn, head of BBDO Advertising, fathers a widely-adopted set of rules for brainstorming.
1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures for Performing a Failure Mode Effects and Criticality Analysis.
1960: Kaoru Ishikawa introduces his now famous cause-and-effect diagram.
1818: Gauss uses the normal curve to explore the mathematics of error analysis for measurement, probability analysis, and hypothesis testing.
1970s: Dr. Noriaki Kano introduces his two-dimensional quality model and the three types of quality.
1986: Bill Smith, a senior engineer and scientist introduces the concept of Six Sigma at Motorola
1994: Larry Bossidy launches Six Sigma at Allied Signal.
1995: Jack Welch launches Six Sigma at GE.
Six Sigma Companies
Six Sigma and Financial Services
4- Definition of Quality
Definition of Quality
Perfection
Providing a
good usable
productConsistency
Elimination
of waste
Fitness for
use
Doing it
right the first
time
Delighting or
pleasing the
customer
Total
Customer
Service and
satisfaction
Defining Quality
Quality is a predictable degree of uniformity and
dependability, at low cost and suited to the market
Deming
Quality is fitness for use
Juran
Quality is conformance to requirements
Crosby
30
Defining Quality
The degree to which a set of inherent
characteristics fulfills requirements.
ISO 9000:2000
31
Quality can be Quantified
Q = P/E
Where Q = Quality
P = Performance
E = Expectations
If Q is greater than 1.0, then the customer has a
good feeling about the product or service.
32
What Is Quality?
The degree of excellence of a thing
(Websters Dictionary)
The totality of features and characteristics
that satisfy needs ( ASQC)
Fitness for use
Modern definition of quality
Quality is inversely proportional to variability
5. Dimensions quality
36
Dimensions of Quality (Garvin (1987) 1. Performance
Will the product do the intended job?
2. Reliability
How often the product fail?
3. Durability
How long the product last?
4. Serviceability
How easy it to repair the product?
5. Aesthetics
What does the product look like?
6. Features
What does the product do?
7. Perceived quality
What is the reputation of the company or its product?
8. Conformance to standards
is the product made exactly as the designer intended?
Service Quality Dimensions and
Examples
Dimension Examples
1. Tangibles Were the facilities clean, personnel neat?
2. Convenience Was the service center conveniently located?
3. Reliability Was the problem fixed?
4. Responsiveness Were customer service personnel willing and able
to answer questions?
5. Time How long did the customer wait?
6. Assurance Did the customer service personnel seem
knowledgeable about the repair?
7. Courtesy Were customer service personnel and the cashier
friendly and courteous?
DMAIC The Improvement
Methodology
Define Measure Analyze Improve Control
Objective:
DEFINE the
opportunity
Objective:
MEASURE current
performance
Objective:
ANALYZE the root
causes of problems
Objective:
IMPROVE the
process to
eliminate root
causes
Objective:
CONTROL the
process
to sustain the gains.
Key Define Tools:
Cost of Poor
Quality (COPQ)
Voice of the
Stakeholder
(VOS)
Project Charter
As-Is Process
Map(s)
Primary Metric
(Y)
Key Measure
Tools:
Critical to Quality
Requirements
(CTQs)
Sample Plan
Capability
Analysis
Failure Modes
and Effect
Analysis (FMEA)
Key Analyze
Tools:
Histograms,
Boxplots, Multi-
Vari Charts, etc.
Hypothesis Tests
Regression
Analysis
Key Improve
Tools:
Solution
Selection Matrix
To-Be Process
Map(s)
Key Control
Tools:
Control Charts
Contingency
and/or Action
Plan(s)
What is the problem? The problem is the Output (a Y
in a math equation Y=f(x1,x2,x3) etc).
What is the cost of this problem
Who are the stake holders / decision makers
Align resources and expectations
Define DMAIC ProjectWhat is the project?
Six Sigma
Project
Charter
Voice of the
Stakeholder
Stakeholders
$
Cost of Poor
Quality
Define As-Is ProcessHow does our existing process work?Move-It! Courier Package HandlingProcess
Acco
un
tin
gF
ina
lizin
gD
eliv
ery
Out-Sort SupervisorOut-Sort ClerkAccounts
SupervisorAccounts
Receivable ClerkWeight Fee ClerkDistance Fee ClerkIn-Sort SupervisorIn-Sort ClerkMail ClerkCourier
Observ e packageweight (1 or 2) onback of package
Look upappropriate
Weight Fee andwrite in top middlebox on package
back
Take packagesf rom Weight FeeClerk Outbox toA/R Clerk Inbox.
Add Distance &Weight Fees
together and writein top right box on
package back
Circle Total Feeand Draw Arrow
f rom total tosender code
Take packagesf rom A/R Clerk
Outbox toAccounts
Superv isor Inbox.
Write Total Feef rom package in
appropriateSender column onAccts. Supv .s log
Add up Total # ofPackages and
Total Fees f romlog and createclient inv oice
Deliv er inv oice toclient
Submit log toGeneral Managerat conclusion of
round.
Take packagesf rom Accounts
Superv isorOutbox to Out-
Sort Clerk Inbox.
Draw 5-point Starin upper right
corner of packagef ront
Sort packages inorder of Sender
Code bef oreplacing in outbox
Take packagesf rom Out-Sort
Clerk Outbox toOut-Sort
Superv isor Inbox.
Observ e senderand receiv er
codes and makeentry in Out-SortSuperv isors log
Deliv er Packagesto customers
according to N, S,E, W route
Submit log toGeneral Managerat end of round
Submit log toGeneral Managerat end of round
Does EVERYONE
agree how the current
process works?
Define the Non Value
Add steps
Define Customer RequirementsWhat are the CTQs? What motivates the customer?
Voice of the Customer Key Customer Issue Critical to QualitySECONDARY RESEARCH
PRIMARY RESEARCH
Surveys
OTM
Market Data
Indust
ry I
nte
lLis
tenin
g P
ost
s
Industry Benchmarking
Focus Groups
Customer Service
Customer Correspondence
Obser-vations
Measure Baselines and CapabilityWhat is our current level of performance?
Sample some data / not all data
Current Process actuals measured
against the Customer expectation
What is the chance that we will succeed
at this level every time?50403020100
95% Confidence Interval for Mu
26.525.524.523.522.521.520.519.5
95% Confidence Interval for Median
Variable: 2003 Output
19.7313
8.9690
21.1423
Maximum3rd QuartileMedian1st QuartileMinimum
NKurtosisSkewnessVarianceStDevMean
P-Value:A-Squared:
26.0572
11.8667
25.1961
55.290729.610023.147516.4134 0.2156
1000.2407710.238483
104.34910.215223.1692
0.8540.211
95% Confidence Interval for Median
95% Confidence Interval for Sigma
95% Confidence Interval for Mu
Anderson-Darling Normality Test
Descriptive Statistics
Others
Amou
ntLa
te
41779
4.017.079.0
100.0 96.0 79.0
100
50
0
100
80
60
40
20
0
Defect
Count
PercentCum %
Pe
rce
nt
Co
unt
Pareto Chart for Txfr Defects
Measure Failures and RisksWhere does our process fail and why? Subjective opinion mapped into an objective risk profile number
Failure Modes and Effects Analysis (FMEA)
Process or
Product Name:Prepared by: Page ____ of ____
Responsible: FMEA Date (Orig) ______________ (Rev) _____________
Process
Step/Part
Number Potential Failure Mode Potential Failure Effects
S
E
V Potential Causes
O
C
C Current Controls
D
E
T
R
P
N
Actions
Recommended Resp. Actions Taken
S
E
V
O
C
C
D
E
T
R
P
N
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
Process/Product
X1
X2
X4
X3
etc
Six Sigma
Analyze Potential Root CausesWhat affects our process?
y = f (x1, x2, x3 . . . xn)
Ishikawa Diagram (Fishbone)
Analyze Validated Root CausesWhat are the key root causes?
Other s
Amou
ntLa
te
41779
4.017.079.0
100.0 96.0 79.0
100
50
0
100
80
60
40
20
0
Defect
Count
PercentCum %
Perc
ent
Count
Pareto Chart for Txfr Defects
Six Sigma
y = f (x1, x2, x3 . . . xn)
Critical Xs
Othe
r
Cleric
al
Curre
ncy
2 312
11.817.670.6
100.0 88.2 70.6
15
10
5
0
100
80
60
40
20
0
Defect
Count
PercentCum %
Perc
ent
Count
Pareto Chart for Amt Defects
Process
Simulatio
n
Data Stratification
Regression Analysis
Experimental Design
Improve Potential SolutionsHow can we address the root causes we identified?
Address the causes, not the symptoms.
y = f (x1, x2, x3 . . . xn)
Critical Xs
Decision
Evalu
ate
Clarify
Gen
erate
Divergent | Convergent
Improve Solution SelectionHow do we choose the best solution?
Time
Qualit
y
Cost
Solution Sigma Time CBA Other Score
Six Sigma
Solution
Implementatio
n Plan
Solution Selection Matrix
Nice
Try
Nice
Idea X
SolutionRight Wrong
Imple
menta
tion
Bad
G
ood
Control Sustainable BenefitsHow do we hold the gains of our new process?
Some variation is normal and OK
How High and Low can an X go yet not materially impact the Y
Pre-plan approach for control exceptions
0 10 20 30
15
25
35
Observation Number
Indiv
idual V
alu
e
Mean=24.35
UCL=33.48
LCL=15.21
Process Owner: Date:
Process Description: CCR:
Measuring and Monitoring
Key
Measure
ments
Specs
&/or
Targets
Measures
(Tools)
Where &
Frequency
Responsibility
(Who)
Contingency
(Quick Fix)Remarks
P1 - activity
duration,
min.
P2 - # of
incomplete
loan
applications
Process Control System (Business Process Framework)
Direct Process Customer:
Flowchart
Customer Sales Branch ManagerProcessingLoan Service
Manager
1.1
Applic
ation &
Revie
w1.2
Pro
cessin
g1.3
Cre
dit r
evi
ew
1.4
Revie
w1.5
Dis
clo
sure
Apply forloan
Reviewappliation forcompleteness
ApplicationComplete?
Completemeeting
informationNo
DFSS The Design MethodologyDesign for Six Sigma
Uses
Design new processes, products, and/or services from scratch
Replace old processes where improvement will not suffice
Differences between DFSS and DMAIC
Projects typically longer than 4-6 months
Extensive definition of Customer Requirements (CTQs)
Heavy emphasis on benchmarking and simulation; less emphasis on baselining
Key Tools
Multi-Generational Planning (MGP)
Quality Function Deployment (QFD)
Define Measure Analyze Develop Verify
Champions
Promote awareness and execution of Six Sigma within lines of business and/or functions
Identify potential Six Sigma projects to be executed by Black Belts and Green Belts
Identify, select, and support Black Belt and Green Belt candidates
Participate in 2-3 days of workshop training
Black Belts
Use Six Sigma methodologies and advanced tools (to execute business improvement projects
Are dedicated full-time (100%) to Six Sigma
Serve as Six Sigma knowledge leaders within Business Unit(s)
Undergo 5 weeks of training over 5-10 months
Green Belts
Use Six Sigma DMAIC methodology and basic tools to execute improvements within their existing job function(s)
May lead smaller improvement projects within Business Unit(s)
Bring knowledge of Six Sigma concepts & tools to their respective job function(s)
Undergo 8-11 days of training over 3-6 months
Evolution of Quality Management (13/19)
6 Sigma
QMS
Taguchi
DOE
SPC
Inspection
1930 1940 1975 1985 1990 2000
Thank You