Six sigma

Topics (Session 1)
♦ Understanding Six Sigma

♦ History of Six Sigma

♦ Six Sigma Methodologies & Tools

♦ Roles & Responsibilities

♦ How YOU can use 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.

What’s in a name?
♦ Sigma is the Greek letter representing the standard
deviation of a population of data.

♦ Sigma is a measure
of variation
(the data spread)
σ

μ

What does variation mean?
20

♦ Variation means that a 15

process does not produce 10

the same result (the “Y”) 5

every time. 0

-5

♦ Some variation will exist in
-10

all processes.
♦ Variation directly affects customer experiences.

Customers do not feel averages!

Measuring Process Performance
The 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 30 min. or less

the variation!
s

0 10 20 x 30 40 50

♦ Managing by the average doesn’t tell the whole story. The
average and the variation together show what’s happening.

Reduce Variation to Improve
Performance
How many standard 30 min. or less
deviations can you
“fit” within
s
customer
expectations?

0 10 20 x 30 40 50

♦ Sigma level measures how often we meet (or fail to meet)
the requirement(s) of our customer(s).

Managing Up the Sigma Scale
Sigma % Good % Bad DPMO
1 30.9% 69.1% 691,462
2 69.1% 30.9% 308,538
3 93.3% 6.7% 66,807
4 99.38% 0.62% 6,210
5 99.977% 0.023% 233
6 99.9997% 0.00034% 3.4

Examples of the Sigma Scale
In a world at 3 sigma. . . In a world at 6 sigma. . .

♦ There are 964 U.S. flight ♦ 1 U.S. flight is cancelled every
cancellations per day. 3 weeks.

♦ The police make 7 false arrests ♦ There are fewer than 4 false
every 4 minutes. arrests per month.

♦ In MA, 5,390 newborns are ♦ 1 newborn is dropped every 4
dropped each year. years in MA.

♦ In one hour, 47,283 ♦ It would take more than
international long distance calls 2 years to see the same number
are accidentally disconnected. of dropped international calls.

Topics
♦ Understanding Six Sigma

♦ History of Six Sigma

♦ Six Sigma Methodologies & Tools

♦ Roles & Responsibilities

♦ How YOU can use Six Sigma

The Six Sigma Evolutionary Timeline

1818: Gauss uses the normal curve 1924: Walter A. Shewhart introduces
to explore the mathematics of error the control chart and the distinction of
analysis for measurement, probability special vs. common cause variation as
analysis, and hypothesis testing. contributors to process problems.

1736: French 1896: Italian sociologist Vilfredo
mathematician Alfredo Pareto introduces the 80/20
Abraham de rule and the Pareto distribution in
Moivre publishes Cours d’Economie Politique.
an article
introducing the
normal curve.
1949: U. S. DOD issues Military
Procedure MIL-P-1629, Procedures
1960: Kaoru Ishikawa
for Performing a Failure Mode Effects
introduces his now famous
and Criticality Analysis.
cause-and-effect diagram.

1941: Alex Osborn, head of 1970s: Dr. Noriaki Kano
BBDO Advertising, fathers a introduces his two-dimensional
widely-adopted set of rules for quality model and the three
“brainstorming”. types of quality.

1986: Bill Smith, a senior engineer
and scientist introduces the 1995: Jack Welch
concept of Six Sigma at Motorola launches Six Sigma at
GE.

1994: Larry Bossidy launches
Six Sigma at Allied Signal.

Six Sigma and Financial Services

DMAIC – The Improvement
Methodology
Define Measure Analyze Improve Control
Objective: Objective: Objective: Objective: Objective:
DEFINE the MEASURE current ANALYZE the IMPROVE the CONTROL the
opportunity performance root causes of process to process
problems eliminate root to sustain the gains.
causes

Key Define Tools: Key Measure Key Analyze Key Improve Key Control
• Cost of Poor Tools: Tools: Tools: Tools:
Quality (COPQ) • Critical to Quality • Histograms, • Solution • Control Charts
• Voice of the Requirements Boxplots, Multi- Selection Matrix • Contingency
Stakeholder (CTQs) Vari Charts, etc. • To-Be Process and/or Action
(VOS) • Sample Plan • Hypothesis Tests Map(s) Plan(s)
• Project Charter • Capability • Regression
• As-Is Process Analysis Analysis
Map(s) • Failure Modes
• Primary Metric and Effect
(Y) Analysis (FMEA)

Define – DMAIC Project
What is the project?
$
Project Cost of
Charter Poor Voice of
Quality S ta k e h o ld e r s the
Stakeholde
r

Six Sigma
♦ 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 – As-Is Process
How does our existing process work?
Move-It! Courier Package Handling
Process
Accounts Accounts
Courier Mail Clerk In-SortClerk In-SortSupervisor DistanceFeeClerk WeightFeeClerk Out-SortClerk Out-SortSupervisor
ReceivableClerk Supervisor

Observ e package
weight (1 or 2) on
back of package

Look up
appropriate
Weight Fee and
write in top middle
box on package
back

Add Distance &
Take packages
Weight Fees
f rom Weight Fee
together and write
Clerk Outbox to
in top right box on
A/R Clerk Inbox.
package back

Circle Total Fee

Does EVERYONE and Draw Arrow
f rom total to
sender code

agree how the current
Accounting

Take packages Write Total Fee
f rom A/R Clerk f rom package in

process works?
Outbox to appropriate
Accounts Sender column on
Superv isorInbox. Accts. Supv .’s log

Take packages
Draw 5-point Star
f rom Accounts
in upper right
Superv isor
corner of package
Outbox to Out-
f ront
Sort Clerk Inbox.

Define the Non Value
Sort packages in
order of Sender
Code bef ore
placing in outbox

Take packages
Add steps Add up Total # of Observ e sender
Finalizing

f rom Out-Sort Packages and and receiv er
Clerk Outbox to Total Fees f rom codes and make
Out-Sort log and create entry in Out-Sort
Superv isorInbox. client inv oice Superv isor’s log

Deliv erPackages
Delivery

to customers
according to N, S,
E, W route

Deliv er inv oiceto
client

Submit log to
Submit log to Submit log to
General Manager
General Manager General Manager
at conclusion of
at end of round at end of round
round.

Define – Customer Requirements
What are the CTQs? What motivates the customer?
Voice of the Customer Key Customer Issue Critical to Quality
SECONDARY RESEARCH

Market
Data Industry
l e n yrt s udn
I

Benchmarking

Customer
t I

Correspondence
Customer
Service
s s o P gn ne s L
i t i

PRIMARY RESEARCH
t

Surve
Surve
ys
ys

OTM

Obser-
Focus Groups vations

Measure – Baselines and
Capability
What is our current level of performance?
Descriptive Statistics
♦ Sample some data / not all data Variable: 2003 Output

♦ Current Process actuals measured against Anderson-Darling Normality Test
A-Squared: 0.211
P-Value: 0.854

the Customer expectation Mean
StDev
23.1692
10.2152

♦
Variance 104.349

What is the chance that we will succeed Skewness
Kurtosis
N
0.238483
0.240771
100
0 10 20 30 40 50

at this level every time? Minimum
1st Quartile
0.2156
16.4134
Median 23.1475
3rd Quartile 29.6100
Maximum 55.2907
Pareto Chart for Txfr Defects 95% Confidence Interval for Mu
95% Confidence Interval for Mu
21.1423 25.1961
19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 95% Confidence Interval for Sigma
100 100 8.9690 11.8667
95% Confidence Interval for Median
80 19.7313 26.0572

60
Percent
Count

50
40

20

0 0
t
un er s
Defect La
te
A mo Oth

Count 79 17 4
Percent 79.0 17.0 4.0
Cum % 79.0 96.0 100.0

Measure – Failures and Risks
Where does our process fail and why?
Subjective opinion mapped into an “objective” risk profile number

Failure Modes and Effects Analysis (FMEA)

Process/Product

Process or
Prepared by: Page ____ of ____
Product Name:

Responsible: FMEA Date (Orig) ______________ (Rev) _____________

Process S O D R S O D R
Step/Part E C E P Actions E C E P
Number Potential Failure Mode Potential Failure Effects V Potential Causes C Current Controls T N Recommended Resp. Actions Taken V C T N

X1 0

0
0

0

X2
0 0

0 0

X3
0 0

0 0

X4
0 0

0 0

0 0

etc 0

0
0

0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

Analyze – Potential Root Causes
What affects our process?
Ishikawa Diagram
(Fishbone)

Six Sigma
y = f (x1, x2, x3 . . . xn)

Analyze – Validated Root Causes
What are the key root causes?
Pareto Chart for Txfr Defects

100 100

80

Percent
60
Count

50
40

20

0 0

nt er s

E x p e r im e n t a l D e s ig n
Defect La
te
Am
ou
Oth

Count 79 17 4
Percent 79.0 17.0 4.0
Cum % 79.0 96.0 100.0

Data Regression
Stratification Analysis
Pareto Chart for Amt Defects

15
100

80
Process
Simulatio
Percent

60
Count

10

40
5
20
n
0 0

cy al er
Defect Cu
rren Cle
ric
Oth

Count 12 3 2
Percent 70.6 17.6 11.8
Cum % 70.6 88.2 100.0

Six Sigma
y = f (x1, x2, x3 . . . xn)
Critical Xs

Improve – Potential Solutions
How can we address the root causes we identified?
♦ Address the causes, not the symptoms.

Generate

Evaluate
Clarify
Decision
y = f (x1, x2, x3 . . . xn)
Critical Xs

Divergent | Convergent

Improve – Solution Selection
How do we choose the best solution?
Solution Selection Matrix
Qualit
Solution Sigma Time CBA Other Score
y

Time Cost

Right
Six Sigma
Solution
Wrong

Nice
☺ Try
Solution
Implementatio
Nice n Plan
X
doo G da B
not a ne ml p m
e I

Idea
i t

Control – Sustainable Benefits
How 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

Process Control System (Business Process Framework)
Process Owner: Direct Process Customer: Date:
Process Description: CCR:

Flowchart Measuring and Monitoring
Measures
Key Specs
(Tools) Responsibility Contingency
Measure &/or Remarks
Where & (Who) (Quick Fix)
ments Targets
Frequency
P1 - activity 35
duration,
min. UCL=33.48

P2 - # of
incomplete

Individual Value
loan
applications

25
Mean=24.35

15 LCL=15.21

0 10 20 30
Observation Number

DFSS – The Design Methodology
Design for Six Sigma

Define Measure Analyze Develop Verify

♦ 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)

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

Other Roles

♦ Subject Matter Experts
– Provide specific process knowledge to Six Sigma teams
– Ad hoc members of Six Sigma project teams

♦ Financial Controllers
– Ensure validity and reliability of financial figures used
by Six Sigma project teams
– Assist in development of financial components of
initial business case and final cost-benefit analysis

Topics for Detailed Discussion
♦ Problem Identification
♦ Cost of Poor Quality
♦ Problem Refinement
♦ Process Understanding
♦ Potential X to Critical X
♦ Improvement

Problem Identification
“If it ain’t broke, why fix it
“This is the way we’ve always done it…”

• First Pass Yield
• Roll Throughput Yield
• Histogram
• Pareto

First Pass Yield (FPY):
The probability that 100 Units
any given unit can go
Step 1 Outputs / Inputs
through a system
defect-free without 100 / 100 = 1

rework. 100

Scrap 10 Units Step 2
90 / 100 = .90

90

Scrap 3 Units Step 3
87 / 90 = .96

87

Scrap 2 Units Step 4 85 / 87 = .97

At first glance, the yield would seem to be When in fact the FPY is (1 x .90 x .96 x .97 = .
85% (85/100 but….) 838)
85

Rolled
100 Units Outputs / Inputs
Throughput
Yield (RTY): Step 1 90 / 100 = .90
The yield of
individual Re-Work
process steps 10 Units 100 Units
multiplied Step 2 97 / 100 = .97
together.
Reflects the
Re-Work
hidden factory 3 Units 100 Units
rework issues
associated with Step 3 98 / 100 = .98
a process.
Re-Work
2 Units 100 Units
Step 4 .90 x .97 x .98 = .855

100 Units

RTY Examples - Widgets
50

Roll Throughput Yield
Function 1 50/50 = 1
(50-5)/50 = .90
50
(50-10)/50 = .80
Function 2
5
(50-5)/50 = .90
50

Function 3
10
1 x .90 x .80 x .90 = .65

50

Function 4
5
Put another way, this process is operating
50 a 65% efficiency

RTY Example - Loan Underwriting
50

Roll Throughput Yield
Application 50/50 = 1
(50-7-2)/50 = .82
2 50 7

Fails (43-6)/43 = .86
Underwrite
Underwriting
(43-1-2)/43 = .93
6 43

Complete Full
Paperwork
1 x .82 x .86 x .93 = .66
2 1
43
Decide not to
Close
borrow

42 Put another way, this process is operating
a 66% efficiency

Histogram – A histogram is a basic graphing tool that displays the
relative frequency or occurrence of continuous data values showing
which values occur most and least frequently. A histogram illustrates the
shape, centering, and spread of data distribution and indicates whether
there are any outliers.

Histogram of Cycle Time

40

30
Frequency

20

10

0

0 100 200 300 400 500
C8

Histogram – Can also help us graphically understand the data

Descriptive Statistics
Variable: CT

Anderson-Darling Normality Test
A-Squared: 6.261
P-Value: 0.000

Mean 80.1824
StDev 67.6003
Variance 4569.81
Skewness 2.31712
Kurtosis 8.26356
N 170
25 100 175 250 325 400
Minimum 1.000
1st Quartile 31.000
Median 66.000
3rd Quartile 105.000
95% Confidence Interval for Mu Maximum 444.000
95% Confidence Interval for Mu
69.947 90.417
54 64 74 84 94 95% Confidence Interval for Sigma
61.098 75.664
55.753 84.494

Pareto – The Pareto principle states that 80% of the impact of the
problem will show up in 20% of the causes. A bar chart that displays by
frequency, in descending order, the most important defects.
Pareto Chart for WEB

100
100
80

Percent
60
Count

50
40

20

0 0
EB ers
Defect No
n-W Oth eb)
(W
Count 96 15
Percent 86.5 13.5
Cum % 86.5 100.0

Topics (Session 2)
♦ Problem Identification
♦ Cost of Poor Quality
♦ Problem Refinement
♦ Process Understanding
♦ Potential X to Critical X
♦ Improvement

Cost of Poor Quality
COPQ - The cost involved in fulfilling the gap between the desired and
actual product/service quality. It also includes the cost of lost opportunity
due to the loss of resources used in rectifying the defect.

Hard Savings - Six Sigma project benefits that allow you to do the same
amount of business with less employees (cost savings) or handle more
business without adding people (cost avoidance).
Soft Savings - Six Sigma project benefits such as reduced time to market,
cost avoidance, lost profit avoidance, improved employee morale,
enhanced image for the organization and other intangibles may result in
additional savings to your organization, but are harder to quantify.

Examples / Buckets–
Roll Throughput Yield Inefficiencies (GAP between desired result and
current result multiplied by direct costs AND indirect costs in the process).
Cycle Time GAP (stated as a percentage between current results and
desired results) multiplied by direct and indirect costs in the process.
Square Footage opportunity cost, advertising costs, overhead costs, etc…

Problem Refinement
Multi Level Pareto – Logically Break down initial Pareto data into sub-
sets (to help refine area of focus)

Pareto Chart for WEB

100
100
80

Percent
60
Count

50
40

20

0 0 Pareto Chart for Type
B
WE ers
Defect No
n- Oth eb)
(W 100
Count 96 15 100
Percent 86.5 13.5
80
Cum % 86.5 100.0

Percent
60

Count
50
40

20

0 0
g
oi n
nG
al ime dO ers
Defect An
nu
On
eT
im
e an Oth
eT
On
Count 45 35 13 16
Percent 41.3 32.1 11.9 14.7
Cum % 41.3 73.4 85.3 100.0

Problem Refinement
Problem Statement – A crisp description of what we are trying to solve.
Primary Metric – An objective measurement of what we are attempting
to solve (the “y” in the y = f(x1, x2, x3….) calculation).
Secondary Metric – An objective measurement that ensures that a Six
Sigma Project does not create a new problem as it fixes the primary
problem. For example, a quality metric would be a good secondary
metric for an improve cycle time primary metric.

Problem Refinement
Fish Bone Diagram - A tool used to solve quality problems by
brainstorming causes and logically organizing them by branches. Also
called the Cause & Effect diagram and Ishikawa diagram

Provides tool for exploring cause / effect and 5 whys

Process Understanding
SIPOC – Suppliers, Inputs, Process, Outputs, Customers
You obtain inputs from suppliers, add value through your process, and
provide an output that meets or exceeds your customer's requirements.

Process Map – should allow people unfamiliar with the process to understand
the interaction of causes during the work-flow. Should outline Value Added
(VA) steps and non-value add (NVA) steps.

Full Form
Control Open
Start Size Sorts Pull & Sort
Receipt / Docs
Extract
Ck / Vouch
Verify

Perfection
Requal Group

No

Yes Prep cks,
Remit
Rulrs route Prep cks Ship to IP
Pass 1 Pass 2
vouch

Vouchers

Key from
Balance
Data Cap image

No
Vouch
OK

Inventory Yes
Prep
Folders / Full Form Ship to
Box QCReview Cust


Create daily peak Action
staff need plan Plan
No

Yes Can they Call employee
Add 30% to To Floor
the required make it? (3x)
no.
Operations No Need OJT Yes Make No
Compare to OJT
Re-Tng it?
Check off original Billet rpt
desired
Manually Review
returnee Yes
Update HR Staff
staff & "need No Yes
Billet Request Billet Need re
to retrain" To Floor
-train
list

Add 40% to Call (3x)
Stop!
staff needed
Create Update
Staff No IPS
No
Billet Rev
Do they original Do they No
Send Letters Yes Yes Have we No Yes Have we No Yes Interview / Meet Fleet
Do they want to billet & want to Call Wait Rank as
to desired hired hired New hiring
respond? work this call work this List pre-hire "1 2 3"
staff enough? enough? criteria
peak? uncheck peak?
ed
What if the
HR sends Hire in 1- Yes
returnee is Yes Yes
req for No No 2 order
Start already
staffing (3's are
HR / working here show up No
nos. not Place into Call
Recruit on another Do they Do they orienta
No No placed) dept 3X
program? want to want to tion
Stop! Stop!
Currently stay on the stay on the
send the ltr list list
anyways Wait List Yes

Yes Yes

New &
Other Take off Set 14
Take off Set 14
People IPS month
IPS month
call in system flag (on
system flag (on
IPS?)
IPS?)

schedule Yes No Gen Event Roster
for Reach
rpt in IPS
training

Show No Call Notify
up? 1X HR

Yes
Training Gen rpt for
Ops Kronos
Recruit
Train

No Yes Update
Pass?
IPS

Potential X to Critical X
“Y” is the dependent output of a variable process. In other
words, output is a function of input variables (Y=f(x1, x2,
x3…).
Through hypothesis testing, Six Sigma allows one to
determine which attributes (basic descriptor (generally
limited or binary in nature) for data we gather – ie. day of
the week, shift, supervisor, site location, machine type,
work type, affect the output. For example, statistically,
does one shift make more errors or have a longer cycle
time than another? Do we make more errors on Fridays
than on Mondays? Is one site faster than another? Once we
determine which attributes affect our output, we determine
the degree of impact using Design of Experiment (DOE).

A Design of Experiment (DOE) is a structured, organized
method for determining the relationship between factors
(Xs) affecting a process and the output of that process (Y).
Not only is the direct affect of an X1 gauged against Y but
also the affect of X1 on X2 against Y is also gauged. In
other words, DOE allows us to determine - does one input
(x1) affect another input (x2) as well as Output (Y).

DOE Example
Main Effects Plot (data means) for Elapsed
Main Effects Plot –
1.4
Lo
w
Hi g
h
Lo
w
Hig
h
Lo
w
Hig
h
Lo
w
Hig
h
Direct impact to Y
1.3
Elapsed

1.2

1.1

1.0
Jams DCDEL SK P2Jam

Interaction Plot (data means) for Elapsed
1 3 1 3 1 3 1 3

1.50
Jams
1 1.25
3 1.00

1.50
DCDEL
3 1.25

Interaction Plot –
1 1.00

1.50
SK
Impacts of X’s on 3

1
1.25

1.00

each other P2Jam 1.50

3 1.25

1 1.00

DOE Optimizer –
Allows us to
statistically predict the
Output (Y) based on
optimizing the inputs
(X) from the Design of
experiment data.

Improvement
Once we know the degree to which inputs (X) affect our
output (Y), we can explore improvement ideas, focusing
on the cost benefit of a given improvement as it relates
to the degree it will affect the output. In other words, we
generally will not attempt to fix every X, only those that
give us the greatest impact and are financially or
customer justified.

Control
Once improvements are made, the question becomes, are the
improvement consistent with predicted Design of Experiment
results (ie – are they what we expected) and, are they statistically
different than pre-improvement results.

Process Capability Analysis for Sept

LSL USL
Process Data
USL 0.23000
Within
Target *
LSL -1.00000 Overall
Mean -0.02391
Sample N 23
StDev (Within) 0.166425
StDev (Overall) 0.221880

Potential (Within) Capability
Z.Bench 1.53
Z.USL 1.53
Z.LSL 5.87
Cpk 0.51

-1.0 -0.5 0.0 0.5 1.0
Cpm *

Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Z.Bench 1.14 % < LSL 0.00 % < LSL 0.00 % < LSL 0.00
Z.USL 1.14 % > USL 13.04 % > USL 6.35 % > USL 12.62
Z.LSL 4.40 % Total 13.04 % Total 6.35 % Total 12.62
Ppk 0.38

Control
Control Chart - A graphical tool for monitoring changes that occur
within a process, by distinguishing variation that is inherent in the
process(common cause) from variation that yields a change to the
process(special cause). This change may be a single point or a series
of points in time - each is a signal that something is different from
what was previously observed and measured.
I and MR Chart for Sept

1
Individual Value

0.5 UCL=0.5293

0.0 Mean=0.03
2

-0.5 LCL=-0.4693

Subgroup Sept 13 Sept 20
Date 9/13 9/25
0.7 1
0.6 UCL=0.6134
Moving Range

0.5
0.4
0.3
0.2 R=0.1877
0.1
0.0 LCL=0

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