An Introduction to
Lean Six Sigma (6σ) in Higher
Education
Dr. Andrew Luna
Director
Institutional Research and Planning
University of West Georgia
Stan DeHoff
University Project Portfolio Manager
Office of Decision Support
Medical College of Georgia
Six Sigma - As Easy to Understand As
Parking Your Car
2
Agenda
•
•
•
•
•
•
History of Quality in Higher Education
The World We Live In
Six Sigma Defined
DMAIC
Lean Defined
Example: Using Statistical Measures for
Quality Control in higher education
• Example: Using Lean Six Sigma at MCG
3
History of Quality in Higher Education
• In 1980, NBC aired “If Japan can…Why
can’t we?” and the Quality movement took
off in the U.S.
• In 1991, IBM offered $1 million ($3 million
in IBM equipment) to those colleges and
universities that could adapt quality
management initiatives
• In 1992 all of higher education went TQM
“crazy”
4
History of Quality in Higher Education,
cont.
• TQM failed in higher education because of lack of
knowledge.
• TQM lost its appeal to many business because of
increased labor and documentation costs and
decreased reliance on Statistical Process Control
• Six Sigma was an effort by Motorola and GE to
bring back statistical measurement to quality
• Six Sigma is now slowly entering the halls of
academe
5
The World We Live In
Sonny Perdue
• Governor, State of Georgia
– Changing the culture of state government
• Principle-centered, people-focused, customer-friendly
– Commission for a New Georgia
• Best managed, growing, educated, healthy, safe
“Our government needed new thinking
from a fresh perspective to see better
ways to manage our assets and services
and map our future.”
6
The World We Live In
Erroll B. Davis, Jr.
• Chancellor, University System of Georgia
– Ongoing series of changes to improve System
communication and institutional engagement
• Reorganization, new System Strategic Plan, more
unified System
– Focus on accountability and quality and “Six Sigma”
“I want our actions and decisions to be
based upon knowledge. So that is an
initial expectation; that we will focus on
data-driven decision-making.”
7
What is Six Sigma (6σ)?
• 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.
Amount of Variation
Effect
Sigma Value
Too much
Hard to produce output within
customer specifications
Low (0 – 2)
Moderate
Most output meets customer
specifications
Middle (3 – 5)
Very little
Virtually all output meets
customer specifications
High (6)
8
Six Sigma Concept
Every Human Activity Has Variability...
Customer
Specification
Customer
Specification
defects
Target
Reducing Variability is the Key to Understanding Six Sigma
9
Six Sigma Concept
Parking Your Car in the Garage
Has Variability...
Customer
Specification
defects
Target
Customer
Specification
defects
10
Six Sigma Concept
Before
3s
A 3 s process because 3 standard deviations
fit between target and spec
Target
Customer
Specification
1s
2s
3s
After
Target
By reducing the variability,
we improve the process
6s !
1s
Customer
Specification
No Defects!
3s
6s
11
What’s Wrong With 99% Quality?
3.8 Sigma
99% Good
Six Sigma
99.99966% Good

20,000 articles of mail lost per hour

7 articles of mail lost per hour

Unsafe drinking water for almost 15
minutes each day

Unsafe drinking water for 1 minute
every 7 months

5,000 incorrect surgical operations
per week

1.7 incorrect surgical operations
per week

2 short or long landings at most major
airports each day

1 short or long landing at most
major airports every 5 years

200,000 wrong drug prescriptions
dispensed each year

68 wrong drug prescriptions
dispensed each year
12
Why Use Sigma as a Metric?
 Focuses on defects
• Even one defect reflects a failure in your
customer’s eye
 Establishes a common
comparisons easier
metric
to
make
 Is a more sensitive indicator than percentage
or average-based metrics …
13
Limitations of Average-Based Metrics
FOXTROT
BY BILL AMEND
14
Where Did 6σ Come From?
• Started at Motorola Corporation in the mid-1980’s,
when the company discovered that products with a
high first-pass yield (i.e., those that made it through
the production process defect-free) rarely failed in
actual use, resulting in higher customer satisfaction.
• Popularized by former General Electric CEO Jack
Welch’s commitment to achieving Six Sigma
capability (realized $12 Billion savings over 5 years).
"Six Sigma is a quality program that improves your
customers' experience, lowers your costs and builds
better leaders."
15
Isn’t 6σ Just For Manufacturing?
• No, Six Sigma is good for ANY business.
– Has been successful in industries such as
banking, retail, software, and medical
– Has been successful in improving processes
throughout operations, sales, marketing,
information technology, finance, customer
services, and human resources
• Why?
– Because every business suffers from the two
key problems that Six Sigma can solve:
defects and delay
16
Six Sigma (6σ) in Academia
Institutions which have implemented some form of Six Sigma
methodology within their operations:
Health Sciences:
Other:
Medical College of Pennsylvania
Medical College of Virginia
Medical College of Wisconsin
Medical U of South Carolina
St. Louis U Health Sciences Center
U of Michigan Health System
U of Tennessee Health Science Center
U of Texas Health Science Center
U of Texas Medical Branch
University System of Georgia:
University of Georgia
University of West Georgia
Valdosta State University
Alabama
Boston University
Cal Poly State
California
Carnegie Mellon
Central Florida
Central Michigan
Clemson
Coastal Carolina
Colorado
Connecticut
Florida Tech
Illinois Central
Jackson State
Johns Hopkins
Kettering
Michigan
Mississippi
Mississippi State
NC State
Ohio
Penn State
Purdue
Rockhurst
Rutgers
San Diego
South Carolina
South Dakota State
Tennessee
Texas
Texas A&M
Tulane
UNC Chapel Hill
Vanderbilt
Vermont
Villanova
Washington
Western Illinois
Western Kentucky
USG Institutions Teaching Six Sigma
Abraham Baldwin
Armstrong Atlantic State
Bainbridge College
Clayton State
Columbus State
Darton College
Georgia State
Georgia Inst of Tech
Kennesaw State
Southern Polytechnic State
University of Georgia
Valdosta State
17
Six Sigma (6σ) Methodologies
Control Define
Improve
Measure
Analyze
Verify
Design
Define
Measure
Analyze
DMAIC: This method is used to
improve the current capabilities of an
existing process. This is by far the most
commonly used methodology of sigma
improvement teams.
DMADV: This method is used when you
need to create or completely redesign a
process, product, or service to meet
customer requirements. DMADV teams
are usually staffed by senior managers
and Six Sigma experts.
18
DMAIC Methodology
DEFINE
Identify, prioritize, and
select the right project(s)
MEASURE
Identify key product
characteristics & process
parameters, understand
processes, and measure
performance
ANALYZE
Identify the key (causative)
process determinants
IMPROVE
Establish prediction model
and optimize performance
CONTROL
Hold the gains
19
Six Sigma Toolbox
Analysis of Variance (ANOVA)
Box Plots
Brainstorming
Cause-effect Diagrams
Correlation & Regression
Design Of Experiments
Graphs and Charts
Histograms
Hypothesis Testing
Pareto Analysis
Process Capability Studies
Process Control Plans
Process Flow Diagrams
Quality Function Deployment
Response Surface Methods
Scatter Diagrams
Standard Operating Procedures
(SOPs)
Statistical Process Control
20
Project Focus
Process
Characterization
Define
The right project(s), the right team(s)

Measure
Y


Analyze
Process
Optimization

Improve

X’s



Control

Process
Problems and
Symptoms
Process outputs
Response variable, Y
Independent variables, Xi
Process inputs
The Vital Few determinants
Causes
Mathematical relationship
Goal: Y = f ( x )
21
Different Views of the Organization
30,000 Ft. – View of Entire Organization
5,000 Ft. – View of One Process
22
So, What is Lean?
• The methodology of increasing the speed
of production by eliminating process steps
which do not add value
– those which delay the product or service
– those which deal with the waste and rework
of defects along the way
23
Where Did Lean Come From?
• Lean thinking originated at Toyota with the Toyota
Production System (TPS). The original ideas were
formulated by Sakichi Toyoda in the 1920s and
1930s,
but
only
made
the
leap
to
full
implementation in the 1950s.
• Many of the principles of lean came from a
surprising source: American supermarkets where
small quantities of a vast selection of inventory is
replenished as customers "pull" them off the shelf.
24
Core Ideas of Lean
• Determine and create value
– What does the customer want?
• Use “pull” instead of “push” systems to avoid
overproduction
– Inventories hide problems and efficiencies.
• One piece flow
– Make the work “flow,” so that there are no
interruptions and no wasted time or material
• Eliminate the seven speed bumps (non-value
adds) caused by waste
• Use the “five whys?” and Six Sigma problem
solving to eliminate defects
25
The Seven Speed Bumps of Lean
Non-value added waste – is any activity which
absorbs money, time, and people but creates no
value.
1.
2.
3.
4.
5.
Over production which creates inventories that take up
space and capital
Excess inventory caused by over production
Waiting for the next value-added process to start
Unnecessary movement of work products
Unnecessary movement of employees
6.
7.
Unnecessary or incorrect processing
Defects leading to repair, rework, or scrap.
26
The Antidote to Waste: The 5 S’s
1. Sort
– Keep only what is needed
2. Straighten
– A place for everything and everything in its place
3. Shine
– Clean systems and work area to expose problems
4. Standardize
– Develop systems and procedures to monitor conformance
to the first three rules. (Six Sigma’s Define and Measure
phases)
5. Sustain
– Maintain a stable workflow. (Six Sigma’s Analyze,
Improve, and Control phases)
27
Synergy of Lean and Six Sigma
Lean reduces non-value-add steps
# of
Steps
±3s
1
93.32%
99.379% 99.976% 99.999%
7
61.63%
95.733% 98.839% 99.997%
10
50.08%
93.96%
99.768% 99.996%
20
25.08%
88.29%
99.536% 99.993%
40
6.29%
77.94%
99.074% 99.986%
±4s
±5s
±6s
Six Sigma improves quality of value-add steps
Source: Motorola Six Sigma Institute
28
The Birth of “Lean Six Sigma”
• Six Sigma improves effectiveness by
eliminating defects (improves Quality)
• Lean improves efficiency by eliminating
delay and waste (improves Speed)
• Most Six Sigma efforts are incorporating
the principles of Lean. Therefore, Six
Sigma is often called Lean Six Sigma.
29
Pareto Chart in Residence Halls
Residential Life Incident Reports – 2 Years
250
100.00%
90.00%
200
70.00%
150
Count
60.00%
50.00%
100
40.00%
30.00%
50
20.00%
Cumlative Percentage
80.00%
10.00%
0
0.00%
M
.
isc
ise
o
N
n
ol
ty
h
e
it o
ism
f
l
o
a
a
c
a
S
d
sit
Al
re
an
Vi
i
V
F
t
ef
h
T
l
l
t
ca
ca
en
i
i
n
m
ed
ha
ss
M
c
a
e
rr
a
M
H
30
Using Pareto and Trend Analysis
Trend Analysis
31
Control Chart for Hot Water in Residence
Hall
Problem
• Survey found that
most residents in a
female hall were
unhappy with the
bathrooms
• Subsequent focus
groups found that
residents were upset
over the quantity and
quality of hot water
• Define – Hot water
variability in high-rise
residence hall
• Measure – Record
temp. of hot water on
high, med., and low
floors for two weeks,
three times a day
• Analyze – Plot hot
water on X-Bar/R
Control Chart
32
Control Chart for Hot Water in Residence
Hall, Cont.
X - Bar
Hugging of the
Mean
Periodicity
Means
140
130
120
110
100
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
2
21
2
2
2
2
2
2
2
2
3
31
3
3
3
3
3
R
3
3
4
41
Exceeding
Control Limit
45
40
Trend
35
Ranges
3
30
Run
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
2
21
2
2
2
2
2
2
2
2
3
31
3
3
3
3
3
3
3
3
4
41
33
Control Chart for Hot Water in Residence
Hall, Cont.
• Improve – After understanding the
process and the control chart, the team
offered suggestions to control variability
• Control – A new control chart was run
after changes to the system and the
process was found to be in control
• Money – The changes decreased utility
costs and increased student retention in
the hall
34
Regression Analysis
• Multiple Regression was used to explain
variability in academic departmental
budget allocations
• Credit hours, professors, degrees, market
of the discipline, and majors were used to
predict budget allocation
• Predicted allocations were compared to
actual allocations and significant
discrepancies were addressed.
35
Reference Our
Master Improvement Story
a.k.a.,
Balanced
Scorecard
A Master Improvement Story links key measures to
improvement efforts. This linkage helps leaders and
employees focus on the customer / stakeholder and align
all of their actions to achieve desired outcomes.
Vision
The Medical College of
Georgia will become
one of the nation's
premier health
sciences universities.
Long-Term Objectives
I - Enhance Educational
Environment and Update
Educational Programs
Annual Objectives
Improve Program
Effectiveness
Improve Student
Performance
II - Enhance the Research
Enterprise
Improve Research
Productivity
Improve Research
Outcomes
III - X
Measures
Number of applications
Enrollment
Number of Degrees conferred
Passage rate
Targets
*
*
*
*
*
*
*
*
*
*
*
Grade point averages
Standard examination scores
Fulfilled requirements
% retained
% promoted
% graduated
% certified/licensed
___
___
___
___
___
___
___
___
___
___
___
* Amount of external funding
* NIH funding
* Comparative ranking
___
___
___
*
*
*
*
*
___
___
___
___
___
Number of new grants
Dollar amount of new grants
Number of research studies
Number of publications
Presentations per Faculty
36
DMAIC: Define the Project
Define the project’s purpose and scope. Collect background information on
the process and your customers’ needs and requirements.
As an example project, let’s focus on the Full-Time
Instructional Faculty (FTI) Turnover Rate metric …
IV - Continuously Enhance
the Quality of Faculty and
Staff
Improve Recruitment
* Incentive packages
* Time to fill open reqs
___
___
Improve Retention
* Competitive salaries
* Tenure status
* Turnover rate
___
___
___
Definition: Number of full-time instructional faculty (FTI) who left during a
fiscal year (July 1 - June 30) divided by the total number of FTI faculty
present as of June 30 of the prior fiscal year.
Source:
37
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
MCG Faculty Turnover Rate
20
15
% Turnover
Most
problems
can be
easily
expressed
as a line
graph
showing
the
current
trend.
10
5
0
91
9
1
92
9
1
93
9
1
94
9
1
95
9
1
96
9
1
97
9
1
98
9
1
99
9
1
MCG Turnover
00
0
2
01
0
2
02
0
2
03
0
2
04
0
2
05
0
2
Trendline
38
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
A Control
Chart is
used to
detect and
monitor
variation
over time.
This chart
tells us that
the process
is unstable.
39
DMAIC: Measure the Current
Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
Stop! Wait a minute! We had an early
retirement program in 2001 and 2002,
where we planned to have a high
faculty turnover rate. What if we were
to flag those years as “special causes”
and
remove
them
from
our
measurement?
Okay, let’s see …
40
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
(excluding early retirement years 2001-2002)
20
15
10
5
MCG Turnover
20
05
20
04
20
03
20
00
19
99
19
98
19
97
19
96
19
95
19
94
19
93
19
92
0
19
91
But is the
process
stable?
1991-2005 Faculty Turnover Rate
% Turnover
If we
remove the
“special
cause” early
retirement
program
years of
2001 2002, our
trend is
actually
downward.
Trendline
41
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
The Control
Chart still
indicates an
unstable
process with
points too
close to the
Upper and
Lower Control
Limits.
But is the
process
capable of
meeting
specifications?
42
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
A Histogram
measures the
process’s
capability of
meeting the
customer’s
specifications.
Our process
is not
capable, as
there is too
much
variation.
The Target and Customer Specification values are examples based on peer reports.
43
DMAIC: Measure the Current
Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
Now that we have seen that our Faculty
Turnover process is both unstable and
incapable of meeting specifications, let’s take
a closer look at the year 2005…
44
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
Calculating Sigma Value Worksheet
Determine the number of defect
opportunities per unit
Determine the number of units
2.
processed
Determine the total number of
3.
defects made
1.
4. Calculate Defects per Opportunity
5.
Calculate Defects per Million
Opportunities
6. Calculate Yield
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
O
=
1
N
=
647
= Fiscal Year End 2004 Faculty
D
=
64
= Faculty Terminations during 2005
DPO =
D
N*O=
=
0.098918
DPMO =
DPO X 1M
=
98,918
Yield = (1 - DPO) x 100 =
Sigma Value
=
90.108%
2.79
= 2005 Faculty Turnover (9.89%)
= % of Units (Faculty) which went
through the process (Fiscal Year)
without a defect (Termination)
= 2005 Faculty Turnover Sigma
In Good To Great, author Jim Collins mentions the need for a BHAG
or Big Hairy Audacious Goal. Using Six Sigma as a guide, you can
measure your current performance and set a BHAG of reaching the
next level sigma.
45
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
A Pareto
Chart helps
you break
down a big
problem into
its parts and
identify which
are the most
important.
Terminations
64
56
48
40
32
24
16
8
0
86%
80%
70%
98%
95%
91%
100%
80%
52%
60%
30%
19
40%
14
20%
12
6
4
3
3
2
1
0%
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lle
gi
at
e
Em
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oy
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er
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ic
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m
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ry
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si
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io
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tir
vo
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Vo
“Voluntary
Collegiate
Employment
Elsewhere”
caused 30%
of the Faculty
turnover, and
“Involuntary
NonReappointment” caused
22%.
2005 MCG Faculty Turnover
Reasons
Pareto Principle: 80% of the problems are caused by 20% of the contributors.
46
DMAIC: Analyze to Identify Causes
Identify the root cause of defects. Confirm them with data.
An Ishikawa (Fishbone) Cause-and-Effect diagram is used to
identify potential causes of the problem.
Resources
Process/Methods
Why?
Why?
Why?
Why?
Why?
Why?
Problem Statement
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
People
During 2005, "Voluntary
Collegiate Employment
Elsewhere" accounted for
30% of Faculty Turnover.
Why?
Why?
Technology
47
DMAIC: Improve
Develop, try out, and implement solutions that address the root causes. Use
data to evaluate results for the solutions and the plans used to carry them out.
A Countermeasures chart is used to identify potential solutions and rank
them for implementation.
Feasibility: 1-low, 5-high
1-Expensive & Difficult to implement
5-Inexpensive and easy to implement
Value ($/period)
Action (Who?)
Specific Actions
Overall
Countermeasure/
Proposed Solutions
Effectiveness
Root Cause
Feasibility
Problem Statement:
During 2005, "Voluntary Collegiate Employment Elsewhere" accounted for 30% of
Faculty Turnover.
0
0
0
0
0
0
0
0
0
0
Effectiveness: 1-low, 5-high
1-Not very effective
5-Very Effective
48
DMAIC: Control
Maintain gains that you have achieved by standardizing your work methods
or processes. Anticipate future improvements and make plans to preserve
the lessons learned from this improvement effort.
After
Before
Before
}
A1
A2
A3
A4
A2
A1
Improvement
A3
4. Calculate Defects per Opportunity
5.
Calculate Defects per Million
Opportunities
6. Calculate Yield
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
}Remaining Gap
A4
Countermeasures
implemented
=
1
1
N
=
647
647
D
=
64
7
DPO =
D
N*O=
=
0.098918
0.010819
DPMO =
DPO X 1M
=
98,918
10,819
90.108%
98.918%
2.79
3.80
Yield = (1 - DPO) x 100 =
=
Target
After
O
Sigma Value
Good
}Improvement
Calculating Sigma Value Worksheet
Before
Determine the number of defect
1.
opportunities per unit
Determine the number of units
2.
processed
Determine the total number of
3.
defects made
After
49
To Recapitulate Six Sigma
• Define – Choose a significant process
• Measure – Track the output of that
process
• Analyze – Determine the causes of
variability within the process
• Improve – Minimize the variability
• Control – Stabilize the process
Remember: Minimize variability, increase quality. Increase quality, decrease costs!
50
QUESTIONS?
51