P_IRP Lean Six Sigma.ppt

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

2
Six Sigma - As Easy to Understand As
Parking Your Car

3
• 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
Agenda

4
• 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”
History of Quality in Higher Education

5
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

6
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.”

7
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.”

8
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)

9
Customer
Specification
Every Human Activity Has Variability...
Reducing Variability is the Key to Understanding Six Sigma
Six Sigma Concept
defects
Target
Customer
Specification
defects
Target
Customer
Specification

10
Six Sigma Concept
Parking Your Car in the Garage
Has Variability...
Target
defects
defects
Customer
Specification
Customer
Specification

11
Six Sigma Concept
A 3s process because 3 standard deviations
fit between target and spec
Target Customer
Specification
1s
2s
3s
3s
Before
Target Customer
Specification
1s
1s
2s
2s
3s
3s
3
s
Before
Target
Customer
Specification
After
1s
3s
6s
6s !
By reducing the variability,
we improve the process
Target
Customer
Specification
After
1s
1s
3s
3s
6s
6s
6s !
No Defects!
By reducing the variability,
we improve the process

12
Six Sigma
99.99966% Good
 20,000 articles of mail lost per hour
 Unsafe drinking water for almost 15
minutes each day
 5,000 incorrect surgical operations
per week
 2 short or long landings at most major
airports each day
 200,000 wrong drug prescriptions
dispensed each year
 7 articles of mail lost per hour
 Unsafe drinking water for 1 minute
every 7 months
 1.7 incorrect surgical operations
per week
 1 short or long landing at most
major airports every 5 years
 68 wrong drug prescriptions
dispensed each year
3.8 Sigma
99% Good
What’s Wrong With 99% Quality?

13
Why Use Sigma as a Metric?
 Focuses on defects
• Even one defect reflects a failure in your
customer’s eye
 Establishes a common metric to make
comparisons easier
 Is a more sensitive indicator than percentage
or average-based metrics …

14
Limitations of Average-Based Metrics
FOXTROT BY BILL AMEND

15
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."

16
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

17
Six Sigma (6σ) in Academia
Abraham Baldwin Columbus State Kennesaw State
Armstrong Atlantic State Darton College Southern Polytechnic State
Bainbridge College Georgia State University of Georgia
Clayton State Georgia Inst of Tech Valdosta State
USG Institutions Teaching Six Sigma
Institutions which have implemented some form of Six Sigma
methodology within their operations:
Health Sciences:
Medical College of Pennsylvania Alabama Jackson State South Carolina
Medical College of Virginia Boston University Johns Hopkins South Dakota State
Medical College of Wisconsin Cal Poly State Kettering Tennessee
Medical U of South Carolina California Michigan Texas
St. Louis U Health Sciences Center Carnegie Mellon Mississippi Texas A&M
U of Michigan Health System Central Florida Mississippi State Tulane
U of Tennessee Health Science Center Central Michigan NC State UNC Chapel Hill
U of Texas Health Science Center Clemson Ohio Vanderbilt
U of Texas Medical Branch Coastal Carolina Penn State Vermont
University System of Georgia: Colorado Purdue Villanova
University of Georgia Connecticut Rockhurst Washington
University of West Georgia Florida Tech Rutgers Western Illinois
Valdosta State University Illinois Central San Diego Western Kentucky
Other:

18
Six Sigma (6σ) Methodologies
Define
Measure
Analyze
Improve
Control
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.
Define
Measure
Analyze
Design
Verify
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.

19
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

20
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
Six Sigma Toolbox

21
 Process
 Problems and
Symptoms
 Process outputs
 Response variable, Y
 Independent variables, Xi
 Process inputs
 The Vital Few determinants
 Causes
 Mathematical relationship
Y
X’s
Measure
Analyze
Improve
Control
Process
Characterization
Process
Optimization
Goal: Y = f ( x )
Define The right project(s), the right team(s)
Project Focus

22
30,000 Ft. – View of Entire Organization
5,000 Ft. – View of One Process
Different Views of the Organization

23
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

24
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.

25
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

26
The Seven Speed Bumps of Lean
1. Over production which creates inventories that take up
space and capital
2. Excess inventory caused by over production
3. Waiting for the next value-added process to start
4. Unnecessary movement of work products
5. Unnecessary movement of employees
6. Unnecessary or incorrect processing
7. Defects leading to repair, rework, or scrap.
Non-value added waste – is any activity which
absorbs money, time, and people but creates no
value.

27
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)

28
Synergy of Lean and Six Sigma
# of
Steps
±3s ±4s ±5s ±6s
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%
Lean
reduces
non-value-add
steps
Six Sigma improves quality of value-add steps
Source: Motorola Six Sigma Institute

29
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.

30
Pareto Chart in Residence Halls
0
50
100
150
200
250
M
i
s
c
.
N
o
i
s
e
V
i
s
i
t
a
t
i
o
n
F
i
r
e
S
a
f
e
t
y
A
l
c
o
h
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V
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d
a
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i
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T
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f
t
H
a
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a
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m
e
n
t
M
e
d
i
c
a
l
M
e
c
h
a
n
i
c
a
l
Count
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Cumlative
Percentage
Residential Life Incident Reports – 2 Years

31
Using Pareto and Trend Analysis
Trend Analysis

32
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

33
Hall, Cont.
100
110
120
130
140
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
0
5
10
15
20
25
30
35
40
45
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
X - Bar
R
Means
Ranges
Run
Trend
Hugging of the
Mean
Periodicity
Exceeding
Control Limit

34
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

35
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.

36
Reference Our
Master Improvement Story
Vision Long-Term Objectives Annual Objectives Measures Targets
The Medical College of
Georgia will become
one of the nation's
premier health
sciences universities.
I - Enhance Educational
Environment and Update
Educational Programs
II - Enhance the Research
Enterprise
Improve Program
Effectiveness
* Number of applications
* Enrollment
* Number of Degrees conferred
* Passage rate
___
___
___
___
Improve Student
Performance
* Grade point averages
* Standard examination scores
* Fulfilled requirements
* % retained
* % promoted
* % graduated
* % certified/licensed
___
___
___
___
___
___
___
Improve Research
Productivity
* Amount of external funding
* NIH funding
* Comparative ranking
___
___
___
Improve Research
Outcomes
* Number of new grants
* Dollar amount of new grants
* Number of research studies
* Number of publications
* Presentations per Faculty
___
___
___
___
___
III - X
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.
a.k.a.,
Balanced
Scorecard

37
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.
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 …
Source:
IV - Continuously Enhance
the Quality of Faculty and
Staff
Improve Recruitment
Improve Retention
* Incentive packages
* Time to fill open reqs
* Competitive salaries
* Tenure status
* Turnover rate
___
___
___
___
___

38
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
Most
problems
can be
easily
expressed
as a line
graph
showing
the
current
trend.
MCG Faculty Turnover Rate
0
5
10
15
20
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
%
Turnover
MCG Turnover Trendline

39
improvement effort.
A Control
Chart is
used to
detect and
monitor
variation
over time.
This chart
tells us that
the process
is unstable.

40
DMAIC: Measure the Current
Situation
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 …
improvement effort.

41
improvement effort.
1991-2005 Faculty Turnover Rate
(excluding early retirement years 2001-2002)
0
5
10
15
20
1
9
9
1
1
9
9
2
1
9
9
3
1
9
9
4
1
9
9
5
1
9
9
6
1
9
9
7
1
9
9
8
1
9
9
9
2
0
0
0
2
0
0
3
2
0
0
4
2
0
0
5
%
Turnover
MCG Turnover Trendline
If we
remove the
“special
cause” early
retirement
program
years of
2001 -
2002, our
trend is
actually
downward.
But is the
process
stable?

42
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?

43
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.

44
DMAIC: Measure the Current
Situation
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…
improvement effort.

45
improvement effort.
1.
Determine the number of defect
opportunities per unit
O = 1
2.
Determine the number of units
processed
N = 647 = Fiscal Year End 2004 Faculty
3.
Determine the total number of
defects made
D = 64 = Faculty Terminations during 2005
D
N * O =
5.
Calculate Defects per Million
Opportunities
DPMO = DPO X 1M = 98,918
6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108%
= % of Units (Faculty) which went
through the process (Fiscal Year)
without a defect (Termination)
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
Sigma Value = 2.79 = 2005 Faculty Turnover Sigma
= 2005 Faculty Turnover (9.89%)
0.098918
Calculating Sigma Value Worksheet
DPO = =
Calculate Defects per Opportunity
4.
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.

46
improvement effort.
2005 MCG Faculty Turnover
19
14 12 6 4 3 3 2 1
30%
52%
70%
80%
86%
91% 95% 98%
0
8
16
24
32
40
48
56
64
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e
Reasons
Terminations
0%
20%
40%
60%
80%
100%
A Pareto
Chart helps
you break
down a big
problem into
its parts and
identify which
are the most
important.
“Voluntary
Collegiate
Employment
Elsewhere”
caused 30%
of the Faculty
turnover, and
“Involuntary
Non-
Reappoint-
ment” caused
22%.
Pareto Principle: 80% of the problems are caused by 20% of the contributors.

47
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.
Process/Methods
Resources
Technology
People
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
Why?
During 2005, "Voluntary
Collegiate Employment
Elsewhere" accounted for
30% of Faculty Turnover.
Problem Statement

48
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.
Problem Statement:
Root Cause
Countermeasure/
Proposed Solutions
Feasibility
Specific Actions
Effectiveness
Overall
Action
(Who?)
Value
($/period)
0
0
0
0
0
0
0
0
0
0
Feasibility: 1-low, 5-high Effectiveness: 1-low, 5-high
1-Expensive & Difficult to implement 1-Not very effective
5-Inexpensive and easy to implement 5-Very Effective
During 2005, "Voluntary Collegiate Employment Elsewhere" accounted for 30% of
Faculty Turnover.

49
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.
} Improvement
Before After
A1 A2 A3 A4 A2 A1 A3 A4
Before After
1.
Determine the number of defect
opportunities per unit
O = 1 1
2.
Determine the number of units
processed
N = 647 647
3.
Determine the total number of
defects made
D = 64 7
D
N * O =
5.
Calculate Defects per Million
Opportunities
DPMO = DPO X 1M = 98,918 10,819
6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108% 98.918%
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
Sigma Value = 2.79 3.80
0.098918
Calculating Sigma Value Worksheet
DPO = =
Calculate Defects per Opportunity
4. 0.010819
Before After
}Improvement
Target
}Remaining Gap
Good
Countermeasures
implemented

50
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!

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