View
215
Download
1
Category
Preview:
Citation preview
An Introduction to Lean Six Sigma (6σ) in
Higher EducationDr. Andrew LunaDirectorInstitutional Research and PlanningUniversity of West Georgia
Stan DeHoffUniversity Project Portfolio ManagerOffice of Decision SupportMedical 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
CustomerSpecification
Every Human Activity Has Variability...
Reducing Variability is the Key to Understanding Six Sigma
Six Sigma Concept
defects
Target
CustomerSpecification
defects
Target
CustomerSpecification
10
Six Sigma Concept
Parking Your Car in the GarageHas Variability...
Target
defectsdefects
CustomerSpecification
CustomerSpecification
11
Six Sigma Concept
A 3 process because 3 standard deviations fit between target and spec
Target CustomerSpecification
1
2
3
3
Before
Target CustomerSpecification
11
22
33
3
Before
Target
CustomerSpecification
After
13
6
6
By reducing the variability,we improve the process
Target
CustomerSpecification
After
1133
66
6
No Defects!
By reducing the variability,we improve the process
12
Six Sigma99.99966% Good
Six Sigma99.99966% 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 Sigma99% Good3.8 Sigma99% 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 StateArmstrong Atlantic State Darton College Southern Polytechnic StateBainbridge College Georgia State University of GeorgiaClayton 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 J ackson State South CarolinaMedical College of Virginia Boston University J ohns Hopkins South Dakota StateMedical College of Wisconsin Cal Poly State Kettering TennesseeMedical U of South Carolina California Michigan TexasSt. Louis U Health Sciences Center Carnegie Mellon Mississippi Texas A&MU of Michigan Health System Central Florida Mississippi State TulaneU of Tennessee Health Science Center Central Michigan NC State UNC Chapel HillU of Texas Health Science Center Clemson Ohio VanderbiltU of Texas Medical Branch Coastal Carolina Penn State Vermont
University System of Georgia: Colorado Purdue VillanovaUniversity of Georgia Connecticut Rockhurst WashingtonUniversity of West Georgia Florida Tech Rutgers Western I llinoisValdosta State University I llinois 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, andselect 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 modeland optimize performance
CONTROL Hold the gains
20
Analysis of Variance (ANOVA)Box Plots BrainstormingCause-effect Diagrams Correlation & RegressionDesign Of ExperimentsGraphs and ChartsHistogramsHypothesis TestingPareto AnalysisProcess Capability StudiesProcess Control PlansProcess Flow DiagramsQuality Function DeploymentResponse Surface MethodsScatter DiagramsStandard 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
Pro
cess
Ch
arac
teri
zati
on
Pro
cess
O
pti
miz
atio
n
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 production3. Waiting for the next value-added process to start4. Unnecessary movement of work products5. Unnecessary movement of employees
6. Unnecessary or incorrect processing7. 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
±3 ±4 ±5 ±6
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
Co
un
t
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Cu
mla
tive
Per
cen
tag
e
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
Control Chart for Hot Water in Residence 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
Mea
nsR
ange
s Run
Trend
Hugging of the Mean
Periodicity
Exceeding Control Limit
34
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
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 OurMaster Improvement Story
Vision Long-Term Objectives Annual Objectives Measures TargetsThe Medical College of Georgia will become one of the nation's
premier health sciences universities.
I - Enhance Educational Environment and Update
Educational Programs
I I - 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
_______________I I I - 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.,BalancedScorecard
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
StaffImprove 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
% T
urn
over
MCG Turnover Trendline
39
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.
Points outside
Upper Control Limit
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 …
Gather information on the current situation to provide a clearer focus for your improvement effort.
41
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your improvement effort.
1991-2005 Faculty Turnover Rate(excluding early retirement years 2001-2002)
0
5
10
15
20
% T
urn
over
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
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?
Points too close to
Control Limits
43
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.
Poin
ts o
utsi
de
Spec
ifica
tions
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…
Gather information on the current situation to provide a clearer focus for your improvement effort.
45
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your 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 Opportunity4.
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
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your improvement effort.
2005 MCG Faculty Turnover
1914 12 6 4 3 3 2 1
30%
52%
70%80%
86% 91% 95% 98%
0
8
16
24
32
40
48
56
64
Reasons
Term
inati
ons
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 toidentify potential causes of the problem.
Process/ MethodsResources
TechnologyPeople
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 CauseCountermeasure/
Proposed Solutions Feasib
ilit
y
Specific Actions Eff
ecti
ven
ess
Overa
ll
Acti
on
(W
ho?)
Valu
e (
$/p
eri
od
)
0000000000
Feasibility: 1-low, 5-high Effectiveness: 1-low, 5-high1-Expensive & Difficult to implement 1-Not very effective5-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 After1.
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 Opportunity4. 0.010819
Improvement
of 1σ!
Before After
}Improvement
Target}Remaining Gap
Good
Countermeasuresimplemented
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!
51
QUESTIONS?
Recommended