An Introduction to Lean Six Sigma (6σ) in Higher Education Dr. Andrew Luna Director Institutional...

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

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Six Sigma - As Easy to Understand As Parking Your Car

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• 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

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• 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

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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

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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.”

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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.”

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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)

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CustomerSpecification

Every Human Activity Has Variability...

Reducing Variability is the Key to Understanding Six Sigma

Six Sigma Concept

defects

Target


defects

Target


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Six Sigma Concept

Parking Your Car in the GarageHas Variability...

Target

defectsdefects



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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


After

13

6

6

By reducing the variability,we improve the process

Target


After

1133

66

6

No Defects!

By reducing the variability,we improve the process

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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?

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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 …

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Limitations of Average-Based Metrics

FOXTROT BY BILL AMEND

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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."

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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

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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:

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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.

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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

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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

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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

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30,000 Ft. – View of Entire Organization

5,000 Ft. – View of One Process

Different Views of the Organization

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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

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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.

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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

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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.

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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)

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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

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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.

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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

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Using Pareto and Trend Analysis

Trend Analysis

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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

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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

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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

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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.

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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

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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

______

_________

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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

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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

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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 …


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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?

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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

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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

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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…


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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.

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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.

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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

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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.

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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

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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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QUESTIONS?

An Introduction to Lean Six Sigma (6σ) in Higher Education Dr. Andrew Luna Director Institutional...

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