six sigma a brief overview

8/10/2019 six sigma a brief overview

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What is Six Sigma?


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Basics

A new way of doing business

Wise application of statistical tools within

a structured methodology Repeated application of strategy to

individual projects

Projects selected that will have asubstantial impact on the bottom line


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A scientific and practical method to achieveimprovements in a company

Scientific:Structured approach.Assuming quantitative data.

Practical:Emphasis on financial result.Start with the voice of the customer.

Show methe data

Show methe money

Six Sigma


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Knowledge

Management

The Six Sigma Initiative

integrates these efforts


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GE Service company - examples

Approving a credit card application

Installing a turbine

Lending money

Servicing an aircraft engine

Answering a service call for an appliance

Underwriting an insurance policy

Developing software for a new CAT productOverhauling a locomotive


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the most important initiative GE has ever

undertaken. Jack WelchChief Executive Officer

General Electric

In 1995 GE mandated each employee to work towardsachieving 6 sigma

The average process at GE was 3 sigma in 1995

In 1997 the average reached 3.5 sigma

GEs goal was to reach 6 sigma by 2001

Investments in 6 sigma training and projects reached

45MUS$ in 1998, profits increased by 1.2BUS$

General Electric


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AtMotorola we use statistical methods dailythroughout all of our disciplines to synthesize an

abundance of data to derive concrete actions.

How has the use of statistical methods within

Motorola Six Sigma initiative, across disciplines,

contributed to our growth? Over the past decade we

have reduced in-process defects by over 300 fold,

which has resulted in cumulative manufacturing cost

savings of over 11 billion dollars*.Robert W. Galvin

Chairman of the Executive Committee

Motorola, Inc.

MOTOROLA

*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998


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Positive quotations

If youre an average Black Belt, proponents sayyoull find ways to save $1 million each year

Raytheon figures it spends 25% of each salesdollar fixing problems when it operates at four

sigma, a lower level of efficiency. But if it raisesits quality and efficiency to Six Sigma, it wouldreduce spending on fixes to 1%

The plastics business, through rigorous SixSigma process work , added 300 million poundsof new capacity (equivalent to a free plant),saved $400 million in investment and will save

another $400 million by 2000


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Barrier #1: Engineers and managers are not interested inmathematical statistics

Barrier #2: Statisticians have problems communicating withmanagers and engineers

Barrier #3: Non-statisticians experience statistical anxietywhich has to be minimized before learning can take place

Barrier # 4: Statistical methods need to be matched tomanagement style and organizational culture

Barriers to implementation


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Technical

Skills

Soft Skills

StatisticiansMaster

Black BeltsBlack Belts

Quality Improvement

Facilitators

BB

M


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Reality

Six Sigma through the correct applicationof statistical tools can reap a companyenormous rewards that will have a positive

effect for years

or

Six Sigma can be a dismal failure if notused correctly

ISRU, CAMT and Sauer Danfoss willensure the former occurs


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Six Sigma

The precise definition of Six Sigma is notimportant; the content of the program is

A disciplined quantitative approach forimprovement of defined metrics

Can be applied to all business

processes, manufacturing, finance andservices


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Focus of Six Sigma*

Accelerating fast breakthroughperformance

Significant financial results in 4-8

monthsEnsuring Six Sigma is an extension of

the Corporate culture, not the program

of the monthResults first, then culture change!

*Adapted from Zinkgraf (1999), Sigma BreakthroughTechnologies Inc., Austin, TX.


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Six Sigma: Reasons for Success

The Success at Motorola, GE andAlliedSignal has been attributed to:

Strong leadership (Jack Welch, LarryBossidy and Bob Galvin personally involved)

Initial focus on operations

Aggressive project selection (potentialsavings in cost of poor quality >$50,000/year)

Training the right people


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The right way!

Plan for quick wins Find good initial projects - fast wins

Establish resource structure

Make sure you know where it is

Publicise success

Often and continually - blow that trumpet

Embed the skills

Everyone owns successes


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The Six Sigma metric


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Consider a 99% quality level

5000 incorrect surgical operations perweek!

200,000 wrong drug prescriptions peryear!

2 crash landings at most major airports

each day! 20,000 lost articles of mail per hour!


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Not very satisfactory!

Companies should strive for Six Sigmaquality levels

A successful Six Sigma programme canmeasure and improve quality levels acrossall areas within a company to achieve

world class status Six Sigma is a continuous improvement

cycle


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Scientific method (after Box)

INDUCTION INDUCTION

DEDUCTION DEDUCTION

Data

Facts

Theory

Hypothesis

Conjecture

Idea

Model

Check

Plan

DoAct


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23

Improvement cycle

PDCA cycle

Plan

Do

Check

Act


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Prioritise (D)

Measure (M)

Interpret

(D/M/A)

Problem (D/M/A)

solve

Improve (I)

Hold

gains (C)

Alternative interpretation


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Statistical background

Target = m

Some Key measure


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+/-3s


Target = m

Control limits


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+/-3sLSL USL


Required Tolerance

Target = m


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+/-3s

+/-6s

LSL USL


Tolerance

Target = m

Six-Sigma


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+/-3s

+/-6s

LSL USL

ppm1350

ppm1350


Tolerance

Target = m


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+/-3s

+/-6s

LSL USL

ppm0.001

ppm1350

ppm1350

ppm0.001


Tolerance

Target = m


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Six-Sigma allows for un-foreseenproblems and longer term issueswhen calculating failure error orre-work rates

Allows for a process shift


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LSL

0ppm ppm3.4

1.5sUSL

ppm3.4ppm

66803

m

+/-6s


Tolerance


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Performance Standards

23

4

5

6

30853766807

6210

233

3.4

s PPM

69.1%93.3%

99.38%

99.977%

99.9997%

Yield

Processperformance

Defects permillion

Long termyield

Current standard

World Class


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Number of processes 3 4 5 6

1

10

100

500

1000

20002955

93.32

50.09

0.1

0

0

00

99.379

93.96

53.64

4.44

0.2

00

99.9767

99.77

97.70

89.02

79.24

62.7550.27

99.99966

99.9966

99.966

99.83

99.66

99.3299.0

First Time Yield in multiple stage process

Performance standards


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Benefits of 6s approach w.r.t. financials

-level Defect rate

(ppm)

Costs of poor quality Status of the

company6 3.4 < 10% of turnover World class

5 233 10-15% of turnover

4 6210 15-20% of turnover Current standard

3 66807 20-30% of turnover2 308537 30-40% of turnover Bankruptcy

Financial Aspects


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Six Sigma and other

Quality programmes


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Comparing three recent developmentsin Quality Management

ISO 9000 (-2000)

EFQM Model

Quality Improvement and Six

Sigma Programs


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ISO 9000

Proponents claim that ISO 9000 is ageneral system for Quality Management

In fact the application seems to involve

an excessive emphasis on Quality Assurance,and

standardization of already existing systems

with little attention to Quality Improvement It would have been better if improvement

efforts had preceded standardization


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Critique of ISO 9000

Bureaucratic, large scale

Focus on satisfying auditors, notcustomers

Certification is the goal; the job is done when

certified Little emphasis on improvement

The return on investment is not transparent

Main driver is: We need ISO 9000 to become a certified supplier, Notwe need to be the best and most cost effective

supplier to win our customers business

Corrupting influence on the quality profession


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EFQM Model

A tool for assessment: Can measure where weare and how well we are doing

Assessment is a small piece of the biggerscheme of Quality Management:

Planning Control

Improvement

EFQM provides a tool for assessment, but notools, training, concepts and managerialapproaches for improvement and planning


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The Success of Change

Programs?Performance improvement efforts

have as much impact onoperational and financial results as a

ceremonial rain dance has on the weather

Schaffer and Thomson,

Harvard Business Review(1992)


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Change Management:Two Alternative Approaches

Activity Centered

Programs

Result Oriented

Programs

ChangeManagement

Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992


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Activity Centered Programs

Activity Centered Programs:The pursuit ofactivities that sound good, but contribute littleto the bottom line

Assumption:If we carry out enough of the

right activities, performance improvementswill follow This many people have been trained

This many companies have been certified

Bias Towards Orthodoxy:Weak or noempirical evidence to assess the relationshipbetween efforts and results


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An Alternative:Result-Driven Improvement Programs

Result-Driven Programs:Focus onachieving specific, measurable,operationalimprovements within a few months

Examples of specific measurable goals:

Increase yield

Reduce delivery time

Increase inventory turns Improved customer satisfaction

Reduce product development time


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Result Oriented Programs

Project based

ExperimentalGuided by empirical evidenceMeasurable results

Easier to assess cause and effectCascading strategy

Wh T f ti


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Why TransformationEfforts Fail!

John Kotter, Professor, Harvard BusinessSchool

Leading scholar on Change Management

Lists 8 common errors in managingchange, two of which are:

Not establishing a sense of urgency

Not systematically planning for andcreating short term wins


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Six Sigma Demystified*

Six Sigma is TQM in disguise, but thistime the focus is:

Alignment of customers, strategy, process

and people Significant measurable business results

Large scale deployment of advancedquality and statistical tools

Data based, quantitative



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Keys to Success*

Set clear expectations for results

Measure the progress (metrics)

Manage for results



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Black Belts

Six Sigma practitioners who are employedby the company using the Six Sigmamethodology

work full time on the implementation of problemsolving & statistical techniques through projectsselected on business needs

become recognised Black Belts afterembarking on Six Sigma training programmeand completion of at least two projects whichhave a significant impact on the bottom-line


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Black Bel t requ ired resou rces

-Training in statistical methods.

-Time to conduct the project!-Software to facilitate data analysis.

-Permissions to make required changes!!

-Coaching by a championor external support.

Black Belt requirements


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In o ther word s the Black Bel t is

-Empowered.

-In the sense that it was always meant!

-As the theroists have been saying for years!

Black Belt role!


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Further down the line - after initial Six Sigmaimplementation package

Master Black Belts

Black Belts who have reached an acquired levelof statistical and technical competence

Provide expert advice to Black Belts

Green Belts

Provide assistance to Black Belts in Six Sigmaprojects

Undergo only two weeks of statistical and

problem solving training


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Six Sigma instructors (ISRU)

Aim: Successfully integrate the Six Sigmamethodology into a companys existing cultureand working practices

Key traitsKnowledge of statistical techniques

Ability to manage projects and reach closure

High level of analytical skillsAbility to train, facilitate and lead teams to

success, soft skills


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Six Sigma training

package


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Aim of training package

To success ful ly integrate Six Sigm a

methodology into Sauer Danfosscu l ture and attain s ign i f icant

improvements in qual ity, service and

operat ional performance


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DMAIC

Define Select a project

Measure Prepare for assimilating information

Analyze Characterise the current situation

Improve Optimize the process

Control Assure the improvements

Six-Sigma -A Roadmap for improvement


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Training (1 week)

Work on project(3 weeks)

Review

Define

Measure

Analyze

Improve

Control

Throughput time project

4 months (full time)

Example of a Classic Training strategy

ISRU program content


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ISRU program content

Week 1 - Six Sigma introductory week(Deployment phase)

Weeks 2-5 - Main Black Belt trainingprogramme

Week 2 - Measurement phaseWeek 3 - Analysis phase

Week 4 - Improve phase

Week 5 - Control phase

Project support for Six Sigma Black Beltcandidates

Access to ISRUs distance learning facility


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Training programme delivery

Lectures supported by appropriate technology

Video case studies

Games and simulations

Experiments and workshops

Exercises

Defined projects

Delegate presentations Homework!

5 k f t i i


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Measure

Analyze

Improve

Control

Define

5 weeks of training


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Deployment (Define) phase

Topics covered include

Team Roles

Presentation skillsProject management skills

Group techniques

QualityPitfalls to Quality Improvement projects

Project strategies

Minitab introduction


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Measurement phase

Topics covered include:

Quality Tools

Risk Assessment

Measurements

Capability & Performance

Measurement Systems Analysis

Quality Function Deployment

FMEA


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Example - QFD

A method for meeting customerrequirements

Uses tools and techniques to set product

strategies

Displays requirements in matrix diagrams,including House of Quality

Produces design initiatives to satisfycustomer and beat competitors


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House Of Quality

6. Technical assessment andtarget values

1. Customerrequirements

4. Relationshipmatrix

3. Productcharacteristics

Importance

2. Competitiveassessment

5. Tradeoffmatrix


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Lead-times - the time to market and timeto stable production

Start-up costs

Engineering changes

QFD can reduce


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Analysis phase

Topics include:

Hypothesis testing

Comparing samples

Confidence Intervals

Multi-Vari analysis

ANOVA (Analysis of Variance)

Regression


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Example - Design of Experiments

What can it do for you?

Minimumcost Maximumoutput

Wh t d it i l ?


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What does it involve?

Brainstorming sessions to identifyimportant factors

Conducting a fewexperimental trials

Recognising significant factorswhichinfluence a process

Setting these factors to get maximumoutput


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Control phase

Topics include:

Control charts

SPC case studies

EWMA

Poka-Yoke

5S

Reliability testing

Business impact assessment


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R lt f SPC


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Results of SPC

An improvement in the process

Reduction in variation

Better control over process

Provides practical experience of

collecting useful information for analysisHopefully some enthusiasm for

measurement!


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P j t ti


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Black Belt

Training

Application

Review

ISRU

ISRU,Champion

ISRU,

Champion

Project execution

Conducting projects


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Tradit ional Six Sigma

-Project leader is obliged tomake an effort.

-Set of tools .

-Focus on technical knowledge.

-Project leader is left to his owndevices.

-Results are fuzzy.

-Safe targets.

-Projects conducted on theside.

-Black Belt is obliged toachieve financial results.

-Well-structured method.

-Focus on experimentation.

-Black Belt is coached bychampion.

-Results are quantified.

-Stretched targets.

-Projects are top priority.

Conducting projects


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The right support

+The right projects

+The right people

+The right tools

+

The right plan=The right results

Ch i R l


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Champions Role

Communicate vision and progress

Facilitate selecting projects and people

Track the progress of Black Belts

Breakdown barriers for Black Belts

Create supporting systems


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Project selection


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Define

Select:

- the project- the process- the Black Belt- the potential savings- time schedule

- team

Project selection

P j t l ti


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Projects may be selected according to:

1. A complete list of requirements of customers.

2. A complete list of costs of poor quality.

3. A complete list of existing problems or targets.

4. Any sensible meaningful criteria

5. Usually improves bottom line - but exceptions

Project selection

Key Quality Characteristics


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Key Quality Characteristics

CTQsHow will you measure them?

How often?

Who will measure?

Is the outcome critical or importantto results?


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Key Criteria

Is the potential gain enough - e.g. -

saving > $50,000 per annum?

Can you do this within 3-4 months?

Will results be usable?

Is this the most important issue at themoment?


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Why is ISRU an effective

Six Sigma practitioner?

Reasons


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Because we are experts in the applicationof industrial statistics and managing theaccompanying change

We want to assist companies in improvingperformance thus helping companies togreater success

We will act as mentors to staff embarkingon Six Sigma programmes

Reasons


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INDUSTRIALSTATISTICS

RESEARCH UNITWe are based in the School of Mechanical andSystems Engineering, University of Newcastle uponTyne, England

Mi i t t t


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Mission statement

"To promote the effective andwidespread use of statisticalmethods throughout European

industry."


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The work we do can be broken

down into 3 main categories:

Consultancy

TrainingMajor Research Projects

All with the common goal of promoting qualityimprovement by implementing statisticaltechniques

Consultancy


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Consultancy

We have long term one to one consultancieswith large and small companies, e.g.

Transco

Prescription Pricing Agency

Silverlink

To name but a few

Training


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Training

In-House courses SPC

QFD

Design of ExperimentsMeasurement Systems Analysis

On-Site courses

As above, tailored courses to suit the company Six Sigma prog rammes

European projects


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European projects

The Unit has provided the statistical input intomany major European projects

Examples include -

Use of sensory panels to assess butter quality

Using water pressures to detect leaks

Assessing steel rail reliability

Testing fire-fighters boots for safety

European projects


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European projects

Eurostat-investigating the multi-dimensionalaspects of innovation using the CommunityInnovation Survey (CIS) II

- 17 major European countries involved -determining the factors that influenceinnovation

Certified Reference materials for assessingwater quality - validating EC Laboratories

New project- Effect on food of the taints

and odours in packaging materials

Typical local projects


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Typical local projects

Assessment of environmental risks inchemical and process industries

Introduction of statistical process control(SPC) into a micro-electronics company

Helping to develop a new catheter foropen-heart surgery via designedexperiments (DoE)

Restaurant of the Year& Pub of the Yearcompetitions!

Benefits


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Benefits

Better monitoring of processes

Better involvement of peopleStaff morale is raised

Throughput is increased

Profits go up

Examples of past successes


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Down time cut by 40% - Villa soft drinks

Waste reduced by 50% - Many projects

Stock holding levels halved - Manyprojects

Material use optimised saving 150k pa -

BootsExpensive equipment shown to be

unnecessary - Wavin



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Faster Payment of Bills (cut by 30 days)

Scrap rates cut by 80%

New orders won (e.g 100,000 for anSME)

Cutting stages from a process

Reduction in materials use (Paper - Ink)


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Distance Learning

Facility

Distance Learning


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Distance Learning

your time

your place your study pattern

your pace

or Flexible training

or Open Learning


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Case study

Case study: project selection


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Roast

Cool

Grind

Pack

Coffeebeans

Sealedcoffee

Moisture

content

Savings:-Savings on rework and scrap-Water costs less than coffee

Potential savings:

500 000 Euros

Case study: project selection

C t d M


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1. Select the Critical to Quality (CTQ)characteristic

2. Define performance standards

3. Validate measurement system

Case study: Measure


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Case study: Measure


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Gauge R&R study

3. Measurement reliability

Measurement system

too unreliable!

y

So fix it!!

Case study: Analyse


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Analyse

4. Establish product capability

5. Define performanceobjectives

6. Identify influence factors

y y

Improvement opportunities


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USL

USL

Improvement opportunities

Diagnosis of problem


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CTQ

CTQ

CTQ

CTQ

Diagnosis of problem

Discovery of causes


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-Brainstorming

-Exploratory data analysis

6. Identify factorsMaterialMachineMan

Method Measure-ment

MotherNature

Amount of

added water

Roasting

machines

Batchsize

Reliability

of Quadra Beam

Weather

conditions

Moisture%

y


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A case study


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- Roasting machines (Nuisance variable)

- Weather conditions (Nuisance variable)

- Stagnations in the transport system(Disturbance)

- Batch size (Nuisance variable)

- Amount of added water (Control

variable)

Potential influence factors

A case study

Case study: Improve


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Improve

7. Screen potential causes

8. Discover variablerelationships

9. Establish operatingtolerances

y

Case study: Improve


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- Relation between humidityand

moisture% not established

- Effect of stagnations confirmed

- Machine differences confirmed

7. Screen potential causes

Design of Experiments (DoE)8. Discover variable relationships

Experimentation


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Experiments are run based on: IntuitionKnowledgeExperiencePowerEmotions

Possible settings for X1

P

ossiblesettingsforX2

X:Settings with whichan experiment is run.

X

X

XX

X

X

X

Actually:were just tryingunsystematicalno design/plan

How do we often conduct experiments?

Experimentation


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A systematical experiment: Organized / discipline

One factor at a timeOther factors kept constant

Procedure:

XX XX OX X X X X

X: First vary X1; X2is kept constant

O: Optimal value for X1.

X:Vary X2; X1is kept constant.

:Optimal value (???)

X

X

X

X

X

X

X

Possible settings for X1

Po

ssible

settings

forX2

p

Design of Experiments (DoE)


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One factor (X)

low high

X1 21

Two factors (Xs)

low

high

high

X2

X1

22

high

Three factors (Xs)

low highX1

X3

X223


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Advantages of multi-factorover one-factor

A case study: Experiment


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Experiment:

Y: moisture%X1: Water (liters)X2: Batch size (kg)

y p

110

105

600

10

Water

11

610

12

100

13

14

620 630

Moisture

95640Batch size

Surface Plot of Moisture

A case study


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Feedback adjustments for influenceof weather conditions

y

9. Establish operating tolerances

A case study: feedback adjustments


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y j

153

105

157

209

261

313

365

417

469

521

573

625

677

729

781

833

885

937

989

Moisture% without adjustments

A case study: feedback adjustments


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153

105

157

209

261

313

365

417

469

521

573

625

677

729

781

833

885

937

989

y j

Moisture% with adjustments

Case study: Control


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Control

10. Validate measurementsystem (Xs)

11. Determine processcapability

12. Implement process

controls

y

Results


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131211109

USLUSL

Process Capability Analysis for Moisture

Within

Overallslong-term< 0.280

Objective

slong-term= 0.532Before

slong-term< 0.100Result

131211109

USL

Process Capability Analysis for Moisture

Within

Overall

Benefits


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Benefits of this projectslong-term< 0.100

Ppk= 1.5

This enables us to increase the mean to

12.1%

Per 0.1% coffee: 100 000 Euros saving

Benefits of this project:

1 100 000 Euros per year

Approved by controller


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Six Sigma approach to this project


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- Step-by-step approach.

- Constant testing and double checking.

- No problem fixing, but: explanation control.

- Interaction of technical knowledge andexperimentation methodology.

- Good research enables intelligent decisionmaking.

- Knowing the financial impact made it easy to findpriority for this project.

Re-cap I!


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Structured approachroadmapSystematic project-based improvement

Plan for quick wins

Find good initial projects - fast winsPublicise success

Often and continually - blow that trumpet

Use modern tools and methodsEmpirical evidence based improvement

Re-cap II!


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DMAIC is a basic training structure Establish your resource structure

- Make sure you know where external help is

Key ingredient is the support for projects- Its the project that wins not the training itself

Fit the training programme around thecompany needs

- not the company around the training

Embed the skills

- Everyone owns the successes

ENBIS


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All joint authors - presenters - are members of:

Pro-Enbis or ENBIS.

This presentation is supported by Pro-Enbis a

Thematic Network funded under the Growth

programme of the European Commissions 5th

Framework research programme - contractnumber G6RT-CT-2001-05059

six sigma a brief overview

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