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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
Statistical background
Target = m
Control limits
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+/-3sLSL USL
Statistical background
Required Tolerance
Target = m
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+/-3s
+/-6s
LSL USL
Statistical background
Tolerance
Target = m
Six-Sigma
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+/-3s
+/-6s
LSL USL
ppm1350
ppm1350
Statistical background
Tolerance
Target = m
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+/-3s
+/-6s
LSL USL
ppm0.001
ppm1350
ppm1350
ppm0.001
Statistical background
Tolerance
Target = m
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Statistical background
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
Statistical background
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
*Adapted from Zinkgraf (1999), Sigma BreakthroughTechnologies Inc., Austin, TX.
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Keys to Success*
Set clear expectations for results
Measure the progress (metrics)
Manage for results
*Adapted from Zinkgraf (1999), Sigma BreakthroughTechnologies Inc., Austin, TX.
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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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Examples of past successes
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
Examples of past successes
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Examples of past successes
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