Description
Six Sigma is a set of tools and strategies for process improvement originally developed by Motorola in 1985.[1][2] Six Sigma became well known after Jack Welch made it a central focus of his business strategy at General Electric in 1995,[3] and today it is used in different sectors of industry
What is Six Sigma?
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 a substantial impact on the „bottom line?
Six Sigma
A scientific and practical method to achieve improvements in a company
Scientific: • Structured approach. • Assuming quantitative data. ”Show me the money”
“Show me the data”
Practical: • Emphasis on financial result. • Start with the voice of the customer.
Where can Six Sigma be applied?
Service Management
Purchase
Design
Administration
Six Sigma Methods
Production
Quality Depart. HRM
IT
M&S
The Six Sigma Initiative integrates these efforts
Knowledge Management
„Six Sigma? companies
? Companies who have successfully adopted „Six Sigma? strategies include:
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 product ? Overhauling a locomotive
General Electric
• In 1995 GE mandated each employee to work towards achieving 6 sigma • The average process at GE was 3 sigma in 1995 • In 1997 the average reached 3.5 sigma • GE?s goal was to reach 6 sigma by 2001 • Investments in 6 sigma training and projects reached 45MUS$ in 1998, profits increased by 1.2BUS$
“the most important initiative GE has ever undertaken”. Jack Welch
Chief Executive Officer General Electric
MOTOROLA
“At Motorola we use statistical methods daily throughout 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.
*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998
Positive quotations
? “If you?re an average Black Belt, proponents say
you?ll find ways to save $1 million each year” ? “Raytheon figures it spends 25% of each sales dollar fixing problems when it operates at four sigma, a lower level of efficiency. But if it raises its quality and efficiency to Six Sigma, it would reduce spending on fixes to 1%” ? “The plastics business, through rigorous Six Sigma process work , added 300 million pounds of new capacity (equivalent to a „free plant?), saved $400 million in investment and will save another $400 million by 2000”
Negative quotations
? “Because managers? bonuses are tied to Six
Sigma savings, it causes them to fabricate results and savings turn out to be phantom” ? “Marketing will always use the number that makes the company look best …Promises are made to potential customers around capability statistics that are not anchored in reality” ? “ Six Sigma will eventually go the way of the other fads”
Barriers to implementation
Barrier #1: Engineers and managers are not interested in mathematical statistics
Barrier #2: Statisticians have problems communicating with managers and engineers Barrier #3: Non-statisticians experience “statistical anxiety” which has to be minimized before learning can take place Barrier # 4: Statistical methods need to be matched to management style and organizational culture
Statisticians
Technical Skills
BB
Master Black Belts
MBB
Black Belts
Quality Improvement Facilitators
Soft Skills
Reality
? Six Sigma through the correct application of statistical tools can reap a company enormous rewards that will have a positive effect for years or ? Six Sigma can be a dismal failure if not used correctly ? ISRU, CAMT and Sauer Danfoss will ensure the former occurs
Six Sigma
? The precise definition of Six Sigma is not important; the content of the program is ? A disciplined quantitative approach for improvement of defined metrics ? Can be applied to all business processes, manufacturing, finance and services
Focus of Six Sigma*
? Accelerating fast breakthrough performance ? Significant financial results in 4-8 months ? Ensuring Six Sigma is an extension of the Corporate culture, not the program of the month ? Results first, then culture change!
*Adapted from Zinkgraf (1999), Sigma Breakthrough
Technologies Inc., Austin, TX.
Six Sigma: Reasons for Success
? The Success at Motorola, GE and AlliedSignal has been attributed to:
?
? ?
?
Strong leadership (Jack Welch, Larry Bossidy and Bob Galvin personally involved) Initial focus on operations Aggressive project selection (potential savings in cost of poor quality > $50,000/year) Training the right people
The right way!
? Plan for “quick wins”
?
Find good initial projects - fast wins
Make sure you know where it is Often and continually - blow that trumpet Everyone owns successes
? Establish resource structure
?
? Publicise success
?
? Embed the skills
?
The Six Sigma metric
Consider a 99% quality level
? 5000 incorrect surgical operations per week! ? 200,000 wrong drug prescriptions per year! ? 2 crash landings at most major airports each day! ? 20,000 lost articles of mail per hour!
Not very satisfactory!
? Companies should strive for „Six Sigma? quality levels ? A successful Six Sigma programme can measure and improve quality levels across all areas within a company to achieve „world class? status ? Six Sigma is a continuous improvement cycle
Scientific method (after Box)
Data Facts
INDUCTION INDUCTION
Theory Hypothesis Conjecture Idea Model
DEDUCTION
DEDUCTION
Plan Act Check Do
Improvement cycle
? PDCA cycle Plan Act Check
23
Do
Alternative interpretation
Prioritise (D) Hold gains (C) Measure (M)
Improve (I) Problem (D/M/A) solve
Interpret (D/M/A)
Statistical background
Some Key measure
Target = m
Statistical background
„Control? limits +/ - 3 s
Target = m
Statistical background
Required Tolerance
LSL USL
+/ - 3 s
Target = m
Statistical background
Tolerance
LSL
+/ - 3 s
USL
Target = m
+/ - 6 s
Six-Sigma
Statistical background
Tolerance
LSL
+/ - 3 s
USL
1350 pp m
1350 pp m
Target = m
+/ - 6 s
Statistical background
Tolerance
LSL
+/ - 3 s
USL
0.001 pp m
1350 pp m
1350 pp m
0.001 pp m
Target = m
+/ - 6 s
Statistical background
? Six-Sigma allows for un-foreseen „problems? and longer term issues when calculating failure error or re-work rates ? Allows for a process „shift?
Statistical background
Tolerance
LSL
1. 5 s
USL
0 ppm
3. 4 ppm
66803 ppm
3. 4 ppm
m
+/ - 6 s
Performance Standards
s
2 3 4 5 6
Process performance
PPM
308537 66807 6210 233 3.4
Defects per million
Yield
69.1% 93.3% 99.38% 99.977% 99.9997%
Long term yield
Current standard
World Class
Performance standards
First Time Yield in multiple stage process Number of processes 1 10 100 500 1000 2000 2955 3? 4? 5? 6?
93.32 99.379 99.9767 99.99966 50.09 93.96 99.77 99.9966 0.1 53.64 97.70 99.966 0 4.44 89.02 99.83 0 0.2 79.24 99.66 0 0 62.75 99.32 0 0 50.27 99.0
Financial Aspects Benefits of 6s approach w.r.t. financials
s-level Defect rate Costs of poor quality Status of the (ppm) company 6 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 turnover 2 308537 30-40% of turnover Bankruptcy
Six Sigma and other Quality programmes
Comparing three recent developments in “Quality Management”
? ISO 9000 (-2000)
? EFQM Model ? Quality Improvement and Six Sigma Programs
ISO 9000
? Proponents claim that ISO 9000 is a general 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
Critique of ISO 9000
? Bureaucratic, large scale ? Focus on satisfying auditors, not customers ? 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, Not “we need to be the best and most cost effective supplier to win our customer?s business”
? Corrupting influence on the quality profession
EFQM Model
? A tool for assessment: Can measure where we are and how well we are doing ? Assessment is a small piece of the bigger scheme of Quality Management: ? Planning ? Control ? Improvement ? EFQM provides a tool for assessment, but no tools, training, concepts and managerial approaches for improvement and planning
The “Success” of Change Programs?
“Performance improvement efforts … have as much impact on operational and financial results as a ceremonial rain dance has on the weather”
Schaffer and Thomson, Harvard Business Review (1992)
Change Management: Two Alternative Approaches
Activity Centered Programs
Change Management Result Oriented Programs
Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992
Activity Centered Programs
? Activity Centered Programs: The pursuit of activities that sound good, but contribute little to the bottom line ? Assumption: If we carry out enough of the “right” activities, performance improvements will follow
? ?
This many people have been trained This many companies have been certified
? Bias Towards Orthodoxy: Weak or no empirical evidence to assess the relationship between efforts and results
ISO 9000
Data
Deduction Induction
Hypothesis
No Checking with Empirical Evidence, No Learning Process
An Alternative: Result-Driven Improvement Programs
? Result-Driven Programs: Focus on achieving specific, measurable, operational improvements within a few months ? Examples of specific measurable goals:
? ? ?
?
?
Increase yield Reduce delivery time Increase inventory turns Improved customer satisfaction Reduce product development time
Result Oriented Programs
? Project based
? Experimental ? Guided by empirical evidence ? Measurable results ? Easier to assess cause and effect ? Cascading strategy
Why Transformation Efforts Fail!
? John Kotter, Professor, Harvard Business School ? Leading scholar on Change Management ? Lists 8 common errors in managing change, two of which are: • Not establishing a sense of urgency • Not systematically planning for and creating short term wins
Six Sigma Demystified*
Six Sigma is TQM in disguise, but this time the focus is:
?
?
?
?
Alignment of customers, strategy, process and people Significant measurable business results Large scale deployment of advanced quality and statistical tools Data based, quantitative
*Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.
Keys to Success* ? Set clear expectations for results
? Measure the progress (metrics) ? Manage for results
*Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.
Key personnel in successful Six Sigma programmes
Black Belts
? Six Sigma practitioners who are employed by the company using the Six Sigma methodology ? work full time on the implementation of problem
solving & statistical techniques through projects selected on business needs ? become recognised „Black Belts? after embarking on Six Sigma training programme and completion of at least two projects which have a significant impact on the „bottom-line?
Black Belt requirements
Black Belt required resources
-Training in statistical methods. -Time to conduct the project!
-Software to facilitate data analysis.
-Permissions to make required changes!! -Coaching by a champion – or external support.
Black Belt role!
In other words the Black Belt is
-Empowered.
-In the sense that it was always meant!
-As the theroists have been saying for years!
Champions or „enablers?
? High-level managers who champion Six Sigma projects ? they have direct support from an executive management committee ? orchestrate the work of Six Sigma Black Belts ? provide Black Belts with the necessary backing at the executive level
Further down the line - after initial Six Sigma implementation package
? Master Black Belts
? Black Belts who have reached an acquired level of statistical and technical competence ? Provide expert advice to Black Belts
? Green Belts
? Provide assistance to Black Belts in Six Sigma projects ? Undergo only two weeks of statistical and problem solving training
Six Sigma instructors (ISRU)
? Aim: Successfully integrate the Six Sigma
methodology into a company?s existing culture and working practices
? Key traits
? Knowledge of statistical techniques ? Ability to manage projects and reach closure ? High level of analytical skills ? Ability to train, facilitate and lead teams to success, „soft skills?
Six Sigma training package
Aim of training package
To successfully integrate Six Sigma methodology into Sauer Danfoss’ culture and attain significant improvements in quality, service and operational performance
Six-Sigma - A “Roadmap” for improvement
Define Measure Analyze Improve
Select a project Prepare for assimilating information Characterise the current situation Optimize the process
Control
Assure the improvements
DMAIC
Example of a Classic Training strategy
Define Measure Throughput time project 4 months (full time)
Analyze Training (1 week) Work on project (3 weeks) Control Review
Improve
ISRU program content
? Week 1 - Six Sigma introductory week
(Deployment phase) ? Weeks 2-5 - Main Black Belt training programme
? ? ? ? Week 2 - Measurement phase Week 3 - Analysis phase Week 4 - Improve phase Week 5 - Control phase
? Project support for Six Sigma Black Belt candidates ? Access to ISRU?s distance learning facility
Draft training schedule
Jan 2003 Feb 2003 2/9 2/16 2/23 3/2 Mar 2003 Apr 2003 May 2003 Jun 2003 Jul 2003
No.
Black Belt work package tasks
Start
End
Duration
1/5 1/12 1/19 1/26 2/2 3/9 3/16 3/23 3/30 4/6 4/13 4/20 4/27 5/4 5/11 5/18 5/25 6/1 6/8 6/15 6/22 6/29 7/6 7/13 7/20 7/27
1 2 3 4 5 6 7 8 9
Champions Day Intial 3-day Black belt sessions Administration Day Project support (Workshop 1) Black Belt training (Measurement phase) Project support (Workshop2) Black Belt training (Analysis phase) Project support (Workshop 3) Black Belt training (Improvement phase)
03/02/03 04/02/03 07/02/03 11/02/03 17/02/03 25/03/03 14/04/03 06/05/03 26/05/03 17/06/03 07/07/03 29/07/03
03/02/03 06/02/03 07/02/03 11/02/03 21/02/03 25/03/03 18/04/03 06/05/03 30/05/03 17/06/03 11/07/03 30/07/03
1d 3d 1d 1d 1w 1d 1w 1d 1w 1d 1w 2d
10 Project support (Workshop 4) 11 Black Belt training (Control phase) 12 Project support (Follow up)
Training programme delivery
? Lectures supported by appropriate technology
? ? ? ? ? ? ? Video case studies Games and simulations Experiments and workshops Exercises Defined projects Delegate presentations Homework!
5 weeks of training
Define
Measure
Analyze
Improve
Control
Deployment (Define) phase
? Topics covered include
? Team Roles ? Presentation skills ? Project management skills ? Group techniques ? Quality ? Pitfalls to Quality Improvement projects ? Project strategies ? Minitab introduction
Measurement phase
? Topics covered include:
? Quality Tools ? Risk Assessment ? Measurements ? Capability & Performance ? Measurement Systems Analysis ? Quality Function Deployment ? FMEA
Example - QFD
? A method for meeting customer requirements ? Uses tools and techniques to set product strategies ? Displays requirements in matrix diagrams, including „House of Quality? ? Produces design initiatives to satisfy customer and beat competitors
House Of Quality
Importance
5. Tradeoff matrix 3. Product characteristics
1. Customer requirements
4. Relationship matrix
2. Competitive assessment
6. Technical assessment and target values
QFD can reduce
? Lead-times - the time to market and time to stable production ? Start-up costs
? Engineering changes
Analysis phase
? Topics include:
? Hypothesis testing ? Comparing samples ? Confidence Intervals ? Multi-Vari analysis ? ANOVA (Analysis of Variance) ? Regression
Improvement phase
? Topics include:
? History of Design of Experiments (DoE) ? DoE Pre-planning and Factors ? DoE Practical workshop ? DoE Analysis ? Response Surface Methodology (Optimisation) ? Lean Manufacturing
Example - Design of Experiments
What can it do for you?
Minimum cost Maximum output
What does it involve?
? Brainstorming sessions to identify important factors
? Conducting a few experimental trials
? Recognising significant factors which influence a process ? Setting these factors to get maximum output
Control phase
? Topics include:
? Control charts ? SPC case studies ? EWMA ? Poka-Yoke ? 5S ? Reliability testing ? Business impact assessment
Example - SPC (Statistical Process Control)
- reduces variability and keeps the process stable
Disturbed process Natural process
Natural boundary
Temporary upsets
Natural boundary
Results of SPC
? An improvement in the process ? Reduction in variation ? Better control over process ? Provides practical experience of collecting useful information for analysis ? Hopefully some enthusiasm for measurement!
Project support
? Initial „Black Belt? projects will be considered in
Week 1 by Executive management committee, „Champions? and „Black Belt? candidates
? Projects will be advanced significantly during the training programme via:
? continuous application of newly acquired statistical techniques ? workshops and on-going support from ISRU and CAMT ? delivery of regular project updates by „Black Belt? candidates
Project execution
Black Belt
Review Training
ISRU, Champion
ISRU Application
ISRU, Champion
Conducting projects
Traditional
-Project leader is obliged to make an effort. -Set of tools . -Focus on technical knowledge. -Project leader is left to his own devices. -Results are fuzzy. -Safe targets. -Projects conducted “on the side”.
Six Sigma
-Black Belt is obliged to achieve financial results. -Well-structured method. -Focus on experimentation. -Black Belt is coached by champion. -Results are quantified. -Stretched targets. -Projects are top priority.
The right support + The right projects + The right people + The right tools + The right plan = The right results
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
Champions Role
• Measure and report Business Impact
• Lead projects overall • Overcome resistance to Change
• Encourage others to Follow
Project selection
Define Select: - the project - the process - the Black Belt - the potential savings - time schedule - team
Project selection
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
Key Quality Characteristics “CTQs”
How will you measure them? How often? Who will measure? Is the outcome critical or important to results?
Outcome Examples
Reduce defective parts per million Increased capacity or yield Improved quality Reduced re-work or scrap Faster throughput
Key Questions
Is this a new product - process? Yes - then potential six-sigma
Do you know how best to run a process? No - then potential six-sigma
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 the moment?
Why is ISRU an effective Six Sigma practitioner?
Reasons
? Because we are experts in the application of industrial statistics and managing the accompanying change ? We want to assist companies in improving performance thus helping companies to greater success ? We will act as mentors to staff embarking on Six Sigma programmes
INDUSTRIAL STATISTICS RESEARCH UNIT
We are based in the School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, England
Mission statement
"To promote the effective and widespread use of statistical methods throughout European industry."
The work we do can be broken down into 3 main categories: ? Consultancy
? Training ? Major Research Projects
All with the common goal of promoting quality improvement by implementing statistical techniques
Consultancy
We have long term one to one consultancies with large and small companies, e.g. ? Transco ? Prescription Pricing Agency ? Silverlink To name but a few
Training
In-House courses
? SPC ? QFD ? Design of Experiments ? Measurement Systems Analysis
On-Site courses
? As above, tailored courses to suit the company ? Six Sigma programmes
European projects
? The Unit has provided the statistical input into many 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-fighter?s boots for safety
European projects
? Eurostat - investigating the multi-dimensional aspects of innovation using the Community Innovation Survey (CIS) II - 17 major European countries involved determining the factors that influence innovation ? Certified Reference materials for assessing water quality - validating EC Laboratories ? New project - „Effect on food of the taints and odours in packaging materials?
Typical local projects
? Assessment of environmental risks in chemical and process industries ? Introduction of statistical process control (SPC) into a micro-electronics company ? Helping to develop a new catheter for open-heart surgery via designed experiments (DoE) ? „Restaurant of the Year? & „Pub of the Year? competitions!
Benefits
?Better monitoring of processes ?Better involvement of people ?Staff morale is raised ?Throughput is increased ?Profits go up
Examples of past successes
? Down time cut by 40% - Villa soft drinks ? Waste reduced by 50% - Many projects ? Stock holding levels halved - Many projects ? Material use optimised saving £150k pa Boots ? Expensive equipment shown to be unnecessary - Wavin
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 an SME) ? Cutting stages from a process ? Reduction in materials use (Paper - Ink)
Distance Learning Facility
Distance Learning
? or Flexible training ? or Open Learning
? your time
? your place ? your study pattern ? your pace
Distance Learning
? http://www.ncl.ac.uk/blackboard ? Clear descriptions ? Step by step guidelines ? Case studies ? Web links, references ? Self assessment exercises in „Microsoft Excel? and „Minitab? ? Help line and discussion forum ? Essentially a further learning resource for Six Sigma tools and methodology
Case study
Case study: project selection
Coffee beans
Roast Savings: -Savings on rework and scrap -Water costs less than coffee Potential savings: 500 000 Euros
Cool
Grind
Pack
Sealed coffee
Moisture content
Case study: Measure
1. Select the Critical to Quality (CTQ) characteristic
2. Define performance standards
3. Validate measurement system
Case study: Measure
1. CTQ Moisture contents of roasted coffee 2. Standards - Unit: one batch - Defect: Moisture% > 12.6%
Case study: Measure
3. Measurement reliability Gauge R&R study
Measurement system too unreliable! So fix it!!
Case study: Analyse
Analyse 4. Establish product capability 5. Define performance objectives 6. Identify influence factors
Improvement opportunities
USL
USL
Diagnosis of problem
CTQ
CTQ
CTQ
CTQ
Discovery of causes
Man Machine Material
6. Identify factors
-Brainstorming -Exploratory data analysis
Roasting machines Batch size
Moisture%
Amount of added water Reliability of Quadra Beam Weather conditions
Method
Measurement
Mother Nature
Discovery of causes
Regelkaart voor for Vocht% Control chart moisture%
5.2 1 1
Individual Value
1 3.0SL=4.410 4.2 X=3.900
-3.0SL=3.390 3.2 0 10 20 30 40 50
Observation Number
A case study
Potential influence factors
- Roasting machines (Nuisance variable)
- Weather conditions (Nuisance variable) - Stagnations in the transport system (Disturbance) - Batch size (Nuisance variable) - Amount of added water (Control variable)
Case study: Improve
Improve 7. Screen potential causes 8. Discover variable relationships 9. Establish operating tolerances
Case study: Improve
7. Screen potential causes
- Relation between humidity and moisture% not established
- Effect of stagnations confirmed - Machine differences confirmed
8. Discover variable relationships
Design of Experiments (DoE)
Experimentation
How do we often conduct experiments?
Experiments are run based on: Intuition Knowledge Experience Power Emotions X X X X X X: Settings with which an experiment is run.
Possible settings for X2
X
X
Actually: • we?re just trying • unsystematical • no design/plan
Possible settings for X1
Experimentation
A systematical experiment: Organized / discipline One factor at a time Other factors kept constant
Possible settings for X2
X
Procedure:
X: First vary X1; X2 is kept constant O: Optimal value for X1. X X X: Vary X2; X1 is kept constant. : Optimal value (???)
X
X X X X X X X XO X X X X
Possible settings for X1
Design of Experiments (DoE)
One factor (X) X1
low high
2
1
Two factors (X?s)
high high
Three factors (X?s)
X2
2
2 X2 2 X3
low
3
low
X1
high
X1
high
Advantages of multi-factor over onefactor
A case study: Experiment
Surface Plot of Moisture
Experiment: Y: moisture% X1: Water (liters) X2: Batch size (kg)
Moisture
14 13 12 11 10 105 600 100 610 620 630 95 640 110
Water
Batch size
A case study
9. Establish operating tolerances Feedback adjustments for influence of weather conditions
A case study: feedback adjustments
4.35
4.25
4.15
4.05
3.95
1
53
105
157
209
261
313
365
417
469
521
573
625
677
729
781
833
885
937
Moisture% Vocht% without adjustments
989
A case study: feedback adjustments
4.35
4.25
4.15
4.05
3.95
1
53
105
157
209
261
313
365
417
469
521
573
625
677
729
781
833
885
937
Controlled Vocht% Moisture% with adjustments
989
Case study: Control
Control 10. Validate measurement system (X?s)
11. Determine process capability
12. Implement process controls
Results
Before slong-term = 0.532
ProcessCapability CapabilityAnalysis Analysisfor forMoisture Moisture Process
ObjectiveProcess Data slong-term < 0.280
Process Data USL 12.6000 USL 13.0000 Target * Target * LSL * LSL 9.0000 Mean 11.0026 Mean 10.9921 Sample N 490 Sample N 200 StDev (Within) 0.335675 StDev (Within) 0.105808 StDev (Overall) 0.531635 StDev (Overall) 0.102497
USL USL
Within Within Overall Overall
Result
slong-term < 0.100
Potential (Within) Capability Potential (Within) Capability Cp * Cp 6.30 CPU 1.54 CPU 6.33 CPL * CPL 6.28 Cpk 1.54 Cpk 6.28 Cpm * Cpm * Overall Capability Pp Overall Capability * PPU 0.96 Pp 6.50
9 9
10 10
11 11
12 12
13 13
Exp. "Overall" Performance PPM LSL Performance * Exp. < "Overall" PPM > 1987.68 < USL LSL 0.00
Observed Performance PPM < LSL Performance * Observed PPM 0.00 PPM > < USL LSL 0.00
Exp. "Within" Performance PPM LSL Performance * Exp. < "Within" PPM 1.79 PPM > < USL LSL 0.00
Benefits
Benefits of this project
slong-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
Case study: control
12. Implement process controls
- SPC control loop - Mistake proofing - Control plan - Audit schedule
Project closure - Documentation of the results and data. - Results are reported to involved persons. - The follow-up is determined
Six Sigma approach to this project
- Step-by-step approach. - Constant testing and double checking. - No problem fixing, but: explanation ? control. - Interaction of technical knowledge and experimentation methodology. - Good research enables intelligent decision making.
- Knowing the financial impact made it easy to find priority for this project.
Re-cap I!
? Structured approach – roadmap ? Systematic project-based improvement ? Plan for “quick wins”
?
Find good initial projects - fast wins Often and continually - blow that trumpet
? Publicise success
?
? Use modern tools and methods ? Empirical evidence based improvement
Re-cap II!
? 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
- It’s the project that ‘wins’ not the training itself
? Fit the training programme around the company needs - not the company around the training ? Embed the skills
- Everyone owns the successes
ENBIS
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 Commission?s 5th Framework research programme - contract number G6RT-CT-2001-05059
doc_768519940.ppt
Six Sigma is a set of tools and strategies for process improvement originally developed by Motorola in 1985.[1][2] Six Sigma became well known after Jack Welch made it a central focus of his business strategy at General Electric in 1995,[3] and today it is used in different sectors of industry
What is Six Sigma?
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 a substantial impact on the „bottom line?
Six Sigma
A scientific and practical method to achieve improvements in a company
Scientific: • Structured approach. • Assuming quantitative data. ”Show me the money”
“Show me the data”
Practical: • Emphasis on financial result. • Start with the voice of the customer.
Where can Six Sigma be applied?
Service Management
Purchase
Design
Administration
Six Sigma Methods
Production
Quality Depart. HRM
IT
M&S
The Six Sigma Initiative integrates these efforts
Knowledge Management
„Six Sigma? companies
? Companies who have successfully adopted „Six Sigma? strategies include:
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 product ? Overhauling a locomotive
General Electric
• In 1995 GE mandated each employee to work towards achieving 6 sigma • The average process at GE was 3 sigma in 1995 • In 1997 the average reached 3.5 sigma • GE?s goal was to reach 6 sigma by 2001 • Investments in 6 sigma training and projects reached 45MUS$ in 1998, profits increased by 1.2BUS$
“the most important initiative GE has ever undertaken”. Jack Welch
Chief Executive Officer General Electric
MOTOROLA
“At Motorola we use statistical methods daily throughout 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.
*From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998
Positive quotations
? “If you?re an average Black Belt, proponents say
you?ll find ways to save $1 million each year” ? “Raytheon figures it spends 25% of each sales dollar fixing problems when it operates at four sigma, a lower level of efficiency. But if it raises its quality and efficiency to Six Sigma, it would reduce spending on fixes to 1%” ? “The plastics business, through rigorous Six Sigma process work , added 300 million pounds of new capacity (equivalent to a „free plant?), saved $400 million in investment and will save another $400 million by 2000”
Negative quotations
? “Because managers? bonuses are tied to Six
Sigma savings, it causes them to fabricate results and savings turn out to be phantom” ? “Marketing will always use the number that makes the company look best …Promises are made to potential customers around capability statistics that are not anchored in reality” ? “ Six Sigma will eventually go the way of the other fads”
Barriers to implementation
Barrier #1: Engineers and managers are not interested in mathematical statistics
Barrier #2: Statisticians have problems communicating with managers and engineers Barrier #3: Non-statisticians experience “statistical anxiety” which has to be minimized before learning can take place Barrier # 4: Statistical methods need to be matched to management style and organizational culture
Statisticians
Technical Skills
BB
Master Black Belts
MBB
Black Belts
Quality Improvement Facilitators
Soft Skills
Reality
? Six Sigma through the correct application of statistical tools can reap a company enormous rewards that will have a positive effect for years or ? Six Sigma can be a dismal failure if not used correctly ? ISRU, CAMT and Sauer Danfoss will ensure the former occurs
Six Sigma
? The precise definition of Six Sigma is not important; the content of the program is ? A disciplined quantitative approach for improvement of defined metrics ? Can be applied to all business processes, manufacturing, finance and services
Focus of Six Sigma*
? Accelerating fast breakthrough performance ? Significant financial results in 4-8 months ? Ensuring Six Sigma is an extension of the Corporate culture, not the program of the month ? Results first, then culture change!
*Adapted from Zinkgraf (1999), Sigma Breakthrough
Technologies Inc., Austin, TX.
Six Sigma: Reasons for Success
? The Success at Motorola, GE and AlliedSignal has been attributed to:
?
? ?
?
Strong leadership (Jack Welch, Larry Bossidy and Bob Galvin personally involved) Initial focus on operations Aggressive project selection (potential savings in cost of poor quality > $50,000/year) Training the right people
The right way!
? Plan for “quick wins”
?
Find good initial projects - fast wins
Make sure you know where it is Often and continually - blow that trumpet Everyone owns successes
? Establish resource structure
?
? Publicise success
?
? Embed the skills
?
The Six Sigma metric
Consider a 99% quality level
? 5000 incorrect surgical operations per week! ? 200,000 wrong drug prescriptions per year! ? 2 crash landings at most major airports each day! ? 20,000 lost articles of mail per hour!
Not very satisfactory!
? Companies should strive for „Six Sigma? quality levels ? A successful Six Sigma programme can measure and improve quality levels across all areas within a company to achieve „world class? status ? Six Sigma is a continuous improvement cycle
Scientific method (after Box)
Data Facts
INDUCTION INDUCTION
Theory Hypothesis Conjecture Idea Model
DEDUCTION
DEDUCTION
Plan Act Check Do
Improvement cycle
? PDCA cycle Plan Act Check
23
Do
Alternative interpretation
Prioritise (D) Hold gains (C) Measure (M)
Improve (I) Problem (D/M/A) solve
Interpret (D/M/A)
Statistical background
Some Key measure
Target = m
Statistical background
„Control? limits +/ - 3 s
Target = m
Statistical background
Required Tolerance
LSL USL
+/ - 3 s
Target = m
Statistical background
Tolerance
LSL
+/ - 3 s
USL
Target = m
+/ - 6 s
Six-Sigma
Statistical background
Tolerance
LSL
+/ - 3 s
USL
1350 pp m
1350 pp m
Target = m
+/ - 6 s
Statistical background
Tolerance
LSL
+/ - 3 s
USL
0.001 pp m
1350 pp m
1350 pp m
0.001 pp m
Target = m
+/ - 6 s
Statistical background
? Six-Sigma allows for un-foreseen „problems? and longer term issues when calculating failure error or re-work rates ? Allows for a process „shift?
Statistical background
Tolerance
LSL
1. 5 s
USL
0 ppm
3. 4 ppm
66803 ppm
3. 4 ppm
m
+/ - 6 s
Performance Standards
s
2 3 4 5 6
Process performance
PPM
308537 66807 6210 233 3.4
Defects per million
Yield
69.1% 93.3% 99.38% 99.977% 99.9997%
Long term yield
Current standard
World Class
Performance standards
First Time Yield in multiple stage process Number of processes 1 10 100 500 1000 2000 2955 3? 4? 5? 6?
93.32 99.379 99.9767 99.99966 50.09 93.96 99.77 99.9966 0.1 53.64 97.70 99.966 0 4.44 89.02 99.83 0 0.2 79.24 99.66 0 0 62.75 99.32 0 0 50.27 99.0
Financial Aspects Benefits of 6s approach w.r.t. financials
s-level Defect rate Costs of poor quality Status of the (ppm) company 6 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 turnover 2 308537 30-40% of turnover Bankruptcy
Six Sigma and other Quality programmes
Comparing three recent developments in “Quality Management”
? ISO 9000 (-2000)
? EFQM Model ? Quality Improvement and Six Sigma Programs
ISO 9000
? Proponents claim that ISO 9000 is a general 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
Critique of ISO 9000
? Bureaucratic, large scale ? Focus on satisfying auditors, not customers ? 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, Not “we need to be the best and most cost effective supplier to win our customer?s business”
? Corrupting influence on the quality profession
EFQM Model
? A tool for assessment: Can measure where we are and how well we are doing ? Assessment is a small piece of the bigger scheme of Quality Management: ? Planning ? Control ? Improvement ? EFQM provides a tool for assessment, but no tools, training, concepts and managerial approaches for improvement and planning
The “Success” of Change Programs?
“Performance improvement efforts … have as much impact on operational and financial results as a ceremonial rain dance has on the weather”
Schaffer and Thomson, Harvard Business Review (1992)
Change Management: Two Alternative Approaches
Activity Centered Programs
Change Management Result Oriented Programs
Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992
Activity Centered Programs
? Activity Centered Programs: The pursuit of activities that sound good, but contribute little to the bottom line ? Assumption: If we carry out enough of the “right” activities, performance improvements will follow
? ?
This many people have been trained This many companies have been certified
? Bias Towards Orthodoxy: Weak or no empirical evidence to assess the relationship between efforts and results
ISO 9000
Data
Deduction Induction
Hypothesis
No Checking with Empirical Evidence, No Learning Process
An Alternative: Result-Driven Improvement Programs
? Result-Driven Programs: Focus on achieving specific, measurable, operational improvements within a few months ? Examples of specific measurable goals:
? ? ?
?
?
Increase yield Reduce delivery time Increase inventory turns Improved customer satisfaction Reduce product development time
Result Oriented Programs
? Project based
? Experimental ? Guided by empirical evidence ? Measurable results ? Easier to assess cause and effect ? Cascading strategy
Why Transformation Efforts Fail!
? John Kotter, Professor, Harvard Business School ? Leading scholar on Change Management ? Lists 8 common errors in managing change, two of which are: • Not establishing a sense of urgency • Not systematically planning for and creating short term wins
Six Sigma Demystified*
Six Sigma is TQM in disguise, but this time the focus is:
?
?
?
?
Alignment of customers, strategy, process and people Significant measurable business results Large scale deployment of advanced quality and statistical tools Data based, quantitative
*Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.
Keys to Success* ? Set clear expectations for results
? Measure the progress (metrics) ? Manage for results
*Adapted from Zinkgraf (1999), Sigma Breakthrough Technologies Inc., Austin, TX.
Key personnel in successful Six Sigma programmes
Black Belts
? Six Sigma practitioners who are employed by the company using the Six Sigma methodology ? work full time on the implementation of problem
solving & statistical techniques through projects selected on business needs ? become recognised „Black Belts? after embarking on Six Sigma training programme and completion of at least two projects which have a significant impact on the „bottom-line?
Black Belt requirements
Black Belt required resources
-Training in statistical methods. -Time to conduct the project!
-Software to facilitate data analysis.
-Permissions to make required changes!! -Coaching by a champion – or external support.
Black Belt role!
In other words the Black Belt is
-Empowered.
-In the sense that it was always meant!
-As the theroists have been saying for years!
Champions or „enablers?
? High-level managers who champion Six Sigma projects ? they have direct support from an executive management committee ? orchestrate the work of Six Sigma Black Belts ? provide Black Belts with the necessary backing at the executive level
Further down the line - after initial Six Sigma implementation package
? Master Black Belts
? Black Belts who have reached an acquired level of statistical and technical competence ? Provide expert advice to Black Belts
? Green Belts
? Provide assistance to Black Belts in Six Sigma projects ? Undergo only two weeks of statistical and problem solving training
Six Sigma instructors (ISRU)
? Aim: Successfully integrate the Six Sigma
methodology into a company?s existing culture and working practices
? Key traits
? Knowledge of statistical techniques ? Ability to manage projects and reach closure ? High level of analytical skills ? Ability to train, facilitate and lead teams to success, „soft skills?
Six Sigma training package
Aim of training package
To successfully integrate Six Sigma methodology into Sauer Danfoss’ culture and attain significant improvements in quality, service and operational performance
Six-Sigma - A “Roadmap” for improvement
Define Measure Analyze Improve
Select a project Prepare for assimilating information Characterise the current situation Optimize the process
Control
Assure the improvements
DMAIC
Example of a Classic Training strategy
Define Measure Throughput time project 4 months (full time)
Analyze Training (1 week) Work on project (3 weeks) Control Review
Improve
ISRU program content
? Week 1 - Six Sigma introductory week
(Deployment phase) ? Weeks 2-5 - Main Black Belt training programme
? ? ? ? Week 2 - Measurement phase Week 3 - Analysis phase Week 4 - Improve phase Week 5 - Control phase
? Project support for Six Sigma Black Belt candidates ? Access to ISRU?s distance learning facility
Draft training schedule
Jan 2003 Feb 2003 2/9 2/16 2/23 3/2 Mar 2003 Apr 2003 May 2003 Jun 2003 Jul 2003
No.
Black Belt work package tasks
Start
End
Duration
1/5 1/12 1/19 1/26 2/2 3/9 3/16 3/23 3/30 4/6 4/13 4/20 4/27 5/4 5/11 5/18 5/25 6/1 6/8 6/15 6/22 6/29 7/6 7/13 7/20 7/27
1 2 3 4 5 6 7 8 9
Champions Day Intial 3-day Black belt sessions Administration Day Project support (Workshop 1) Black Belt training (Measurement phase) Project support (Workshop2) Black Belt training (Analysis phase) Project support (Workshop 3) Black Belt training (Improvement phase)
03/02/03 04/02/03 07/02/03 11/02/03 17/02/03 25/03/03 14/04/03 06/05/03 26/05/03 17/06/03 07/07/03 29/07/03
03/02/03 06/02/03 07/02/03 11/02/03 21/02/03 25/03/03 18/04/03 06/05/03 30/05/03 17/06/03 11/07/03 30/07/03
1d 3d 1d 1d 1w 1d 1w 1d 1w 1d 1w 2d
10 Project support (Workshop 4) 11 Black Belt training (Control phase) 12 Project support (Follow up)
Training programme delivery
? Lectures supported by appropriate technology
? ? ? ? ? ? ? Video case studies Games and simulations Experiments and workshops Exercises Defined projects Delegate presentations Homework!
5 weeks of training
Define
Measure
Analyze
Improve
Control
Deployment (Define) phase
? Topics covered include
? Team Roles ? Presentation skills ? Project management skills ? Group techniques ? Quality ? Pitfalls to Quality Improvement projects ? Project strategies ? Minitab introduction
Measurement phase
? Topics covered include:
? Quality Tools ? Risk Assessment ? Measurements ? Capability & Performance ? Measurement Systems Analysis ? Quality Function Deployment ? FMEA
Example - QFD
? A method for meeting customer requirements ? Uses tools and techniques to set product strategies ? Displays requirements in matrix diagrams, including „House of Quality? ? Produces design initiatives to satisfy customer and beat competitors
House Of Quality
Importance
5. Tradeoff matrix 3. Product characteristics
1. Customer requirements
4. Relationship matrix
2. Competitive assessment
6. Technical assessment and target values
QFD can reduce
? Lead-times - the time to market and time to stable production ? Start-up costs
? Engineering changes
Analysis phase
? Topics include:
? Hypothesis testing ? Comparing samples ? Confidence Intervals ? Multi-Vari analysis ? ANOVA (Analysis of Variance) ? Regression
Improvement phase
? Topics include:
? History of Design of Experiments (DoE) ? DoE Pre-planning and Factors ? DoE Practical workshop ? DoE Analysis ? Response Surface Methodology (Optimisation) ? Lean Manufacturing
Example - Design of Experiments
What can it do for you?
Minimum cost Maximum output
What does it involve?
? Brainstorming sessions to identify important factors
? Conducting a few experimental trials
? Recognising significant factors which influence a process ? Setting these factors to get maximum output
Control phase
? Topics include:
? Control charts ? SPC case studies ? EWMA ? Poka-Yoke ? 5S ? Reliability testing ? Business impact assessment
Example - SPC (Statistical Process Control)
- reduces variability and keeps the process stable
Disturbed process Natural process
Natural boundary
Temporary upsets
Natural boundary
Results of SPC
? An improvement in the process ? Reduction in variation ? Better control over process ? Provides practical experience of collecting useful information for analysis ? Hopefully some enthusiasm for measurement!
Project support
? Initial „Black Belt? projects will be considered in
Week 1 by Executive management committee, „Champions? and „Black Belt? candidates
? Projects will be advanced significantly during the training programme via:
? continuous application of newly acquired statistical techniques ? workshops and on-going support from ISRU and CAMT ? delivery of regular project updates by „Black Belt? candidates
Project execution
Black Belt
Review Training
ISRU, Champion
ISRU Application
ISRU, Champion
Conducting projects
Traditional
-Project leader is obliged to make an effort. -Set of tools . -Focus on technical knowledge. -Project leader is left to his own devices. -Results are fuzzy. -Safe targets. -Projects conducted “on the side”.
Six Sigma
-Black Belt is obliged to achieve financial results. -Well-structured method. -Focus on experimentation. -Black Belt is coached by champion. -Results are quantified. -Stretched targets. -Projects are top priority.
The right support + The right projects + The right people + The right tools + The right plan = The right results
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
Champions Role
• Measure and report Business Impact
• Lead projects overall • Overcome resistance to Change
• Encourage others to Follow
Project selection
Define Select: - the project - the process - the Black Belt - the potential savings - time schedule - team
Project selection
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
Key Quality Characteristics “CTQs”
How will you measure them? How often? Who will measure? Is the outcome critical or important to results?
Outcome Examples
Reduce defective parts per million Increased capacity or yield Improved quality Reduced re-work or scrap Faster throughput
Key Questions
Is this a new product - process? Yes - then potential six-sigma
Do you know how best to run a process? No - then potential six-sigma
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 the moment?
Why is ISRU an effective Six Sigma practitioner?
Reasons
? Because we are experts in the application of industrial statistics and managing the accompanying change ? We want to assist companies in improving performance thus helping companies to greater success ? We will act as mentors to staff embarking on Six Sigma programmes
INDUSTRIAL STATISTICS RESEARCH UNIT
We are based in the School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, England
Mission statement
"To promote the effective and widespread use of statistical methods throughout European industry."
The work we do can be broken down into 3 main categories: ? Consultancy
? Training ? Major Research Projects
All with the common goal of promoting quality improvement by implementing statistical techniques
Consultancy
We have long term one to one consultancies with large and small companies, e.g. ? Transco ? Prescription Pricing Agency ? Silverlink To name but a few
Training
In-House courses
? SPC ? QFD ? Design of Experiments ? Measurement Systems Analysis
On-Site courses
? As above, tailored courses to suit the company ? Six Sigma programmes
European projects
? The Unit has provided the statistical input into many 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-fighter?s boots for safety
European projects
? Eurostat - investigating the multi-dimensional aspects of innovation using the Community Innovation Survey (CIS) II - 17 major European countries involved determining the factors that influence innovation ? Certified Reference materials for assessing water quality - validating EC Laboratories ? New project - „Effect on food of the taints and odours in packaging materials?
Typical local projects
? Assessment of environmental risks in chemical and process industries ? Introduction of statistical process control (SPC) into a micro-electronics company ? Helping to develop a new catheter for open-heart surgery via designed experiments (DoE) ? „Restaurant of the Year? & „Pub of the Year? competitions!
Benefits
?Better monitoring of processes ?Better involvement of people ?Staff morale is raised ?Throughput is increased ?Profits go up
Examples of past successes
? Down time cut by 40% - Villa soft drinks ? Waste reduced by 50% - Many projects ? Stock holding levels halved - Many projects ? Material use optimised saving £150k pa Boots ? Expensive equipment shown to be unnecessary - Wavin
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 an SME) ? Cutting stages from a process ? Reduction in materials use (Paper - Ink)
Distance Learning Facility
Distance Learning
? or Flexible training ? or Open Learning
? your time
? your place ? your study pattern ? your pace
Distance Learning
? http://www.ncl.ac.uk/blackboard ? Clear descriptions ? Step by step guidelines ? Case studies ? Web links, references ? Self assessment exercises in „Microsoft Excel? and „Minitab? ? Help line and discussion forum ? Essentially a further learning resource for Six Sigma tools and methodology
Case study
Case study: project selection
Coffee beans
Roast Savings: -Savings on rework and scrap -Water costs less than coffee Potential savings: 500 000 Euros
Cool
Grind
Pack
Sealed coffee
Moisture content
Case study: Measure
1. Select the Critical to Quality (CTQ) characteristic
2. Define performance standards
3. Validate measurement system
Case study: Measure
1. CTQ Moisture contents of roasted coffee 2. Standards - Unit: one batch - Defect: Moisture% > 12.6%
Case study: Measure
3. Measurement reliability Gauge R&R study
Measurement system too unreliable! So fix it!!
Case study: Analyse
Analyse 4. Establish product capability 5. Define performance objectives 6. Identify influence factors
Improvement opportunities
USL
USL
Diagnosis of problem
CTQ
CTQ
CTQ
CTQ
Discovery of causes
Man Machine Material
6. Identify factors
-Brainstorming -Exploratory data analysis
Roasting machines Batch size
Moisture%
Amount of added water Reliability of Quadra Beam Weather conditions
Method
Measurement
Mother Nature
Discovery of causes
Regelkaart voor for Vocht% Control chart moisture%
5.2 1 1
Individual Value
1 3.0SL=4.410 4.2 X=3.900
-3.0SL=3.390 3.2 0 10 20 30 40 50
Observation Number
A case study
Potential influence factors
- Roasting machines (Nuisance variable)
- Weather conditions (Nuisance variable) - Stagnations in the transport system (Disturbance) - Batch size (Nuisance variable) - Amount of added water (Control variable)
Case study: Improve
Improve 7. Screen potential causes 8. Discover variable relationships 9. Establish operating tolerances
Case study: Improve
7. Screen potential causes
- Relation between humidity and moisture% not established
- Effect of stagnations confirmed - Machine differences confirmed
8. Discover variable relationships
Design of Experiments (DoE)
Experimentation
How do we often conduct experiments?
Experiments are run based on: Intuition Knowledge Experience Power Emotions X X X X X X: Settings with which an experiment is run.
Possible settings for X2
X
X
Actually: • we?re just trying • unsystematical • no design/plan
Possible settings for X1
Experimentation
A systematical experiment: Organized / discipline One factor at a time Other factors kept constant
Possible settings for X2
X
Procedure:
X: First vary X1; X2 is kept constant O: Optimal value for X1. X X X: Vary X2; X1 is kept constant. : Optimal value (???)
X
X X X X X X X XO X X X X
Possible settings for X1
Design of Experiments (DoE)
One factor (X) X1
low high
2
1
Two factors (X?s)
high high
Three factors (X?s)
X2
2
2 X2 2 X3
low
3
low
X1
high
X1
high
Advantages of multi-factor over onefactor
A case study: Experiment
Surface Plot of Moisture
Experiment: Y: moisture% X1: Water (liters) X2: Batch size (kg)
Moisture
14 13 12 11 10 105 600 100 610 620 630 95 640 110
Water
Batch size
A case study
9. Establish operating tolerances Feedback adjustments for influence of weather conditions
A case study: feedback adjustments
4.35
4.25
4.15
4.05
3.95
1
53
105
157
209
261
313
365
417
469
521
573
625
677
729
781
833
885
937
Moisture% Vocht% without adjustments
989
A case study: feedback adjustments
4.35
4.25
4.15
4.05
3.95
1
53
105
157
209
261
313
365
417
469
521
573
625
677
729
781
833
885
937
Controlled Vocht% Moisture% with adjustments
989
Case study: Control
Control 10. Validate measurement system (X?s)
11. Determine process capability
12. Implement process controls
Results
Before slong-term = 0.532
ProcessCapability CapabilityAnalysis Analysisfor forMoisture Moisture Process
ObjectiveProcess Data slong-term < 0.280
Process Data USL 12.6000 USL 13.0000 Target * Target * LSL * LSL 9.0000 Mean 11.0026 Mean 10.9921 Sample N 490 Sample N 200 StDev (Within) 0.335675 StDev (Within) 0.105808 StDev (Overall) 0.531635 StDev (Overall) 0.102497
USL USL
Within Within Overall Overall
Result
slong-term < 0.100
Potential (Within) Capability Potential (Within) Capability Cp * Cp 6.30 CPU 1.54 CPU 6.33 CPL * CPL 6.28 Cpk 1.54 Cpk 6.28 Cpm * Cpm * Overall Capability Pp Overall Capability * PPU 0.96 Pp 6.50
9 9
10 10
11 11
12 12
13 13
Exp. "Overall" Performance PPM LSL Performance * Exp. < "Overall" PPM > 1987.68 < USL LSL 0.00
Observed Performance PPM < LSL Performance * Observed PPM 0.00 PPM > < USL LSL 0.00
Exp. "Within" Performance PPM LSL Performance * Exp. < "Within" PPM 1.79 PPM > < USL LSL 0.00
Benefits
Benefits of this project
slong-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
Case study: control
12. Implement process controls
- SPC control loop - Mistake proofing - Control plan - Audit schedule
Project closure - Documentation of the results and data. - Results are reported to involved persons. - The follow-up is determined
Six Sigma approach to this project
- Step-by-step approach. - Constant testing and double checking. - No problem fixing, but: explanation ? control. - Interaction of technical knowledge and experimentation methodology. - Good research enables intelligent decision making.
- Knowing the financial impact made it easy to find priority for this project.
Re-cap I!
? Structured approach – roadmap ? Systematic project-based improvement ? Plan for “quick wins”
?
Find good initial projects - fast wins Often and continually - blow that trumpet
? Publicise success
?
? Use modern tools and methods ? Empirical evidence based improvement
Re-cap II!
? 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
- It’s the project that ‘wins’ not the training itself
? Fit the training programme around the company needs - not the company around the training ? Embed the skills
- Everyone owns the successes
ENBIS
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 Commission?s 5th Framework research programme - contract number G6RT-CT-2001-05059
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