What is Six Sigma? : What is Six Sigma? Basics : 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’ Slide 3: A scientific and practical method to achieve improvements in a company Scientific:
Assuming quantitative data.
Emphasis on financial result.
Start with the voice of the customer. “Show me
the data” ”Show me
the money” Six Sigma Slide 4: Six Sigma Methods Production Design Service Purchase HRM Administration Quality
Depart. Management M & S IT Where can Six Sigma be applied? Slide 5: DOE SPC Knowledge
Management Benchmarking The Six Sigma Initiative
integrates these efforts Improvement teams Problem
Solving teams ISO 9000 Strategic
planning and more ‘Six Sigma’ companies : ‘Six Sigma’ companies Companies who have successfully adopted ‘Six Sigma’ strategies include: GE “Service company” - examples : GE “Service company” - examples Approving a credit card application
Installing a turbine
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 Slide 8: “the most important initiative GE has ever undertaken”. Jack Welch
Chief Executive Officer
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$ General Electric Slide 9: “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. MOTOROLA *From the forward to MODERN INDUSTRIAL STATISTICS by Kenett and Zacks, Duxbury, 1998 Positive quotations : 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 : 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” Slide 12: 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 Barriers to implementation Slide 13: Technical Skills Soft Skills Statisticians Master Black Belts Black Belts Quality Improvement Facilitators BB MBB Reality : Reality Six Sigma through the correct application of statistical tools can reap a company enormous rewards that will have a positive effect for years
Six Sigma can be a dismal failure if not used correctly
ISRU, CAMT and Sauer Danfoss will ensure the former occurs Six Sigma : 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* : 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 : 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! : The right way! Plan for “quick wins”
Find good initial projects - fast wins
Establish resource structure
Make sure you know where it is
Often and continually - blow that trumpet
Embed the skills
Everyone owns successes The Six Sigma metric : The Six Sigma metric Consider a 99% quality level : 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! : 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 Slide 22: Scientific method (after Box) Improvement cycle : 23 Improvement cycle PDCA cycle Plan Do Check Act Slide 24: Prioritise (D) Measure (M) Interpret
(D/M/A) Problem (D/M/A)
solve Improve (I) Hold
gains (C) Alternative interpretation Slide 25: Statistical background Target = m Some Key measure Slide 26: + / - 3 s Statistical background Target = m ‘Control’ limits Slide 27: + / - 3 s L S L U S L Statistical background Required Tolerance Target = m Slide 28: + / - 3 s + / - 6 s L S L U S L Statistical background Tolerance Target = m Six-Sigma Slide 29: + / - 3 s + / - 6 s L S L U S L p p m 1 3 5 0 p p m 1 3 5 0 Statistical background Tolerance Target = m Slide 30: + / - 3 s + / - 6 s L S L U S L p p m 0 . 0 0 1 p p m 1 3 5 0 p p m 1 3 5 0 p p m 0 . 0 0 1 Statistical background Tolerance Target = m Statistical background : 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’ Slide 32: L S L 0 p p m p p m 3 . 4 1 . 5 s U S L p p m 3 . 4 p p m 6 6 8 0 3 m + / - 6 s Statistical background Tolerance Slide 33: Performance Standards 2
3.4 ? PPM 69.1%
99.9997% Yield Process
performance Defects per
million Long term
yield Current standard World Class Slide 34: Number of processes 3s 4s 5s 6s 1
99.0 First Time Yield in multiple stage process Performance standards Slide 35: Benefits of 6s approach w.r.t. financials Financial Aspects Six Sigma and other Quality programmes : Six Sigma and other Quality programmes Comparing three recent developments in “Quality Management” : Comparing three recent developments in “Quality Management” ISO 9000 (-2000)
Quality Improvement and Six Sigma Programs ISO 9000 : 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 : 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 : 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:
EFQM provides a tool for assessment, but no tools, training, concepts and managerial approaches for improvement and planning The “Success” of Change Programs? : 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 : Change Management:Two Alternative Approaches Activity Centered
Programs Result Oriented
Management Reference: Schaffer and Thomson, HBR, Jan-Feb. 1992 Activity Centered Programs : 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 No Checking with Empirical Evidence, No Learning Process : No Checking with Empirical Evidence, No Learning Process ISO 9000 An Alternative: Result-Driven Improvement Programs : 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:
Reduce delivery time
Increase inventory turns
Improved customer satisfaction
Reduce product development time Result Oriented Programs : Result Oriented Programs Project based
Guided by empirical evidence
Easier to assess cause and effect
Cascading strategy Why Transformation Efforts Fail! : 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 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* : 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 : Key personnel in successful Six Sigma programmes Black Belts : 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’ Slide 52: 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 requirements Slide 53: In other words the Black Belt is Empowered.
In the sense that it was always meant!
As the theroists have been saying for years! Black Belt role! Champions or ‘enablers’ : 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 : 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
Provide assistance to Black Belts in Six Sigma projects
Undergo only two weeks of statistical and problem solving training Six Sigma instructors (ISRU) : Six Sigma instructors (ISRU) Aim: Successfully integrate the Six Sigma methodology into a company’s existing culture and working practices
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 : Six Sigma training package Aim of 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 Slide 59: DMAIC Six-Sigma - A “Roadmap” for improvement Slide 60: Define Throughput time project 4 months (full time) Example of a Classic Training strategy ISRU program content : 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 : Draft training schedule Training programme delivery : Training programme delivery Lectures supported by appropriate technology
Video case studies
Games and simulations
Experiments and workshops
Homework! 5 weeks of training : 5 weeks of training Deployment (Define) phase : Deployment (Define) phase Topics covered include
Project management skills
Pitfalls to Quality Improvement projects
Minitab introduction Measurement phase : Measurement phase Topics covered include:
Capability & Performance
Measurement Systems Analysis
Quality Function Deployment
FMEA Example - QFD : 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 QFD can reduce : Lead-times - the time to market and time to stable production
Engineering changes QFD can reduce Analysis phase : Analysis phase Topics include:
ANOVA (Analysis of Variance)
Regression Improvement phase : Improvement phase Topics include:
History of Design of Experiments (DoE)
DoE Pre-planning and Factors
DoE Practical workshop
Response Surface Methodology (Optimisation)
Lean Manufacturing Example - Design of Experiments : Example - Design of Experiments What can it do for you? Minimum cost Maximum output What does it involve? : 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 : Control phase Topics include:
SPC case studies
Business impact assessment Example - SPC (Statistical Process Control) - reduces variability and keeps the process stable : Example - SPC (Statistical Process Control) - reduces variability and keeps the process stable Disturbed process Natural process Temporary upsets Natural boundary Natural boundary Results of SPC : 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 : 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 Slide 78: Black Belt Training Application Review ISRU ISRU,
Champion Project execution Slide 79: Traditional Six Sigma 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.
Projects conducted “on the side”. Black Belt is obliged to achieve financial results.
Focus on experimentation.
Black Belt is coached by champion.
Results are quantified.
Projects are top priority. Conducting projects Slide 80: The right support
The right projects
The right people
The right tools
The right plan
The right results Champions Role : 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 : Champions Role Measure and report Business Impact
Lead projects overall
Overcome resistance to Change
Encourage others to Follow Slide 83: Define Select:
- the project
the Black Belt
the potential savings
team Project selection Slide 84: Projects may be selected according to:
A complete list of requirements of customers.
A complete list of costs of poor quality.
A complete list of existing problems or targets.
Any sensible meaningful criteria
Usually improves bottom line - but exceptions Project selection Key Quality Characteristics “CTQs” : Key Quality Characteristics “CTQs” How will you measure them?
Who will measure?
Is the outcome critical or important to results? Outcome Examples : Outcome Examples Reduce defective parts per million
Increased capacity or yield
Reduced re-work or scrap
Faster throughput Key Questions : 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 : 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? : Why is ISRU an effective Six Sigma practitioner? Slide 90: 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 Reasons INDUSTRIAL STATISTICSRESEARCH UNIT : INDUSTRIAL STATISTICSRESEARCH UNIT We are based in the School of Mechanical and Systems Engineering, University of Newcastle upon Tyne, England Mission statement : 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: : The work we do can be broken down into 3 main categories: Consultancy
Major Research Projects All with the common goal of promoting quality improvement by implementing statistical techniques Consultancy : Consultancy We have long term one to one consultancies with large and small companies, e.g.
Prescription Pricing Agency
To name but a few Training : Training In-House courses
Design of Experiments
Measurement Systems Analysis
As above, tailored courses to suit the company
Six Sigma programmes European projects : 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 : 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 : 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 : Benefits Better monitoring of processes
Better involvement of people
Staff morale is raised
Throughput is increased
Profits go up Examples of past successes : 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 : 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 Facility Distance Learning : Distance Learning your time
your study pattern
your pace or Flexible training
or Open Learning Distance Learning : Distance Learning http://www.ncl.ac.uk/blackboard
Step by step guidelines
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 Slide 106: Roast Cool Grind Pack Coffee
coffee Moisture content Savings:
Savings on rework and scrap
Water costs less than coffee
500 000 Euros Case study: project selection Slide 107: Select the Critical to Quality (CTQ) characteristic
Define performance standards
Validate measurement system Case study: Measure Slide 108: Moisture contents of
roasted coffee 1. CTQ Unit: one batch
Defect: Moisture% > 12.6% 2. Standards Case study: Measure Slide 109: Gauge R&R study 3. Measurement reliability Measurement system too unreliable! Case study: Measure So fix it!! Slide 110: Analyse 4. Establish product capability
5. Define performance objectives
6. Identify influence factors Case study: Analyse Slide 111: Improvement opportunities Slide 112: Diagnosis of problem Slide 113: Brainstorming
Exploratory data analysis 6. Identify factors Material Machine Man Method Measure- ment Mother Nature Amount of added water Roasting machines Batch size Reliability of Quadra Beam Weather conditions Moisture% Discovery of causes Slide 114: Control chart for moisture% Discovery of causes Slide 115: 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 Slide 116: Improve 7. Screen potential causes
8. Discover variable relationships
9. Establish operating tolerances Case study: Improve Slide 117: Relation between humidity and moisture% not established
Effect of stagnations confirmed
Machine differences confirmed 7. Screen potential causes Design of Experiments (DoE) 8. Discover variable relationships Case study: Improve Slide 118: Experiments are run based on: Intuition
Emotions Possible settings for X1 Possible settings for X2 X: Settings with which
an experiment is run. X X X X X X X Actually:
we’re just trying
no design/plan How do we often conduct experiments? Experimentation Slide 119: A systematical experiment: Organized / discipline
One factor at a time
Other factors kept constant Procedure: X X X X O X X X X X X: First vary X1; X2 is kept constant
O: Optimal value for X1.
X: Vary X2; X1 is kept constant.
: Optimal value (???) X X X X X X X Possible settings for X1 Possible settings for X2 Experimentation Slide 120: Design of Experiments (DoE) Advantages of multi-factor over one-factor : Advantages of multi-factor over one-factor Slide 122: Experiment:
X1: Water (liters)
X2: Batch size (kg) A case study: Experiment Slide 123: Feedback adjustments for influence of weather conditions A case study 9. Establish operating tolerances Slide 124: A case study: feedback adjustments Moisture% without adjustments Slide 125: A case study: feedback adjustments Moisture% with adjustments Slide 126: Control 10. Validate measurement system (X’s)
11. Determine process capability
12. Implement process controls Case study: Control Slide 127: ?long-term = 0.532 Before Results Slide 128: Benefits of this project ?long-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 Benefits Approved by controller Slide 129: SPC control loop
Audit schedule 12. Implement process controls Case study: control Documentation of the results and data.
Results are reported to involved persons.
The follow-up is determined Project closure Slide 130: 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. Six Sigma approach to this project Re-cap I! : 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
Use modern tools and methods
Empirical evidence based improvement Re-cap II! : 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 : 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