untitled 6σ
(Quality Assurance: A historical perspective)
(Quality: What have been done and what are needed) 6σ (GE) (Design for Six Sigma: GE as an example) 6σ
(Does 6σ work in Taiwan?)

Bradley Efron (2004)
Statistics in Nature Assumption: Nature has no will and run by rules that makes no exception—no magic, no miracles, no answered prayers, no appeals to higher authority. Mechanics astronomy physics chemistry Biology medicine
Statistics in humanity (human will actually play a major role)
Economics and psychology Life itself might be exempt from common physical rules.
Theory and Reality
Theorem 1 Don’t put all eggs into one basket.
Theorem 2 If you put all eggs into one basket, Watch the basket!
1988
Quality Management
Not to teach them how to improve, But teach them how to speed up the improvement.
How long a minute is depends upon Which side of the bathroom door you’re on.

Quality Revolution
What is Quality Control All About?
What is Quality?
Balance & Profit
S. Thomas Foster
What is Quality?
? ? ? Index Management
How to Determine Quality Characteristics?
Mission Vision Action Items
BSC Six Sigma Mission/Vision Mission
To develop and implement the tools to help foster a culture of continuous improvement on existing products
and processes, and a “Do it Right The First Time” philosophy on new generations of products and
processes. Through appropriate use of these tools, BSC will benefit from significant advances in quality and
Customer satisfaction. Vision
We will drive significant business improvement utilizing the Six Sigma processes. To ensure success, we will create a sustainable system of training to facilitate the usage of the Six Sigma tools as a core competency at
BSC.




QUALITY ASSURANCE: HISTORICAL HIGHLIGHTS
1920/30’s: Walter Shewhart introduces statistical process control (SPC) at AT&T 1940’s: War effort accelerates applications; development of MIL STD 105, MIL STD 414 and other sampling acceptance plans (Dodge & Romig) 1946: American Society for Quality Control (ASQC) established (and start of numerous professional conferences on quality control) Late 1940’s: Box et al adapt design of experiments to industrial applications 1950’s: Deming hailed for bringing total quality management (TQM) to Japan; Deming Award established 1970’s: Taguchi introduces “off-line quality control” and robust designs Early 1980’s: Deming, Juran, Crosby, Shanian, Golomski et al bring their versions of TQM to U.S. Mid-1980’s: Six Sigma introduced at Motorola 1986: Malcom Baldridge Award (for performance excellence) launched; Motorola Six Sigma program among first winners 1987: ISO 9000 international standards established 1995: Six Sigma comes out of the closet with adoption at GE 1997: ASQC becomes American Society for Quality (ASQ)
Some Key players
W. Edwards Deming Joseph M. Juran Kaoru Ishikawa Armand Feigenbaum Philip Crosby Genichi Taguchi Others
Shewhart Control Charts
73.995
74.005
74.015
Two Main Issues
Western Sensitizing Rules
1. Outside 3-sigma limits 2. 8 points in a row in zone C 3. 6 points in a row increasing or decreasing 4. 14 points in a row alternating up or down 5. 2 out of 3 points in a row in Zone A 6. 4 out of 5 points in a row in Zone B 7. 15 points in a row in Zone C 8. 8 points in a row not in Zone C
see also Balkin and Lin (2001)
Deming (1986)
The biggest problem that most any company in the Western world faces is not its competitors, nor the Japan. The biggest problems are self-inflicted.
Everyone doing his best is not the answer. Everyone is doing his best! It is necessary that people understand the reason for the changes that are necessary.
Total Quality Management
DEMING’S 14 POINTS
1. Constant of purpose 2. Adopt the new philosophy 3. Build in quality 4. Awarding business on value, not price 5. Continually eliminate problems 6. Training on the job 7. Adopt and institute leadership 8. Drive out fear 9. Not Departmentalism but team work 10. Not slogans, but how to 11. No quotas 12. Allow pride in work 13. Education and retraining 14. Top Management Push
DEMING’S 14 POINTS (From Out of the Crisis)
1. Create constancy of purpose for improvement of product and service 2. Adopt the new philosophy 3. Cease dependency on inspection to achieve quality 4. End practice of awarding business on basis of price tag alone. Instead,
minimize total cost by working with a single supplier 5. Improve constantly and forever every process for planning, production
and service 6. Institute training on the job 7. Adopt and institute leadership 8. Drive out fear 9. Break down barriers between staff areas 10. Eliminate slogans, exhortations, and targets for the work force 11. Eliminate numerical quotas for the work force and numerical goals for
management 12. Remove barriers that rob people of pride of workmanship. Eliminate the
annual rating or merit system 13. Institute a vigorous program of education and self-improvement for
everyone. 14. Put everybody in the company to work to accomplish the transformation
Goal
S/N Ratio ()
Quality Loss Function
Robust Design (Taguchi)
What do the customers want? What don’t the customers want?
Variation Deduction
Output
Input
Clay tiles fired in kiln Problem: Size variation in tiles
(Possibly due to temperature gradient in kiln)
Possible Remedies: 1. Buy a new kiln with precise controllability of
temperature and temperature gradient 2. Seek a more “robust” recipe for tile clay
Design of Experiment Solution: Add 5% Lime
Size variation reduced No additional cost
— (Taguchi Three-Stage Design)
(System Design)
(Parameter Design)
(Tolerance Design)
Quality Creation (Kano)
Konica Example (1970s)
What is the major complaint from the customer?
Picture failure
Konica Example
Under exposure Out of focus Blank film
Konica Example
Under exposure ==> Built in flash bulb Out of focus ==> Auto focus Blank film ==> Auto loading and winding
Standard & Award
Malcom Baldridge Award
Similarities: Bladrige & Six Sigma
6σ focuses on training specialist vs. broad based training in organization
EmphasizeEmployee Education, Training & Development
Work Systems
Information Management
6σ address through project improvement & process monitoring; Bladrige encourage more organizational-wise approaches
Emphasize Customer Relationship & Satisfaction
Specified at the project level in 6σCenter piece for improvement
Customer & Market Knowledge
Not specified in 6σStrategy Development
Not considered in 6σPublic Responsibility & Citizenship
Visionary LeadershipOrganization Leadership
Cumulative Financial SavingOrganizational Effectiveness
Project LevelFinancial & Market Results
Support Processes
Business Support Processes
Product & Service Processes
6σ does not explicitly considerEffective use of team should promote employee satisfaction
Employee Well-Being & Satisfaction
ISO 9000
Theoretical Model Uderlying the Deming method
Theoretical Framework for
Ishikawa Seven Tools:
Check Sheets Pareto Chart Cause and Effect Diagram Histogram Scatter Plot Regression/Correlation Control Chart
How to construct it? How to read it?
Check List
Pareto Chart
Cause-and-Effect Diagram
73.995
74.005
74.015
Too Little Information vs Too Many Information
Ishikawa Seven Steps Problem:
Recognition of the features of the problem Analysis:
Finding out the main causes Action:
Action to eliminate the causes Check:
Confirmation of the effectiveness of the action Standardization:
Permanent elimination of the causes Conclusion:
Review of the activities and planning for future work
Tools of Quality Plan-Do-Check-Act Cycle Ishikawa Seven Tools
Histogram, Pareto Charts, Cause-and-Effect Diagram, Check Sheets, Scatter Diagrams, Flowcharts, Control Charts.
Seven New Tools The Affinity Diagram, The Interrelationship Digraph, Tree Diagram, Prioritization Grid, Matrix Diagram, Process Decision Program Chart, Activity Network Diagram
Five S’s Seiri (organize); Seiton (neatness); Seiso (cleaning); Seiketsu (standardization); Shitsuke (discipline).
Mt. Rushmore, front site
Six Sigma History
Late 1700’s Carl Frederick Gauss introduced the concept of the Normal Curve 1920’s Walter Shewhart set 3 sigma deviations from the mean as the correction point for a process 1979 an exasperated Motorola executive named Art Sundry said, at a meeting, "The real problem at Motorola is that our quality stinks!" and in 1985 Motorola Engineer / Executive, Bill Smith coined the term “Six Sigma” Mid 1980’s, Motorola Chairman Bob Galvin set a new standard of 6-sigma saving $2.2 billion in the process. 1988 Motorola won the Malcolm Baldrige National Quality Award
Six Sigma History 1992 Allied Signal's Larry Bossidy adopted Six Sigma and developed an entire system of leadership and support systems began to form around the statistical problem solving tools developed by Motorola. 1995, Jack Welch, CEO of GE, began to study Six Sigma and eventually dove in. When GE does something, it does it all the way. GE reported to have saved billions of dollars with Six Sigma. GE’s success prompted a number of other companies to adopt Six Sigma during the final years of the 1990's such as Honeywell, Kodak. Six Sigma is well established in US industry and is currently sweeping across Europe
WHAT IS SIX SIGMA?
Six Sigma is a quality metric, a highly disciplined improvement process, a culture Brief history
Developed at Motorola (mid ‘80’s—publicized in mid ’90’s) Embraced by Allied Signal, Eastman Kodak, ABB, IBM, TI, etc. (‘90’s) Adopted as a key company initiative world-wide by GE (Sep ‘95)
Top-down management commitment: Be “lunatic” about quality Welch (GE ex-CEO): Change from fixing products to fixing processes for perfection Formal Goal: Reduce defects to 3.4 per million opportunities (6 sigma quality) Implementation via hierarchy of trained
Green belts: Member of team responsible for implementing Six Sigma Black belts: Leader of team responsible for implementing Six Sigma Master black belts: Teacher and mentor of black belts Champions: Senior management person responsible for obtaining resources and generating (and maintaining) enthusiasm for Six Sigma
WHAT IS SIX SIGMA? (continued) Built upon
Disciplined approach with overall road-map and focus on improvement Stepwise improvement process: Measure, Analyze, Improve, Control (MAIC)
Measure: Identify customer needs (CTQs: critical to quality characteristics) and quantify existing defect levels Analyze: Determine root causes of defects Improve: Permanently eliminate root causes of defects Control: Maintain gains over time
Define (problem and goals up-front) added (DMAIC) Highly data-oriented (emphasis on classical statistical methods: gage R&R, SPC, DOE, regression, etc. ) and various quality/management tools (e.g., QFD, FMEA) Emphasis on common language for metrics, tools, goals, etc.
Initially focused on “fixing the factory,” e.g., reduce rework and scrap reduction
Evolved to include design, processes, customer focus, and “everything we do”
The Goal
3.4 defects per million opportunities for all processes critical to customer satisfaction
Companies Implementing 6-Sigma
General Electric ABB Vetco CitiGroup DuPont Bombardier Ceridian Polaroid Carlson Co. Sony IBM
Motorola American Express Eastman Kodak Amazon.com Honeywell Dow Chemical Ford Motor Cargill Unisys Iomega Pacific
AlliedSignal Seagate Technology 3M Toshiba Navistar Raytheon Lockheed Martin US Bank Eaton Texas Instruments LG Electronics
-6σ -5σ -4σ -3σ -2σ -1σ y +1σ +2σ +3σ +4σ +5σ +6σ
Lower Specification
±2σ 95.45 45,500
±1σ 68.27 317,300
±6σ 99.9999998 0.002 ±5σ 99.999943 0.57
±4σ 99.9937 63 ±3σ 99.73 2,700
-6σ -5σ -4σ -3σ -2σ -1σ y +1σ +2σ +3σ +4σ +5σ +6σ
Lower Specification
The Six Sigma Metric ±1.5σ Shift
The 1.5 Sigma Shift is expected to occur over the very long-term (typically years)
The Six Sigma Metric Specification
Limit Percent within Specification
Defects per million (Centered)
Defects per million (1.5σ shift)
± 1σ 68.27 30.23 317,300 697,700
± 2σ 95.45 69.13 45,500 308,700
± 3σ 99.73 93.32 2,700 66,810 ± 4σ 99.9937 99.3790 63 6,210 ± 5σ 99.999943 99.97670 0.57 233 ± 6σ 99.9999998 99.999660 0.002 3.4
A common definition of a 6 Sigma process is one which achieves 3.4 defects per million or less.
SIX SIGMA METRICS (for normally distributed property) ““Short Term CenteredShort Term Centered”” versus versus ““Long Term ShiftedLong Term Shifted””
Six Sigma CenteredSix Sigma Centered
Lower Spec LimitLower Spec Limit Upper Spec LimitUpper Spec Limit TargetTarget
6 6 Sigma Process CapabilitySigma Process Capability (without mean shift)(without mean shift)
SHORT TERM
.001 .001 parts per millionparts per million .001 .001 parts per millionparts per million
+6σ
LONG TERM
Lower Spec LimitLower Spec Limit Upper Spec LimitUpper Spec Limit TargetTarget 3.4 3.4 parts per millionparts per million
Mean Shifted 1.5Mean Shifted 1.5σσ
6 6 Sigma Process CapabilitySigma Process Capability (with mean shift)(with mean shift)
GOAL: REDUCE VARIABILITY (SIGMA) AND CENTER PROCESS ON TARGETGOAL: REDUCE VARIABILITY (SIGMA) AND CENTER PROCESS ON TARGET
-6σ
4.5σ1.5σ
20,000 lost articles of mail per hour Unsafe drinking water almost 15 minutes per day 5,000 incorrect surgical operations per week 2 short or long landings at most major airports
each day 200,000 wrong drug prescriptions each year No electricity for almost 7 hours each month
3σ Capability 93.32% Historical Standard
4σ Capability 99.379% Recent Standard
6σ Capability 99.99966% Industry Vision
*
*
*
Room for much improvement perceived quality levels
• U.S. Internal Revenue Service phone tax advice: 2 to 2.5 sigma.
• Restaurant bills: 3 to 3.5 sigma.
• Prescriptions: 3.5 to 4 sigma.
• Airline baggage handling: 3.75 to 4.25 sigma
• World-class quality companies: 4.5 to 5 sigma
• US airline fatality rate: Over 6 sigma
Sigma: a measure of quality
99% Good (3.8 Sigma) 20,000 lost articles of mall per hour Unsafe drinking water for almost 15 minutes each day 5,000 incorrect surgical operations per week Two short or long landings at most major airports each day 200,000 wrong drug prescriptions each year
99.99966% Good (6 Sigma) Seven articles lost per hour
One unsafe minute every seven months 1.7 incorrect operations per week One short or long landing every five years
68 wrong prescriptions per year
Why Six Sigma?
Strategy for Running a Business Tool to Eliminate Variation Vision of Product & Service Excellence Value to Our Customers Metric of World Class Companies Goal for Competitive Strength
WHY NOW? • Accessibility to
– Automatic monitoring
– Large/immense databases
• General availability of statistical software to “tame” the data (“democratization” of statistics)
• Improved communications: E-mail, video-conferencing, Internet
• (Maybe) Impact of Deming, Juran, Taguchi et al
• (Definitely) Emphasis on bottom-line & gaining competitive edge
• Room for much improvement (low-lying fruit); perceived quality levels:
• U.S. Internal Revenue Service phone tax advice: 2 to 2.5 sigma
• Restaurant bills: 3 to 3.5 sigma
• Prescriptions: 3.5 to 4 sigma
• Airline baggage handling: 3.75 to 4.25 sigma
• World-class quality companies: 4.5 to 5 sigma
• US airline fatality rate: Over 6 sigma
Motorola Best Practice More than Manufacturing ... All Functions Customer and Supplier Linkages Critical Top Level Commitment Critical ... Not a “Program” Detailed, Extensive Training - 40 Hours/Employee/Year Grew Shares in Highly Competitive Global Markets Shared What Worked and What Didn't
Six Sigma Journey
For Each Product or Process CTQ → Measure, Analyze, Improve,& Control 1. Identify/Define CTQ’s: What are the customer needs & key processes? (survey/interview/inquiries) 2. Measure: What is the frequency of defects? (measurement system/process mapping/sigma rating) 3. Analyze: When and where do defects occur? (Statistics/Pareto/benchmarking/etc.) 4. Improve: How can we fix the process? (design of experiments/expert brainstorming/etc.) 5. Control: How can we ensure the process remains fixed? (measurement feedback control/procedural/etc.)
Changing focus from Y to X
Y Dependent Output Effect Symptom Monitor
X1, …, Xn Independent Input-Process Cause Problem Control
Identifying and fixing root causes will help us obtain the desired output
Premises underlying Six Sigma
All Processes Have Variability Variability Has Definable Causes Typically Only a Few Causes Are Significant If Causes Can Be Identified and Understood ... They Can Be Controlled Designs Must Be Robust to the Effects of the Remaining Process Variations
Meco, Inc.: A Case Study
Meco, Inc. Located in Greenville, TN More than 600 Full Time Employees Furniture Line:
Banquet tables Card tables Folding chairs Step ladders Stepping stools
Grill Line: Charcoal grills Electric grills Portable Grills Rotisserie Grills Smokers
Goals: Determine settings for paint guns so that a procedure can be written for painting Elimination of reinforcement (manual) paint position. Gain process knowledge.
Plan: Form team to carry out plan and goals. Study current process (control charts). Design and run experiment.
Results: Procedure written for painting balck grill bowls. Reduced average paint thickness approximately 42.5% Reduced variation in paint thickness approximately 74.9%
Process Flowchart Hang product on line
Pre-treatment
Historical Data
Paint Thickness by Location and Hook, Current Data: Routine Paint Process Settings Bowls Hoods Loc. Top X Bot X Top R Bot R Top X Top X Top R Bot R 1 1.72 1.33 0.9 0.8 1.0 1.01 0.7 1.5/0.9 2 1.80 1.39 0.5 1.0/0.4 1.27 0.98 0.7 0.7 3 1.43 1.36 0.6 0.6 0.82 1.03 0.3 0.9 4 1.63 1.36 1.3/0.7 0.7 0.86 0.91 0.5 0.4 5 2.42 2.19 1.1 1.8/0.9 2.10 2.12 0.9 0.8 6 2.31 2.39 1.4 1.4 2.44 2.20 0.8 0.8 7 1.95 2.14 0.8 0.9 1.67 1.92 1.0 0.9 8 2.18 2.26 1.1 1.1 1.79 2.12 1.5/0.9 0.7 9 2.13 2.55 0.7 1.4 2.04 2.26 0.8 1.2/0.8 10 2.24 2.44 1.3 1.4 2.15 2.49 1.2/0.8 1.6/1.2
Paint Thickness by Location and Hook, Current Data Bowls
Special Powder Pressure Setting
Bowls Routine Setting Without Manual Reinforcement
Loc. Top X Bot X Top R Bot R Top X Top X Top R Bot R 1 1.59 1.2 0.7 0.6 1.35 1.3/1.03 0.4 1/0.1 2 1.575 1.12 0.7 0.5 1.45 1.075 0.8/0.4 0.7 3 1.14 1.26 0.6 0.6 0.925 0.9 0.3 0.4 4 1.21 1.28 1.0 0.9 1.00 1.05 0.2 0.3 5 1.86 2.08 0.6 0.6 2.95 2.425 0.4 0.7 6 1.89 2.19 0.8 0.9 2.35 2.175 0.4 0.4 7 1.59 2.1 0.6 1.1 1.925/1.7 2.3 1.2/0.8 0.4 8 1.56 2.32 0.6 0.6 1.55 2.625 0.6 0.5 9 1.99 2.15 0.8 1.4 2.525 2.8 0.4 0.3 10 1.73 2.375 0.7 0.8 2.05 2.975 0.6 1.3
Uncontrollable Variables: Line speed Ambient temperature Humidity ground Forward/Dilution air pressure Fluidizing
Experimental Variables: Gun 1 position Gun 8 position Gun 9 position Powder pressure Gun 7 position Gun 11 position
Controlled variables: Experimental Levels Variable Coded-level (-1) Coded level (0) Coded level (+1) Gun 1 position 58.3/74 59.3/77 Gun 8 position 48.3/81 49.3/84 Gun 9 position 37.5/80 38.5/83 Powder pressure 25 lbs 35 lbs 45 lbs Gun 7 position 64.5/80 65.5/83 66.5/86 Gun 11 position 52/79 53/82 54/85
Experimental Responses Hook Location Response Top Hook
Inside (1-4) Side (5,6) Outside (7-10)
Average and range of paint thickness Average and range of paint thickness Average and range of paint thickness
Bottom Hook Inside (1-4) Side (5,6) Outside (7-10)
Average and range of paint thickness Average and range of paint thickness Average and range of paint thickness
Weight Top and bottom hook
Weight of paint covering parts
How would you run the experiment?
Experimental Design
Exp. No.
Run Order
Gun 1 Gun 8 Gun 9 Pressure Gun 7 Gun 11
1 20 -1 -1 1 -1 -1 0 2 19 1 -1 -1 -1 -1 0 3 17 -1 1 -1 -1 -1 0 4 36 1 1 1 -1 -1 0 5 34 -1 -1 1 0 -1 1 6 2 1 -1 -1 0 -1 1 7 23 -1 1 -1 0 -1 1 8 6 1 1 1 0 -1 1 9 22 -1 -1 1 1 -1 -1 10 26 1 -1 -1 1 -1 -1 11 28 -1 1 -1 1 -1 -1 12 10 1 1 1 1 -1 -1 13 27 -1 -1 1 -1 0 1 14 12 1 -1 -1 -1 0 1 15 31 -1 1 -1 -1 0 1 16 8 1 1 1 -1 0 1 17 9 -1 -1 1 0 0 -1 18 33 1 -1 -1 0 0 -1 19 14 -1 1 -1 0 0 -1 20 1 1 1 1 0 0 -1 21 18 -1 -1 1 1 0 0 22 21 1 -1 -1 1 0 0 23 24 -1 1 -1 1 0 0 24 35 1 1 1 1 0 0 25 29 -1 -1 1 -1 1 -1 26 15 1 -1 -1 -1 1 -1 27 32 -1 1 -1 -1 1 -1 28 16 1 1 1 -1 1 -1 29 13 -1 -1 1 0 1 0 30 30 1 -1 -1 0 1 0 31 3 -1 1 -1 0 1 0 32 11 1 1 1 0 1 0 33 5 -1 -1 1 1 1 1 34 25 1 -1 -1 1 1 1 35 7 -1 1 -1 1 1 1 36 4 1 1 1 1 1 1
Significant Variables for Each Response Variable
Top Hook Response 1% Significance 5% Significance 10% Significance Inside Avg
Range P 9*7 7
8*11 9*P 1*7 1*7 8*11 8*7 1*P
Avg Range
8*7 1*7 9 7
Avg Range
P 11
Bottom Hook Response 1% Significance 5% Significance 10% Significance Avg
Range P
1*11 9*11 9 9*P 8*P 9 1*7
Avg Range
P 9
8*7 1*11 8*P 1*P 9*11
Weight Response 1% Significance 5% Significance 10% Significance Weight P 1 9*P 1*7 7 9*7
How would you analyze the data?
P is significant, set at level 0. Gun 8 is significant, set at level -1. Gun 7 is significant, set at -1 or 0. Both 1x7 and 9x11 interactions are significant.
Gun 1 and gun 7 have a negative coefficient, thus must have different levels. Gun 9 and gun 11 also have a negative coefficient.
How would you run the confirmation runs?
Confirmation Run Order and Settings Variable Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Gun 1 -1 -1 +1 +1 -1 -1 +1 +1 -1 Gun 8 -1 -1 -1 -1 -1 -1 -1 -1 -1 Gun 9 +1 -1 +1 -1 -1 +1 -1 -1 -1 Pressure 0 0 0 0 0 0 0 0 0 Gun 7 -1 0 -1 -1 0 0 -1 -1 0 Gun 11 +1 -1 0 -1 +1 0 0 +1 0
Paint Thickness by Location and Hook, Current and New Paint Process Settings Current
Process New
Process Loc. Top X Bot X Top R Bot R Top X Top X Top R Bot R 1 1.72 1.33 0.9 0.8 0.95 0.8 0.6/0.3 0.2 2 1.80 1.39 0.5 1.0/0.4 0.9/0.7 0.9 0.6/0 0.2 3 1.43 1.36 0.6 0.6 0.83 0.6 0.2 0.2 4 1.63 1.36 1.3/0.7 0.7 0.60 0.87 0.5 0.1 5 2.42 2.19 1.1 1.8/0.9 1.67 1.23 0.1 0.4 6 2.31 2.39 1.4 1.4 1.43 1.1 0.2 0.2 7 1.95 2.14 0.8 0.9 1.43 1.47 0.2 0.2 8 2.18 2.26 1.1 1.1 1.13 1.63 0.1 0.3 9 2.13 2.55 0.7 1.4 1.33 1.07 0.4 0.3 10 2.24 2.44 1.3 1.4 1.47 1.43 0.3 0.3
The New Process Will ...
Reduce the black paint cost by $7,681 to $15,361 per year Decrease the amount of recycled paint in the paint booth Eliminate the cost of the touch-up paint process Decrease the time and cost in setting up the paint process Decrease the number of bowls reworked (stripping and repainting)
Procedure
1) Define your goal as clear as possible 2) Use SPC technique (historical data) to understand
the process 3) Identify controllable / uncontrollable factors 4) Use DOE technique to find the “optimal” setting
for potential improvements 5) Run confirmation experiments
Define Measure Analysis Improve Control
Six Sigma Improvement Process
Analyze
Define
How should I Bound the Problem?
Understand the Customer's Critical to Quality (CTQ) Factors Link the CTQs to Key Internal Processes Prioritize Key Processes on Which to Focus
Phase I: Measure
How Good Is the Process Today?
Determine How to Measure the Capability of the Process Validate That the Measuring Approach is Reliable (Gage R&R) Gather the Correct Sampling of Data
Process capability
Over time, a “typical” process will shift and drift by approximately 1.5σ
Phase II: Analyze
What Causes Defects in the Process Today? Use the Sample Data to Distinguish Current Performance vs. “Entitlement” Hypothesize What the Underlying Factors Are That Are Causing the Difference Between Current Performance and “Entitlement” Design Experiments to Confirm the Importance of the Underlying Factors
Baselining & Benchmarking an existing process
Baselining = current process / benchmarking = ultimate goal
Phase III: Improve
How do I Fix the Underlying Causes of Variation? Conduct Experiments to Quantify the Impact of the Underlying Factors on the Capability of the Process Design Fixes to Control the Underlying Factors Within Acceptable Bounds Confirm That the Economics of the Proposed Fixes are Positive
Design of Experiments (DOE) SCREENING
For Experiments involving a Large Number of Factors Useful in Isolating the “Vital Few” from the Trivial Many
CHARACTERIZATION For Experiments Involving a Relatively Small Number of Factors Useful When Studying Relatively Uncomplicated Effects & Interactions
OPTIMIZATION For Experiments Involving Only few Factors Useful When Studying Highly Complicated Effects & Relationships
Phase IV: Control
How do I Control the Variation in the Underlying Factors?
Deploy Measurement Tools to Confirm Process Is in Control (SPC, Cheek Sheets, etc.) Establish Feedback Mechanisms/Alarms to Confirm Process Is in Control
Some basic six-sigma tools
Some basic six-sigma tools
Some basic six-sigma tools
Seven Basic Tools Analysis of Variance Variance Components Regression Analysis Fault tree Analysis Failure Modes & Effects Analysis Mistake Proofing Acceptance Sampling Reliability/Survival Analysis
Black Belt
Belt Definitions People with a Yellow Belt (YB): participate in Six Sigma Problem Solving Projects People with a Green Belt (GB): lead less complex Six Sigma Projects, participate in BB Teams & support YB Teams People with a Black Belt (BB): lead complex Six Sigma Projects, mentor, support and develop GB’s & YB’s; Part & Full Time People with a Master Black Belt (MBB): are the experts in application & training
Yellow Belt
Green Belt
A 125-year old, $126B High-Tech Growth Company
Technology is Key to Growth
Founded by Thomas Edison
A diversified technology, manufacturing and services company with a commitment to achieving
world leadership in each of its key businesses
Aircraft EnginesAircraft Engines Capital ServicesCapital Services
Consumer ProductsConsumer Products Industrial SystemsIndustrial Systems
Information ServicesInformation Services Medical SystemsMedical Systems
PlasticsPlastics Power SystemsPower Systems
NBCNBC
General Electric Overview
20 KEY LESSONS LEARNED (Source: Hahn, G., Six Sigma Forum Magazine, May 2002)
The time is right: Intense competitive pressures, consumer demand, computers The enthusiastic commitment of top management is essential (as shown by GE)
Be “lunatic” about quality Make Six Sigma a major factor in promotion, incentive compensation, etc. Set up structure: Champion, master black belts, black belts, etc. Provide the needed resources and priorities Ensure everybody is paying attention Take overall responsibility Follow up
Develop structure—not extra-curricular activity: Organization, budget, objectives, responsibilities, measurement of results Commit top people (champions, MBBs, BBs) Invest in relevant hands-on training
Knowledgeable trainers who are outstanding communicators Customize to needs of specific businesses Ensure common vocabulary Incorporate hands-on involvement Consider engaging external gurus to expedite Six Sigma introduction
Select initial projects to build credibility rapidly: Importance evident, viable and doable in short time, readily quantifiable (reap “low-lying fruit”—often in manufacturing)
SIX SIGMA: 20 KEY LESSONS LEARNED (Continued)
Make it all pervasive, and involve everybody (“the way we work”) Emphasize Design for Six Sigma (DFSS) And don’t forget Design for Reliability Focus on the entire system: Consider all CTQ’s simultaneously Emphasize customer CTQ’s Include commercial quality improvement Recognize all savings and costs—including savings from averting problems Customize to meet business needs, e.g.,
Basic concepts of design of experiments universally applicable Mixture experiments especially relevant for chemical processing Planning of consumer surveys especially relevant for marketing and servicing operations
Consider variability as well as the mean (“variability is evil”) Plan to get the right data
In God we trust, all others bring data Advance planning is critical Data quality generally more important than data quantity
SIX SIGMA: 20 KEY LESSONS LEARNED (Continued)
Beware of dogmatism: Goal is to gain quality improvement—not to use any specific tool Avoid nonessential bureaucracy—a criticism of Six Sigma Keep the toolbox vital; with experience
Some tools have been found more important than others Some not originally included have proven their value
Expect Six Sigma to become a more silent partner over time
No more headline news when it is “the way we work” Retain momentum by continued successes
THE FUN HAS JUST BEGUN!
A LOOK INTO THE FUTURE OF SIX SIGMA
At GE Will continue to evolve Will be de-emphasized per se--but accepted as “way we work” Will be meshed with other initiatives: e.g., e-Business Impact of training will remain; but some practitioners might become rusty on tools
Rest of world Some companies will jump on Six Sigma wagon
Actively being promoted by ASQ, etc. Opportunity for academia Similar cycle as GE seems likely
Others might not adopt Six Sigma per se--but will face similar challenges Not limited to businesses; also applicable to banks, social agencies, government, schools, etc.
Summary Define
Measure Identify CTQ & CTP (Critical to Process) Variables Do Process Mapping Develop and Validate Measurement Systems
Analyze Benchmark and Baseline Processes Calculate Yield and Sigma Target Opportunities and Establish Improvement Goals Use of Pareto Chart & Fishbone Diagrams
Key components of breakthrough strategy: A mix of concepts and tools
Improve Use Design of Experiments Isolate the“Vital Few”from the“Trivial Many” Sources of Variation Test for Improvement in Centering Use of Brainstorming and Action Workouts
Control Set up Control Mechanisms Monitor Process Variation Maintain in Control Processes Use of Control Charts and Procedures


QC … TQM … 6σ …
--
:




Appendix
More Readings
STILL QUESTION?
Send $500 to Dennis Lin Department of Management Science & Information Systems Penn State University
+1 814 865-0377 (phone)
+1 814 863-7076 (fax)
Summary of Last Four Lectures
Reality




What’s Statistics All About?
Making Sense Out of Numbers
Math Notations x x, y, z, u, v, w x1, x2,…,xn y1, y2,…,yn; z1, z2,…,zn; w1, w2,…,wn … α, β, γ, ... αi, βj, γk, ... αijk, βj.k.pqr,
,





E.F. Schumacher When the Lord created the world and people to live in it—an enterprise which, according to modern science, took a very long time—I could well imagine that He reasoned with Himself as follows: “If I make everything predictable, these human beings, whom I have endowed with pretty good brains, will undoubtedly learn to predict everything, and they will thereupon have no motive to do anything at all, because they will recognize that the future is totally determined and cannot be influenced by any human action. On the other hand, if I make everything unpredictable, they will gradually discover that there is no rational basis for any decision whatsoever and, as in the first case, they will thereupon have no motive to do anything at all. Neither scheme would make sense. I must therefore create a mixture of the two. Let some things be predictable and let others be unpredictable. They will then, amongst many other things, have the very important task of finding out which is which.”
I love noise ! I love noise ! I love noise !






Black Belt Training
Module 1 (Define) Six Sigma Overview The Six Sigma Problem Solving Process (overview) Seven Basic Tools for Quality Improvement
DMAIC Introduction Basic Statistics (includes Data Transformation) Customer Focus (includes Process Mapping)
Project Reviews / DMAIC Review
Black Belt Training Topics
Module 2 (Measure) DMAIC Review Drill Down Chart / Process Mapping Control Charts Capability Studies Analysis of Variance Measurement Systems Analysis Change Point Analyzer Project Reviews / DMAIC Review
Black Belt Training Topics
Module 3 (Analyze) DMAIC Review Fault Tree Analysis Multi-Vari Charts Confidence Intervals Linear & Polynomial Regression Design of Experiments Project Reviews / DMAIC Review
Black Belt Training Topics
Module 4 (Improve) DMAIC Review Multiple Regression Response Surface Methodology VarTran Augmentation Robust Design Project Reviews / DMAIC Review
Black Belt Training Topics
Module 5 (Control) DMAIC Review FMEA Mistake Proofing Hypothesis Testing Acceptance Sampling Process Validation DMAIC Workshop Project Reviews / DMAIC Review
Black Belt Training Topics
Course Curriculum at GE
INTRODUCTION OF SIX SIGMA AT GE CEO Jack Welch
Decides on company-wide quality initiative (“most important ever”) Sold on Six Sigma (by Larry Bossidy); launches in September ‘95 Tied to promotion and management incentive compensation
Implementation centered within each GE business Champion (senior manager) reporting to business leader, provides resources Master Black Belts (MBBs)—full time mentor, trainer, change agent Black Belts (BBs)—full time project leader Green Belts (GB)—Implementer (part-time)
Plus small corporate group to provide coordination, share best practices, etc.
Start-up Brought in Mikel Harry for MAIC (trained first MBBs)
(later brought in Maurice Berryman for Design for Six Sigma (DFSS)) Rapid transition to GE leaders
Training and certification All GE employees trained in strategies, tools and techniques All professionals green belt (or black belt) trained: 4 one week sessions plus two successful projects--administered by MBBs Master black belt: Special training beyond GB/BB & mentor 20 projects
GG/BB DMAIC CURRICULUM-WEEK 1: DEFINE/MEASURE
Classical Training Define/Measure
Overview and process improvement planning—The MAIC roadmap Quality Function Deployment (QFD) – Matrix to relate what customer wants to design requirements via “house of quality” Failure Mode and Effects Analysis (FMEA) Organizational effectiveness concepts Basic Statistics using Minitab Process capability Measurement systems analysis
Revised Recommendation (Hoerl 2001) Context
Why Six Sigma? DMAIC & DFSS process Project management fundamentals Team effectiveness fundamentals
Define Project selection Scoping projects Developing project plan Multi-generational projects Process identification
Measure QFD (and House of Quality)
Identifying customer needs Developing measurable CTQ metrics
Sampling (data quality and quantity) Measurement system analysis (not just gauge R&R) SPC Part I
Statistical Control (process stability) Implications of instability
Capability analysis
Classical Training Statistical thinking Hypothesis testing (F, t, etc.) Correlation Simple regression
Revised Recommendation (Hoerl 2001) Basic graphical improvement tools (“Magnificent 7”: Flow chart, Pareto chart, cause & effect diagram, histogram, scatter plot, stratification, and run chart) Management and planning tools Confidence intervals (emphasized) Hypothesis testing (de- emphasized) Analysis of variance (de- emphasized) Regression Developing conceptual designs in DFSS
GG/BB DMAIC CURRICULUM-WEEK 3: IMPROVE
Classical Training Design of Experiments (DOE)
Factorial experiments Fractional factorials Balanced block designs Response surface designs
Analysis of Variance (ANOVA) Multiple Regression Facilitation Tools
Revised Recommendation (Hoerl 2001) DOE, focusing on
Two level factorials Screening designs (fractional factorials) Response surface designs
Piloting (of DMAIC improvements) FMEA Mistake-proofing DFSS design tools
CTQ flowdown Capability flowup Simulation
(Note: Discussion might spill into week 4)
GG/BB DMAIC CURRICULUM-WEEK 4: CONTROL
Classical Training Control plans Mistake-proofing Team development Parallel, discrete, continuous process, administration and design tracks
Note: 1) Taught by MBBs/BBs 2) ~9 day classroom training, plus
~3 weeks project for each phase
Revised Recommendation (Hoerl 2001) Development control plans Statistical process control-Part II: Using control charts Piloting new designs in DFSS
Note: 1) Taught by MBBs/BBs 2) ~9 day classroom training, plus
~3 weeks project for each phase
ABOVE IS MANUFACTURING ORIENTED: CHANGE FOR DFSS, FINANCE, ETC.
More Readings
See the following web sites: www.isixsigma.com www.asq.org
See the following texts: Books on Quality Improvement by Thomas P. Ryan or Douglas Montgomery Books on Six Sigma by Peter Pande or Forrest Breyfogle
See the following article: Snee, Ronald (1999), “Why should statisticians pay attention to Six Sigma”, Quality Progress 32(9):100- 103.
WHERE TO GO FOR MORE INFORMATION BOOKS LISTED ON WEB (www.isigma.com/books): 2000 and before
Breyfolge et al, Managing Six Sigma: A Practical Guide (2000) Pande et al, The Six Sigma Way: How GE, Motorola and other Top Companies are Honing their Performance (2000) Rath & Strong, Rath and Strong’s Six Sigma Pocket Guide (2000) Arthur, Six Sigma Simplified (2000) Snyder: Understanding the Essentials of the Six Sigma Quality Initiative (2000) Naumann, Hoisington, Customer Centered Six Sigma:Linking Customers, Process Improvement and Financial Results (2000) Oriel Incorporated, Guiding Successful Six Sigma Projects (2000) Tennant, Six Sigma: SPC & TQM in Manufacturing and Services (2000) Eckes, GE’s 6 Sigma Revolution: How GE & Others turned Process into Profits (2000) Brue: Six Sigma for Team Members (2000) Harry and Schroeder, Six Sigma: The Breakthrough Management Strategy Revolutionizing the World’s Top Corporations (1999) (also audio) Breyfogle, Implementing Six Sigma: Smarter Solutions using Statistical Methods (1999) Perez-Wilson, Six Sigma: Understanding the Concept, Implications and Challenges (1999) Lawson, Measuring Six Sigma and Beyond (1997) Prins, Six Sigma Metrics (1993) Harry, Vision of Six Sigma (1997) -- 8 volumes Harry et al, Six Sigma Mechanical Design Tolerancing (1988) Harry, The Nature of Six Sigma Quality (1988)
WHERE TO GO FOR MORE INFORMATION BOOKS LISTED ON WEB (www.isigma.com/books): 2001
Stamatis, Six Sigma and Beyond: 8 Volumes: Foundations, Problem Solving & Basic Maths, Statistics & Probability, Statistical Process Control, Design of Experiments, Design for Six Sigma, Implementation Process (2001/2) Breyfogle et al, Wisdom on the Green: Smarter Six Sigma Business Solutions (2001) Eckes, Making Six Sigma Last: Managing Cultural and Technical Change (2001) Brassard & Ritter, Sailing Through Six Sigma (2001) Chowdhury, The Power of Six Sigma: How 6 Sigma is Transforming the Way we Work (2001) Arthur, Six Sigma Instructor Guide (2001) Pyzded, Six Sigma Handbook: Complete Guide for GBs, BBs and Managers (2001) Pande et al, What is Six Sigma? (2001) Lowenthal, Six Sigma Project Management: A Pocket Guide (2001) Tayntor, Six Sigma Software Development (2001) Mills et al, The Path to Integration of Lean Enterprise and Six Sigma (2001) Munro, Six Sigma for the Shop Floor: A Pocket Guide (2001) Juran Institute, Implementing Juran’s 6-Step Quality Improvement Process and Six Sigma Tools (2001) Harry, Six Sigma Knowledge Guide (2001) Bhote, The Ultimate Six Sigma: Beyond Quality Excellence (2001) American Productivity & Quality Center, Deploying Six Sigma (2001) Perez-Wilson, Six Sigma: Understanding the Concept, Implications and Challenges (2001) Dodson et al, Six Sigma Study Guide (2001) Keller, Six Sigma Deployment: A Guide for Implementing in Your Organization (2001) Tenant, Six Sigma and TQM in Manufacturing and Services (2001) ?, The Power of Six Sigma: An Inspiring Tale of an Extraordinary Process Transforming the Way we Work (2001)
WHERE TO GO FOR MORE INFORMATION BOOKS LISTED ON WEB (www.isigma.com/books): 2002 (and 2003)
Harry, The Six Sigma Fieldbook: How to Successfully Implement Six Sigma Breakthrough Strategy (2003) Harrington, Six Sigma Toolkit Set (2003) Wheat et al, A Parable of the Journey to Six Sigma and Lean Enterprise (2003) Eckes, Six Sigma for Everyone (2003) Rath & Strong, Six Sigma Leadership Handbook (2003) Andell, Six Sigma Leadership (2002) Snee and Hoerl, Leading Six Sigma: A Step by Step Guide (2002) Ehrlich, Transactional Six Sigma and Servicing (2002) Barney & McCarty, The New Six Sigma: A Leader’s Guide (2002) Gaudard & Ramsey, Six Sigma Companion Guide: Experimental Design for Practitioners (2002) Smith et al, Strategic Six Sigma: Best Practices from the Executive Suite (2002) Craveling et al, Design for Six Sigma in Technology and Product Development (2002) Shina, Six Sigma for Electronics Design and Manufacturing (2002) Eckes, Six Sigma Team Dynamics: The Elusive Key to Project Success (2002) Plotkin, Sis Sigma: What It Is and How to Use It (2002) George, Lean Six Sigma, Combining Six Sigma Quality with Lean Production Speed (2002) Walters, The Six Sigma Journey from Art to Science (2002) Tennant, Design for Six Sigma: Launching New Products and Services without Failure (2002) Lientz & Rea, Reach Six Sigma Goals Without the Pain (2002) Campbell, Applying Project Management to Six Sigma Projects (2002) Adams et al, Six Sigma Deployment (2002) Dedeke, What Makes Six Sigma Work (2002) Barry et al, The Six Sigma Book for Healthcare (2002) Conner, Six Sigma and other Continuous Improvement Tools for the Small Shop (2002) Gordon, Six Sigma Quality for Business and Manufacture (2002) ? Six Sigma and Accelerated Testing (2002)
WHERE TO GO FOR MORE INFORMATION (Continued) Six Sigma Forum, American Society for Quality publication, (First issue November
2001); articles to date (August 2002) include: Gregory Watson: Cycles of Learning: Observations of Jack Welch Janet Young: Driving Performance Results at American Express Larry Smith: Six Sigma and the Evolution of Quality in Product Development Mark Goldstein: Six Sigma Program Success Factors Larry Bossidy et al: The Honeywell Edge Dennis Attenello and John Uzzi: Achieving Six Sigma in Financial Services Debbie Phillips-Donaldson: Six Sigma Payoff Charles Gowen: How to Implement Six Sigma for Maximum Benefit Tracy Thurkow: Manage Behavioral Changes Tim Young: Merging Six Sigma and IT Matt Barney: Motorola’s Second Generation Andrea Kabcenell & Donald Berwick: Pursuing Perfection in Healthcare Greg Stock: Taking Performance to a Higher Level Gerry Hahn: 20 Key Lessons Learned Roger Hoerl: An Inside Look at Six Sigma at GE Robert Galvin: In the Beginning (at Motorola) Rick Edgeman & David Bigio: Six Sigma in the Academic World Douglas Gorman & Keith Bower: Measurement System Analysis and Destructive Testing Charles Huber: Straight Talk on DFSS Gregory Watson: Selling Six Sigma to Upper Management Larry Smith: Are Your Champions Doing a Good Job?
GUIDE TO ABBREVIATIONS ABB: Asea, Brown, Boveri, Ltd. ANOVA: Analysis of Variance ASQ: American Society for Quality ASQC: American Society for Quality Control AT&T: American Telephone and Telegraph Company BB: Black Belt CART: Classification and Regression Trees CEO: Chief Executive Officer CT: Computer Tomography Imaging CTQ: Critical to Quality characteristic DFSS: Design for Six Sigma DMAIC: Define, Measure, Analyze, Improve, Control DMADOV: Define, Measure, Analyze, Design, Optimize and Verify (in DFSS)