1. © G. A. Motter, 2006 & 2008 ECE 480 Six Sigma Overview & Introduction to Design for Six Sigma Gregg Motter Motter@MSU.EDU

2. © G. A. Motter, 2006 & 2008 Six Sigma Companies 3MADT Security Air Products Allied Signal American StandardAmerichem ArmstrongArmstrong World IndustriesAsahi Kasai Astrazeneca AT&T Avery Denison AvnetBank of AmericaBASF Bayer BC HydroBecton Dickenson Black & DeckerBoeing Bombardier Boston Financial ServicesCalloway Golf Caterpillar CelaneseCity BankChlorox Conoco Corning Cott Beverages Covenant HealthCromptonDannon DecomaDegussa Dell Delphi Deutsche Bank Dow Chemical DuPont Eastman Chemical Eastman KodakEaton Corp.Eli Lily EmersonEnergizerFlorida Light & Power Ford Fortis Health General Electric General Motors Georgia PacificGillette GlaxoSmithKlineGoodrichGoodyear Harley Davidson HPHitachi

3. © G. A. Motter, 2006 & 2008 More Six Sigma Companies HoneywellIBMIntel ITT IndustriesITW John Deere Johns Manville Corp. Johnson & Johnson Kellogg Kohler Corp.LG Chemical Lockheed Martin Lord LubrizolMaytag MckessonMoog Motorola National Semiconductor NBC NewsNCR Corp. Noranda Northrop Grumman Noveon Omnova Solutions Owens CorningPhillips Pitney Bowes PPG IndustriesPraxair Raytheon Rogers Corp. Royal Bank of CanadaSaint-Gobain Samsung SAS Inst. Scott Seeds Seagate Technologies Sherwin Williams SiemensSilicon Graphics SonySprintSquare D Schlumberger SunbeamSwagelok Timken Toyota Trane TransfreightTRW US Filter Visteon WR Grace Xerox

4. © G. A. Motter, 2006 & 2008 Six Sigma “Belts” “Belt”Responsibilities Master Black Belt Lead the Six Sigma Program within the Company / Organization Develop & update training materials Train Black Belts Mentor & Certify Black Belts Black Belt Lead & Execute Projects utilizing Six Sigma Methodology Train Green Belts Mentor & Certify Green Belts Green Belt Project Team Members working under Black Belt Leadership

5. © G. A. Motter, 2006 & 2008 Six Sigma is... A Systematic Data Driven Approach for: © G. A. Motter, 2006 & 2008 Continuous Improvement MAIC Problem Solving DFSS

6. © G. A. Motter, 2006 & 2008 WOWing Customers with Six Sigma Products... via Design for Six Sigma Define Quality, Defect, and Sigma Level Describe generic DFSS Process Flow Highlight the DFSS Process with an Example Explain “What’s different about DFSS from traditional Engineering Design approach”

7. © G. A. Motter, 2006 & 2008 First, Must Understand Definitions: Quality, Defect, & Process Capability Sigma Level % Out of Spec 60.00034 50.02327 40.62097 36.68072 230.85375 169.14625 Defects Lower Spec Limit Upper Spec Limit Manufacturing Voice of Customer Customer Wants Mean Source: Implementing Six Sigma, F. Breyfogle III Assuming a Long Term 1.5 Sigma Shift

8. © G. A. Motter, 2006 & 2008 Document Design Final Report Sponsor Docs. Design Day Define the Project Charter Identify CCRs Voice of Customer House of Quality Identify Conceptual Design Function Analysis Solution Selection Matrix Design Proposal End of Week 6 Build Prototype Proof-of-Concept Bread Board Circuit Discrete Components Wire Wrap Board End of Week 7 Demo #1 Analyze Prototype Perform. Collect data Statistically Analyze Data Design Meet CCRs? Optimize Design Surface Mount Chips Optimized Power PC Boards Manufacturability Robustness Test & Validate Design Collect Data Statistically Analyze Data Run Charts Package Design Survive Use Environment Heat Management Shock & Vibration Week 15 – Demo #2 DFSS Process Flow

9. © G. A. Motter, 2006 & 2008 Intense Focus on what the Customer wants Relationships Strong - 9 Medium - 3 Weak - 1 Hows Customer Importance System Level Importance Thrust Weight Tot Maint Cost Mission Fuel Burn Cycle Life Limits Time To Remove Disk Burst Speed Lb $/Flt Hr Gal/Flt Flt Cycles Lb Minutes RPM Target Direction More is better Less is better Targeted amount 1 4 5 3 2 4 2 35 2257 36 68 2241 Customer Needs Whats High Power Low Oper. Cost Meet Range Req'ts Long Life High Payload Easy To Maintain Easy To Troubleshoot Voice of the Customer Surveys Focus groups Conjoint Analysis Identify Voice of the Customer and Quality Function Deployment

10. © G. A. Motter, 2006 & 2008 Requirements ‘Flow Down’ from Customer and Design Capabilities ‘Flow Up’ System Requirements Flow Down Subsystem Design System Design Assembly Design Part Design Customer Design for Robustness, Reliability & Manufacturability at Specified Sigma Level Source: Design for Six Sigma, C. Creveling Capability Flow Up

11. © G. A. Motter, 2006 & 2008 System Design Locomotive Platform Engine Generator Inverter Truck Traction Coupler SanderControl Console System Subsystems Assemblies Parts Customers buy System Performance and Reliability Design Decisions are made at Subsystem, Assembly and Parts Systems engineering allows –Flow Down of Customer Requirements to lower design levels –Rational Design Decisions to achieve system-level goals Requirements Flow Down Design

12. © G. A. Motter, 2006 & 2008 Quantify relationships between CCRs & Design Parameters –First principles models –Numerical models (finite elements, lumped parameter, …) –Designed experiments (DOE) –QFD Design for Robust Performance DOE Main Effects Plot Fuel Economy Octane Level Air Temp X1X1 X2 X2 Y = 50 90 8080 7070 60 Response Surface Steep gradient, high sensitivity to variabilities in X’s Optimum Regression to obtain Transfer Function: Y = f(X 1, X 2, X 3 )  a 0 + a 1 X 1 + a 2 X 2 + a 3 X 3 + a 4 X 1 X 2 + a 5 X 1 X 3 + a 6 X 2 X 3 +... 2-Way Interactions Main Effects Capture knowledge in Transfer Function libraries & design templates Optimize

13. © G. A. Motter, 2006 & 2008 Statistical vs Deterministic Design: Switching Power Supply Example Baseline design Isolated switching converter/ feedback section Low cost, combine power MOSFET and control circuit in a 3-pin package Input Filter Isolated Switching Converter Feedback V o = 5 Vdc, +/- 5%V in = 85 - 275 Vac System Requirements: V in : 85 - 275 V V o : 5 V, +/- 5% 6  quality Low cost

14. © G. A. Motter, 2006 & 2008 Deterministic Design Analysis: Transfer function Choose values for design parameters: Substituting: Output voltage = 5.04 volts Baseline design meets 5V, +/- 5% performance requirement But, quality level is not yet determined Baseline design meets 5V, +/- 5% performance requirement But, quality level is not yet determined Design ParameterValue LM 431I ref voltage, V ref (volts)2.495 R 1 (ohms)10000 R 2 (ohms) 10000 Bias current, I b (amps)5.0E-06 V ref V o = V ref + R 2 ____ + I b R 1 ( )

15. © G. A. Motter, 2006 & 2008 Statistical Design Design ParameterMeanStd DevTolerances Lower Upper LM 431I V ref (volts)2.4950.02830.0850.085 R 1 (ohms)1000033.33331%1% R 2 (ohms) 1000033.33331%1% Bias current, I b (amps)5.0E-061.15E-062.00E-062.00E-06 Analysis: Transfer Function Design parameters are statistical. Engineer selects mean values and a measure of variability (e.g., standard deviation, based on component tolerance). Baseline design meets 5V, +/- 5% performance But quality level is only 5  Baseline design meets 5V, +/- 5% performance But quality level is only 5  Do a statistical analysis (e.g., Monte Carlo), using Transfer Function and statistical parameter values Results: V o mean5.04 volts V o std dev0.059 volts Defects/million188 (5.06s) V ref V o = V ref + R 2 ____ + I b R 1 ( ) Volts 4.754.8755.005.1255.25.000.009.017.026.035 Probability

16. © G. A. Motter, 2006 & 2008 Design optimization analysis: Use Transfer Function to understand response surface shape and output voltage sensitivity to each design parameter Reduce defect rate by: (1) shift mean values or (2) reduce design parameter variance Statistical Design: Approaching “6  ” Design ParameterMeanStd DevSensitivity LM 431I V ref (volts)2.4950.02832 R 1 (ohms)1000033.3333-0.0002495 R 2 (ohms) 1000033.33330.0002545 Bias current, I b (amps)5.0E-061.15E-0610000 Design Mod 1: Center distribution by increasing R 1 to 10160 ohms Results: V o mean5.00 volts V o std dev0.058 volts Defects/million20 (5.61s) 4.754.8755.005.1255.25 Base Centered.000.009.019.028.038 Volts Probability

17. © G. A. Motter, 2006 & 2008 Statistical Design: Reaching “6  ” Design Mod 2: Mod 1 plus reduce variance by using 0.1% resistors Design Mod 3: Mod 2 plus LM 431AI MOSFET to reduce V ref variance Summary Statistical design enables prediction of performance, quality and cost during the design process Statistical design enables prediction of performance, quality and cost during the design process 4.754.8755.005.1255.25 Base0.1% ResistorsMOSFET Upgrade.000.012.025.037.050 Volts Probability Centered 0.1% Resistors.000.009.019.028.038 4.754.8755.005.1255.25 Volts Probability MeanStd DevDPMO Z ST Cost Baseline Design5.040.0591895.06100% Mod 1: Centered via R 1 5.000.058205.61100% 5.000.057135.7101% Mod 3: LM 431AI 5.000.041~07.58105% Mod 2: 0.1% Resistors

18. © G. A. Motter, 2006 & 2008 What’s Different About DFSS? Disciplined, comprehensive process applicable to all Designs “Line of Sight” from Customer Needs to all System Design levels Statistical design to understand... and reduce Variation “New” tools: QFD, Function Analysis, TRIZ, DOE, DFM, statistical tolerance, Robust Design, multi-variable optimizations Quality prediction throughout development Dedicated Team can develop a Breakthrough Design in months But, does not replace need for sound Engineering Judgment But, does not replace need for sound Engineering Judgment

19. © G. A. Motter, 2006 & 2008 Questions & Discussion