Looking into the Future of Design for Six Sigma (DFSS)

1. Jesse Peplinski January 16, 2012 • Description of past deployments • Comparison and observations • Suggestions for the future Looking into the Futureof Design for Six Sigma (DFSS)

2. Measure the Requirements Analyze the Root Causes Improve the Design Control the Root Causes Six Sigma Versus DFSS Define theDesign Problem Capture the Voiceof the Customer Identify Critical Requirements • You can use a flexibleapproach to let each design problem dictate which process is followed • Use Six Sigma (MAIC) as a data-driven method for design improvements • Use DFSS as a rigorous method for creating a design to satisfy multiple requirements SelectApproach Improve or Create New Design Improve Existing Design Create Design Concept Build Math Models DFSS MAIC Optimizethe Design Validatethe Design

3. What is a “Deployment”? • A company-specific attempt to inject Six Sigma and/or DFSS into its culture and daily activities • Typically a customized mixture of: • Training classes with tailored content • Structure for projects and “belt” certification • Supporting software tools • Strategic communication by management and leadership • Scope of implementation can vary widely • All employees vs. targeted teams • Local vs. global

4. Past DFSS Deployments

5. Past DFSS Deployments

6. Observations • Pendulum swing • Larger, top-down deployments often end up with lower levels of long-term practice. • Backlash against projects and certification • Long-term health of deployment correlated with selective, low-key implementation • Challenge of demonstrating DFSS savings • Heroes get visibility for fixing mistakes; cost avoidance is difficult to recognize. • Tools stand the test of time • Six Sigma: Gage R&R, SOP’s, DOE, process control • DFSS: QFD, Pugh Matrix, Monte Carlo, Optimization

7. Suggestions for the Future • Design for Six Sigma: • DFSS tools fit naturally within a systems engineering group. (If you don’t have a systems engineering group, consider starting one.) • In addition, DFSS tools should be leveraged by your key participants in design reviews. (Principals, architects, etc.) • DFSS success hinges on modeling and simulation capability. Be prepared for resistance. • Six Sigma: • Let DMAIC flow naturally from leadership asking questions and demanding answers with data • Let plans for training and employee reward be driven by the forces above. (Not vice-versa.)

8. Product Development Process Best Practice SE/DFSS Enablers & Tools Voice of the Customer Quality Function Deployment Exploration TRIZ & Design Selection Identify CriticalRequirements Failure Modes & Effects Analysis Conceptual Design S E & D F S S Physics and First Principles Create Design Concept DOE and Regression Build Models Detail Design Statistical Allocation • Optimize the Design • Allocate Variability • Analyze Variability • Optimize Variability Sensitivity and Monte Carlo Analysis Design Verification Cost and Reliability Analysis Multi-Objective Optimization Initial Production Validate the Design FMEA & Fault Tree Analysis Test Effectiveness Analysis Final Production Design that best meets all requirements Scorecards SE/DFSS Process How does DFSS fit within Systems Engineering? • First – use the Tools to support the Process

9. A Product Model (equation, simulation, workbook, hardware, etc.) Y B PNC C D “Non-compliant” “Non-compliant” “Compliant” E LL T UL Modeling and Analysis within DFSS Require that this be done everywhere, and if it isn’t, explain why not! Understanding Requirements, Specifications, & Capabilities Non-Compliance refers to any condition that results in Defects or Off-Spec conditions Applying Models & Analyses Predicting Probability of Non-Compliance The fundamental metric is the Probability of Non-Compliance (PNC)

10. Modeling: Easier than It May Appear Gather Design Parameter Information Can equations be developed? Fast, Accurate Math Model Yes Key Design Parameters (X’s) No Yes Critical Requirements (Y’s) Identify Existing Models A simulation of sufficient accuracy exists? Simulation computes very quickly? Yes No No Best Design Alternative(s) Historicaldata exists? Perform Regression Analysis Yes No Create New Models Prototypesexist? Perform a Design of Experiments No Yes

11. ~ ~ ~ ~ ~ ~ ~ ~ Six Sigma Examples It starts with hard problems: Our goal is to get solid answers: • What can we do to improve our process yield? • How can we reduce operating temperatures and fix our thermal issues? • What can we do to increase sales volume? • How can we increase the throughput of our call center? • Switching from supplier A to supplier B will improve yields by 8%. • This power supply redesign will reduce operating temperatures by 11 °C. • A $50 rebate would increase sales by 15%. • Adding two more operators will increase throughput by 100 calls per day. How do we bridge the gap with high levels of confidence based on solid evidence?

12. Guiding Questions Answer these questions to bridge the gap: 1. What is our current state? • Product or process performance inmeasurable terms (Y’s) 2. What is our desired state? • How much improvement is needed in our measurable Y’s? 3. How good are our measurement systems? • If we measure the same thing twice, do we get the same answer? • If we made a process improvement, could we detect it? 4. What data do we need to collect? • Responses (Y’s) and Parameters (potential X’s) • How much data? Time period? Shifts? • Existing data? Or new data collection effort? If we can’t measure it, we don’t know where we are. If we can’t measure it, we can never know if we get there.

13. Guiding QuestionsContinued 5. If the Y is plotted versus the X’s, is there evidence of correlation (patterns) for some of the X’s? Which ones? • May begin to indicate the significant drivers for improvement 6. Is there statistical evidence that the Y changes when some X’s change? Which ones? • Type of analysis used (t-Test, F-Test, ANOVA, etc.) • Confidence level 7. What changes in the X’s are needed to achieve the desired state? Implement Six Sigma as a process foranswering these questions.

14. Thank you… Questions? Contact: jpeplinski@stat-design.com