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1 Prepared by Dr. Leonard R. Hepp All Rights Reserved Simply Left Mouse Click to advance through animations & slides.

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1 1 Prepared by Dr. Leonard R. Hepp All Rights Reserved Simply Left Mouse Click to advance through animations & slides.

2 2 Prepared by Dr. Leonard R. Hepp All Rights Reserved A Methodology… For Continuous Improvement Six Sigma is a highly disciplined data-based methodology of problem solving leveraging tools & techniques where appropriate. What is Six Sigma? Six Sigma follows two rigorous approaches: DMAIC Methodology …for improving EXISTING processes DMADOV Methodology …for CREATING a new product or process Lets Look At Each Method DefineMeasure Analyze Improve Control Verify DefineMeasure Analyze Design Optimize

3 3 Prepared by Dr. Leonard R. Hepp All Rights Reserved

4 4 Prepared by Dr. Leonard R. Hepp All Rights Reserved Target Customer Specification X X X X XXXX X X X X X X X X X X X X X X X X X XX X X X XX X XX X X X X X X X X Every Human Activity Has Variability... 1 defects Variation & Defects are the Enemy

5 5 Prepared by Dr. Leonard R. Hepp All Rights Reserved Target Customer Specification A 3 process because 3 standard deviations fit between target and spec Target Customer Specification No Defects Reliability thru Variance Reduction

6 6 Prepared by Dr. Leonard R. Hepp All Rights Reserved And … What About Discrete Data? We have an Invoice Payment Process of 45 Days or less and recent data for last month shows that of 100 invoices, 92 were paid in 45 days or less. 92% On Time Question…How many were paid in 30 days? Between Days? What was my shortest payment cycles? With Discrete Data youd have to go back and re-measure! And you cant model new performance limits. And…

7 7 Prepared by Dr. Leonard R. Hepp All Rights Reserved Average Customer Specification Defects per Million Opportunities 6 _________________________________________________________________________ 3 Average Customer Specification 67,000 Defects per Million Opportunities 3 Considerations Customer Specification Average Variation Six Sigma - A Stretch Goal For many processes BUT Not Good Enough for Some! What is Six Sigma Quality?

8 8 Prepared by Dr. Leonard R. Hepp All Rights Reserved Sigma is a statistical unit of measure that reflects process capabilityDPMO % % 4 6,210 99% 3 66,807 93% 2 308,537 69% % Process Capability Defects Per Million Opportunities Percentage Good Increase Requires Exponential DPMO Reduction What is Six Sigma Quality?

9 9 Prepared by Dr. Leonard R. Hepp All Rights Reserved 1,000,000 10,000 1, Sigma Scale of Measure Restaurant Bills Doctor Prescription Writing Order Write-up Domestic Airline Fatality Rate (0.43 PPM) Airline Baggage Handling Average Industrial Company Best-in-Class Industrial Company Defects per Million Typical Service Industry Processes are 1.5 to 3 IRS Tax Advice (phone in) Sigma Quality Level - Examples

10 10 Prepared by Dr. Leonard R. Hepp All Rights Reserved Measure Analyze Improve Define Six Sigma DMAIC Process Control 6 Characterization Optimization Characterization Optimization

11 11 Prepared by Dr. Leonard R. Hepp All Rights Reserved Measure Analyze Improve Define Control Six Sigma DMAIC Overview Practical Problem: Low Yield Statistical Problem: Mean Off Target Statistical Solution: Isolate Key Variables Practical Solution: Install Automatic Controller Practical Solution Statistical Solution Practical Problem Problem Solving Flow Need Statistical Problem Do Practical Problem Whats The Problem?

12 12 Prepared by Dr. Leonard R. Hepp All Rights Reserved Six Sigma DMAIC The 12+3 Step DMAIC Strategy DMAIC – The Steps Formulating the Practical Problem Changing to a Statistical Problem Developing a Statistical Solution Implementing the Practical Solution Step 0: Build the House of Quality A. Identify Needs B. Team Charter C. Process/SIPOC Step 1: Select the CTQ Characteristic Step 2: Define Performance Standards Step 3: Validate MSA and Data Collection DMAIC Step 4: Establish Process Capability Step 5: Define Performance Objectives Step 6: Identify Variation Sources DMAIC Step 7: Screen Potential Causes Step 8: Discover Variable Relationships Step 9: Establish Operating Tolerances DMAIC Step 10: Validate MSA on the Xs Step 11: Determine Process Capability Step 12: Implement Process Controls DMAIC How good am I today? How good do I need to be? What factors make a difference? How good am I today? How good do I need to be? What factors make a difference? How do my customers look at me? What do I want to improve? Whats the best way to measure? Can I trust the output data? What do I want to improve? Whats the best way to measure? Can I trust the output data? Whats at the root of the problem? How can I predict the output? How tight does the control have to be? Whats at the root of the problem? How can I predict the output? How tight does the control have to be? Can I trust the in-process data? Have I reached my goal? How can I sustain the improvement? Can I trust the in-process data? Have I reached my goal? How can I sustain the improvement?

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14 14 Prepared by Dr. Leonard R. Hepp All Rights Reserved Yes AnalyzeMeasureImproveControlDefine Is the Improvement a New or Redesigned Product/ Service? AnalyzeMeasureDesign Verify Define Is Incremental Improvement Enough? Does a Process Exist? No YesNo DMAIC/DMADOV Transition Points: Optimize When To Use DMADOV

15 15 Prepared by Dr. Leonard R. Hepp All Rights Reserved DMADOV – The 5 Phases and 14 Steps

16 16 Prepared by Dr. Leonard R. Hepp All Rights Reserved 1. Identify customer needs (CTQs) and set performance goals. 8. Generate and validate models - Identify transfer functions. 9. Capability flow-up utilizing scorecards…watch for: Low Z st on scorecard. Lack of transfer function. Unknown process capability. 10. Optimize design Statistical analysis of variance drivers Robustness Error proofing 11. Generate process specs and verify measurement system Xs 12. Statistically confirm predictions. 13. Develop control plan for CTQs (mean and variance). 14. Document the effort and results. The DMADOV Methodology – 14 Steps 2. Perform QFD/CTQ flowdown…Needs to Design Requirements 3. Establish measurement system capability. 4. Develop conceptual designs 5. Reliability Analysis of Designs 6. Build Scorecard of Customer Needs (CTQs) 7. Perform risk assessment QUALITY BY DESIGN! DMADOVFundamentals (Key Concepts) QFD-CTQ Flow-Down FMEA Business Model (Transfer Function) Scorecards

17 17 Prepared by Dr. Leonard R. Hepp All Rights Reserved

18 18 Prepared by Dr. Leonard R. Hepp All Rights Reserved Project Charter Problem Statement & Goal Project Scope Project Milestones (with firm dates for DMA, targets for DOV) High-Level CTQs Project Team (Leader, Champion, Sponsor, Black Belt, Master Black Belt, Team Members, Other Resources) Internal Communication Plan Business Case Project Risk Assessment (FMEA or written assessment) Multi-Generational Plan Cost-Benefit Analysis Identify, segment & prioritize customer User Profiling (how many, how often, from where) Identify & prioritize CTQs Interviews, surveys, or focus groups Measurement Plan Acceptance Criteria As-Is Process Documentation QFD to determine how to satisfy CTQs Benchmarking (within your team, within company, external) To-Be Process Map/High-Level Solution Make vs. Buy Analysis Vendor/Technology Selection Detailed Functional Specification Prototype (use to iteratively refine functional specification) Define Test Cases Final Project Schedule and Project Plan Phased Rollout Schedule End-User Communication and Marketing Plan Technical Specification Interface Design Application Architecture Information Architecture (DB) Server Architecture Code Reuse Strategy System FMEA Security Plan (engage SSO team) Backup & DR Plan Monitoring Solution Peer Technical Reviews Packaged Software Customization Review Help Desk Strategy Purchase Hardware and Software Schedule Stress Test and Security Review Code Application Develop User & Training Documentation System Documentation Application Kit (for Production Support) Help Desk Documentation Unit Test Integration Test Browser Lab Test Peer Code Review Freeze Code Performance & Load Test Security Code Review Data Migration Production Deployment Preliminary Acceptance Testing Launch Monitoring Tools User Training Production Pilot Update System FMEA Transition to Production Support Transition to Help Desk GO LIVE Performance and Usage Monitoring Issues Log Feedback Management Bug Fixes and Further Optimization Final Acceptance Testing and CTQ Measurement Document & Share Best Practices DMADOV Project Progress Overview Define Measure Analyze Optimize Verify Design


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