Recent Advances and Future Directions for Quality Engineering

1. Recent Advances and Future Directions for Quality Engineering Geoff Vining Virginia Tech USA

2. Outline • Recent Advances • Extending Standard Methodologies to Hard, Practical Problems • “Statistical Thinking” • Applications in Areas Other than Manufacturing • Advances in Software (However, Need Caution) • Truly Global Reach

3. Outline • Future Directions • Integrating Quality Engineering Concepts over Complex Systems • Large Data Sets • Dealing with Image Data • Greater Emphasis on Reliability • Innovation • Strong Need to Train Practitioners Properly (Dangers of Current Software!) • Statistical Engineering

4. Background • Past Department Head of Statistics at Virginia Tech • Past Editor of the Journal of Quality Technology (1998-2000) • Past Editor of Quality Engineering (2009-2010) • Past Chair of the ASQ Publications Management Board • Member of the ASQ Board of Directors

5. Background - Journals • Quality Engineering • Co-Published by ASQ and Taylor & Francis • Practitioner Focus • Journal of Quality Technology • Published by ASQ • Focus on High Level Practitioner/Academic

6. Background - Journals • Technometrics • Co-Published by ASQ and ASA • Similar Focus as JQT, Tends to be More Mathematical • Quality and Reliability Engineering International • Published by Wiley • More European • Publishes “Best” Papers from ENBIS

7. Extending Standard Methodologies to Hard Problems • Experiments with Hard-to-Change and Easy-to-Change Factors • Very Common Practical Problem • Extensive Literature for Agricultural Applications • Jones and Nachtsheim • Profile Monitoring • Characteristic of Interest Is a Profile (Function) • Woodall • Computer Experiments

8. “Statistical Thinking” • Originated in the mid 90s • Basic Idea: • All work occurs in a system of interconnected processes. • Variation exists in all processes. • The keys to success are: • understanding variation • reducing variation.

9. “Statistical Thinking” • Roger Hoerl and Ron Snee (2012) Statistical Thinking: Improving Business Performance (Wiley and SAS Business Series) • Point: Biggest contribution that quality practitioners can make: get senior managers to understand variation and its sources • Data Analysis in North America is easy to send off-shore!

10. Applications in Areas Other than Manufacturing • See Quality Engineering for Examples • Service Functions • Accounts Payable • Product Delivery • Costumer Relations • Risk Management • Security • Healthcare • Several People in Israel Have Done Very Nice Work!

11. Advances in Software • Current Software Can Do Much More Sophisticated Statistical Analyses to Support Quality Engineering • Hard-to-Change versus Easy-to-Change Factors • Integrated Variance Optimal Designs • Space-Filling Designs (Computer Experiments) • Gaussian Stochastic Processes (Comp. Exp.)

12. Advances in Software • Exercise Caution with Software “Claims”! • You Do Not Need to Think about Data Collection • Give Us the Factors and the Levels • We Give You the Plan • You Do Not Need to Think about The Analysis • We Plan the Data Collection • We Know the Best Analysis • Consequence: Potential for Major Disasters!

13. Advances in Software • Software Is an Extremely Important Tool • Requires Intelligent Use • “Fisher in a Box”/”George Box in a Box” Does Not Exist! • Data Collection Requires Intelligent Collaboration • Ask the Right Questions • Think Carefully about the Science • Translate Everything Properly into the Analysis

14. Global Reach • Foundations to Quality Engineering are North American and Japanese Manufacturing • North America: Statistical Theory and Methods • Japan: • Quality Management • “Soft Tools” • Teamwork • Deming, Box, Taguchi, “The Gurus”

15. Global Reach • Important Influences • Movement of Manufacturing Away from North America • Asian Tigers • China • India • Latin America (Brazil and Mexico) • Recognition in Europe of Need for Quality Engineering: ENBIS

16. Impact of Global Reach • Editorial Boards Are Truly Global • Authors Publishing in the Quality Engineering Journals Are Truly Global • Proliferation of Outstanding Quality Engineering Conferences • ASQ - Global

17. Future Directions • Current Directions Will Continue to Grow • New Directions • “Research” • “Practice” • Be Aware of the Divide!

18. Integrating Quality Engineering Concepts Across Complex Processes • Complex Processes as Opposed to “Data Mining” (Next Topic!) • Developmental Testing of Weapon Systems • Multiyear • Multistage • Different Objectives at Each Stage • Competing Interests! • Complex Manufacturing Processes • Multistage • Often, Multi-location

19. Integrating Quality Engineering Concepts Across Complex Processes • Good Quality Engineering Practices May or May Not Being Used at Substages • In Some Cases, Just Applying Current Quality Engineering Methods to the System Work • In Many More Cases, Need New Methodology • Formal/Informal Bayesian Methods • Belief Networks

20. Large Data Sets • Data Mining Is Becoming Extremely Important • Great Deal of Good Work in Israel! • Emergence of Massive Data Warehouses (Planet Scale!) • Standard Statistical Approaches • Not Valid • Not Informative • Often Most Interesting Phenomena: Outliers!

21. Image Data • Ability to Monitor Processes via Image Data • Basic Analysis of Image Data Becoming “Mature” • In Some Cases, May Be Able to Adapt Standard Statistical Process Control Techniques • In Many Cases, Must Create New Monitoring Procedures Based on Image of Every Item

22. Greater Emphasis on Reliability • Reliability: Quality Over Time • Customers Beginning to Demand Highly Reliable Products and Processes • Simple Accelerated Life Tests Not Sufficient • Strong Need: • Experimental Design and Analysis for Reliabilty Data • Process Control with Reliability Data

23. Innovation • Not Long Ago, Building Better Quality Was Significant Innovation • High Quality Now Viewed as Expectation • New Issue: Next Way to “Delight” Customers • “Improved” Current Products • New Products Customers Never Imagined • Issue: How Can Quality Engineering Facilitate Innovation • See January 2012 Issue of Quality Engineering

24. Proper Training of Practitioners • Six Sigma Brought Quality Engineering into the Hands of Subject Matter Experts • Typical Training Barely Scratched Surface • “3 Month Wonders” • Often, Do Not Know When to Call an Expert • Software Developments • Proper Follow-Up Training Essential

25. Statistical Engineering • How to best use known statistical principles and tools to solve high impact problems for the benefit of humanity. • tactical integration of statistical thinking with the application of statistical methods and tools (at the operational level • drive proper application of statistical methods based on solid understanding of statistical thinking principles. • typically involves the appropriate selection and use of multiple statistical tools, integrated into a comprehensive approach to solving complex problems. • Focus on Large, Unstructured, Complex Problems • Most Recent Issue of Quality Engineering (April 2012)