Recent Advances and Future Directions for Quality Engineering

Geoff ViningVirginia Tech

USA

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

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

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

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

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

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

“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.

“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!

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!

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.)

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!

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

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”

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

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

Future Directions

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

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

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

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!

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

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

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

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

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)