1、Recent Advances and Future Directions for Quality EngineeringGeoff ViningVirginia TechUSAOutline Recent AdvancesExtending Standard Methodologies to Hard, Practical Problems“Statistical Thinking”Applications in Areas Other than ManufacturingAdvances in Software (However, Need Caution)Truly Global Rea
2、chOutline Future DirectionsIntegrating Quality Engineering Concepts over Complex SystemsLarge Data SetsDealing with Image DataGreater Emphasis on ReliabilityInnovationStrong Need to Train Practitioners Properly (Dangers of Current Software!) Statistical EngineeringBackground Past Department Head of
3、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 DirectorsBackground - Journals Quality EngineeringCo-Published by ASQ and Taylor & Fr
4、ancisPractitioner Focus Journal of Quality TechnologyPublished by ASQFocus on High Level Practitioner/AcademicBackground - Journals TechnometricsCo-Published by ASQ and ASASimilar Focus as JQT, Tends to be More Mathematical Quality and Reliability Engineering InternationalPublished by WileyMore Euro
5、peanPublishes “Best” Papers from ENBIS Extending Standard Methodologies to Hard Problems Experiments with Hard-to-Change and Easy-to-Change FactorsVery Common Practical ProblemExtensive Literature for Agricultural ApplicationsJones and Nachtsheim Profile MonitoringCharacteristic of Interest Is a Pro
6、file (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 variationreducing variation.“Statistical Thinking” Roger Hoerl and Ro
7、n 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 Are
8、as Other than Manufacturing See Quality Engineering for Examples Service FunctionsAccounts PayableProduct DeliveryCostumer 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
9、Analyses to Support Quality EngineeringHard-to-Change versus Easy-to-Change FactorsIntegrated Variance Optimal DesignsSpace-Filling Designs (Computer Experiments)Gaussian Stochastic Processes (Comp. Exp.)Advances in Software Exercise Caution with Software “Claims”! You Do Not Need to Think about Dat
10、a CollectionGive Us the Factors and the LevelsWe Give You the Plan You Do Not Need to Think about The AnalysisWe Plan the Data CollectionWe Know the Best Analysis Consequence: Potential for Major Disasters!Advances in Software Software Is an Extremely Important ToolRequires Intelligent Use“Fisher in
11、 a Box”/”George Box in a Box” Does Not Exist! Data Collection Requires Intelligent CollaborationAsk the Right QuestionsThink Carefully about the ScienceTranslate Everything Properly into the AnalysisGlobal Reach Foundations to Quality Engineering are North American and Japanese ManufacturingNorth Am
12、erica: Statistical Theory and MethodsJapan: Quality Management “Soft Tools” TeamworkDeming, Box, Taguchi, “The Gurus”Global Reach Important InfluencesMovement of Manufacturing Away from North America Asian Tigers China India Latin America (Brazil and Mexico)Recognition in Europe of Need for Quality
13、Engineering: ENBISImpact 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 - GlobalFuture Directions Current Directions Will Continue to Grow New Directions“Resear
14、ch”“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 P
15、rocesses Multistage Often, Multi-locationIntegrating 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 Method
16、ologyFormal/Informal Bayesian MethodsBelief NetworksLarge 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 ValidNot Informative Often Most Interesting Phenomena: Outliers!
17、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 ItemGreater Emphasis on Reliability R
18、eliability: 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 DataProcess Control with Reliability DataInnovation Not Long Ago, Building Better Quality Was Si
19、gnificant Innovation High Quality Now Viewed as Expectation New Issue: Next Way to “Delight” Customers“Improved” Current ProductsNew Products Customers Never Imagined Issue: How Can Quality Engineering Facilitate Innovation See January 2012 Issue of Quality EngineeringProper Training of Practitioner
20、s Six Sigma Brought Quality Engineering into the Hands of Subject Matter ExpertsTypical Training Barely Scratched Surface“3 Month Wonders”Often, Do Not Know When to Call an Expert Software Developments Proper Follow-Up Training EssentialStatistical Engineering How to best use known statistical princ
21、iples 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)
