Mary A. Meyer

Eliciting and analyzing expert judgment: A practical guide

Eliciting and analyzing expert judgment , Eliciting and analyzing expert judgment , کتابخانه دیجیتال جندی شاپور اهوازPreface to ASA-SIAM Edition Preface List of Figures List of Tables List of Examples Part I. Introduction to Expert Judgment. 1. Introduction 2. Common Questions and Pitfalls Concerning Expert Judgment 3. Background on Human Problem Solving and Bias Part II. Elicitation Procedures. 4. Selecting the Question Areas and Questions 5. Refining the Questions 6. Selecting and Motivating the Experts 7. Selecting the Components of Elicitation 8. Designing and Tailoring the Elicitation 9. Practicing the Elicitation and Training the Project Personnel 10. Conducting the Elicitation Part III. Analysis Procedures. 11. Introducing the Techniques for Analysis of Expert Judgment Data 12. Initial Look at the Data_The First Analyses 13. Understanding the Data Base Structure 14. Correlation and Bias Detection 15. Model Formation 16. Combining Responses_Aggregation 17. Characterizing Uncertainties 18. Making Inferences Appendix A. SAATY Appendix B. MCBETA Appendix C. EMPIRICAL Appendix D. BOOT Glossary of Expert Judgment Terms References Index.

Ch. 11. Predict: A new approach to product development and lifetime assessment using information integration technology

Publisher Summary Performance and reliability evaluation with diverse information combination and tracking (PREDICT) is a successful example of information integration technology that has been applied in two parallel applications: automotive system development and stockpile physics packages in nuclear weapons. This chapter outlines the applications, implementation steps, expert judgment, statistical tools, and decision making that make up the PREDICT methodology. PREDICT has demonstrated its effectiveness for expertise capture, reliability, and performance estimation in the nuclear weapons program and for concept system development in the automotive industry. In the post–Cold War era, the basic philosophy of information integration has been positively affecting the certification process of the nuclear systems. This same philosophy has been providing the formal structure for taking advantage of a companys greatest asset—the knowledge and expertise of its engineers and designers.

Characterizing reliability in a product/process design-assurance program

Just as estimates of cost and program timing are critical factors to be known and monitored during a new product development program, so too is the reliability perspective. This paper describes an approach to reliability modeling that encompasses the impact of both product and manufacturing process design on the distribution (characterizing the uncertainty) of reliability over time. It further describes the elicitation of expert judgment which is used to quantify the initial reliability estimate, including uncertainty. Finally, it describes a Bayesian updating approach which is applicable throughout the development program, and which accommodates a wide variety of possible new information. Although the model is rigorous in its execution; a user-friendly approximation is also described which may be useful to the product development team for purposes of test and validation planning.

PREDICT: a case study, using fuzzy logic

Los Alamos National Laboratory, Design For Reliability, Inc., and others, have worked together to develop PREDICT, a new methodology to characterize the reliability of a new product during its development program. Rather than conducting testing after hardware has been built, and developing statistical confidence bands around the results, this updating approach starts with an early reliability estimate characterized by large uncertainty, and then proceeds to reduce the uncertainty by folding in fresh information in a Bayesian framework. A considerable amount of knowledge is available at the beginning of a program in the form of expert judgment that helps to provide the initial estimate. This estimate is then continually updated as substantial and varied information becomes available during the course of the development program. This paper presents a case study of the application of PREDICT, including an example of the use of fuzzy logic, with the objective of further describing the methodology.