Necip Doganaksoy

THE EVOLUTION OF SIX SIGMA

[This abstract is based on the authors abstract.] Modes of operation and profitability have been significantly impacted at companies that have used the Six Sigma approach to quality improvement. In some form or another, Six Sigma represents the w..

Algorithmic statistical process control: concepts and an application

The goal of algorithmic statistical process control is to reduce predictable quality variations using feedback and feedforward techniques and then monitor the complete system to detect and remove unexpected root causes of variation. This methodology seeks to exploit the strengths of both automatic control and statistical process control (SPC), two fields that have developed in relative isolation from one another. Recent experience with the control and monitoring of intrinsic viscosity from a particular General Electric polymerization process has led to a better understanding of how SPC and feedback control can be united into a single system. Building on past work by MacGregor, Box, Astrom, and others, the article covers the application from statistical identification and modeling to implementing feedback control and final SPC monitoring. Operational and technical issues that arose are examined, and a general approach is outlined.

Handbook of reliability engineering

Partial table of contents: PROBABILITY. Basic Concepts, Measures, and Definition. Units. Unrenewable Equipment. Renewable Systems. Repairable Dual Systems. Systems with Network Structures. Evaluation of System Effectiveness. Systems with Time Redundancy. Queuing Systems with Unreliable Service Channels. Mechanical Equipment. STATISTICS. Estimation of Equipment Reliability from Tests. Acceptance-Rejection Tests. Accelerated Tests. Reliability Growth. Monte Carlo Simulations. OPTIMIZATION. Optimal Redundancy. Optimal Supply of Spare Parts. Optimal Control of Inventories of Spare Parts. Optimal Maintenance. Appendices. References. Index.

Identification of out of control quality characteristics in a multivariate manufacturing environment

There are many instances in which the quality of a product or constancy of a process is determined by the joint levels of several attributes or properties. During the conduct of such a process or the production of such a product, one wishes to detect as quickly as possible any departure from a satisfactory state, while at the same time identifying which attributes are responsible for the deviation. In most cases of practical interest, however, there exist correlations among the several properties of interest; this makes it advisable to monitor certain aggregate characteristics of the process, rather than observing its various components separately. When the mean vector of the quality attributes is the major concern, this aggregate monitoring function is most commonly implemented via a T 2 chart. The dependencies among attributes, however, complicate the determination of which are responsible when a deviation occurs. This paper presents an approach to help identify aberrant variables when Shewhart type mul...

Joint Optimization of Mean and Standard Deviation Using Response Surface Methods

Taguchis robust parameter design calls for simultaneous optimization of the mean and standard deviation responses. The dual response optimization procedures have been adapted to achieve this goal by taking into account both the mean and standard deviation response functions. The popular formulations of the dual response problem typically impose a restriction on the value of the secondary response (i.e., keeping the standard deviation below a specified value) and optimize the primary response function (i.e., maximize or minimize the mean). Restrictions on the secondary response, however, may rule out better conditions, since an acceptable value for the secondary response is usually unknown. In fact, process conditons that result in a smaller standard deviation are often preferable. A more flexible formulation of the problem can be achieved by considering the secondary response as another primary response. The proposed method will generate more alternative solutions, called Pareto optimal solutions. This gives more flexibility to the decision-maker in exploring alternative solutions. It is also insightful to examine graphically how the controllable variables simultaneously impact the mean and standard deviation. The procedure is illustrated with three examples, using both the NIMBUS software for nonlinear multiobjective programming and the Solver in the Excel spreadsheet.

Comparisons of Approximate Confidence Intervals for Distributions Used in Life-Data Analysis

This article evaluates the accuracy of approximate confidence intervals for parameters and quantiles of the smallest extreme value and normal distributions. The findings also apply to the Weibull and the lognormal distributions. The interval estimates are based on (a) the asymptotic normality of the maximum likelihood estimator, (b) the asymptotic x 2 distribution of the likelihood ratio (LR) statistic, (c) a mean and variance correction to the signed square roots of the LR statistic, and (d) the Bartlett correction to the LR statistic. The extensive Monte Carlo results about true error probabilities and average lengths under various degrees of censoring show advantages of the LR-based intervals. For complete or moderately censored samples, the mean and variance correction to the LR statistic gives nearly exact and symmetric error probabilities. In small samples with heavy censoring, the Bartlett correction tends to give conservative error probabilities, whereas the uncorrected LR interval is often antico...

Quality and Reliability of Technical Systems: Theory, Practice, Management

This text presents state-of-the-art methods and procedures necessary for cost and time effective quality and reliability assurance during the design and production of equipment and systems. It is based on more than 20 years experience gained by the author in research and industry. The book covers theory, practice and management aspects, and addresses the needs of scientists, system-oriented engineers, and project and quality assurance managers. Procedures and methods are presented in such a way that they can be tailored to cover the needs of a specific task. Because of its structure, the text can be used for training programmes in industry and universities, or for self-study.

The Future of Industrial Statistics: A Panel Discussion

Technometrics was founded in 1959 as a forum for publishing statistical methods and applications in engineering and the physical and chemical sciences. The expanding role of statistics in industry was a major stimulus, and, throughout the years many articles in the journal have been motivated by industrial problems. In this panel discussion we look ahead to the future of industrial statistics. Ten experts, encompassing a range of backgrounds, experience, and expertise, answered my request to share with us their thoughts on what lies ahead in industrial statistics. Short biographical sketches of the panelists are provided at the end of the discussion. The panelists wrote independent essays, which I have combined into an integrated discussion. Most of the essays were written as responses to a list of 10 questions that I provided to help the participants direct their thoughts. I have organized the discussion in that same fashion, stating the questions and then providing the related responses. Several discussants added remarks on the role of statistics journals, particularly of Technometrics, and I have added that as a final question. We see this article, not as the end of the story, but rather as the takeoff point for further discussion. To that end, we are initiating an open discussion forum; to participate, go to http://www.asq.org/pub/techno/ and click on Networking and Events. The American Society for Quality will host the forum and Bert Gunter has graciously agreed to serve as moderator.

Interval estimation from censored and masked system-failure data

The author addresses confidence interval (CI) estimation in a competing risk (or multiple failure mode) framework where sample data are singly time-censored on the right and partially masked. A three-component series system with exponentially-distributed component-failure times is considered in order to represent cases involving full as well as partial masking. The approximate CIs considered are based on: asymptotic-normal theory for maximum likelihood estimators; cube-root transformation of the exponential distribution rate parameter; and inverted likelihood ratio tests. The small-sample coverage properties of these approximate CIs are estimated via computer simulation. These results also apply to models where component-failure times are Weibull distributed with known shape parameters. >

A Useful Property of Best Linear Unbiased Predictors with Applications to Life-Testing

Abstract This article shows, for the Gauss–Markov model, that the best linear unbiased estimators of the model parameters remain unchanged if the predicted values of the dependent variable (based on best linear unbiased predictors) are used as observed values in estimating the parameters. This result not only provides a useful insight into the interpretation of best linear unbiased predictors, but it also simplifies calculation of predictions in some cases. We also use this result to construct large-sample approximate predictors for scale and location-scale parameter distributions. Examples from life-testing are given.