Errors in inspection and grading distributional aspects ofscreening and merarchal screening

Continuing the studies of Johnson et al (1980) and Johnson and Kotz (1981), further distributions arising from models of errors in inspection and grading of samples from finite, possibly stratified lots are obtained. Screening, and hierarchal screening forms of inspection are also considered, and the effects of errors on the advantages of these techniques assessed.     

Moment estimators of defect pattern frequencies,allowing for inspection error

Unbiased estimators of the number of individuals in a lot possessing various patterns of types of defects are constructed. Explicit formulas are given for cases of two and three types of defect. Application of the formulas requires knowledge of the probabilities of various kinds of errors in the inspection process.     

The Modified Pulak and Al-Sultan's Model for Determining the Optimum Process Parameters

Economic selection of process parameters has been an important topic in modern statistical process control. The optimum process parameters setting have a major effect on the expected profit/cost per item. There are some concerns on the problem of setting process parameters. Boucher and Jafari (1991 Boucher , T. O. , Jafari , M. A. ( 1991 ). The optimum target value for single filling operations with quality sampling plans . J. Qual. Technol. 23 : 44 – 47 . [CSA] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) first considered the attribute single sampling plan applied in the selection of process target. Pulak and Al-Sultan (1996 Pulak , M. F. S. , Al-Sultan , K. S. ( 1996 ). The optimum targeting for a single filling operation with rectifying inspection . Omega 24 : 727 – 733 . [CSA] [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) extended Boucher and Jafari's model and presented the rectifying inspection plan for determining the optimum process mean. In this article, we further propose a modified Pulak and Al-Sultan model for determining the optimum process mean and standard deviation under the rectifying inspection plan with the average outgoing quality limit (AOQL) protection. Taguchi's (1986 Taguchi , G. ( 1986 ). Introduction to Quality Engineering . Asian Productivity Organization . [Google Scholar]) symmetric quadratic quality loss function is adopted for evaluating the product quality. By solving the modified model, we can obtain the optimum process parameters with the maximum expected profit per item and the specified quality level can be reached.     

Grey Predictive Process Control Charts

The present article intends to develop some imputation methods to reduce the impact of non response at both the occasions in two-occasion successive (rotation) sampling. Utilizing the auxiliary information, which is only available at the current occasion, estimators have been proposed for estimating the population mean at the current occasion. Estimators for the current occasion are also derived as a particular case when there is non response either on the first occasion or second occasion. Behaviors of the proposed estimators are studied and their respective optimum replacement policies are also discussed. To study the effectiveness of the suggested imputation methods, performances of the proposed estimators are compared in two different situations, with and without non response. The results obtained are demonstrated with the help of empirical studies.     

Concurrent Design for Process Quality,Statistical Tolerance,and SPC

The rapid response to the requirements of customers and markets promotes the concurrent engineering (CE) technique in product and process design. The decision making for process quality target, SPC method, sampling plan, and control chart parameter design can be done at the stage of process quality plan based on historical data and process knowledge database. Therefore, it is a reasonable trend to introduce the concepts and achievements on process quality evaluation and process capability analysis, CE, and SPC techniques into process plan and tolerance design. A new systematic method for concurrent design of process quality, statistical tolerance (ST), and control chart is presented based on a NSFC research program. A set of standardized process quality indices (PQIs) for variables is introduced for meeting the measurement and evaluation to process yield, process centering, and quality loss. This index system that has relatively strong compatibility and adaptability is based on raisonne grading by using the series of preferred numbers and arithmetical progression. The expected process quality based on this system can be assured by a standardized interface between PQIs and SPC, that is, quality-oriented statistical tolerance zone. A quality-oriented ST and SPC approach that quantitatively specifies what a desired process is and how to assure it will realize the optimal control for a process toward a predetermined quality target.     

Estimation of a process capability index for inverse gaussian distribution

In this article, interval estimates of Clements' process capability index are studied through bootstrapping when the underlying distribution is Inverse Gaussian. The standard bootstrap, the percentile bootstrap, and the bias-corrected percentile bootstrap confidence intervals are compared.     

A generalized negative binomial and applications

A generalized negative binomial distribution is derived from the Markov Bernoulli sequence of successes and failures. We study the properties and applications of this distribution. The properties are illustrated by two examples of discrete time queueing systems. The distribution is then fitted to two data sets, the eruption record of Mt. Sangay, and a record of computer disk failure accesses. In the first case there is a strong serial dependence in the data and the generalized negative binomial provides a good fit, while in the second case, although there is a significant serial dependence, it is insufficient to justify the additional parameter of the distribution. We conclude by demonstrating the usefulness of the distribution in the field of statistical quality control.     

Culture and identity: two different notions

A clear, precise conceptual distinction between ‘culture’ and ‘identity’ is an essential precondition for analyzing social processes. The anthropological concept of ‘identity’ has been built up over time and enriched by studies on interethnic relationships, ethnic borders and ethnicity. The objective of this essay is to add to an already well-defined concept of culture by incorporating decisive contributions from theories on the nation. Culture and nation are not only highly complex theoretical notions with a long history; they both deal with heterogeneous and conflictive entities. The essay asserts that culture and identity allude to analytically different aspects of social processes. No relationship between the two can be presupposed or generalized to fit all cases. It is necessary to analyze cultural and identitary aspects separately.     

Identifying Dispersion Effects in Robust Design Experiments—Issues and Improvements

The two experimental methods most commonly used for reducing the effect of noise factors on a response of interest Y aim either to estimate a model of the variability (V(Y), or an associated function), that is transmitted by the noise factors, or to estimate a model of the ratio between the response (Y) and all the control and noise factors involved therein. Both methods aim to determine which control factor conditions minimise the noise factors' effect on the response of interest, and a series of analytical guidelines are established to reach this end. Product array designs allow robustness problems to be solved in both ways, but require a large number of experiments. Thus, practitioners tend to choose more economical designs that only allow them to model the surface response for Y. The general assumption is that both methods would lead to similar conclusions. In this article we present a case that utilises a design based on a product design and for which the conclusions yielded by the two analytical methods are quite different. This example casts doubt on the guidelines that experimental practice follows when using either of the two methods. Based on this example, we show the causes behind these discrepancies and we propose a number of guidelines to help researchers in the design and interpretation of robustness problems when using either of the two methods.