No description available.

No description available.

Old concepts, views, and systems must be updated, corrected, or abandoned so as to make the great progress of all human beings. As for an example, Peters and Waterman (1982, page42; listed in Paper 1) explained: “An old but excellent example is the Ptolemaic view of the universe (which held until the sixteenth century) that the earth was at the centre of the universe, and the moon, sun, planets, and stars were embedded in concentric spheres around it. Elaborate mathematical formulas and models were developed that would accurately predict astronomical events based on the Ptolemaic paradigm. Not until Copernicus and Kepler found that the formula worked more easily when the sun replaced the earth as the centre of it all did an instance of paradigm shift begin.” It is quite apparent that, in the field of business management, many old concepts, views, and systems have neither led to a right direction for modern business management nor resolved many management problems; therefore, all the three papers of this portfolio try to remedy those shortcomings and insufficiencies of the research problems so as to make potentially valuable contributions to both the academic and business world. The three papers are independent and interrelated. Each of them is briefly described below. / Thesis (DBA(DoctorateofBusinessAdministration))--University of South Australia, 2005.

Thesis (M.S.)--Ohio University, August, 1981. / Title from PDF t.p.

Thesis (M. Soc. Sc.)--University of Hong Kong, 1991.

Thesis (Ph. D.)--Princeton University, 1995. / Includes bibliographical references (leaves 334-366).

Thesis (M.Soc.Sc.)--University of Hong Kong, 1991. / Also available in print.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Vita. Includes bibliographical references (leaves 129-133). Also available in electronic version.

Most quality control and quality improvement procedures involve making assumptions about the distributional form of data it uses; usually that the data is normally distributed. It is common place to find processes that generate data which is non-normally distributed, e.g. Weibull, logistic or mixture data is increasingly encountered. Any method that seeks to avoid the use of transformation for non-normal data requires techniques for identification of the appropriate distributions. In cases where the appropriate distributions are known it is often intractable to implement. This research is concerned with statistical process control (SPC), where SPC can be apply for variable and attribute data. The objective of SPC is to control a process in an ideal situation with respect to a particular product specification. One of the several measurement tools of SPC is control chart. This research is mainly concerned with control chart which monitors process and quality improvement. We believe, it is a useful process monitoring technique when a source of variability is present. Here, control charts provides a signal that the process must be investigated. In general, Shewhart control charts assume that the data follows normal distribution. Hence, most of SPC techniques have been derived and constructed using the concept of quality which depends on normal distribution. In reality, often the set of data such as, chemical process data and lifetimes data, etc. are not normal. So when a control chart is constructed for x or R, assuming that the data is normal, if in reality, the data is nonnormal, then it will provide an inaccurate results. Schilling and Nelson has (1976) investigated under the central limit theory, the effect of non-normality on charts and concluded that the non-normality is usually not a problem for subgroup sizes of four or more. However, for smaller subgroup sizes, and especially for individual measurements, non-normality can be serious problem. The literature review indicates that there are real problems in dealing with statistical process control for non-normal distributions and mixture distributions. This thesis provides a quantile approach to deal with non-normal distributions, in order to construct median rankit control chart. Here, the quantile approach will also be used to calculate process capability index, average run length (ARL), multivariate control chart and control chart for mixture distribution for non-normal situations. This methodology can be easily adopted by the practitioner of statistical process control.

Thesis (M.B.A.)--Boston University / Quality control as a concept and working tool in modern industry takes many shapes and forms. It is one of the more recent management tools to be incorporated into the complex manufacturing organizations in present day industry. There are almost as many different working concepts and definitions of quality control as there are industrial organizations. When attempting to define quality control, its purposes and objectives, we can find general agreement on its overall scope. W. A. MacCrehan defines quality as "a planned, continuing effort to maintain product quality in manufacturing". 1 A. V. Feigenbaum of General Electric Co., a noted authority in the field of quality control, goes a step further and defines it, "as an effective system for coordinating the quality maintainance and quality improvement efforts of the various groups in an organization so as to enable production at the most economical levels which allow for full customer satisfaction". These text definitions are generally carried over and incorporated in working company philosophies. The General Tire and Rubber Co. carries the following definition in its corporate manual for quality control: "Quality control is the act of assuring that outgoing product levels meet the established quality levels and of coordinating the activities of all departments in such a manner that established quality levels are maintained at the lowest possible cost" .3 This general concept of quality control, however, takes many varying manifestations when translated into specific working tools for use in the day to day activities of each industrial concern.