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In this thesis an attempt is made to present a framework for designing and improving pharmaceutical manufacturing processes based on a methodology that integrates quantitative risk management. Conducting an in-depth case study at a pharmaceutical manufacturer allows for the development of new theory regarding potential trade-offs between process design objectives. Industry practitioners and previous research studies have focused on flexibility, throughput time, efficiency, automation and quality as main objectives during process improvement efforts. Among those five variables, product quality is the main operational risk and mostly assessed qualitatively. This thesis contributes to the existing literature by introducing a more quantitative approach to risk assessment in the pharmaceutical industry. The proposed model relies on a Monte Carlo simulation to determine the quality loss distribution and assess the distributional tail through the principles of Extreme Value Theory. When combined with the other process objectives, the quantification of quality risk leads to novel insights into the trade-offs faced by pharmaceutical manufacturers during process design decisions. In particular, the findings are synthesized by describing process designs through their performance on these identified objectives. Furthermore, products are grouped by distinctive characteristics and then matched to their ideal process designs; an ideal product-process combination is one which exploits reinforcing relationships amongst process objectives and avoids trade-offs between them. The general framework resulting out of these matches places quality risk at the center of attention for future process design and improvement initiatives in the pharmaceutical industry.
It appears that we live in an age of disasters: the mighty Missis sippi and Missouri flood millions of acres, earthquakes hit Tokyo and California, airplanes crash due to mechanical failure and the seemingly ever increasing wind speeds make the storms more and more frightening. While all these may seem to be unexpected phenomena to the man on the street, they are actually happening according to well defined rules of science known as extreme value theory. We know that records must be broken in the future, so if a flood design is based on the worst case of the past then we are not really prepared against floods. Materials will fail due to fatigue, so if the body of an aircraft looks fine to the naked eye, it might still suddenly fail if the aircraft has been in operation over an extended period of time. Our theory has by now penetrated the so cial sciences, the medical profession, economics and even astronomy. We believe that our field has come of age. In or~er to fully utilize the great progress in the theory of extremes and its ever increasing acceptance in practice, an international conference was organized in which equal weight was given to theory and practice. This book is Volume I of the Proceedings of this conference. In selecting the papers for Volume lour guide was to have authoritative works with a large variety of coverage of both theory and practice.
This book is a comprehensive guide to extreme value theory in engineering. Written for the end user with intermediate and advanced statistical knowledge, it covers classical methods as well as recent advances. A collection of 150 examples illustrates the theoretical results and takes the reader from simple applications through complex cases of dependence.
Volume is indexed by Thomson Reuters CPCI-S (WoS). The objective of this collection of peer-reviewed papers is to provide a forum where researchers, educators, engineers, and government officials involved in the general area of Manufacturing Systems Engineering can disseminate the latest research results and views concerning the future research directions of the field.
This important book provides an up-to-date comprehensive and down-to-earth survey of the theory and practice of extreme value distributions OCo one of the most prominent success stories of modern applied probability and statistics. Originated by E J Gumbel in the early forties as a tool for predicting floods, extreme value distributions evolved during the last 50 years into a coherent theory with applications in practically all fields of human endeavor where maximal or minimal values (the so-called extremes) are of relevance. The book is of usefulness both for a beginner with a limited probabilistic background and to expert in the field. Sample Chapter(s). Chapter 1.1: Historical Survey (139 KB). Chapter 1.2: The Three Types of Extreme Value Distributions (146 KB). Chapter 1.3: Limiting Distributions and Domain of Attraction (210 KB). Chapter 1.4: Distribution Function and Moments of Type 1 Distribution (160 KB). Chapter 1.5: Order Statistics, Record Values and Characterizations (175 KB). Contents: Univariate Extreme Value Distributions; Generalized Extreme Value Distributions; Multivariate Extreme Value Distributions. Readership: Applied probabilists, applied statisticians, environmental scientists, climatologists, industrial engineers and management experts."
Abstracts of dissertations available on microfilm or as xerographic reproductions.
This Handbook addresses the methodology of social science research and the appropriate use of different methods.