Peter-Theodor Wilrich – författare

Ein Klassiker mit hoher Verbreitung in der Praxis. Die Zusammenstellung der für die Praxis der mathematisch-statistischen Arbeiten wichtigen Verfahren mit ihren Grundlagen, den benötigten Formeln und den erforderlichen Tabellen oder Nomogrammen ist einmalig. Der Verzicht auf Beweise und Ableitungen gelingt, weil durchgerechnete Beispiele die wesentlichsten Methoden anschaulich erläutern.Anwendungen finden sich in vielen Bereichen der Naturwissenschaften, Ingenieurwissenschaften, Wirtschaftswissenschaften, Medizin und Sozialwissenschaften. Sie dienen beispielsweise dazu, anfallende Daten auszuwerten und Zusammenhänge zwischen Einfluß- und Zielgrößen darzulegen.

In the 1920''s, Walter Shewhart visualized that the marriage of statistical methods and manufacturing processes would produce reliable and consistent quality products. Shewhart (1931) conceived the idea of statistical process control (SPC) and developed the well-known and appropriately named Shewhart control chart. However, from the 1930s to the 1990s, literature on SPC schemes have been "captured" by the Shewhart paradigm of normality, independence and homogeneous variance. When in fact, the problems facing today''s industries are more inconsistent than those faced by Shewhart in the 1930s. As a result of the advances in machine and sensor technology, process data can often be collected on-line. In this situation, the process observations that result from data collection activities will frequently not be serially independent, but autocorrelated. Autocorrelation has a significant impact on a control chart: the process may not exhibit a state of statistical control when in fact, it is in control. As the prevalence of this type of data is expected to increase in industry (Hahn 1989), so does the need to control and monitor it. Equivalently, literature has reflected this trend, and research in the area of SPC with autocorrelated data continues so that effective methods of handling correlated data are available. This type of data regularly occurs in the chemical and process industries, and is pervasive in computer-integrated manufacturing environments, clinical laboratory settings and in the majority of SPC applications across various manufacturing and service industries (Alwan 1991).

Like the preceding volumes, and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under four main headings: sampling inspection, process quality control, data analysis and process capability studies and finally experimental design.

Ein Klassiker mit hoher Verbreitung in der Praxis. Die Zusammenstellung der für die Praxis der mathematisch-statistischen Arbeiten wichtigen Verfahren mit ihren Grundlagen, den benötigten Formeln und den erforderlichen Tabellen oder Nomogrammen ist einmalig. Der Verzicht auf Beweise und Ableitungen gelingt, weil durchgerechnete Beispiele die wesentlichsten Methoden anschaulich erläutern.Anwendungen finden sich in vielen Bereichen der Naturwissenschaften, Ingenieurwissenschaften, Wirtschaftswissenschaften, Medizin und Sozialwissenschaften. Sie dienen beispielsweise dazu, anfallende Daten auszuwerten und Zusammenhänge zwischen Einfluß- und Zielgrößen darzulegen.

Like the first three volumes, published in 1981, 1984 and 1987 and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under three main headings; sampling inspection, process quality control and experimental design. In the first group there are nine papers on acceptance sampling. The second large group of papers deal with control charts and process control and the third group of papers includes contributions on experimental design.

This volume treats the four main categories of Statistical Quality Control: General SQC Methodology, On-line Control including Sampling Inspection and Statistical Process Control, Off-line Control with Data Analysis and Experimental Design, and, fields related to Reliability. Experts with international reputation present their newest contributions.

Like the first three volumes, published in 1981, 1984 and 1987 and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under three main headings; sampling inspection, process quality control and experimental design. In the first group there are nine papers on acceptance sampling. The second large group of papers deal with control charts and process control and the third group of papers includes contributions on experimental design.

Like the preceding volumes, and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under four main headings: sampling inspection, process quality control, data analysis and process capability studies and finally experimental design.

In the 1920's, Walter Shewhart visualized that the marriage of statistical methods and manufacturing processes would produce reliable and consistent quality products. Shewhart (1931) conceived the idea of statistical process control (SPC) and developed the well-known and appropriately named Shewhart control chart. However, from the 1930s to the 1990s, literature on SPC schemes have been "captured" by the Shewhart paradigm of normality, independence and homogeneous variance. When in fact, the problems facing today's industries are more inconsistent than those faced by Shewhart in the 1930s. As a result of the advances in machine and sensor technology, process data can often be collected on-line. In this situation, the process observations that result from data collection activities will frequently not be serially independent, but autocorrelated. Autocorrelation has a significant impact on a control chart: the process may not exhibit a state of statistical control when in fact, it is in control. As the prevalence of this type of data is expected to increase in industry (Hahn 1989), so does the need to control and monitor it. Equivalently, literature has reflected this trend, and research in the area of SPC with autocorrelated data continues so that effective methods of handling correlated data are available. This type of data regularly occurs in the chemical and process industries, and is pervasive in computer-integrated manufacturing environments, clinical laboratory settings and in the majority of SPC applications across various manufacturing and service industries (Alwan 1991).

In Canada, acceptance sampling has been used in legal metrology applications for nearly four decades. One of its principal uses has been in the quality control of utility meters that measure electricity or natural gas supplied to consumers. By law, such meters must be inspected for conformance to specification requirements prior to use and be periodically inspected while huse. With few exceptions, due to the numerous utility companies in the country and their varied practices, the meters exist in the form of isolated lots for inspection purposes. The proportion of nonconforming meters in a lot has traditionally defined lot quality for utility meter sampling inspection purposes. Another principal application of acceptance sampling has been in the quality control of the net contents of packaged products sold in the marketplace. Such products include those sold on the basis of such measures as weight, volume, length, and area. In this particular application, products are also usually inspected on an isolated-lot basis for regulatory purposes. However, lot quality is usually measured on the basis of two criteria for such products: the proportion of nonc- forming packages in the lot and the lot mean quantity. This section reviews Canadian quality control practices in these two areas of application, highlighting some of the deficiencies and issues. Three-class s- pling plans are proposed as a possible solution to some of these deficiencies and issues.

In Canada, acceptance sampling has been used in legal metrology applications for nearly four decades. One of its principal uses has been in the quality control of utility meters that measure electricity or natural gas supplied to consumers. By law, such meters must be inspected for conformance to specification requirements prior to use and be periodically inspected while huse. With few exceptions, due to the numerous utility companies in the country and their varied practices, the meters exist in the form of isolated lots for inspection purposes. The proportion of nonconforming meters in a lot has traditionally defined lot quality for utility meter sampling inspection purposes. Another principal application of acceptance sampling has been in the quality control of the net contents of packaged products sold in the marketplace. Such products include those sold on the basis of such measures as weight, volume, length, and area. In this particular application, products are also usually inspected on an isolated-lot basis for regulatory purposes. However, lot quality is usually measured on the basis of two criteria for such products: the proportion of nonc- forming packages in the lot and the lot mean quantity. This section reviews Canadian quality control practices in these two areas of application, highlighting some of the deficiencies and issues. Three-class s- pling plans are proposed as a possible solution to some of these deficiencies and issues.

The twenty-three papers in this volume are carefully selected, reviewed and revised for this volume, and are divided into two parts: Part 1: "On-line Control" with subchapters 1.1 "Control Charts" and 1.2 "Surveillance Sampling and Sampling Plans" and Part 2:"Off-line Control".

The 10th International Workshop on Intelligent Statistical Quality Control took place in Seattle, USA, Aug 18-20, 2010. It was hosted by Professor C. M. Mastrangelo, Department of Industrial and Systems Engineering, University of Washington, Seattle. The workshop was jointly organized by Professors H. J. Lenz, C. M. Mastrangelo, W. Schmid and P.T. Wilrich. The twenty-seven papers in this volume were carefully selected by the scientific program committee, reviewed by its members, revised by the authors and, finally, adapted for this volume by the editors.The book is divided into two parts: Part I "On-line Control" covers fields like control charting, monitoring and surveillance as well as acceptance sampling. Part II "Off-line Control" is devoted to experimental design, process capability analysis and data quality.The purpose of the book is on the one hand to provide insights into important new developments in the area of statistical quality control – especially surveillance and monitoring – and on the other hand to critically discuss methods used in on-line and off-line statistical quality control.