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Frontiers in Statistical Quality Control 6

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. Hans-Joachim Lenz

    1. Institut für Statistik und Ökonometrie, Freie Universität Berlin, Berlin, Germany

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  2. Peter-Theodor Wilrich

    1. Institut für Statistik und Ökonometrie, Freie Universität Berlin, Berlin, Germany

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Frontiers in Statistical Quality Control (FSQC, volume 6)

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Table of contents (22 papers)

  1. Front Matter

    Pages I-XII
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  2. Sampling Inspection

    1. Front Matter

      Pages 1-1
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    2. Methodological Foundations of Statistical Lot Inspection

      • Rainer Göb
      Pages 3-24

    3. Credit-based Accept-zero Sampling Schemes for the Control of Outgoing Quality

      • David H. Baillie, Chris A. J. Klaassen
      Pages 25-35

    4. Acceptance Sampling Plans by Attributes with Fuzzy Risks and Quality Levels

      • Przemyslaw Grzegorzewski
      Pages 36-46

    5. Acceptance Sampling by Variables under Measurement Uncertainty

      • Henrik Melgaard, Poul Thyregod
      Pages 47-57

  3. Statistical Process Control

    1. Front Matter

      Pages 59-59
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    2. Simultaneous Shewhart-Type Charts for the Mean and the Variance of a Time Series

      • Sven Knoth, Wolfgang Schmid, Alexander Schöne
      Pages 61-79

    3. Application of ISO 3951 Acceptance Sampling Plans to the Inspection by Variables in Statistical Process Control (SPC)

      • Olgierd Hryniewicz
      Pages 80-92

    4. Optimal Set-Up of a Manufacturing Process with Unequal Revenue from Oversized and Undersized Items

      • Shaul P. Ladany
      Pages 93-101

    5. Frequency Distribution Supporting Recognition of Unnatural Patterns on Shewhart X-bar Chart

      • Jacek Nieckula
      Pages 102-117

    6. Monitoring Processes with Data Available in Tabular Form Using Multiple Correspondence Analysis

      • Ross S. Sparks
      Pages 118-154

    7. Monitoring a Proportion Using CUSUM and SPRT Control Charts

      • Marion R. Reynolds, Zachary G. Stoumbos
      Pages 155-175

    8. The Effect of Non-Normality on the Performance of CUSUM Procedures

      • Thomas P. Ryan, Belinda J. Faddy
      Pages 176-193

    9. Process Control for Non-Normal Populations Based on an Inverse Normalizing Transformation

      • Haim Shore
      Pages 194-206

    10. On Nonparametric Multivariate Control Charts Based on Data Depth

      • Zachary G. Stoumbos, L. Allison Jones, William H. Woodall, Marion R. Reynolds Jr.
      Pages 207-227

    11. Multivariate Process Monitoring for Nylon Fiber Production

      • Christina M. Mastrangelo, Joseph M. Porter, Robert V. Baxley Jr.
      Pages 228-246

    12. The Management of SPC

      • Erwin M. Saniga, Michael F. Pohlen
      Pages 247-259

  4. Data Analysis and Process Capability Studies

    1. Front Matter

      Pages 261-261
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    2. Application of Statistical Causal Analysis to Process Analysis

      • Ken Nishina
      Pages 263-274
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Keywords

About this book

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).

Editors and Affiliations

  • Institut für Statistik und Ökonometrie, Freie Universität Berlin, Berlin, Germany

    Hans-Joachim Lenz, Peter-Theodor Wilrich

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