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Springer Handbook of Engineering Statistics pp 173–192Cite as

Statistical Methods for Quality and Productivity Improvement

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Part of the book series: Springer Handbooks ((SHB))

Abstract

The first section of this chapter introduces statistical process control SPC and robust design RD, two important statistical methodologies for quality and productivity improvement. Section 10.1 describes in-depth SPC theory and tools for monitoring independent and autocorrelated data with a single quality characteristic. The relationship between SPC methods and automatic process control methods is discussed and differences in their philosophies, techniques, efficiencies, and design are contrasted. SPC methods for monitoring multivariate quality characteristics are also briefly reviewed.

Section 10.2 considers univariate RD, with emphasis on experimental design, performance measures and modeling of the latter. Combined and product arrays are featured and performance measures examined, include signal-to-noise ratios SNR, PerMIAs, process response, process variance and desirability functions. Of central importance is the decomposition of the expected value of squared-error loss into variance and off-target components which sometimes allows the dimensionality of the optimization problem to be reduced.

Section 10.3 deals with multivariate RD and demonstrates that the objective function for the multiple characteristic case is typically formed by additive or multiplicative combination of the univariate objective functions. Some alternative objective functions are examined as well as strategies for solving the optimization problem.

Section 10.4 defines dynamic RD and summarizes related publications in the statistics literature, including some very recent entries. Section 10.5 lists RD case studies originating from applications in manufacturing, reliability and tolerance design.

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Abbreviations

APC:

automatic process control

ARL:

average run length

ARMA:

autoregressive and moving average

COT:

cumulative sum of T

CV:

coefficient of variance

DRD:

dynamic robust design

EWMAST:

exponentially weighted moving average chart for stationary processes

GLM:

general linear model

GLRT:

generalized likelihood ratio test

MMSE:

minimum mean squared error

NBM:

nonoverlapping batch means

NTB:

nominal-the-best case

OBM:

overlapping batch means

PID:

proportional-integral-derivative

RD:

Robust design

RSM:

response surface method

RSM:

response surface models

SCC:

special-cause charts

SNR:

signal-to-noise ratios

SPC:

statistical process control

STS:

standardized time series

UBM:

unified batch mean

WBM:

weighted batch mean

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Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, Stevens Institute of Technology, Castle Point of Hudson, 07030, Hoboken, NJ, USA

    Wei Jiang

  2. Department of Internal Medicine, Yale University School of Medicine, 1 Church St, 06437, New Haven, CT, USA

    Terrence Murphy

  3. School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, 30332, Atlanta, GA, USA

    Kwok-Leung Tsui

Authors
  1. Wei Jiang
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  2. Terrence Murphy
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  3. Kwok-Leung Tsui
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Corresponding authors

Correspondence to Wei Jiang , Terrence Murphy or Kwok-Leung Tsui .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Rutgers the State University of New Jersey, 96 Frelinghuysen Road, 08854, Piscataway, NJ, USA

    Hoang Pham Prof.

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© 2006 Springer-Verlag

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Jiang, W., Murphy, T., Tsui, KL. (2006). Statistical Methods for Quality and Productivity Improvement. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-84628-288-1_10

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  • DOI: https://doi.org/10.1007/978-1-84628-288-1_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-806-0

  • Online ISBN: 978-1-84628-288-1

  • eBook Packages: EngineeringEngineering (R0)

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