Introduction to statistical quality control 7th ed.

Author

• Montgomery, Douglas C.

Additional Author(s)

-

Publisher

Hoboken, NJ.: John Wiley and Sons, Inc., 2013

Language

English

ISBN

9781118146811

Series

Subject(s)

• PROCESS CONTROL-STATISTICAL METHODS
• QUALITY CONTROL-STATISTICAL METHODS

Notes

• Appendix: p. 709-722

. Bibliography: p. 723-737 . Index: p. 749-754

Abstract

This Edition continues to explore the modern practice of statistical quality control, providing comprehensive coverage of the subject from basic principles to state-of-the-art concepts and applications. The objective is to give the reader a thorough grounding in the principles of statistical quality control and a basis for applying those principles in a wide variety of both product and nonproduct situations. Divided into four parts, it contains numerous changes, including a more detailed discussion of the basic SPC problem-solving tools and two new case studies, expanded treatment on variable control charts with new examples, a chapter devoted entirely to cumulative-sum control charts and exponentially-weighted, moving-average control charts, and a new section on process improvement with designed experiments.

Physical Dimension

Number of Page(s)

xiv, 754 p.

Dimension

26 cm.

Other Desc.

ill.

Summary / Review / Table of Content

PART 1 INTRODUCTION --
1 Quality Improvement in the Modern Business Environment --
Chapter Overview and Learning Objectives --
1.1 The Meaning of Quality and Quality Improvement --
1.1.1 Dimensions of Quality --
1.1.2 Quality Engineering Terminology --
1.2 A Brief History of Quality Control and Improvement --
1.3 Statistical Methods for Quality Control and Improvement --
1.4 Management Aspects of Quality Improvement --
1.4.1 Quality Philosophy and Management Strategies --
1.4.2 The Link Between Quality and Productivity --
1.4.3 Supply Chain Quality Management --
1.4.4 Quality Costs --
1.4.5 Legal Aspects of Quality --
1.4.6 Implementing Quality Improvement --
2 The DMAIC Process --
Chapter Overview and Learning Objectives --
2.1 Overview of DMAIC --
2.2 The Define Step --
2.3 The Measure Step --
2.4 The Analyze Step --
2.5 The Improve Step --
2.6 The Control Step --
2.7 Examples of DMAIC --
2.7.1 Litigation Documents --
2.7.2 Improving On-Time Delivery --
2.7.3 Improving Service Quality in a Bank PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT --
3 Modeling Process Quality --
Chapter Overview and Learning Objectives --
3.1 Describing Variation --
3.1.1 The Stem-and-Leaf Plot --
3.1.2 The Histogram --
3.1.3 Numerical Summary of Data --
3.1.4 The Box Plot --
3.1.5 Probability Distributions --
3.2 Important Discrete Distributions --
3.2.1 The Hypergeometric Distribution --
3.2.2 The Binomial Distribution --
3.2.3 The Poisson Distribution --
3.2.4 The Negative Binomial and Geometric Distributions --
3.3 Important Continuous Distributions --
3.3.1 The Normal Distribution --
3.3.2 The Lognormal Distribution --
3.3.3 The Exponential Distribution --
3.3.4 The Gamma Distribution --
3.3.5 The Weibull Distribution --
3.4 Probability Plots --
3.4.1 Normal Probability Plots --
3.4.2 Other Probability Plots --
3.5 Some Useful Approximations --
3.5.1 The Binomial Approximation to the Hypergeometric --
3.5.2 The Poisson Approximation to the Binomial --
3.5.3 The Normal Approximation to the Binomial --
3.5.4 Comments on Approximations --
4 Inferences about Process Quality --
Chapter Overview and Learning Objectives --
4.1 Statistics and Sampling Distributions --
4.1.1 Sampling from a Normal Distribution --
4.1.2 Sampling from a Bernoulli Distribution --
4.1.3 Sampling from a Poisson Distribution --
4.2 Point Estimation of Process Parameters 4.3 Statistical Inference for a Single Sample --
4.3.1 Inference on the Mean of a Population, Variance Known --
4.3.2 The Use of P-Values for Hypothesis Testing --
4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown --
4.3.4 Inference on the Variance of a Normal Distribution --
4.3.5 Inference on a Population Proportion --
4.3.6 The Probability of Type II Error and Sample Size Decisions --
4.4 Statistical Inference for Two Samples --
4.4.1 Inference for a Difference in Means, Variances Known --
4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown --
4.4.3 Inference on the Variances of Two Normal Distributions --
4.4.4 Inference on Two Population Proportions --
4.5 What If There Are More Than Two Populations? The Analysis of Variance --
4.5.1 An Example --
4.5.2 The Analysis of Variance --
4.5.3 Checking Assumptions: Residual Analysis --
4.6 Linear Regression Models --
4.6.1 Estimation of the Parameters in Linear Regression Models --
4.6.2 Hypothesis Testing in Multiple Regression --
4.6.3 Confidance Intervals in Multiple Regression --
4.6.4 Prediction of New Observations --
4.6.5 Regression Model Diagnostics PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS --
5 Methods and Philosophy of Statistical Process Control --
Chapter Overview and Learning Objectives --
5.1 Introduction --
5.2 Chance and Assignable Causes of Quality Variation --
5.3 Statistical Basis of the Control Chart --
5.3.1 Basic Principles --
5.3.2 Choice of Control Limits --
5.3.3 Sample Size and Sampling Frequency --
5.3.4 Rational Subgroups --
5.3.5 Analysis of Patterns on Control Charts --
5.3.6 Discussion of Sensitizing Rules for Control Charts --
5.3.7 Phase I and Phase II of Control Chart Application --
5.4 The Rest of the Magnificent Seven --
5.5 Implementing SPC in a Quality Improvement Program --
5.6 An Application of SPC --
5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses --
6 Control Charts for Variables --
Chapter Overview and Learning Objectives --
6.1 Introduction --
6.2 Control Charts for -x and R --
6.2.1 Statistical Basis of the Charts --
6.2.2 Development and Use of -x and R Charts --
6.2.3 Charts Based on Standard Values --
6.2.4 Interpretation of -x and R Charts --
6.2.5 The Effect of Nonnormality on -x and R Charts --
6.2.6 The Operating-Characteristic Function --
6.2.7 The Average Run Length for the -x Chart --
6.3 Control Charts for -x and s PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS --
5 Methods and Philosophy of Statistical Process Control --
Chapter Overview and Learning Objectives --
5.1 Introduction --
5.2 Chance and Assignable Causes of Quality Variation --
5.3 Statistical Basis of the Control Chart --
5.3.1 Basic Principles --
5.3.2 Choice of Control Limits --
5.3.3 Sample Size and Sampling Frequency --
5.3.4 Rational Subgroups --
5.3.5 Analysis of Patterns on Control Charts --
5.3.6 Discussion of Sensitizing Rules for Control Charts --
5.3.7 Phase I and Phase II of Control Chart Application --
5.4 The Rest of the Magnificent Seven --
5.5 Implementing SPC in a Quality Improvement Program --
5.6 An Application of SPC --
5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses --
6 Control Charts for Variables --
Chapter Overview and Learning Objectives --
6.1 Introduction --
6.2 Control Charts for -x and R --
6.2.1 Statistical Basis of the Charts --
6.2.2 Development and Use of -x and R Charts --
6.2.3 Charts Based on Standard Values --
6.2.4 Interpretation of -x and R Charts --
6.2.5 The Effect of Nonnormality on -x and R Charts --
6.2.6 The Operating-Characteristic Function --
6.2.7 The Average Run Length for the -x Chart --
6.3 Control Charts for -x and s --
6.3.1 Const 8.3 Process Capability Ratios --
8.3.1 Use and Interpretation of Cp --
8.3.2 Process Capability Ratio for an Off-Center Process --
8.3.3 Normality and the Process Capability Ratio --
8.3.4 More about Process Centering --
8.3.5 Confidence Intervals and Tests on Process Capability Ratios --
8.4 Process Capability Analysis Using a Control Chart --
8.5 Process Capability Analysis Using Designed Experiments --
8.6 Process Capability Analysis with Attribute Data --
8.7 Gauge and Measurement System Capability Studies --
8.7.1 Basic Concepts of Gauge Capability --
8.7.2 The Analysis of Variance Method --
8.7.3 Confidence Intervals in Gauge R & R Studies --
8.7.4 False Defectives and Passed Defectives --
8.7.5 Attribute Gauge Capability --
8.7.6 Comparing Customer and Supplier Measurement Systems --
8.8 Setting Specification Limits on Discrete Components --
8.8.1 Linear Combinations --
8.8.2 Nonlinear Combinations --
8.9 Estimating the Natural Tolerance Limits of a Process --
8.9.1 Tolerance Limits Based on the Normal Distribution --
8.9.2 Nonparametric Tolerance Limits PART 4 OTHER STATISTICAL PROCESSMONITORING AND CONTROL TECHNIQUES --
9 Cumulative Sum and Exponentially Weighted Moving Average Control Charts --
Chapter Overview and Learning Objectives --
9.1 The Cumulative Sum Control Chart --
9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean --
9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean --
9.1.3 Recommendations for CUSUM Design --
9.1.4 The Standardized CUSUM --
9.1.5 Improving CUSUM Responsiveness for Large Shifts --
9.1.6 The Fast Initial Response or Headstart Feature --
9.1.7 One-Sided CUSUMs --
9.1.8 A CUSUM for Monitoring Process Variability --
9.1.9 Rational Subgroups --
9.1.10 CUSUMs for Other Sample Statistics --
9.1.11 The V-Mask Procedure --
9.1.12 The Self-Starting CUSUM --
9.2 The Exponentially Weighted Moving Average Control Chart --
9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean --
9.2.2 Design of an EWMA Control Chart --
9.2.3 Robustness of the EWMA to Nonnormality --
9.2.4 Rational Subgroups --
9.2.5 Extensions of the EWMA --
9.3 The Moving Average Control Chart --
10 Other Univariate Statistical Process-Monitoring and Control Techniques --
Chapter Overview and Learning Objectives --
10.1 Statistical Process Control for Short Production Runs PART 4 OTHER STATISTICAL PROCESSMONITORING AND CONTROL TECHNIQUES --
9 Cumulative Sum and Exponentially Weighted Moving Average Control Charts --
Chapter Overview and Learning Objectives --
9.1 The Cumulative Sum Control Chart --
9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean --
9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean --
9.1.3 Recommendations for CUSUM Design --
9.1.4 The Standardized CUSUM --
9.1.5 Improving CUSUM Responsiveness for Large Shifts --
9.1.6 The Fast Initial Response or Headstart Feature --
9.1.7 One-Sided CUSUMs --
9.1.8 A CUSUM for Monitoring Process Variability --
9.1.9 Rational Subgroups --
9.1.10 CUSUMs for Other Sample Statistics --
9.1.11 The V-Mask Procedure --
9.1.12 The Self-Starting CUSUM --
9.2 The Exponentially Weighted Moving Average Control Chart --
9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean --
9.2.2 Design of an EWMA Control Chart --
9.2.3 Robustness of the EWMA to Nonnormality --
9.2.4 Rational Subgroups --
9.2.5 Extensions of the EWMA --
9.3 The Moving Average Control Chart --
10 Other Univariate Statistical Process-Monitoring and Control Techniques --
Chapter Overview and Learning Objectives --
10.1 Statistical Process Control for Short Production Runs --
10.1.1 x and R Cha 10.11.7 Monitoring Bernoulli Processes --
10.11.8 Nonparametric Control Charts --
11 Multivariate Process Monitoring and Control --
Chapter Overview and Learning Objectives --
11.1 The Multivariate Quality-Control Problem --
11.2 Description of Multivariate Data --
11.2.1 The Multivariate Normal Distribution --
11.2.2 The Sample Mean Vector and Covariance Matrix --
11.3 The Hotelling T2 Control Chart --
11.3.1 Subgrouped Data --
11.3.2 Individual Observations --
11.4 The Multivariate EWMA Control Chart --
11.5 Regression Adjustment --
11.6 Control Charts for Monitoring Variability --
11.7 Latent Structure Methods --
11.7.1 Principal Components --
11.7.2 Partial Least Squares --
12 Engineering Process Control and SPC --
Chapter Overview and Learning Objectives --
12.1 Process Monitoring and Process Regulation --
12.2 Process Control by Feedback Adjustment --
12.2.1 A Simple Adjustment Scheme: Integral Control --
12.2.2 The Adjustment Chart --
12.2.3 Variations of the Adjustment Chart --
12.2.4 Other Types of Feedback Controllers --
12.3 Combining SPC and EPC PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS --
13 Factorial and Fractional Factorial Experiments for Process Design and Improvement --
Chapter Overview and Learning Objectives --
13.1 What is Experimental Design? --
13.2 Examples of Designed Experiments In Process and Product Improvement --
13.3 Guidelines for Designing Experiments --
13.4 Factorial Experiments --
13.4.1 An Example --
13.4.2 Statistical Analysis --
13.4.3 Residual Analysis --
13.5 The 2k Factorial Design --
13.5.1 The 22 Design --
13.5.2 The 2k Design for k ≥ 3 Factors --
13.5.3 A Single Replicate of the 2k Design --
13.5.4 Addition of Center Points to the 2k Design --
13.5.5 Blocking and Confounding in the 2k Design --
13.6 Fractional Replication of the 2k Design --
13.6.1 The One-Half Fraction of the 2k Design --
13.6.2 Smaller Fractions: The 2k-p Fractional Factorial Design --
14 Process Optimization with Designed Experiments --
Chapter Overview and Learning Objectives --
14.1 Response Surface Methods and Designs --
14.1.1 The Method of Steepest Ascent --
14.1.2 Analysis of a Second-Order Response Surface --
14.2 Process Robustness Studies --
14.2.1 Background --
14.2.2 The Response Surface Approach to Process Robustness Studies --
14.3 Evolutionary Operation PART 6 ACCEPTANCE SAMPLING --
15 Lot-By-Lot Acceptance Sampling for Attributes --
Chapter Overview and Learning Objectives --
15.1 The Acceptance-Sampling Problem --
15.1.1 Advantages and Disadvantages of Sampling --
15.1.2 Types of Sampling Plans --
15.1.3 Lot Formation --
15.1.4 Random Sampling --
15.1.5 Guidelines for Using Acceptance Sampling --
15.2 Single-Sampling Plans for Attributes --
15.2.1 Definition of a Single-Sampling Plan --
15.2.2 The OC Curve --
15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve --
15.2.4 Rectifying Inspection --
15.3 Double, Multiple, and Sequential Sampling --
15.3.1 Double-Sampling Plans --
15.3.2 Multiple-Sampling Plans --
15.3.3 Sequential-Sampling Plans --
15.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859) --
15.4.1 Description of the Standard --
15.4.2 Procedure --
15.4.3 Discussion --
15.5 The Dodge-Romig Sampling Plans --
15.5.1 AOQL Plans --
15.5.2 LTPD Plans --
15.5.3 Estimation of Process Average --
16 Other Acceptance-Sampling Techniques --
Chapter Overview and Learning Objectives --
16.1 Acceptance Sampling by Variables --
16.1.1 Advantages and Disadvantages of Variables Sampling --
16.1.2 Types of Sampling Plans Available --
16.1.3 Caution in the Use of Variables Sampling

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	00088/17	658.562015195 Mon I	Library - 7th Floor	Available