Statistical Quality Control, 7th Edition

Statistical Quality Control, 7th Edition

by Douglas C. Montgomery
Released June 2012
Publisher(s): Wiley
ISBN: 9781118146811

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Book description

The Seventh Edition of Introduction to Statistical Quality Control provides a comprehensive treatment of the major aspects of using statistical methodology for quality control and improvement. Both traditional and modern methods are presented, including state-of-the-art techniques for statistical process monitoring and control and statistically designed experiments for process characterization, optimization, and process robustness studies. The seventh edition continues to focus on DMAIC (define, measure, analyze, improve, and control--the problem-solving strategy of six sigma) including a chapter on the implementation process. Additionally, the text includes new examples, exercises, problems, and techniques. Statistical Quality Control is best suited for upper-division students in engineering, statistics, business and management science or students in graduate courses.

Table of contents

  1. Coverpage
  2. Titlepage
  3. Copyright
  4. About the Author
  5. Preface
  6. Contents
  7. PART 1 INTRODUCTION
    1. 1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT
      1. Chapter Overview and Learning Objectives
      2. 1.1 The Meaning of Quality and Quality Improvement
        1. 1.1.1 Dimensions of Quality
        2. 1.1.2 Quality Engineering Terminology
      3. 1.2 A Brief History of Quality Control and Improvement
      4. 1.3 Statistical Methods for Quality Control and Improvement
      5. 1.4 Management Aspects of Quality Improvement
        1. 1.4.1 Quality Philosophy and Management Strategies
        2. 1.4.2 The Link Between Quality and Productivity
        3. 1.4.3 Supply Chain Quality Management
        4. 1.4.4 Quality Costs
        5. 1.4.5 Legal Aspects of Quality
        6. 1.4.6 Implementing Quality Improvement
    2. 2 THE DMAIC PROCESS
      1. Chapter Overview and Learning Objectives
      2. 2.1 Overview of DMAIC
      3. 2.2 The Define Step
      4. 2.3 The Measure Step
      5. 2.4 The Analyze Step
      6. 2.5 The Improve Step
      7. 2.6 The Control Step
      8. 2.7 Examples of DMAIC
        1. 2.7.1 Litigation Documents
        2. 2.7.2 Improving On-Time Delivery
        3. 2.7.3 Improving Service Quality in a Bank
  8. PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT
    1. 3 MODELING PROCESS QUALITY
      1. Chapter Overview and Learning Objectives
      2. 3.1 Describing Variation
        1. 3.1.1 The Stem-and-Leaf Plot
        2. 3.1.2 The Histogram
        3. 3.1.3 Numerical Summary of Data
        4. 3.1.4 The Box Plot
        5. 3.1.5 Probability Distributions
      3. 3.2 Important Discrete Distributions
        1. 3.2.1 The Hypergeometric Distribution
        2. 3.2.2 The Binomial Distribution
        3. 3.2.3 The Poisson Distribution
        4. 3.2.4 The Negative Binomial and Geometric Distributions
      4. 3.3 Important Continuous Distributions
        1. 3.3.1 The Normal Distribution
        2. 3.3.2 The Lognormal Distribution
        3. 3.3.3 The Exponential Distribution
        4. 3.3.4 The Gamma Distribution
        5. 3.3.5 The Weibull Distribution
      5. 3.4 Probability Plots
        1. 3.4.1 Normal Probability Plots
        2. 3.4.2 Other Probability Plots
      6. 3.5 Some Useful Approximations
        1. 3.5.1 The Binomial Approximation to the Hypergeometric
        2. 3.5.2 The Poisson Approximation to the Binomial
        3. 3.5.3 The Normal Approximation to the Binomial
        4. 3.5.4 Comments on Approximations
    2. 4 INFERENCES ABOUT PROCESS QUALITY
      1. Chapter Overview and Learning Objectives
      2. 4.1 Statistics and Sampling Distributions
        1. 4.1.1 Sampling from a Normal Distribution
        2. 4.1.2 Sampling from a Bernoulli Distribution
        3. 4.1.3 Sampling from a Poisson Distribution
      3. 4.2 Point Estimation of Process Parameters
      4. 4.3 Statistical Inference for a Single Sample
        1. 4.3.1 Inference on the Mean of a Population, Variance Known
        2. 4.3.2 The Use of P-Values for Hypothesis Testing
        3. 4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown
        4. 4.3.4 Inference on the Variance of a Normal Distribution
        5. 4.3.5 Inference on a Population Proportion
        6. 4.3.6 The Probability of Type II Error and Sample Size Decisions
      5. 4.4 Statistical Inference for Two Samples
        1. 4.4.1 Inference for a Difference in Means, Variances Known
        2. 4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown
        3. 4.4.3 Inference on the Variances of Two Normal Distributions
        4. 4.4.4 Inference on Two Population Proportions
      6. 4.5 What If There Are More Than Two Populations? The Analysis of Variance
        1. 4.5.1 An Example
        2. 4.5.2 The Analysis of Variance
        3. 4.5.3 Checking Assumptions: Residual Analysis
      7. 4.6 Linear Regression Models
        1. 4.6.1 Estimation of the Parameters in Linear Regression Models
        2. 4.6.2 Hypothesis Testing in Multiple Regression
        3. 4.6.3 Confidance Intervals in Multiple Regression
        4. 4.6.4 Prediction of New Observations
        5. 4.6.5 Regression Model Diagnostics
  9. PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS
    1. 5 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL
      1. Chapter Overview and Learning Objectives
      2. 5.1 Introduction
      3. 5.2 Chance and Assignable Causes of Quality Variation
      4. 5.3 Statistical Basis of the Control Chart
        1. 5.3.1 Basic Principles
        2. 5.3.2 Choice of Control Limits
        3. 5.3.3 Sample Size and Sampling Frequency
        4. 5.3.4 Rational Subgroups
        5. 5.3.5 Analysis of Patterns on Control Charts
        6. 5.3.6 Discussion of Sensitizing Rules for Control Charts
        7. 5.3.7 Phase I and Phase II of Control Chart Application
      5. 5.4 The Rest of the Magnificent Seven
      6. 5.5 Implementing SPC in a Quality Improvement Program
      7. 5.6 An Application of SPC
      8. 5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses
    2. 6 CONTROL CHARTS FOR VARIABLES
      1. Chapter Overview and Learning Objectives
      2. 6.1 Introduction
      3. 6.2 Control Charts for X and R
        1. 6.2.1 Statistical Basis of the Charts
        2. 6.2.2 Development and Use of X and R Charts
        3. 6.2.3 Charts Based on Standard Values
        4. 6.2.4 Interpretation of X and R Charts
        5. 6.2.5 The Effect of Nonnormality on X and R Charts
        6. 6.2.6 The Operating-Characteristic Function
        7. 6.2.7 The Average Run Length for the X Chart
      4. 6.3 Control Charts for X and s
        1. 6.3.1 Construction and Operation of X and s Charts
        2. 6.3.2 The X and s Control Charts with Variable Sample Size
        3. 6.3.3 The s2 Control Chart
      5. 6.4 The Shewhart Control Chart for Individual Measurements
      6. 6.5 Summary of Procedures for X, R, and s Charts
      7. 6.6 Applications of Variables Control Charts
    3. 7 CONTROL CHARTS FOR ATTRIBUTES
      1. Chapter Overview and Learning Objectives
      2. 7.1 Introduction
      3. 7.2 The Control Chart for Fraction Nonconforming
        1. 7.2.1 Development and Operation of the Control Chart
        2. 7.2.2 Variable Sample Size
        3. 7.2.3 Applications in Transactional and Service Businesses
        4. 7.2.4 The Operating-Characteristic Function and Average Run Length Calculations
      4. 7.3 Control Charts for Nonconformities (Defects)
        1. 7.3.1 Procedures with Constant Sample Size
        2. 7.3.2 Procedures with Variable Sample Size
        3. 7.3.3 Demerit Systems
        4. 7.3.4 The Operating-Characteristic Function
        5. 7.3.5 Dealing with Low Defect Levels
        6. 7.3.6 Nonmanufacturing Applications
      5. 7.4 Choice Between Attributes and Variables Control Charts
      6. 7.5 Guidelines for Implementing Control Charts
    4. 8 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS
      1. Chapter Overview and Learning Objectives
      2. 8.1 Introduction
      3. 8.2 Process Capability Analysis Using a Histogram or a Probability Plot
        1. 8.2.1 Using the Histogram
        2. 8.2.2 Probability Plotting
      4. 8.3 Process Capability Ratios
        1. 8.3.1 Use and Interpretation of Cp
        2. 8.3.2 Process Capability Ratio for an Off-Center Process
        3. 8.3.3 Normality and the Process Capability Ratio
        4. 8.3.4 More about Process Centering
        5. 8.3.5 Confidence Intervals and Tests on Process Capability Ratios
      5. 8.4 Process Capability Analysis Using a Control Chart
      6. 8.5 Process Capability Analysis Using Designed Experiments
      7. 8.6 Process Capability Analysis with Attribute Data
      8. 8.7 Gauge and Measurement System Capability Studies
        1. 8.7.1 Basic Concepts of Gauge Capability
        2. 8.7.2 The Analysis of Variance Method
        3. 8.7.3 Confidence Intervals in Gauge R & R Studies
        4. 8.7.4 False Defectives and Passed Defectives
        5. 8.7.5 Attribute Gauge Capability
        6. 8.7.6 Comparing Customer and Supplier Measurement Systems
      9. 8.8 Setting Specification Limits on Discrete Components
        1. 8.8.1 Linear Combinations
        2. 8.8.2 Nonlinear Combinations
      10. 8.9 Estimating the Natural Tolerance Limits of a Process
        1. 8.9.1 Tolerance Limits Based on the Normal Distribution
        2. 8.9.2 Nonparametric Tolerance Limits
  10. PART 4 OTHER STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
    1. 9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS
      1. Chapter Overview and Learning Objectives
      2. 9.1 The Cumulative Sum Control Chart
        1. 9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean
        2. 9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean
        3. 9.1.3 Recommendations for CUSUM Design
        4. 9.1.4 The Standardized CUSUM
        5. 9.1.5 Improving CUSUM Responsiveness for Large Shifts
        6. 9.1.6 The Fast Initial Response or Headstart Feature
        7. 9.1.7 One-Sided CUSUMs
        8. 9.1.8 A CUSUM for Monitoring Process Variability
        9. 9.1.9 Rational Subgroups
        10. 9.1.10 CUSUMs for Other Sample Statistics
        11. 9.1.11 The V-Mask Procedure
        12. 9.1.12 The Self-Starting CUSUM
      3. 9.2 The Exponentially Weighted Moving Average Control Chart
        1. 9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean
        2. 9.2.2 Design of an EWMA Control Chart
        3. 9.2.3 Robustness of the EWMA to Non-normality
        4. 9.2.4 Rational Subgroups
        5. 9.2.5 Extensions of the EWMA
      4. 9.3 The Moving Average Control Chart
    2. 10 OTHER UNIVARIATE STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
      1. Chapter Overview and Learning Objectives
      2. 10.1 Statistical Process Control for Short Production Runs
        1. 10.1.1 X and R Charts for Short Production Runs
        2. 10.1.2 Attributes Control Charts for Short Production Runs
        3. 10.1.3 Other Methods
      3. 10.2 Modified and Acceptance Control Charts
        1. 10.2.1 Modified Control Limits for the X Chart
        2. 10.2.2 Acceptance Control Charts
      4. 10.3 Control Charts for Multiple-Stream Processes
        1. 10.3.1 Multiple-Stream Processes
        2. 10.3.2 Group Control Charts
        3. 10.3.3 Other Approaches
      5. 10.4 SPC With Autocorrelated Process Data
        1. 10.4.1 Sources and Effects of Autocorrelation in Process Data
        2. 10.4.2 Model-Based Approaches
        3. 10.4.3 A Model-Free Approach
      6. 10.5 Adaptive Sampling Procedures
      7. 10.6 Economic Design of Control Charts
        1. 10.6.1 Designing a Control Chart
        2. 10.6.2 Process Characteristics
        3. 10.6.3 Cost Parameters
        4. 10.6.4 Early Work and Semieconomic Designs
        5. 10.6.5 An Economic Model of the X Control Chart
        6. 10.6.6 Other Work
      8. 10.7 Cuscore Charts
      9. 10.8 The Changepoint Model for Process Monitoring
      10. 10.9 Profile Monitoring
      11. 10.10 Control Charts in Health Care Monitoring and Public Health Surveillance
      12. 10.11 Overview of Other Procedures
        1. 10.11.1 Tool Wear
        2. 10.11.2 Control Charts Based on Other Sample Statistics
        3. 10.11.3 Fill Control Problems
        4. 10.11.4 Precontrol
        5. 10.11.5 Tolerance Interval Control Charts
        6. 10.11.6 Monitoring Processes with Censored Data
        7. 10.11.7 Monitoring Bernoulli Processes
        8. 10.11.8 Nonparametric Control Charts
    3. 11 MULTIVARIATE PROCESS MONITORING AND CONTROL
      1. Chapter Overview and Learning Objectives
      2. 11.1 The Multivariate Quality-Control Problem
      3. 11.2 Description of Multivariate Data
        1. 11.2.1 The Multivariate Normal Distribution
        2. 11.2.2 The Sample Mean Vector and Covariance Matrix
      4. 11.3 The Hotelling T2 Control Chart
        1. 11.3.1 Subgrouped Data
        2. 11.3.2 Individual Observations
      5. 11.4 The Multivariate EWMA Control Chart
      6. 11.5 Regression Adjustment
      7. 11.6 Control Charts for Monitoring Variability
      8. 11.7 Latent Structure Methods
        1. 11.7.1 Principal Components
        2. 11.7.2 Partial Least Squares
    4. 12 ENGINEERING PROCESS CONTROL AND SPC
      1. Chapter Overview and Learning Objectives
      2. 12.1 Process Monitoring and Process Regulation
      3. 12.2 Process Control by Feedback Adjustment
        1. 12.2.1 A Simple Adjustment Scheme: Integral Control
        2. 12.2.2 The Adjustment Chart
        3. 12.2.3 Variations of the Adjustment Chart
        4. 12.2.4 Other Types of Feedback Controllers
      4. 12.3 Combining SPC and EPC
  11. PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS
    1. 13 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT
      1. Chapter Overview and Learning Objectives
      2. 13.1 What is Experimental Design?
      3. 13.2 Examples of Designed Experiments In Process and Product Improvement
      4. 13.3 Guidelines for Designing Experiments
      5. 13.4 Factorial Experiments
        1. 13.4.1 An Example
        2. 13.4.2 Statistical Analysis
        3. 13.4.3 Residual Analysis
      6. 13.5 The 2k Factorial Design
        1. 13.5.1 The 22 Design
        2. 13.5.2 The 2k Design for k ≥ 3 Factors
        3. 13.5.3 A Single Replicate of the 2k Design
        4. 13.5.4 Addition of Center Points to the 2k Design
        5. 13.5.5 Blocking and Confounding in the 2k Design
      7. 13.6 Fractional Replication of the 2k Design
        1. 13.6.1 The One-Half Fraction of the 2k Design
        2. 13.6.2 Smaller Fractions: The 2k−p Fractional Factorial Design
    2. 14 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS
      1. Chapter Overview and Learning Objectives
      2. 14.1 Response Surface Methods and Designs
        1. 14.1.1 The Method of Steepest Ascent
        2. 14.1.2 Analysis of a Second-Order Response Surface
      3. 14.2 Process Robustness Studies
        1. 14.2.1 Background
        2. 14.2.2 The Response Surface Approach to Process Robustness Studies
      4. 14.3 Evolutionary Operation
  12. PART 6 ACCEPTANCE SAMPLING
    1. 15 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES
      1. Chapter Overview and Learning Objectives
      2. 15.1 The Acceptance-Sampling Problem
        1. 15.1.1 Advantages and Disadvantages of Sampling
        2. 15.1.2 Types of Sampling Plans
        3. 15.1.3 Lot Formation
        4. 15.1.4 Random Sampling
        5. 15.1.5 Guidelines for Using Acceptance Sampling
      3. 15.2 Single-Sampling Plans for Attributes
        1. 15.2.1 Definition of a Single-Sampling Plan
        2. 15.2.2 The OC Curve
        3. 15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve
        4. 15.2.4 Rectifying Inspection
      4. 15.3 Double, Multiple, and Sequential Sampling
        1. 15.3.1 Double-Sampling Plans
        2. 15.3.2 Multiple-Sampling Plans
        3. 15.3.3 Sequential-Sampling Plans
      5. 15.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859)
        1. 15.4.1 Description of the Standard
        2. 15.4.2 Procedure
        3. 15.4.3 Discussion
      6. 15.5 The Dodge–Romig Sampling Plans
        1. 15.5.1 AOQL Plans
        2. 15.5.2 LTPD Plans
        3. 15.5.3 Estimation of Process Average
    2. 16 OTHER ACCEPTANCE-SAMPLING TECHNIQUES
      1. Chapter Overview and Learning Objectives
      2. 16.1 Acceptance Sampling by Variables
        1. 16.1.1 Advantages and Disadvantages of Variables Sampling
        2. 16.1.2 Types of Sampling Plans Available
        3. 16.1.3 Caution in the Use of Variables Sampling
      3. 16.2 Designing a Variables Sampling Plan with a Specified OC Curve
      4. 16.3 MIL STD 414 (ANSI/ASQC Z1.9)
        1. 16.3.1 General Description of the Standard
        2. 16.3.2 Use of the Tables
        3. 16.3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9
      5. 16.4 Other Variables Sampling Procedures
        1. 16.4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean
        2. 16.4.2 Sequential Sampling by Variables
      6. 16.5 Chain Sampling
      7. 16.6 Continuous Sampling
        1. 16.6.1 CSP-1
        2. 16.6.2 Other Continuous-Sampling Plans
      8. 16.7 Skip-Lot Sampling Plans
  13. APPENDIX
    1. I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
    2. II. Cumulative Standard Normal Distribution
    3. III. Percentage Points of the X2 Distribution
    4. IV. Percentage Points of the t Distribution
    5. V. Percentage Points of the F Distribution
    6. VI. Factors for Constructing Variables Control Charts
    7. VII. Factors for Two-Sided Normal Tolerance Limits
    8. VIII. Factors for One-Sided Normal Tolerance Limits
  14. BIBLIOGRAPHY
  15. ANSWERS TO SELECTED EXERCISES
  16. INDEX

Product information

  • Title: Statistical Quality Control, 7th Edition
  • Author(s): Douglas C. Montgomery
  • Release date: June 2012
  • Publisher(s): Wiley
  • ISBN: 9781118146811