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Statistical Quality Control, 7th Edition

Name: Statistical Quality Control, 7th Edition
Author: Douglas C. Montgomery
ISBN: 9781118146811

by Douglas C. Montgomery

June 2012

Intermediate to advanced

768 pages

28h 23m

English

Wiley

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Coverpage
Titlepage
Copyright
About the Author
Preface
Contents
PART 1 INTRODUCTION
1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT
Chapter Overview and Learning Objectives1.1 The Meaning of Quality and Quality Improvement1.1.1 Dimensions of Quality1.1.2 Quality Engineering Terminology1.2 A Brief History of Quality Control and Improvement1.3 Statistical Methods for Quality Control and Improvement1.4 Management Aspects of Quality Improvement1.4.1 Quality Philosophy and Management Strategies1.4.2 The Link Between Quality and Productivity1.4.3 Supply Chain Quality Management1.4.4 Quality Costs1.4.5 Legal Aspects of Quality1.4.6 Implementing Quality Improvement
2 THE DMAIC PROCESS
Chapter Overview and Learning Objectives2.1 Overview of DMAIC2.2 The Define Step2.3 The Measure Step2.4 The Analyze Step2.5 The Improve Step2.6 The Control Step2.7 Examples of DMAIC2.7.1 Litigation Documents2.7.2 Improving On-Time Delivery2.7.3 Improving Service Quality in a Bank
PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT

3 MODELING PROCESS QUALITY
Chapter Overview and Learning Objectives3.1 Describing Variation3.1.1 The Stem-and-Leaf Plot3.1.2 The Histogram3.1.3 Numerical Summary of Data3.1.4 The Box Plot3.1.5 Probability Distributions3.2 Important Discrete Distributions3.2.1 The Hypergeometric Distribution3.2.2 The Binomial Distribution3.2.3 The Poisson Distribution3.2.4 The Negative Binomial and Geometric Distributions3.3 Important Continuous Distributions3.3.1 The Normal Distribution3.3.2 The Lognormal Distribution3.3.3 The Exponential Distribution3.3.4 The Gamma Distribution3.3.5 The Weibull Distribution3.4 Probability Plots3.4.1 Normal Probability Plots3.4.2 Other Probability Plots3.5 Some Useful Approximations3.5.1 The Binomial Approximation to the Hypergeometric3.5.2 The Poisson Approximation to the Binomial3.5.3 The Normal Approximation to the Binomial3.5.4 Comments on Approximations
4 INFERENCES ABOUT PROCESS QUALITY
Chapter Overview and Learning Objectives4.1 Statistics and Sampling Distributions4.1.1 Sampling from a Normal Distribution4.1.2 Sampling from a Bernoulli Distribution4.1.3 Sampling from a Poisson Distribution4.2 Point Estimation of Process Parameters4.3 Statistical Inference for a Single Sample4.3.1 Inference on the Mean of a Population, Variance Known4.3.2 The Use of P-Values for Hypothesis Testing4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown4.3.4 Inference on the Variance of a Normal Distribution4.3.5 Inference on a Population Proportion4.3.6 The Probability of Type II Error and Sample Size Decisions4.4 Statistical Inference for Two Samples4.4.1 Inference for a Difference in Means, Variances Known4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown4.4.3 Inference on the Variances of Two Normal Distributions4.4.4 Inference on Two Population Proportions4.5 What If There Are More Than Two Populations? The Analysis of Variance4.5.1 An Example4.5.2 The Analysis of Variance4.5.3 Checking Assumptions: Residual Analysis4.6 Linear Regression Models4.6.1 Estimation of the Parameters in Linear Regression Models4.6.2 Hypothesis Testing in Multiple Regression4.6.3 Confidance Intervals in Multiple Regression4.6.4 Prediction of New Observations4.6.5 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 Objectives5.1 Introduction5.2 Chance and Assignable Causes of Quality Variation5.3 Statistical Basis of the Control Chart5.3.1 Basic Principles5.3.2 Choice of Control Limits5.3.3 Sample Size and Sampling Frequency5.3.4 Rational Subgroups5.3.5 Analysis of Patterns on Control Charts5.3.6 Discussion of Sensitizing Rules for Control Charts5.3.7 Phase I and Phase II of Control Chart Application5.4 The Rest of the Magnificent Seven5.5 Implementing SPC in a Quality Improvement Program5.6 An Application of SPC5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses
6 CONTROL CHARTS FOR VARIABLES
Chapter Overview and Learning Objectives6.1 Introduction6.2 Control Charts for X and R6.2.1 Statistical Basis of the Charts6.2.2 Development and Use of X and R Charts6.2.3 Charts Based on Standard Values6.2.4 Interpretation of X and R Charts6.2.5 The Effect of Nonnormality on X and R Charts6.2.6 The Operating-Characteristic Function6.2.7 The Average Run Length for the X Chart6.3 Control Charts for X and s6.3.1 Construction and Operation of X and s Charts6.3.2 The X and s Control Charts with Variable Sample Size6.3.3 The s2 Control Chart6.4 The Shewhart Control Chart for Individual Measurements6.5 Summary of Procedures for X, R, and s Charts6.6 Applications of Variables Control Charts
7 CONTROL CHARTS FOR ATTRIBUTES
Chapter Overview and Learning Objectives7.1 Introduction7.2 The Control Chart for Fraction Nonconforming7.2.1 Development and Operation of the Control Chart7.2.2 Variable Sample Size7.2.3 Applications in Transactional and Service Businesses7.2.4 The Operating-Characteristic Function and Average Run Length Calculations7.3 Control Charts for Nonconformities (Defects)7.3.1 Procedures with Constant Sample Size7.3.2 Procedures with Variable Sample Size7.3.3 Demerit Systems7.3.4 The Operating-Characteristic Function7.3.5 Dealing with Low Defect Levels7.3.6 Nonmanufacturing Applications7.4 Choice Between Attributes and Variables Control Charts7.5 Guidelines for Implementing Control Charts
8 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS
Chapter Overview and Learning Objectives8.1 Introduction8.2 Process Capability Analysis Using a Histogram or a Probability Plot8.2.1 Using the Histogram8.2.2 Probability Plotting8.3 Process Capability Ratios8.3.1 Use and Interpretation of Cp8.3.2 Process Capability Ratio for an Off-Center Process8.3.3 Normality and the Process Capability Ratio8.3.4 More about Process Centering8.3.5 Confidence Intervals and Tests on Process Capability Ratios8.4 Process Capability Analysis Using a Control Chart8.5 Process Capability Analysis Using Designed Experiments8.6 Process Capability Analysis with Attribute Data8.7 Gauge and Measurement System Capability Studies8.7.1 Basic Concepts of Gauge Capability8.7.2 The Analysis of Variance Method8.7.3 Confidence Intervals in Gauge R & R Studies8.7.4 False Defectives and Passed Defectives8.7.5 Attribute Gauge Capability8.7.6 Comparing Customer and Supplier Measurement Systems8.8 Setting Specification Limits on Discrete Components8.8.1 Linear Combinations8.8.2 Nonlinear Combinations8.9 Estimating the Natural Tolerance Limits of a Process8.9.1 Tolerance Limits Based on the Normal Distribution8.9.2 Nonparametric Tolerance Limits
PART 4 OTHER STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS
Chapter Overview and Learning Objectives9.1 The Cumulative Sum Control Chart9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean9.1.3 Recommendations for CUSUM Design9.1.4 The Standardized CUSUM9.1.5 Improving CUSUM Responsiveness for Large Shifts9.1.6 The Fast Initial Response or Headstart Feature9.1.7 One-Sided CUSUMs9.1.8 A CUSUM for Monitoring Process Variability9.1.9 Rational Subgroups9.1.10 CUSUMs for Other Sample Statistics9.1.11 The V-Mask Procedure9.1.12 The Self-Starting CUSUM9.2 The Exponentially Weighted Moving Average Control Chart9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean9.2.2 Design of an EWMA Control Chart9.2.3 Robustness of the EWMA to Non-normality9.2.4 Rational Subgroups9.2.5 Extensions of the EWMA9.3 The Moving Average Control Chart
10 OTHER UNIVARIATE STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
Chapter Overview and Learning Objectives10.1 Statistical Process Control for Short Production Runs10.1.1 X and R Charts for Short Production Runs10.1.2 Attributes Control Charts for Short Production Runs10.1.3 Other Methods10.2 Modified and Acceptance Control Charts10.2.1 Modified Control Limits for the X Chart10.2.2 Acceptance Control Charts10.3 Control Charts for Multiple-Stream Processes10.3.1 Multiple-Stream Processes10.3.2 Group Control Charts10.3.3 Other Approaches10.4 SPC With Autocorrelated Process Data10.4.1 Sources and Effects of Autocorrelation in Process Data10.4.2 Model-Based Approaches10.4.3 A Model-Free Approach10.5 Adaptive Sampling Procedures10.6 Economic Design of Control Charts10.6.1 Designing a Control Chart10.6.2 Process Characteristics10.6.3 Cost Parameters10.6.4 Early Work and Semieconomic Designs10.6.5 An Economic Model of the X Control Chart10.6.6 Other Work10.7 Cuscore Charts10.8 The Changepoint Model for Process Monitoring10.9 Profile Monitoring10.10 Control Charts in Health Care Monitoring and Public Health Surveillance10.11 Overview of Other Procedures10.11.1 Tool Wear10.11.2 Control Charts Based on Other Sample Statistics10.11.3 Fill Control Problems10.11.4 Precontrol10.11.5 Tolerance Interval Control Charts10.11.6 Monitoring Processes with Censored Data10.11.7 Monitoring Bernoulli Processes10.11.8 Nonparametric Control Charts
11 MULTIVARIATE PROCESS MONITORING AND CONTROL
Chapter Overview and Learning Objectives11.1 The Multivariate Quality-Control Problem11.2 Description of Multivariate Data11.2.1 The Multivariate Normal Distribution11.2.2 The Sample Mean Vector and Covariance Matrix11.3 The Hotelling T2 Control Chart11.3.1 Subgrouped Data11.3.2 Individual Observations11.4 The Multivariate EWMA Control Chart11.5 Regression Adjustment11.6 Control Charts for Monitoring Variability11.7 Latent Structure Methods11.7.1 Principal Components11.7.2 Partial Least Squares
12 ENGINEERING PROCESS CONTROL AND SPC
Chapter Overview and Learning Objectives12.1 Process Monitoring and Process Regulation12.2 Process Control by Feedback Adjustment12.2.1 A Simple Adjustment Scheme: Integral Control12.2.2 The Adjustment Chart12.2.3 Variations of the Adjustment Chart12.2.4 Other Types of Feedback Controllers12.3 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 Objectives13.1 What is Experimental Design?13.2 Examples of Designed Experiments In Process and Product Improvement13.3 Guidelines for Designing Experiments13.4 Factorial Experiments13.4.1 An Example13.4.2 Statistical Analysis13.4.3 Residual Analysis13.5 The 2k Factorial Design13.5.1 The 22 Design13.5.2 The 2k Design for k ≥ 3 Factors13.5.3 A Single Replicate of the 2k Design13.5.4 Addition of Center Points to the 2k Design13.5.5 Blocking and Confounding in the 2k Design13.6 Fractional Replication of the 2k Design13.6.1 The One-Half Fraction of the 2k Design13.6.2 Smaller Fractions: The 2k−p Fractional Factorial Design
14 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS
Chapter Overview and Learning Objectives14.1 Response Surface Methods and Designs14.1.1 The Method of Steepest Ascent14.1.2 Analysis of a Second-Order Response Surface14.2 Process Robustness Studies14.2.1 Background14.2.2 The Response Surface Approach to Process Robustness Studies14.3 Evolutionary Operation
PART 6 ACCEPTANCE SAMPLING
15 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES
Chapter Overview and Learning Objectives15.1 The Acceptance-Sampling Problem15.1.1 Advantages and Disadvantages of Sampling15.1.2 Types of Sampling Plans15.1.3 Lot Formation15.1.4 Random Sampling15.1.5 Guidelines for Using Acceptance Sampling15.2 Single-Sampling Plans for Attributes15.2.1 Definition of a Single-Sampling Plan15.2.2 The OC Curve15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve15.2.4 Rectifying Inspection15.3 Double, Multiple, and Sequential Sampling15.3.1 Double-Sampling Plans15.3.2 Multiple-Sampling Plans15.3.3 Sequential-Sampling Plans15.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859)15.4.1 Description of the Standard15.4.2 Procedure15.4.3 Discussion15.5 The Dodge–Romig Sampling Plans15.5.1 AOQL Plans15.5.2 LTPD Plans15.5.3 Estimation of Process Average
16 OTHER ACCEPTANCE-SAMPLING TECHNIQUES
Chapter Overview and Learning Objectives16.1 Acceptance Sampling by Variables16.1.1 Advantages and Disadvantages of Variables Sampling16.1.2 Types of Sampling Plans Available16.1.3 Caution in the Use of Variables Sampling16.2 Designing a Variables Sampling Plan with a Specified OC Curve16.3 MIL STD 414 (ANSI/ASQC Z1.9)16.3.1 General Description of the Standard16.3.2 Use of the Tables16.3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.916.4 Other Variables Sampling Procedures16.4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean16.4.2 Sequential Sampling by Variables16.5 Chain Sampling16.6 Continuous Sampling16.6.1 CSP-116.6.2 Other Continuous-Sampling Plans16.7 Skip-Lot Sampling Plans
APPENDIX
I. Summary of Common Probability Distributions Often Used in Statistical Quality ControlII. Cumulative Standard Normal DistributionIII. Percentage Points of the X2 DistributionIV. Percentage Points of the t DistributionV. Percentage Points of the F DistributionVI. Factors for Constructing Variables Control ChartsVII. Factors for Two-Sided Normal Tolerance LimitsVIII. Factors for One-Sided Normal Tolerance Limits
BIBLIOGRAPHY
ANSWERS TO SELECTED EXERCISES
INDEX

Content preview from Statistical Quality Control, 7th Edition

12 Engineering Process Control and SPC

CHAPTER OUTLINE

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

14.2.4 Other Types of Feedback Controllers

12.3 COMBINING SPC AND EPC

CHAPTER OVERVIEW AND LEARNINC OBJECTIVES

Throughout this book we have stressed the importance of process control and variability reduction as essential ingredients of modern business strategy. There are two statistically based approaches for addressing this problem. The first of these is statistical process monitoring by control charts, or statistical process control (SPC). The focus of SPC is on identifying assignable causes so that they can be removed, thereby leading to permanent process improvement or reduction in variability. The second approach is based on adjusting the process using information about its current level or deviation from a desired target. This approach is often called feedback adjustment, and it is a form of engineering process control or EPC. Feedback adjustment regulates the process to account for sources of variability that cannot be removed by the SPC approach.

This chapter introduces simple methods of feedback adjustment and shows how these techniques can be easily implemented in processes where there is a manipulatable variable that affects the process output. We also show how simple SPC schemes can be combined ...

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Statistical Quality Control, 7th Edition

by Douglas C. Montgomery

12 Engineering Process Control and SPC

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