CHAPTER 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 Quality Costs
1-4.4 Legal Aspects of Quality
1-4.5 Implementing Quality Improvement
PART1 Statistical Methods Useful in Quality Control and Improvement
CHAPTER 2 Modeling Process Quality
Chapter Overview and Learning Objectives
2-1 Describing Variation
2-1.1 The Stern-and-Leaf Plot
2-1.2 The Histogram
2-1.3 Numerical Summary of Data
2-1.4 The Box Plot
2-1.5 Probability Distributions
2-2 Important Discrete Distributions
2-2.1 The Hypergeometric Distribution
2-2.2 The Binomial Distribution
2-2.3 The Poisson Distribution
2-2.4 The Pascal and Related Distributions
2-3 Important Continuous Distributions
2-3.1 The Normal Distribution
2-3.2 The Lognomal Distribution
2-3.3 The Exponential Distribution
2-3.4 The Gamma Distribution
2-3.5 The Weibull Distribution
2-4 Probability Plots
2-4.1 Normal Probability Plots
2-4.2 Other Probability Plots
2-5 Some Useful Approximations
2-5.1 The Binomial Approximation to the Hypergeometric
2-5.2 The Poisson Approximation to the Binomial
2-5.3 The Normal Approximation to the Binomial
2-5.4 Comments on Approximations
CHAPTER 3 Inferences about Process Quality
Chapter Overview and Learning Objectives
3-1 Statistics and Sampling Distributions
3-1.1 Sampling from a Normal Distribution
3-1.2 Sampling from a Bernouli Distribution
3-1.3 Sampling from a Poisson Distribution
3-2 Point Estimation of Process Parameters
3-3 Statistical Inference for a Single Sample
3-3.1 Inference on the Mean of a Population, Variance Known
3-3.2 The Use of P-Values for Hypothesis Testing
3-3.3 Inference on the Mean of a Normal Distribution, Variance Unknown
3-3.4 Inference on the Variance of a Normal Distribution
3-3.5 Inference on a Population Proportion
3-3.6 The Probability of Type II Error and Sample Size Decisions
3-4 Statistical Inference for Two Samples
3-4.1 Inference for a Difference in Means, Variances Known
3-4.2 Inference for Difference in Means of Two Normal Distribution, Variances Unknown
3-4.3 Inference on the Variances of Two Normal Distributions
3-4.4 Inference on Two Population Proportions
3-5 What If There Are More Than Two Populations? The Analysis of Variance
3-5.1 An Example
3-5.2 The Analysis of Variance
3-5.3 Checking Assumtions: Residual Analysis
PART II Basic Methods of Statistical Process Control and Capability Analysis
CHAPTER 4 Methods and Philosophy of Statistical Process Control
Chapter Overiew and Learning Objectives
4-1 Introduction
4-2 Chance and Assignable Causes of Quality Variation
4-3 Statistical Basis of the Control Chart
4-3.1 Basic Principles
4-3.2 Choice of Control Limits
4-3.3 Sample Size and Sampling Frequency
4-3.4 Rational Subgroups
4-3.5 Analysis of Patterns on Control
4-3.6 Discussion of Sensitizing Rules for control Charts
4-3.7 Phase I and Phase II of Control Chart Application
4-4 The Rest of the "Magnificent Seven"
4-5 Implementing SPC
4-6 An Application of SPC
4-7 Nonmanufacturing Application of Statistical Process Control
CHAPTER 5 Control Charts for Variables
Chapter Overiew and Learning Objectives
5-1 Introduction
5-2 Control Charts for xbar and R
5-2.1 Statistical Basis of the Charts
5-2.2 Development and Use of xbar and R Charts
5-2.3 Charts Based on Standard Values
5-2.4 Interpretation of xbar and R Charts
5-2.5 The Effect of Nonnormality on xbar and R Charts
5-2.6 The Operating-Characteristic Function
5-2.7 The Average Run Length for the xbar Chart
5-3 Control Charts for xbar and s
5-3.1 Construction and Operation of xbar and s Charts
5-3.2 The xbar and s Control Charts with Variable Sample Size
5-3.3 The S-squre Control Chart
5-4 The Shewhart Control Chart for Individual Measurements
5-5 Summary of Procedures for xbar, R, and s Charts
5-6 Applications of Variables Control Charts
CHAPTER 6 Control Charts for Attributes
Chapter Overview and Learning Objectives
6-1 Introduction
6-2 The Control Chart for Fraction Nonconforming
6-2.1 Devlopment and Operation of the Control Chart
6-2.2 Variable Sample Size
6-2.3 Nonmanufacturing Applications
6-2.4 The Operating-Characteristic Function and Average Run Length Calculations
6-3 Control Charts for Nonconformities (Defects)
6-3.1 Procedures with Constant Sample Size
6-3.2 Procedures with Variable Sample Size
6-3.3 Demerit Systems
6-3.4 The Operating-Characteristic Function
6-3.5 Dealing with Low Defect Levels
6-3.6 Nonmanufacturing Applications
6-4 Choice Between Attributes and Variables Control Charts
6-5 Guidelines for Implementing Control Charts
CHAPTER 7 Process and Mesaurement System Capability Analysis
Chapter Overview and Learning Objectives
7-1 Introduction
7-2 Process Capability Analysis Using a Histogram or a Probability Plot
7-2.1 Using the Histogram
7-2.2 Probability Plotting
7-3 Process Capability Ratios
7-3.1 Use and Interpretation of Cp
7-3.2 Process Capability Ration for an Off-Center Process
7-3.3 Normality and the Process Capability Ratio
7-3.4 More about Process Centering
7-3.5 Confidence Intervals and Tests on Process Capability Ratios
7-4 Process Capability Analysis Using a Control Chart
7-5 Process Capability Analysis Using Designed Experiments
7-6 Gauge and Measurement System Capability Studies
7-6.1 Basic Concepts of Gauge Capability
7-6.2 The Analysis of Variance Method
7-6.3 Confidence Intervals in Gauge R & R Studies
7-6.4 False Defectives and Passed Defectives
7-7 Setting Specification Limitson Discrete Components
7-7.1 Linear Combinaions
7-7.2 Nonlinear Combinations
7-8 Estimating the Natural Tolerance Limits of a Process
7-8.1 Tolerance Limits Based on the Normal Distribution
7-8.2 Nonparametric Tolerlance Limits
PART III Other Statistical Process-Monitoring and Control Techniques
CHAPTER 8 Cumulative Sum and Exponentially Weighted Moving Average Control Charts
Chapter Overview and Learning Objectives
8-1 The Cumulative Sum Control Chart
8-1.1 Basic Principles: The Cusum Control Chart for Monitoring the Process Mean
8-1.2 The Tabular or Algorithmic Cusum for Monitoring the Process Mean
8-1.3 Recommendations for Cusum Design
8-1.4 The Standardized Cusum
8-1.5 Improving Cusum Responsiveness for Large Shifts
8-1.6 The Fast Initial Response or Headstart Feature
8-1.7 One-Sided cusums
8-1.8 A Cusum for Monitoring Process Variability
8-1.9 Rational Subgroups
8-1.10 Cusums for Other Sample Statistics
8-1.11 The V-Mask Procedure
8-2 The Exponentially Weighted Moving Average Control Chart
8-2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean
8-2.2 Design of an EWMA Control Chart
8-2.3 Robustness of the EWMA to Nonnormality
8-2.4 Rational Subgroups
8-2.5 Extensions of the EWMA
8-3 The Moving Average Control Chart
CHAPTER 9 Other Univariate Statistical Process Monitoring and Control Techniques
Chapter Overview and Learning Objectives
9-1 Statistical Process Control for Short Production Runs
9-1.1 xbar and R Charts for Short Production Runs
9-1.2 Attributes Control Charts for Short Production Runs
9-1.3 Other Methods
9-2 Modified and Acceptance Control Charts
9-2.1 Modified Control Limits for the xbar Chart
9-2.2 Acceptance Control Charts
9-3 Control Charts for Multiple-Stream Processes
9-3.1 Multiple-Stream Processes
9-3.2 Group Control Charts
9-3.3 Other Approaches
9-4 SPC With Autocorrelated Process Data
9-4.1 Sources and Effects of Autocorrelation in Process Data
9-4.2 Model-Based Approaches
9-4.3 A Model-Free Approach
9-5 Adaptive Sampling Procedures
9-6 Economic Design of Control Charts
9-6.1 Designing a Control Chart
9-6.2 Process Characteristics
9-6.3 Cost Parameters
9-6.4 Early Work and Semieconomic Designs
9-6.5 An Economic Model of the xbar Control Chart
9-6.6 Other Work
9-7 Cuscore Charts
9-8 The Changepoint Model for Process Monitoring
9-9 Overview of Other Procedures
9-9.1 Tool Wear
9-9.2 Control Charts Based on Other Sample Statistics
9-9.3 Fill Control Problems
9-9.4 Precontrol
9-9.5 Tolerance Interval Control Charts
9-9.6 Monitoring Processes with Censored Data
9-9.7 Nonparametric Control Charts
CHAPTER 10 Multivariate Process Monitoring and Control
Chapter Overview and Learning Objectives
10-1 The Multivariate Quality-Control Problem
10-2 Description of Multivariate Data
10-2.1 The Multivariate Normal Distribution
10-2.2 The Sample Mean Vector and Covariance Matrix
10-3 The Hotelling T-squre Control Chart
10-3.1 Subgrouped Data
10-3.2 Individual Observations
10-4 The Multivariate EWMA Control Chart
10-5 Regression Adjustment
10-6 Control Charts for Monitoring Variability
10-7 Latent Structure Methods
10-7.1 Principal Components
10-7.2 Partial Least Squares
10-8 Profile Monitoring
CHAPTER 11 Engineering Process Control and SPC
Chapter Overview and Learning Objectives
11-1 Process Monitoring and Process Regulation
11-2 Process Control by Feedback Adjustment
11-2.1 A Simple Adjustment Scheme: Integral Control
11-2.2 The Adjustment Chart
11-2.3 Variations of the Adjustment Chart
11-2.4 Other Types of Feedback Controllers
11-3 Combining SPC and EPC
PART IV Process Design and Improvement with Designed Experiments
CHAPTER 12 Fractorial and Fractional Fractorial Experiments for Process Design and Improvement
Chapter Overview and Learning Objectives
12-1 What is Experimental Design
12-2 Examples of Designed Experiments In Process Improvement
12-3 Guidelines for Designing Experiments
12-4 Factorial Experiments
12-4.1 An Example
12-4.2 Statistical Analysis
12-4.3 Residual Analysis
12-5 The 2^k Factorial Design
12-5.1 The 2^2 Design
12-5.2 The 2^k Design for k>=3 Factors
12-5.3 A Single Replicate of the 2^k Design
12-5.4 Addition of Center Points to the 2^k Design
12-5.5 Blocking and Confounding in the 2^k Design
12-6 Fractional Replication of the 2^k Design
12-6.1 The One-Half Fraction of the 2^k Design
12-6.2 Smaller Fractions: The 2^(k-p) Fractional Factorial Design
CHAPTER 13 Process Optimization with Designed Experiments
13-1 Response Surface Methods and Designs
13-1.1 The Method of Steepest Ascent
13-1.2 Analysis of a Second-Order Response Surface
13-2 Process Robustness Studies
13-2.1 Background
13-2.2 The Response Surface Approach to Process Robustness Studies
13-3 Evolutionary Operation
PART V Acceptance Sampling
CHAPTER 14 Lot-by-Lot Acceptance Sampling for Attributes
Chapter Overview and Learning Objectives
14-1 The Acceptance-Sampling Problem
14-1.1 Advantages and Disadvantages of Sampling
14-1.2 Types of Sampling Plans
14-1.3 Lot Formation
14-1.4 Random Sampling
14-1.5 Guidelines for Using Acceptance Sampling
14-2 Single-Sampling Plans for Attributes
14-2.1 Definition of a Single-Sampling Plan
14-2.2 The OC Curve
14-2.3 Designing a Single-Sampling Plan with a Specified OC Curve
14-2.4 Rectifying Inspection
14-3 Double, Multiple, and Sequential Sampling
14-3.1 Double-Sampling Plans
14-3.2 Multiple-Sampling Plans
14-3.3 sequential-Sampling Plans
14-4 Military Standard 105E (ANSI/ASQC Z1.4, ISO2859)
14-4.1 Description of the Standard
14-4.2 Procedure
14-4.3 Discussion
14-5 The Dodge-Romig Sampling Plans
14-5.1 AOQL Plans
14-5.2 LTPD Plans
14-5.3 Estimation of Process Average
CHAPTER 15 Other Cacceptance-Sampling Techniques
15-1 Acceptance Samplling by Variables
15-1.1 Advantages and Disadvantages of Variables Sampling
15-1.2 Types of Sampling Plans Available
15-1.3 Caution in the Use of Variables Sampling
15-2 Designing a Variables Sampling Plan with a Specified OC Curve
15-3 MIL STD 414 (ANSI/ASQC Z1.9)
15-3.1 General Description of the Standard
15-3.2 Use of the Tables
15-3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9
15-4 Other Variabls Sampling Procedures
15-4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean
15-4.2 Sequential Sampling by Variables
15-5 Chain Sampling
15-6 Continuous Sampling
15-6.1 CSP-1
15-6.2 Other Continuous Sampling Plans
15-7 Skip-Lot Sampling Plans
Appendix
I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
II. Cumulative Standard Normal Distribution
III. Percentage Points of the Chi-squre Distribution
IV. Percentage Points of the t Distribution
V. Percentage Points of the F Distribution
VI. Factors for Constructing Variables Control
VII. Factors for Two-Sided Normal Tolerance Limits
VIII. Factors for One-Sided Nomral Tolerance Limits
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