Bhisham C. Gupta - Statistical Quality Control

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STATISTICAL QUALITY CONTROL
Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors Statistical Quality Control: Using MINITAB, R, JMP and Python
Statistical Quality Control: Using MINITAB, R, JMP and Python

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Table of Contents

1 Cover

2 Title Page

3 Copyright Page

4 Dedication Page

5 Preface Audience Topics Covered in This Book Approach Hallmark Features Student Resources Instructor Resources Errata Acknowledgments

6 About the Companion Website

7 1 Quality Improvement and Management1.1 Introduction 1.2 Statistical Quality Control 1.3 Implementing Quality Improvement 1.4 Managing Quality Improvement 1.5 Conclusion

8 2 Basic Concepts of the Six Sigma Methodology2.1 Introduction 2.2 What Is Six Sigma? 2.3 Is Six Sigma New? 2.4 Quality Tools Used in Six Sigma 2.5 Six Sigma Benefits and Criticism Review Practice Problems

9 3 Describing Quantitative and Qualitative Data3.1 Introduction 3.2 Classification of Various Types of Data 3.3 Analyzing Data Using Graphical Tools 3.4 Describing Data Graphically 3.5 Analyzing Data Using Numerical Tools 3.6 Some Important Probability Distributions Review Practice Problems

10 4 Sampling Methods4.1 Introduction 4.2 Basic Concepts of Sampling 4.3 Simple Random Sampling 4.4 Stratified Random Sampling 4.5 Systematic Random Sampling 4.6 Cluster Random Sampling Review Practice Problems

11 5 Phase I Quality Control Charts for Variables5.1 Introduction 5.2 Basic Definition of Quality and Its Benefits 5.3 Statistical Process Control 5.4 Control Charts for Variables 5.5 Shewhart картинка 1and R Control Charts 5.6 Shewhart картинка 2and R Control Charts When the Process Mean and Standard Deviation are Known 5.7 Shewhart картинка 3and R Control Charts for Individual Observations 5.8 Shewhart картинка 4and S Control Charts with Equal Sample Sizes 5.9 Shewhart картинка 5and S Control Charts with Variable Sample Sizes 5.10 Process Capability Review Practice Problems

12 6 Phase I Control Charts for Attributes6.1 Introduction 6.2 Control Charts for Attributes 6.3 The p Chart: Control Charts for Nonconforming Fractions with Constant Sample Sizes 6.4 The p Chart: Control Chart for Nonconforming Fractions with Variable Samples Sizes 6.5 The np Chart: Control Charts for the Number of Nonconforming Units 6.6 The c Control Chart – Control Charts for Nonconformities per Sample 6.7 The u Chart

13 7 Phase II Quality Control Charts for Detecting Small Shifts7.1 Introduction 7.2 Basic Concepts of CUSUM Control Charts 7.3 Designing a CUSUM Control Chart 7.4 Moving Average (MA) Control Charts 7.5 Exponentially Weighted Moving Average (EWMA) Control Charts Review Practice Problems

14 8 Process and Measurement System Capability Analysis8.1 Introduction 8.2 Development of Process Capability Indices 8.3 Various Process Capability Indices 8.4 Pre‐control 8.5 Measurement System Capability Analysis Review Practice Problems

15 9 Acceptance Sampling Plans9.1 Introduction 9.2 The Intent of Acceptance Sampling Plans 9.3 Sampling Inspection vs. 100% Inspection 9.4 Classification of Sampling Plans 9.5 Acceptance Sampling by Attributes 9.6 Single Sampling Plans for Attributes 9.7 Other Types of Sampling Plans for Attributes 9.8 ANSI/ASQ Z1.4‐2003 Sampling Standard and Plans 9.9 Dodge‐Romig Tables 9.10 ANSI/ASQ Z1.9‐2003 Acceptance Sampling Plans by Variables 9.11 Continuous‐Sampling Plans Review Practice Problems

16 10 Computer Resources to Support SQC: Minitab, R, JMP, and Python9.1 Introduction

17 Appendix A: Statistical Tables

18 Appendix B: Answers to Selected Practice ProblemsChapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9

19 Bibliography

20 Index

21 End User License Agreement

List of Tables

1 Chapter 2 Table 2.1 Defects per million opportunities (DPMO) for various sigma levels; ... Table 2.2 Root causes of the Eight Wastes. Table 2.3 The 5S methods.

2 Chapter 3 Table 3.1 Annual revenues of 100 small to mid‐size companies located in the m... Table 3.2 Frequency distribution for data in Table 3.1. Table 3.3 Frequency distribution table for the data in Example 3.2. Table 3.4 Frequency table for the data in Example 3.3. Table 3.5 Understanding defect rates as a function of various process steps. Table 3.6 Frequency distribution table for the data in Example 3.7. Table 3.7 Frequency distribution table for the survival time of parts.Table 3.8 Cholesterol level and systolic BP of 30 randomly selected US males ...Table 3.9 Portion of Table A.5 in the Appendix for n = 5.Table 3.10 Portion of Table A.6 from the Appendix.Table 3.11 A portion of standard normal distribution table, Table A.7, from t...

3 Chapter 4Table 4.1 Table of random numbers.

4 Chapter 5Table 5.1 Check sheet summarizing study data over a period of four weeks.Table 5.2 Frequencies and weighted frequencies when different types of defect...Table 5.3 Percentage of nonconforming units in different shifts over a period...Table 5.4 Lengths (cm) of bolts used in the assembly of mid‐size car engines.Table 5.5 Lengths (cm) of bolts used in the assembly of mid‐size car engines.Table 5.6 Inside diameter measurements (cm) of piston rings for car engines.

5 Chapter 6Table 6.1 Control charts for attributes.Table 6.2 Number of nonconforming APLs inspected each day during the study pe...Table 6.3 Number of nonconforming APLs with 100% inspection each day.Table 6.4 Total number of nonconformities in samples consisting of five paper...Table 6.5 Number of nonconformities on printed boards for laptops per sample;...Table 6.6 Number of nonconformities on printed boards with varying sample siz...

6 Chapter 7Table 7.1 Data from a manufacturing process of medical equipment parts before...Table 7.2a Values of h for given value of k when ARL 0= 370 for two‐sided tabu...Table 7.2b ARL values for certain mean shifts for CUSUM and Shewhart control ...Table 7.3 Tabular CUSUM control chart for the data given in Table 7.1.Table 7.4 Data from a manufacturing process of medical equipment before and a...Table 7.5 Tabular CUSUM control chart using FIR feature for data in Table 7.4...Table 7.6 Tabular CUSUM control chart using the FIR feature for data in Table...Table 7.7 Moving average chart ( M i) for data in Table 7.4 with μ = 15, σ ...Table 7.8 Control limits for each sample point.Table 7.9 A selection of EWMA charts with ARL 0≅ 500.Table 7.10 Exponentially weighted moving average control chart ( z i) for data ...Table 7.11 Control limits for each sample point.

7 Chapter 8Table 8.1 Measurement data for tie rods.Table 8.2 Values of C pkand C pas the process mean changes.Table 8.3 Different processes with the same value of C pk.Table 8.4 PPM of nonconforming units for different values of C pk.Table 8.5 The values of C pkand C pmas μ deviates from the target.Table 8.6 (σ = 2) values of C p, C pk, C pm, C pmk, and C pnstfor μ = 20, 22, 24, ...Table 8.7 (μ = 20) Values of C p, C pk, C pm, C pmk, and C pnstfor σ = 2, 2.5, 3.0...Table 8.8 Pre‐control rules.Table 8.9 Data for an experiment involving three operators, 10 parts (con rod...Table 8.10 Two‐way ANOVA table with interaction.Table 8.11 Gauge R&R variance components.Table 8.12 Gauge evaluation.Table 8.13 Measurement data for Problem 8.34.Table 8.14 Measurement data for Problem 8.36.Table 8.15 Measurement data for Problem 8.37.

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