Statistical Quality Control

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 This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept. Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide alsoFocuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the fieldDiscusses aspects of Six Sigma MethodologyTeaches readers to use MINITAB, R, JMP and Python to create and analyze chartsRequires no previous knowledge of statistical theoryIs supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems Statistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.

Оглавление

Bhisham C. Gupta. Statistical Quality Control

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Statistical Quality Control. Using Minitab, R, JMP, and Python

Preface

Audience

Topics Covered in This Book

Approach

Hallmark Features

Student Resources

Instructor Resources

Errata

Acknowledgments

About the Companion Website

1 Quality Improvement and Management. 1.1 Introduction

1.2 Statistical Quality Control

Definition 1.1

1.2.1 Quality and the Customer

1.2.2 Quality Improvement

1.2.3 Quality and Productivity

1.3 Implementing Quality Improvement

1.3.1 Outcomes of Quality Control

1.3.2 Quality Control and Quality Improvement

1.3.2.1 Acceptance Sampling Plans

1.3.2.2 Process Control

1.3.2.3 Removing Obstacles to Quality

1.3.2.4 Eliminating Productivity Quotas

1.3.3 Implementing Quality Improvement

1.4 Managing Quality Improvement

1.4.1 Management and Their Responsibilities

1.4.2 Management and Quality

1.4.3 Risks Associated with Making Bad Decisions

Definition 1.2

Definition 1.3

1.5 Conclusion

2 Basic Concepts of the Six Sigma Methodology. 2.1 Introduction

2.2 What Is Six Sigma?

2.2.1 Six Sigma as a Management Philosophy

2.2.2 Six Sigma as a Systemic Approach to Problem Solving

2.2.3 Six Sigma as a Statistical Standard of Quality

2.2.3.1 Statistical Basis for Six Sigma

2.2.4 Six Sigma Roles

2.3 Is Six Sigma New?

2.4 Quality Tools Used in Six Sigma

2.4.1 The Basic Seven Tools and the New Seven Tools

2.4.2 Lean Tools

2.4.2.1 Eight Wastes

2.4.2.2 Visual Management

2.4.2.3 The 5S Method

2.4.2.4 Value‐Stream Mapping

2.4.2.5 Mistake‐Proofing

2.4.2.6 Quick Changeover

2.5 Six Sigma Benefits and Criticism

2.5.1 Why Do Some Six Sigma Initiatives Fail?

Review Practice Problems

3 Describing Quantitative and Qualitative Data. 3.1 Introduction

3.2 Classification of Various Types of Data

3.3 Analyzing Data Using Graphical Tools

3.3.1 Frequency Distribution Tables for Qualitative and Quantitative Data

3.3.1.1 Qualitative Data

Example 3.1 Industrial Revenue Data with the Following Example

Solution

Example 3.2 Manufacturing Data

Solution

3.3.1.2 Quantitative Data

Example 3.3 Rod Manufacturing Data

Solution

3.4 Describing Data Graphically. 3.4.1 Dot Plots

Example 3.4 Shipment Data

Solution

3.4.2 Pie Charts

Example 3.5 Manufacturing Process

Minitab

R

3.4.3 Bar Charts

Example 3.6 Manufacturing Employee Data

Solution

Minitab

R

Example 3.7 Auto Parts Data

Solution

Example 3.8 Auto Parts Data

Solution

R

3.4.4 Histograms

Example 3.9 Fuel Pump Data

Solution

Minitab

R

3.4.5 Line Graphs

Example 3.10 Flu Vaccine Data

Solution

R

3.4.6 Measures of Association

Example 3.11 Medical Data

Solution

Minitab

R

3.5 Analyzing Data Using Numerical Tools

3.5.1 Numerical Measures

Definition 3.1

Definition 3.2

3.5.2 Measures of Centrality

3.5.2.1 Mean

Example 3.12 Hourly Wage Data

Solution

Example 3.13 Age Data

Solution

3.5.2.2 Median

Example 3.14 Rod Data

Solution

Example 3.15 Hourly Wage Data

Solution

3.5.2.3 Mode

Example 3.16

Solution

Example 3.17

Solution

Example 3.18

Solution

3.5.3 Measures of Dispersion

3.5.3.1 Range

Example 3.19

Solution

3.5.3.2 Variance

3.5.3.3 Standard Deviation

Example 3.20 Machining Data

Solution

Example 3.21

Solution

3.5.3.4 Empirical Rule

Example 3.22 Soft Drink Data

Solution

Example 3.23 Financial Data

Solution

3.5.3.5 Interquartile Range

Example 3.24 Nurse Salary Data

Solution

3.5.4 Box‐and‐Whisker Plot

Example 3.25 Noise Data

Solution

3.6 Some Important Probability Distributions

3.6.1 The Binomial Distribution

Definition 3.3

Example 3.26

Solution

3.6.1.1 Binomial Probability Tables

Definition 3.4

Example 3.27

Solution

3.6.2 The Hypergeometric Distribution

Definition 3.5

Example 3.28

Solution

Example 3.29

Solution

3.6.2.1 Mean and Standard Deviation of a Hypergeometric Distribution

Example 3.30 Computer Shipments Data

Solution

Minitab

R

3.6.3 The Poisson Distribution

Definition 3.6

Definition 3.7

Example 3.31

Solution

3.6.3.1 Mean and Standard Deviation of a Poisson Distribution

3.6.3.2 Poisson Probability Tables

Example 3.32

Solution

Minitab (Binomial Distribution)

R

Minitab (Poisson Distribution)

R

3.6.4 The Normal Distribution

Definition 3.8

Definition 3.9

Example 3.33

Solution

Example 3.34

Solution

Minitab

R

Example 3.35

Solution

Minitab

R

Review Practice Problems

4 Sampling Methods. 4.1 Introduction

4.2 Basic Concepts of Sampling

Definition 4.1

Definition 4.2

Definition 4.3

Definition 4.4

Definition 4.5

4.2.1 Introducing Various Sampling Designs

Example 4.1

Definition 4.6

Definition 4.7

Definition 4.8

4.3 Simple Random Sampling

Definition 4.9

Definition 4.10

4.3.1 Estimating the Population Mean and Population Total

Example 4.2 Steel Casting Data

Solution

Example 4.3 Manufacturing Time

Solution

4.3.2 Confidence Interval for the Population Mean and Population Total

Example 4.4

Solution

4.3.3 Determining Sample Size

Example 4.5

Solution

4.4 Stratified Random Sampling

4.4.1 Estimating the Population Mean and Population Total

4.4.2 Confidence Interval for the Population Mean and Population Total

Example 4.6 Labor Cost Data

Solution

4.4.3 Determining Sample Size

Example 4.7

Solution

4.5 Systematic Random Sampling

4.5.1 Estimating the Population Mean and Population Total

4.5.2 Confidence Interval for the Population Mean and Population Total

4.5.3 Determining the Sample Size

Example 4.8 Timber Volume Data

Solution

Example 4.9

Solution

4.6 Cluster Random Sampling

4.6.1 Estimating the Population Mean and Population Total

4.6.2 Confidence Interval for the Population Mean and Population Total

Example 4.10 Repair Cost of Hydraulic Pumps

Solution

4.6.3 Determining the Sample Size

Example 4.11

Solution

Review Practice Problems

5 Phase I Quality Control Charts for Variables. 5.1 Introduction

5.2 Basic Definition of Quality and Its Benefits

5.3 Statistical Process Control

Definition 5.1

5.3.1 Check Sheets

Example 5.1 Paper Mill Data

5.3.2 Pareto Chart

5.3.3 Cause‐and‐Effect (Fishbone or Ishikawa) Diagrams

5.3.4 Defect‐Concentration Diagrams

5.3.5 Run Charts

5.4 Control Charts for Variables

5.4.1 Process Evaluation

5.4.2 Action on the Process

5.4.3 Action on the Output

5.4.4 Variation

5.4.4.1 Common Causes (Random Causes)

5.4.4.2 Special Causes (Assignable Causes)

5.4.4.3 Local Actions and Actions on the System

5.4.4.4 Relationship Between Two Types of Variation

5.4.5 Control Charts

5.4.5.1 Preparation for Using Control Charts

5.4.5.2 Benefits of Control Charts

5.4.5.3 Rational Samples for control Charts

5.4.5.3.1 Average Run Length. Definition 5.2

Definition 5.3

Example 5.2

Solution

5.4.5.3.2 Operating Characteristic Curve (OC Curve) Definition 5.4

Definition 5.5

Definition 5.6

5.5 Shewhart and R Control Charts

5.5.1 Calculating Sample Statistics

Example 5.3

Solution

5.5.2 Calculating Control Limits

Example 5.4

Solution

Minitab

R

5.5.3 Interpreting Shewhart and R Control Charts

5.5.4 Extending the Current Control Limits for Future Control

Example 5.5

Solution

5.6 Shewhart and R Control Charts When the Process Mean and Standard Deviation are Known

R

5.7 Shewhart and R Control Charts for Individual Observations

Example 5.6

Solution

Minitab

R

5.8 Shewhart and S Control Charts with Equal Sample Sizes

Example 5.7

Solution

Minitab

R

5.9 Shewhart and S Control Charts with Variable Sample Sizes

Example 5.8 Car Manufacturing Data

Solution

Minitab

R

5.10 Process Capability

Example 5.9

Solution

Review Practice Problems

6 Phase I Control Charts for Attributes. 6.1 Introduction

6.2 Control Charts for Attributes

Definition 6.1

6.3 The p Chart: Control Charts for Nonconforming Fractions with Constant Sample Sizes

6.3.1 Control Limits for the p Control Chart

6.3.2 Interpreting the Control Chart for Nonconforming Fractions

Example 6.1 Fuel Pump Data

Solution

Minitab

R

6.4 The p Chart: Control Chart for Nonconforming Fractions with Variable Samples Sizes

Example 6.2 Fuel Pump Data

Solution

Minitab

R

6.5 The np Chart: Control Charts for the Number of Nonconforming Units

6.5.1 Control Limits for np Control Charts

Example 6.3

Minitab

R

6.6 The c Control Chart – Control Charts for Nonconformities per Sample

Example 6.4 Paper Mill Data

Solution

Minitab

R

6.7 The u Chart

Example 6.5 Printed Boards

Solution

Minitab

R

Example 6.6

Solution

Minitab

R

Review Practice Problems

7 Phase II Quality Control Charts for Detecting Small Shifts. 7.1 Introduction

7.2 Basic Concepts of CUSUM Control Charts. 7.2.1 CUSUM Control Charts vs. Shewhart and R Control Charts

Example 7.1 Medical Equipment Data

Solution

7.3 Designing a CUSUM Control Chart

7.3.1 Two‐Sided CUSUM Control Charts Using the Numerical Procedure

Example 7.2

Solution

Example 7.3

Solution

7.3.2 The Fast Initial Response (FIR) Feature for CUSUM Control Charts

Example 7.4

Solution

Example 7.5

Solution

Minitab

R

7.3.3 One‐Sided CUSUM Control Charts

7.3.4 Combined Shewhart‐CUSUM Control Charts

7.3.5 CUSUM Control Charts for Controlling Process Variability

7.4 Moving Average (MA) Control Charts

Example 7.6

Solution

Example 7.7

Solution

Minitab

R

7.5 Exponentially Weighted Moving Average (EWMA) Control Charts

Example 7.8

Solution

Minitab

R

Review Practice Problems

8 Process and Measurement System Capability Analysis. 8.1 Introduction

8.2 Development of Process Capability Indices

8.3 Various Process Capability Indices. 8.3.1 Process Capability Index: Cp

Example 8.1 Car Engine Data

Solution

Example 8.2

Solution

Example 8.3

Solution

Example 8.4

Solution

8.3.2 Process Capability Index: Cpk

8.3.3 Process Capability Index: Cpm

Example 8.5

Solution

8.3.4 Process Capability Index: Cpmk

Example 8.6

Solution

8.3.5 Process Capability Index: Cpnst

8.3.5.1 Comparing Cpnst with Cpk and Cpm. Example 8.7 Bothe (2002)

Example 8.8 Bothe (2002)

Example 8.9 Pearn et al. (1992)

8.3.5.2 Other Features of Cpnst

8.3.6 Process Performance Indices: Pp and Ppk

8.4 Pre‐control

8.4.1 Global Perspective on the Use of Pre‐control – Understanding the Color‐Coding Scheme

8.4.2 The Mechanics of Pre‐control

8.4.3 The Statistical Basis for Pre‐control

8.4.4 Advantages and Disadvantages of Pre‐control

8.4.4.1 Advantages of Pre‐control

8.4.4.2 Disadvantages of Pre‐control

8.5 Measurement System Capability Analysis

Definition 8.1

Definition 8.2

Definition 8.3

8.5.1 Evaluating Measurement System Performance

8.5.2 The Range Method

Definition 8.4

Definition 8.5

Definition 8.6

Definition 8.7

Definition 8.8

Definition 8.9

Example 8.10

Solution

8.5.3 The ANOVA Method

Minitab

R

Interpretation of Two‐Way ANOVA Table with Interaction

Interpretation of Two‐Way ANOVA Table without Interaction

8.5.4 Graphical Representation of a Gauge R&R Study

8.5.5 Another Measurement Capability Index

Review Practice Problems

9 Acceptance Sampling Plans. 9.1 Introduction

9.2 The Intent of Acceptance Sampling Plans

9.3 Sampling Inspection vs. 100% Inspection

9.4 Classification of Sampling Plans

9.4.1 Formation of Lots for Acceptance Sampling Plans

9.4.2 The Operating Characteristic (OC) Curve

Example 9.1

Solution

9.4.3 Two Types of OC Curves

9.4.4 Some Terminology Used in Sampling Plans

Example 9.2

Solution

Method

Minitab

R

9.5 Acceptance Sampling by Attributes

9.5.1 Acceptable Quality Limit (AQL)

9.5.2 Average Outgoing Quality (AOQ)

9.5.3 Average Outgoing Quality Limit (AOQL)

9.5.4 Average Total Inspection (ATI)

Example 9.3

Solution

Example 9.4

Solution

9.6 Single Sampling Plans for Attributes

9.7 Other Types of Sampling Plans for Attributes

9.7.1 Double‐Sampling Plans for Attributes

9.7.2 The OC Curve

Example 9.5

Solution

Example 9.6

Solution

9.7.3 Multiple‐Sampling Plans

9.7.4 Average Sample Number

Example 9.7

Solution

9.7.5 Sequential‐Sampling Plans

Example 9.8

Solution

9.8 ANSI/ASQ Z1.4‐2003 Sampling Standard and Plans

9.8.1 Levels of Inspection

9.8.2 Types of Sampling

Example 9.9

Solution

9.9 Dodge‐Romig Tables

9.10 ANSI/ASQ Z1.9‐2003 Acceptance Sampling Plans by Variables

9.10.1 ANSI/ASQ Z1.9‐2003 – Variability Known

9.10.2 Variability Unknown – Standard Deviation Method

Example 9.10 Operation Temperature Data

Solution

9.10.3 Variability Unknown – Range Method

Example 9.11 Electrical Resistance Data

Solution

9.11 Continuous‐Sampling Plans

9.11.1 Types of Continuous‐Sampling Plans

9.11.2 Dodge’s Continuous Sampling Plans

9.11.3 MIL‐STD‐1235B

Review Practice Problems

10 Computer Resources to Support SQC: Minitab, R, JMP, and Python. 9.1 Introduction

Appendix A. Statistical Tables

Appendix B Answers to Selected Practice Problems. Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Bibliography

End Notes

Index. A

B

C

D

E

F

G

H

I

J

L

M

N

O

P

Q

R

S

T

U

V

W

Z

WILEY END USER LICENSE AGREEMENT

Отрывок из книги

Bhisham C. Gupta

.....

Hiring a consulting firm that “specializes in quality improvement” and not taking control into their own hands is the biggest mistake made by top management of any organization. Hiring a consulting firm means top management is relinquishing their responsibility for sending the important message to their employees that quality improvement is a never‐ending policy of the company.

Top management should play an important role in team building and, as much as possible, should be part of the teams. Top management and supervisors should understand the entire process of quality improvement so that they can guide their employees appropriately on how to be the team players. Teamwork can succeed only if management supports it and makes teamwork part of their new policy. Forming a team helps to achieve quality improvement, and establishing plans is essential: it is part of the job or process of managing quality improvement.

.....

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