Introduction to Statistical Quality Control, EMEA Edition

Häftad, Engelska, 2019

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Beskrivning

Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge.

Produktinformation

Utgivningsdatum:2019-12-30
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Häftad
Språk:Engelska
Antal sidor:768
Upplaga:8
Förlag:John Wiley & Sons Inc
ISBN:9781119657118

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Tillverkningsteknik inom Naturvetenskap och teknik

Innehållsförteckning

About the Author iiiPreface vPart 1 Introduction 11 Quality Improvement in the Modern Business Environment 3Chapter Overview and Learning Objectives 31.1. The Meaning of Quality and Quality Improvement 31.2. A Brief History of Quality Control and Improvement 91.3. Statistical Methods for Quality Control and Improvement 131.4. Management Aspects of Quality Improvement 162 The DMAIC Process 47Chapter Overview and Learning Objectives 472.1. Overview of DMAIC 472.2. The Define Step 502.3. The Measure Step 522.4. The Analyze Step 532.5. The Improve Step 542.6. The Control Step 552.7. Examples of DMAIC 56Part 2 Statistical Methods Useful in Quality Control and Improvement 633 Modeling Process Quality 65Chapter Overview and Learning Objectives 653.1. Describing Variation 653.2. Important Discrete Distributions 793.3. Important Continuous Distributions 853.4. Probability Plots 963.5. Some Useful Approximations 1004 Inferences About Process Quality 103Chapter Overview and Learning Objectives 1034.1. Statistics and Sampling Distributions 1044.2. Point Estimation of Process Parameters 1094.3. Statistical Inference for a Single Sample 1114.4. Statistical Inference for Two Samples 1284.5. What if There are More than Two Populations? The Analysis of Variance 1434.6. Linear Regression Models 152Part 3 Basic Methods of Statistical Process Control and Capability Analysis 1735 Methods and Philosophy of Statistical Process Control 175Chapter Overview and Learning Objectives 1755.1. Introduction 1755.2. Chance and Assignable Causes of Quality Variation 1765.3. Statistical Basis of the Control Chart 1775.4. The Rest of the Magnificent Seven 1955.5. Implementing SPC in a Quality Improvement Program 2015.6. An Application of SPC 2025.7. Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses 2086 Control Charts for Variables 218Chapter Overview and Learning Objectives 2186.1. Introduction 2186.2. Control Charts for x̅ and R 2196.3. Control Charts for x̅ and s 2426.4. The Shewhart Control Chart for Individual Measurements 2506.5. Summary of Procedures for x̅, R, and s Charts 2606.6. Applications of Variables Control Charts 2617 Control Charts for Attributes 265Chapter Overview and Learning Objectives 2657.1. Introduction 2657.2. The Control Chart for Fraction Nonconforming 2667.3. Control Charts for Nonconformities (Defects) 2897.4. Choice Between Attributes and Variables Control Charts 3077.5. Guidelines for Implementing Control Charts 3118 Process and Measurement System Capability Analysis 317Chapter Overview and Learning Objectives 3178.1. Introduction 3178.2. Process Capability Analysis Using a Histogram or a Probability Plot 3198.3. Process Capability Ratios 3238.4. Process Capability Analysis Using a Control Chart 3368.5. Process Capability Analysis Using Designed Experiments 3388.6. Process Capability Analysis with Attribute Data 3388.7. Describing Capability for Many Processes 3408.8. Gauge and Measurement System Capability Studies 3418.9. Setting Specification Limits on Discrete Components 3608.10. Estimating the Natural Tolerance Limits of a Process 366Part 4 Other Statistical Process-Monitoring and Control Techniques 3699 Cumulative Sum and Exponentially Weighted Moving Average Control Charts 371Chapter Overview and Learning Objectives 3719.1. The Cumulative Sum Control Chart 3729.2. The Exponentially Weighted Moving Average Control Chart 3909.3. The Moving Average Control Chart 40010 Other Univariate Statistical Process-Monitoring and Control Techniques 403Chapter Overview and Learning Objectives 40310.1. Statistical Process Control for Short Production Runs 40410.2. Modified and Acceptance Control Charts 40710.3. Control Charts for Multiple-Stream Processes 41210.4. SPC with Autocorrelated Process Data 41510.5. Adaptive Sampling Procedures 43110.6. Economic Design of Control Charts 43310.7. Cuscore Charts 44210.8. The Changepoint Model for Process Monitoring 44410.9. Profile Monitoring 44510.10. Control Charts in Health Care Monitoring and Public Health Surveillance 44910.11. Overview of Other Procedures 45011 Multivariate Process Monitoring and Control 458Chapter Overview and Learning Objectives 45811.1. The Multivariate Quality-Control Problem 45911.2. Description of Multivariate Data 46011.3. The Hotelling T2 Control Chart 46211.4. The Multivariate EWMA Control Chart 47311.5. Regression Adjustment 47611.6. Control Charts for Monitoring Variability 47911.7. Latent Structure Methods 48212 Engineering Process Control and SPC 488Chapter Overview and Learning Objectives 48812.1. Process Monitoring and Process Regulation 48812.2. Process Control by Feedback Adjustment 48912.3. Combining SPC and EPC 500Part 5 Process Design and Improvement with Designed Experiments 50513 Factorial and Fractional Factorial Experiments for Process Design and Improvement 507Chapter Overview and Learning Objectives 50713.1. What is Experimental Design? 50713.2. Examples of Designed Experiments in Process and Product Improvement 50913.3. Guidelines for Designing Experiments 51213.4. Factorial Experiments 51413.5. The 2k Factorial Design 52313.6. Fractional Replication of the 2k Design 55114 Process Optimization with Designed Experiments 563Chapter Overview and Learning Objectives 56314.1. Response Surface Methods and Designs 56314.2. Process Robustness Studies 57214.3. Evolutionary Operation 583Part 6 Acceptance Sampling 58915 Lot-by-Lot Acceptance Sampling for Attributes 591Chapter Overview and Learning Objectives 59115.1. The Acceptance-Sampling Problem 59115.2. Single-Sampling Plans for Attributes 59615.3. Double, Multiple, and Sequential Sampling 60615.4. Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859) 61515.5. The Dodge-Romig Sampling Plans 62316 Other Acceptance-Sampling Techniques 627Chapter Overview and Learning Objectives 62716.1. Acceptance Sampling by Variables 62716.2. Designing a Variables-Sampling Plan with a Specified OC Curve 63016.3. MIL STD 414 (ANSI/ASQC Z1.9) 63116.4. Other Variables Sampling Procedures 63516.5. Chain Sampling 63616.6. Continuous Sampling 63816.7. Skip-Lot Sampling Plans 641Problems (Available in e-text for students) P-1Appendix A-1Bibliography (Available in e-text for students) B-1Index I-1