A First Course in Quality

Engineering

Integrating Statistical and

Management Methods of Quality

Third Edition

www.EngineeringBooksPDF.comhttp://taylorandfrancis.com

www.EngineeringBooksPDF.comA First Course in Quality

Engineering

Integrating Statistical and

Management Methods of Quality

Third Edition

K. S. Krishnamoorthi

V. Ram Krishnamoorthi

Arunkumar Pennathur

www.EngineeringBooksPDF.comCRC Press

Taylor & Francis Group

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Library of Congress Cataloging‑in‑Publication Data

Names: Krishnamoorthi, K. S., author. | Krishnamoorthi, V. Ram, author. |

Pennathur, Arunkumar, author.

Title: A first course in quality engineering : integrating statistical and

management methods of quality / K.S. Krishnamoorthi, V. Ram

Krishnamoorthi, and Arunkumar Pennathur.

Description: Third edition. | Boca Raton : Taylor & Francis, CRC Press, 2018.

| Includes bibliographical refeferences and index.

Identifiers: LCCN 2018011965| ISBN 9781498764209 (hardback : alk. paper) |

ISBN 9781498764216 (ebook)

Subjects: LCSH: Quality control. | Process control--Statistical methods.

Classification: LCC TS156.8 .K75 2018 | DDC 658.5/62--dc23

LC record available at https://lccn.loc.gov/2018011965

Visit the Taylor & Francis Web site at

http://www.taylorandfrancis.com

and the CRC Press Web site at

http://www.crcpress.com

www.EngineeringBooksPDF.comContents

P r e fa c e t o t h e th i r d e d i t i o n

P r e fa c e t o t h e S e c o n d e d i t i o n

P r e fa c e t o t h e fi r S t e d i t i o n

a u t h o r S

c h a P t e r 1

i n t r o d u c t i o n t o Q ua l i t y

1.1 A Historical Overview

1.1.1 A Note about “Quality Engineering”

1.2 Defining Quality

1.2.1 Product Quality vs. Service Quality

1.3 The Total Quality System

1.4 Total Quality Management

1.5 Economics of Quality

1.6 Quality, Productivity, and Competitive Position

1.7 Quality Costs

1.7.1 Categories of Quality Costs

1.7.1.1 Prevention Cost

1.7.1.2 Appraisal Cost

Internal Failure Cost

1.7.1.3

1.7.1.4 External Failure Cost

1.7.2

Steps in Conducting a Quality Cost Study

1.7.3 Projects Arising from a Quality Cost Study

1.7.4 Quality Cost Scoreboard

1.7.5 Quality Costs Not Included in the TQC

1.7.6 Relationship among Quality Cost Categories

1.7.7

1.7.8 A Case Study in Quality Costs

Success Stories

Summary on Quality Costs

1.8

1.9 Exercise

1.9.1 Practice Problems

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1.9.2 Mini-Projects

Mini-Project 1.1

Mini-Project 1.2

Mini-Project 1.3

Mini-Project 1.4

References

c h a P t e r 2 S tat i S t i c S f o r Q ua l i t y

2.1 Variability in Populations

Some Definitions

2.2

2.2.1 The Population and a Sample

2.2.2 Two Types of Data

2.3 Quality vs. Variability

2.4 Empirical Methods for Describing Populations

2.4.1 The Frequency Distribution

2.4.5 Exercises in Empirical Methods

2.5 Mathematical Models for Describing Populations

2.5.1 Probability

2.4.2 Numerical Methods for Describing Populations

2.4.3 Other Graphical Methods

2.4.4 Other Numerical Measures

Stem-and-Leaf Diagram

2.4.2.1 Calculating the Average and Standard Deviation

2.4.3.1

2.4.3.2 Box-and-Whisker Plot

2.4.4.1 Measures of Location

2.4.4.2 Measures of Dispersion

2.4.1.1 The Histogram

2.4.1.2 The Cumulative Frequency Distribution

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2.6.2.1 CI for the μ of a Normal Population When σ Is Known 113

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2.6.2.2

2.5.3.1 Random Variable

2.5.3.2 Probability Mass Function

2.5.3.3 Probability Density Function

2.5.3.4 The Cumulative Distribution Function

2.5.3.5 The Mean and Variance of a Distribution

Some Important Probability Distributions

2.5.4.1 The Binomial Distribution

2.5.4.2 The Poisson Distribution

2.5.4.3 The Normal Distribution

2.5.4.4 Distribution of the Sample Average X

2.5.4.5 The Central Limit Theorem

2.5.1.1 Definition of Probability

2.5.1.2 Computing the Probability of an Event

2.5.1.3 Theorems on Probability

2.5.1.4 Counting the Sample Points in a Sample Space

Interpretation of CI

2.5.2 Exercises in Probability

2.5.3 Probability Distributions

2.5.4

2.6

Summary on Probability Distributions

2.5.5 Exercises in Probability Distributions

Inference of Population Quality from a Sample

2.6.1 Definitions

2.6.2 Confidence Intervals

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2.6.2.3 CI for μ When σ Is Not Known

2.6.2.4 CI for σ2 of a Normal Population

2.6.3 Hypothesis Testing

Two Types of Errors

2.6.3.1 Test Concerning the Mean µ of a Normal Population

When σ Is Known

2.6.3.2 Why Place the Claim Made about a Parameter in H1?

2.6.3.3 The Three Possible Alternate Hypotheses

2.6.3.4 Test Concerning the Mean μ of a Normal Population

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When σ Is Not Known

2.6.4 Tests for Normality

2.6.5 The P-Value

Summary on Inference Methods

2.6.6 Exercises in Inference Methods

2.6.6.1 Confidence Intervals

2.6.6.2 Hypothesis Testing

2.6.6.3 Goodness-of-Fit Test

2.6.4.1 Use of the Normal Probability Plot

2.6.4.2 Normal Probability Plot on the Computer

2.6.4.3 A Goodness-of-Fit Test

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2.6.3.5 Test for Difference of Two Means When σs Are Known 125

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Mini-Project 2.1

Mini-Project 2.2

Mini-Project 2.3

Mini-Project 2.4

2.7 Mini-Projects

3.1

3.2

Planning for Quality

3.1.1 The Product Creation Cycle

Product Planning

3.2.1 Finding Customer Needs

3.2.1.1 Customer Survey

3.2.2 Quality Function Deployment

3.2.2.1 Customer Requirements and Design Features

3.2.2.2 Prioritizing Design Features

3.2.2.3 Choosing a Competitor as Benchmark

3.2.2.4 Targets

3.2.3 Reliability Fundamentals

3.2.3.1 Definition of Reliability

3.2.3.2 Hazard Function

3.2.3.3 The Bathtub Curve

3.2.3.4 Distribution of Product Life

3.2.3.5 The Exponential Distribution

3.2.3.6 Mean Time to Failure

3.2.3.7 Reliability Engineering

3.3

Product Design

3.3.1 Parameter Design

3.3.2 Design of Experiments

22 Factorial Design

3.3.2.1

3.3.2.2 Randomization

3.3.2.3 Experimental Results from a 22 Design

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Interaction Effects

3.3.2.4 Calculating the Factor Effects

3.3.2.5 Main Effects

3.3.2.6

3.3.2.7 A Shortcut for Calculating Effects

3.3.2.8 Determining the Significance of Effects

3.3.2.9 The 23 Design

3.3.2.10 Interpretation of the Results

3.3.2.11 Model Building

3.3.2.12 Taguchi Designs

3.3.3 Tolerance Design

3.3.3.1 Traditional Approaches

3.3.3.2 Tolerancing According to Dr. Taguchi

3.3.3.3 Assembly Tolerances

3.3.3.4 The RSS Formula

3.3.3.5 Natural Tolerance Limits

3.3.4 Failure Mode and Effects Analysis

3.3.5 Concurrent Engineering

3.3.5.1 Design for Manufacturability/Assembly

3.3.5.2 Design Reviews

3.4

Process Design

3.4.1 The Process Flow Chart

3.4.2 Process Parameter Selection: Experiments

3.4.3 Floor Plan Layout

3.4.4 Process FMEA

3.4.5 Process Control Plan

3.4.6 Other Process Plans

3.4.6.1 Process Instructions

3.4.6.2 Packaging Standards

3.4.6.3 Preliminary Process Capabilities

3.4.6.4 Product and Process Validation

3.4.6.5 Process Capability Results

3.4.6.6 Measurement System Analysis

3.4.6.7 Product/Process Approval

3.4.6.8

Feedback, Assessment, and Corrective Action

3.5 Exercise

3.5.1 Practice Problems

3.5.2 Mini-Projects

Mini-Project 3.1

Mini-Project 3.2

Mini-Project 3.3

References

c h a P t e r 4 Q ua l i t y i n P r o d u c t i o n — P r o c e S S c o n t r o l i

Process Control

4.1

4.2 The Control Charts

4.2.1 Typical Control Chart

4.2.2 Two Types of Data

4.3 Measurement Control Charts

4.3.1 X- and R-Charts

4.3.2 A Few Notes about the X- and R-Charts

4.3.2.1 The Many Uses of the Charts

4.3.2.2

Selecting the Variable for Charting

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False Alarm in the X-Chart

4.3.2.3 Preparing Instruments

4.3.2.4 Preparing Check Sheets

4.3.2.5

4.3.2.6 Determining Sample Size

4.3.2.7 Why 3-Sigma Limits?

4.3.2.8

Frequency of Sampling

4.3.2.9 Rational Subgrouping

4.3.2.10 When the Sample Size Changes for X- and R-Charts

4.3.2.11 Improving the Sensitivity of the X-Chart

4.3.2.12 Increasing the Sample Size

4.3.2.13 Use of Warning Limits

4.3.2.14 Use of Runs

4.3.2.15 Patterns in Control Charts

4.3.2.16 Control vs. Capability

4.3.3 X and S-Charts

4.3.4 The Run Chart

4.4 Attribute Control Charts

4.4.1 The P-Chart

4.4.2 The C-Chart

4.4.3

Some Special Attribute Control Charts

4.4.3.1 The P-Chart with Varying Sample Sizes

4.4.3.2 The nP-Chart

4.4.3.3 The Percent Defective Chart (100P-Chart)

4.4.3.4 The U-Chart

4.4.4 A Few Notes about the Attribute Control Charts

4.4.4.1 Meaning of the LCL on the P- or C-Chart

4.4.4.2

P-Chart for Many Characteristics

4.4.4.3 Use of Runs

4.4.4.4 Rational Subgrouping

4.5

4.6

Implementing SPC on Processes

Summary on Control Charts

4.5.1

Process Capability

4.6.1 Capability of a Process with Measurable Output

4.6.2 Capability Indices Cp and Cpk

4.6.3 Capability of a Process with Attribute Output

4.7 Measurement System Analysis

4.7.1 Properties of Instruments

4.7.2 Measurement Standards

4.7.3 Evaluating an Instrument

4.7.3.1 Properties of a Good Instrument

4.7.3.2 Evaluation Methods

4.7.3.3 Resolution

4.7.3.4 Bias

4.7.3.5 Variability (Precision)

4.7.3.6 A Quick Check of Instrument Adequacy

4.8 Exercise

4.8.1 Practice Problems

4.8.2 Mini-Projects

Mini-Project 4.1

Mini-Project 4.2

Mini-Project 4.3

References

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c h a P t e r 5 Q ua l i t y i n P r o d u c t i o n — P r o c e S S c o n t r o l ii

5.1 Derivation of Limits

5.1.1 Limits for the X-Chart

5.1.2 Limits for the R-Chart

5.1.3 Limits for the P-Chart

5.1.4 Limits for the C-Chart

5.2 Operating Characteristics of Control Charts

5.2.1 Operating Characteristics of an X-Chart

5.2.1.1 Computing the OC Curve of an X-Chart

5.2.2 OC Curve of an R-Chart

5.2.3 Average Run Length

5.2.4 OC Curve of a P-Chart

5.2.5 OC Curve of a C-Chart

5.3 Measurement Control Charts for Special Situations

5.3.1 X - and R-Charts When Standards for μ and/or σ Are Given

5.3.1.1 Case I: μ Given, σ Not Given

5.3.1.2 Case II: μ and σ Given

5.3.2 Control Charts for Slow Processes

5.3.2.1 Control Chart for Individuals (X-Chart)

5.3.2.2 Moving Average and Moving Range Charts

5.3.2.3 Notes on Moving Average and Moving Range Charts

5.3.3 The Exponentially Weighted Moving Average Chart

5.3.3.1 Limits for the EWMA Chart

5.3.4 Control Charts for Short Runs

5.3.4.1 The DNOM Chart

5.3.4.2 The Standardized DNOM Chart

5.4 Topics in Process Capability

5.4.1 The Cpm Index

5.4.2 Comparison of Cp, Cpk, and Cpm

5.4.3 Confidence Interval for Capability Indices

5.4.4 Motorola’s 6σ Capability

5.5 Topics in the Design of Experiments

5.5.1 Analysis of Variance

5.5.2 The General 2k Design

5.5.3 The 24 Design

5.5.4

5.5.5 Fractional Factorials: One-Half Fractions

2k Design with Single Trial

5.5.5.1 Generating the One-Half Fraction

5.5.5.2 Calculating the Effects

5.5.6 Resolution of a Design

5.6 Exercise

5.6.1 Practice Problems

5.6.2 Mini-Projects

Mini-Project 5.1

Mini-Project 5.2

References

c h a P t e r 6 M a n a g i n g f o r Q ua l i t y

6.1 Managing Human Resources

Importance of Human Resources

6.1.1

6.1.2 Organizations

6.1.2.1 Organization Structures

6.1.2.2 Organizational Culture

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6.1.3 Quality Leadership

6.1.3.1 Characteristics of a Good Leader

6.1.4 Customer Focus

6.1.5 Open Communications

6.1.6 Empowerment

6.1.7 Education and Training

6.1.7.1 Need for Training

6.1.7.2 Benefits from Training

6.1.7.3 Planning for Training

6.1.7.4 Training Methodology

6.1.7.5

6.1.7.6 Evaluating Training Effectiveness

Finding Resources

6.1.8 Teamwork

Selecting Team Members

6.1.8.1 Team Building

6.1.8.2

6.1.8.3 Defining the Team Mission

6.1.8.4 Taking Stock of the Team’s Strength

6.1.8.5 Building the Team

6.1.8.6 Basic Training for Quality Teams

6.1.8.7 Desirable Characteristics among Team Members

6.1.8.8 Why a Team?

6.1.8.9 Ground Rules for Running a Team Meeting

6.1.8.10 Making the Teams Work

6.1.8.11 Different Types of Teams

6.1.8.12 Quality Circles

6.2

6.1.9 Motivation Methods

6.1.10 Principles of Management

Strategic Planning for Quality

6.2.1 History of Planning

6.2.2 Making the Strategic Plan

Strategic Plan Deployment

6.2.3

6.3 Exercise

6.3.1 Practice Problems

6.3.2 Mini-Project

Mini-Project 6.1

References

c h a P t e r 7 Q ua l i t y i n P r o c u r e M e n t

7.1

Importance of Quality in Supplies

7.2 Establishing a Good Supplier Relationship

7.2.1 Essentials of a Good Supplier Relationship

7.3 Choosing and Certifying Suppliers

Single vs. Multiple Suppliers

7.3.1

7.3.2 Choosing a Supplier

7.3.3 Certifying a Supplier

Specifying the Supplies Completely

7.4

7.5 Auditing the Supplier

7.6

Supply Chain Optimization

7.6.1 The Trilogy of Supplier Relationship

7.6.2 Planning

7.6.3 Control

7.6.4

Improvement

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7.7 Using Statistical Sampling for Acceptance

7.7.1 The Need for Sampling Inspection

7.7.2

Single Sampling Plans for Attributes

7.7.2.1 The Operating Characteristic Curve

7.7.2.2 Calculating the OC Curve of a Single Sampling Plan

7.7.2.3 Designing an SSP

7.7.2.4 Choosing a Suitable OC Curve

7.7.2.5 Choosing a Single Sampling Plan

7.7.3 Double Sampling Plans for Attributes

7.7.3.1 Why Use a DSP?

7.7.3.2 The OC Curve of a DSP

7.7.4 The Average Sample Number of a Sampling Plan

7.7.5 MIL-STD-105E (ANSI Z1.4)

7.7.5.1

Selecting a Sampling Plan from MIL-STD-105E

7.7.6 Average Outgoing Quality Limit

7.7.7

Some Notes about Sampling Plans

7.7.7.1 What Is a Good AQL?

7.7.7.2 Available Choices for AQL Values in the

MIL-STD-105E

7.7.7.3 A Common Misconception about Sampling Plans

7.7.7.4

7.7.7.5 Variable Sampling Plans

Sampling Plans vs. Control Charts

7.8 Exercise

References

c h a P t e r 8 c o n t i n u o u S i M P r o v e M e n t o f Q ua l i t y

8.1 The Need for Continuous Improvement

8.2 The Problem-Solving Methodology

8.2.1 Deming’s PDCA Cycle

8.2.2

8.2.3 The Generic Problem-Solving Methodology

Juran’s Breakthrough Sequence

8.3 Quality Improvement Tools

8.3.1 Cause-and-Effect Diagram

8.3.2 Brainstorming

8.3.3 Benchmarking

8.3.4 Pareto Analysis

8.3.5 Histogram

8.3.6 Control Charts

8.3.7

Scatter Plots

8.3.8 Regression Analysis

Simple Linear Regression

8.3.8.1

8.3.8.2 Model Adequacy

8.3.8.3 Test of Significance

8.3.8.4 Multiple Linear Regression

8.3.8.5 Nonlinear Regression

8.3.9 Correlation Analysis

8.3.9.1

Significance in Correlation

8.4 Lean Manufacturing

8.4.1 Quality Control

8.4.2 Quantity Control

8.4.3 Waste and Cost Control

8.4.4 Total Productive Maintenance

Stable, Standardized Processes

8.4.5

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8.4.6 Visual Management

8.4.7 Leveling and Balancing

8.4.8 The Lean Culture

8.5 Exercise

8.5.1 Practice Problems

8.5.2 Term Project

References

c h a P t e r 9 a S y S t e M f o r Q ua l i t y

9.1 The Systems Approach

9.2 Dr. Deming’s System

9.2.1 Long-Term Planning

9.2.2 Cultural Change

9.2.3 Prevention Orientation

9.2.4 Quality in Procurement

9.2.5 Continuous Improvement

9.2.6 Training, Education, Empowerment, and Teamwork

9.3 Dr. Juran’s System

9.3.1 Quality Planning

9.3.2 Quality Control

9.3.3 Quality Improvement

9.4 Dr. Feigenbaum’s System

9.5 Baldrige Award Criteria

9.5.1 Criterion 1: Leadership

9.5.1.1

9.5.1.2 Governance and Societal Responsibilities

Senior Leadership

9.5.2 Criterion 2: Strategic Planning

9.5.2.1

9.5.2.2

Strategy Development

Strategy Implementation

9.5.3 Criterion 3: Customers

9.5.3.1 Voice of the Customer

9.5.3.2 Customer Engagement

9.5.4 Criterion 4: Measurement, Analysis,

and Knowledge Management

9.5.4.1 Measurement, Analysis, and Improvement

of Organizational Performance

Information and Knowledge Management

9.5.4.2

9.5.5 Criterion 5: Workforce

9.5.5.1 Workforce Environment

9.5.5.2 Workforce Engagement

9.5.6 Criterion 6: Operations

9.5.6.1 Work Processes

9.5.6.2 Operational Effectiveness

9.5.7 Criterion 7: Results

9.5.7.1 Product and Process Results

9.5.7.2 Customer Results

9.5.7.3 Workforce Results

9.5.7.4 Leadership and Governance Results

9.5.7.5

Financial and Market Outcomes

9.6

ISO 9000 Quality Management Systems

9.6.1 The ISO 9000 Standards

9.6.2 The Seven Quality Management Principles

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9.7

Scope

ISO 9001:2015 Quality Management Systems—Requirements

9.7.1

9.7.2 Normative Reference

9.7.3 Terms and Definitions

9.7.4 Context of the Organization

9.7.4.1 Understanding the Organization and its Context

9.7.4.2 Understanding the Needs and Expectation

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9.7.5 Leadership

9.7.6. Planning

9.7.7. Support

of Interested Parties

9.7.6.1 Actions to Address Risks and Opportunities

9.7.6.2 Quality Objectives and Planning to Achieve Them

9.7.6.3 Planning of Changes

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9.7.4.4 Quality Management System and its Processes

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9.7.5.1 Leadership and Commitment

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9.7.5.2 Policy

9.7.5.3 Organizational Roles, Responsibilities, and Authorities 540

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9.7.7.1 Resources

9.7.7.2 Competence

9.7.7.3 Awareness

9.7.7.4 Communication

9.7.7.5 Documented Information

9.7.8.1 Operation Planning and Control

9.7.8.2 Requirements for Products and Services

9.7.8.3 Design and Development of Products and Services

9.7.8.4 Control of Externally Provided Processes, Products,

9.7.8. Operation

and Services

9.7.8.5 Production and Service Provision

9.7.8.6 Release of Products and Services

9.7.8.7 Control of Nonconforming Outputs

9.7.9. Performance Evaluation

9.7.9.1 Monitoring, Measurement Analysis, and Evaluation

9.7.9.2

9.7.9.3 Management Review

Internal Audit

9.7.10. Improvement

9.7.10.1 General

9.7.10.2 Nonconformity and Corrective Action

9.7.10.3 Continual Improvement

9.8 The Six Sigma System

Six Themes of Six Sigma

9.8.1

9.8.2 The 6σ Measure

9.8.3 The Three Strategies

9.8.4 The Two Improvement Processes

9.8.5 The Five-Step Road Map

9.8.6 The Organization for the Six Sigma System

Summary of Quality Management Systems

9.9

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9.10 Exercise

9.10.1 Practice Problems

9.10.2 Mini-Projects

Mini-Project 9.1

Mini-Project 9.2

References

a P P e n d i x 1: S tat i S t i c a l ta b l e S

a P P e n d i x 2: a n S w e r S t o S e l e c t e d e x e r c i S e S

i n d e x

x v

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www.EngineeringBooksPDF.comhttp://taylorandfrancis.com

www.EngineeringBooksPDF.comPreface to the Third Edition

We are gratified that the second edition of the book was well received by students

and teachers. Notably, since 2015, our text has been used in a Massive Open Online

Course offered online on edX.org by the School of Management of the Technical

University of Munich, Germany. At least a thousand participants each year take the

MOOC course and complete the requirements to receive a certificate. Reviews sug-

gest that the balanced treatment of statistical tools and management methods in qual-

ity is the strength of our book. We will continue to emphasize the importance of

learning the statistical tools along with management methods for quality for design-

ing and producing products and services that will satisfy customer needs. We will

also continue to stress the value in learning the theoretical basis of statistical tools for

process improvement.

The Third Edition improves on the strengths of the earlier editions both in content

and presentation. An important feature of the book is inclusion of examples from real

world to illustrate use of quality methods in solving quality problems. We are adding

several such examples in the new edition, in Chapter 4, from the healthcare industry,

to show the practical use of control charts in healthcare.

In the new edition, we have revised the text to make all the chapters suitable for

self-study. Wherever necessary, new examples have been added and additional expla-

nations have been provided for self-study. We hope we have succeeded in this effort.

The sections of Chapter 9 relating to Baldrige Award Criteria and ISO 9000 have

been fully revised to reflect the latest versions of these quality system documents. The

discussion on the Baldrige Award is based on 2017–18 Baldrige Excellence Framework;

the discussion on the ISO 9000 system is based on ISO 9000–2015.

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Special thanks are due to Professor Dr. Holly Ott of the Technical University of

Munich for the many questions she raised that identified for us places where more

explanations were needed, or typos had to be corrected, or corrections were needed in

the mathematics.

KSK, Peoria, IL

VRK, Chicago, IL

AP, Iowa City, IA

www.EngineeringBooksPDF.comPreface to the Second Edition

“The average Japanese worker has a more in-depth knowledge of statistical methods

than an average American engineer,” explained a U.S. business executive returning

from a visit to Japan, as a reason why the Asian rivals were able to produce better qual-

ity products than U.S. manufacturers. That statement, made almost 30 years ago, may

be true even today as Japanese cars are continuously sought by customers who care for

quality and reliability. Dr. Deming, recognized as the guru who taught the Japanese

how to make quality products, said: “Industry in America needs thousands of statisti-

cally minded engineers, chemists, doctors of medicine, purchasing agents, managers”

as a remedy to improve the quality of products and services produced in the U.S. He

insisted that engineers, and other professionals, should have the capacity for statistical

thinking, which comes from learning the statistical tools and the theory behind them.

The engineering accreditation agency in the U.S., ABET, a body made up of academ-

ics and industry leaders, stipulates that every engineering graduate should have “an

ability to design and conduct experiments, as well as to analyze data and interpret

results” as part of the accreditation criteria.

Yet, we see that most of the engineering majors from a typical college of engineer-

ing in the U.S. (at least 85% of them by our estimate) have no knowledge of qual-

ity methods or ability for statistical thinking when they graduate. Although some

improvements are visible in this regard, most engineering programs apart from indus-

trial engineering do not require formal classes in statistics or quality methodology.

The industry leaders have spoken; the engineering educators have not responded fully.

One of the objectives in writing this book was to make it available as a vehicle for

educating all engineering majors in statistics and quality methods; it can be used to

teach statistics and quality in one course.

The book can serve two different audiences: those who have prior education in sta-

tistics, and those who have no such prior education. For the former group, Chapter 2,

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which contains fundamentals of probability and statistics, needs to be only lightly

reviewed; and for the latter, Chapter 2 must be strongly emphasized. Chapter 5, which

has advanced material in theory of control charts, design of experiments, and process

capability, should be included for the former group whereas it can be partly or com-

pletely omitted for the latter. Besides, a teacher has the option to pick and choose

topics in other chapters as well, according to the needs of an audience.

The second edition has been completely revised to provide clearer explanation of

concepts and to improve the readability throughout by adding a few figures and exam-

ples where necessary. An overview of lean manufacturing methods has been added to

Chapter 8 as yet another set of tools for improving productivity and reducing waste so

that quality products can be produced at minimum cost. More mini-projects, which

expose the students to real-world quality problems, have been added to the end of each

chapter. The coverage of ISO 9000 and the Baldrige Award Criteria has been updated

to reflect the latest revisions of these documents.

The publishing staff at CRC Press have been extremely helpful in publishing this

second edition. We are thankful to them. Some very special thanks are due to Ms.

Cindy Carelli, our contact editor, for her prompt and expert response whenever help

was needed.

Any suggestions to improve the content or its presentation are gratefully welcomed.

K. S. Krishnamoorthi

V. Ram Krishnamoorthi

www.EngineeringBooksPDF.comPreface to the First Edition

In the 20-plus years that I have been teaching classes in quality methods for engi-

neering majors, my objective has been to provide students with the knowledge and

training that a typical quality manager would want of new recruits in his or her

department. Most quality managers would agree that a quality engineer should have

a good understanding of the important statistical tools for analyzing and resolving

quality problems. They would also agree that the engineer should have a good grasp

of management methods, such as those necessary for finding the needs of custom-

ers, organizing a quality system, and training and motivating people to participate in

quality efforts.

Many good textbooks are available that address the topics needed in a course on

quality methods for engineers. Most of these books, however, deal mainly with one or

the other of the two areas in the quality discipline—statistical tools or management

methods—but not both. Thus, we will find books on statistical methods with titles

such as Statistical Process Control and Introduction to Statistical Quality Assurance, and

we will find books on management methods with titles similar to Introduction to Total

Quality, or Total Quality Management. The former group will devote very little cover-

age to management topics; the latter will contain only a basic treatment of statistical

tools. A book with an adequate coverage of topics from both areas, directed toward

engineering majors, is hard to find. This book is an attempt to fill this need. The term

quality engineering, used in the title of this book, signifies the body of knowledge

comprising the theory and application of both statistical and management methods

employed in creating quality in goods and services.

When discussing the statistical methods in this book, one overarching goal has

been to provide the information in such a manner that students can see how the

methods are put to use in practice. They will then be able to recognize when and

where the different methods are appropriate to use, and they will use them effectively

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to obtain quality results. For this reason, real-world examples are used to illustrate the

methods wherever possible, and background information on how the methods have

been derived is provided. The latter information on the theoretical background of the

methods is necessary for an engineer to be able to tackle the vast majority of real-world

quality problems that do not lend themselves to solution by simple, direct application

of the methods. Modification and improvisation of the methods then become neces-

sary to suit the situation at hand, and the ability to make such modifications comes

from a good understanding of the fu...

A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality

A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality