Completely revised and updated, A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality, Second Edition contains virtually all the information an engineer needs to function as a quality engineer. The authors not only break things down very simply but also give a full understanding of why each topic covered is essential to learning proper quality management. They present the information in a manner that builds a strong foundation in quality management without overwhelming readers. See what’s new in the new edition: Reflects changes in the latest revision of the ISO 9000 Standards and the Baldrige Award criteria Includes new mini-projects and examples throughout Incorporates Lean methods for reducing cycle time, increasing throughput, and reducing waste Contains increased coverage of strategic planning This text covers management and statistical methods of quality engineering in an integrative manner, unlike other books on the subject that focus primarily on one of the two areas of quality. The authors illustrate the use of quality methods with examples drawn from their consulting work, using a reader-friendly style that makes the material approachable and encourages self-study. They cover the must-know fundamentals of probability and statistics and make extensive use of computer software to illustrate the use of the computer in solving quality problems. Reorganized to make the book suitable for self study, the second edition discusses how to design Total Quality System that works. With detailed coverage of the management and statistical tools needed to make the system perform well, the book provides a useful reference for professionals who need to implement quality systems in any environment and candidates preparing for the exams to qualify as a certified quality engineer (CQE).

Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.

This hands-on book provides a simplified systems for solving quality problems in design and manufacturing using statistics. It covers methods for problem identificaiton, root cause analysist, and corrective action implementation. It aims to provide engineers and managers with proactive tools to avoid costly errors in prodcut desing and manufacturing.

Through years of teaching experience, John S. Lawson and John Erjavec have learned that it doesn't take much theoretical background before engineers can learn practical methods of data collections, analysis, and interpretation that will be useful in real life and on the job. With this premise in mind, the authors wrote ENGINEERING AND INDUSTRIAL STATISTICS, which includes the basic topics of engineering statistics but puts less emphasis on the theoretical concepts and elementary topics usually found in an introductory statistics book. Instead, the authors put more emphasis on techniques that will be useful for engineers. With fewer details of traditional probability and inference and more emphasis on the topics useful to engineers, the book is flexible for instructors and interesting for students.

In today’s industrial and complex world, the progress of change is incredible. The amount of information which needs to be analyzed is very large and time has become more and more limited. Industries and firms of all sizes desire to increase productivity and sustainability to keep their competitive edge in the marketplace. One of the best tools for achieving this is the application of Quality Engineering Techniques (QET). This book will introduce the integrated model and the numerical applications for implementing it.