Optimization And Variation Reduction In Quality

This book gives the reader tools for optimizing performance and reducing variation in manufacturing. Statistical process control and Taguchi methods are featured in the book along with other less published techniques. The book covers the product life cycle from design to manufacturing and is aimed at improving industrial practice. Methods are shown through case studies and extensive graphics are used to illustrate points.

Optimization in Quality Control presents a broad survey of the state of the art in optimization in quality, and focuses on industrial and national competitiveness. Each chapter has been carefully developed and refereed anonymously by experts in the area of optimization in quality control. Some of the topics covered in this volume include: fundamentals of optimization techniques contemporary approaches to optimization models in process control economic design of control charts determining optimal target values in multiple criteria economic selection models examining quality improvement schemes by trading off between expected warranty servicing costs and increasing manufacturing costs designing optimal inspection plans. This book will serve as an important reference source for academics, professionals and researchers.

A key to be competitive in today's economy is to produce high-quality products at low production cost, to meet or exceed customer's requirements. Product and process variations cost manufacturing industry significant money in terms of high rework cost, scrap, and costly inspections. Reducing product and process variation in a production process is a vital issue in quality improvement programs, because variation grows into hundreds-of-thousands of dollars in added product cost per year. The objectives of this research are (1) to study the variability of a generic pharmaceutical filling process; (2) to generate the process capability and conduct process validation based on statistical process control (SPC); (3) to develop economic optimization models for the filling process; and then (4) to optimize the process mean (e.g., reduce the deviation of the average fill from the target value), as well as minimize the variability around the process mean in a generic liquid pharmaceutical filling operation. This research, motivated by the fact that many production processes are being run at sub-optimal settings, utilizes the combination of control charts and SPC to study the current variability, capability, and validation of our filling process. Then, we developed a model that accounts for both the controllable and uncontrollable factors, and the response variable. Based on the model, we used mixed-level factorial design and robust design methods to effectively determine the optimal level settings of controllable factors that minimize the variability in the fill weights, while keeping the mean fill weight on target. As a result, the response variable (the fill weight) was insensitive or robust to the variations in uncontrollable noise factors. We derived optimum specification limits for the filling process. This research provides consistent methods for process optimization and variation reduction that has been implemented to improve the performance of our filling operations. Consequently, we have decreased the amount of scrap, rework and the cost incurred by the firm. The approach and the models, based on Taguchi's robust design, can be applied to other similar production processes. -- Abstract.

The Six Sigma concept has enjoyed triumphant success throughout the business world for two decades, contributing to significant net income improvement for many companies. This successful concept has been supplemented with the tools of Lean Management, a set of strategies for improving product quality and delivery performance and reducing cost. Six Sigma+Lean links the tried-and-tested tools of both programs in the proven DMAIC process model. The chronology matches that of the approach taken in a Six Sigma+Lean improvement project.

In this volume of the Six Sigma and Beyond series, quality engineering expert D.H. Stamatis focuses on how Statistical Process Control (SPC) relates to Six Sigma. He emphasizes the "why we do" and "how to do" SPC in many different environments. The book provides readers with an overview of SPC in easy-to-follow, easy-to-understand terms. The author reviews and explains traditional SPC tools and how they relate to Six Sigma and goes on to cover the use of advanced techniques. In addition, he addresses issues that concern service SPC and short run processes, explores the issue of capability for both the short run and the long run, and discusses topics in measurement.

As quality becomes an increasingly essential factor for achieving business success, building quality improvement into all stages—product planning, product design, and process design—instead of just manufacturing has also become essential. Quality Engineering: Off-Line Methods and Applications explores how to use quality engineering methods and other modern techniques to ensure design optimization at every stage. The book takes a broad approach, focusing on the user’s perspective and building a well-structured framework for the study and implementation of quality engineering. Starting with the basics, this book presents an overall picture of quality engineering. The author delineates quality engineering methods such as DOE, Taguchi, and RSM as well as computational intelligence approaches. He discusses how to use a general computational intelligence approach to improve product quality and process performance. He also provides extensive examples and case studies, numerous exercises, and a glossary of basic terms. By adopting quality engineering, the defect rate during manufacturing shows noticeable improvement, the production cost is significantly lower, and the quality and reliability of products can be enhanced. Taking an integrated approach that makes the methods of upstream quality improvement accessible, without extensive mathematical treatments, this book is both a practical reference and an excellent textbook.

Quality is a discipline that focuses on product and service excellence. This book is about improving the quality of products and services. The improved quality and reliability lead to higher perceived value and increased market share for a company, thereby increasing revenue and profitability. The book discusses the concepts and dimensions of quality, costs of poor quality, the importance of quality in this highly competitive global economy, and quality programs-Six Sigma and Lean Six Sigma that focus on improving quality in industries. The text integrates quality concepts, statistical methods, and one of the major tools of quality-Statistical Process Control (SPC)-a major part of Six Sigma control phase. A significant part of the book is devoted to process control and the tools of SPC-control charts-used for monitoring, controlling, and improving the processes by identifying the causes of process variation. The fundamentals of control charts, along with SPC techniques for variables and attributes, and process capability analysis and their computer applications are discussed in detail. This book fills a gap in this area by showing the readers comprehensive and step-wise solutions to model and solve quality problems using computers.