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Synopsis
For a manufacturing activity to remain competitive, engineers must rigorously apply statistics methodologies that enable them to understand the sources and consequences of uncontrolled process variation. In this example-rich text, author Jack Reece explains simple comparative statistics and demonstrates how to use JMP to examine raw data graphically and to generate regression models involving fixed and random effects. The following major topics are addressed: Characterizing the measurement process Analyzing process performance Developing appropriate control mechanisms for monitoring measurement and performance Although many of the examples rely on case studies, largely in the semiconductor manufacturing area, the principles described and the methods used apply generally to the study of any process or manufacturing activity. This book assumes that the reader has some knowledge of JMP software, but does not assume extensive statistical knowledge.
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About the Author
Jack E. Reece, Ph.D., writes from 20 years' experience in converting laboratory results into successful commercial operations. During a 15-year career at Minnesota Mining and Manufacturing Company, Jack made fundamental contributions in a variety of industry applications, including fine organics, photographic chemistry, and solvent-case coating technology for unconventional imaging systems. In addition, Jack contributed his process engineering and applied statistics expertise at several leading companies, including Phillips Petroleum Company, ECD, Honeywell, Inc., and SEMATECH. He continued to share his knowledge of the industry with companies in the U.S. and abroad through his consulting company, Reece Associates Ltd. Jack became a Senior Fellow in statistical methods using SAS software in 1992 and became a JMP user in 2002.
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