American industry is becoming more aware of the importance of applying statistical methods to improve its competitive edge in the world market. Examples of real industrial applications can serve as a major motivator for industries that want to increase their use of statistical methods. This book contains a broad selection of case studies written by professionals in the semiconductor industry that illustrate the use of statistical methods to improve manufacturing processes. These case studies offer engineers, scientists, technicians, and managers numerous examples of best-in-class practices by their peers. Because of the universal nature of statistical applications, the methods described here can be applied to a wide range of industries, including the chemical, biotechnology, automotive, steel, plastics, textile, and food industries.
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Editorial Reviews
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‘A wonderful collection of superbly documented industrial statistical experiences … This book will inspire experienced researchers. Beginners, in addition, will learn a good deal about statistical techniques for industrial problem solving.’ Lloyd S. Nelson, Journal of Quality Technology
‘ … A good investment for those who are directly involved in the study or application of statistical methods.’ George G. R. Maharage, Manufacturing Engineer
‘ … A good investment for those who are directly involved in the study or application of statistical methods.’ George G. R. Maharage, Manufacturing Engineer
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4.0 out of 5 stars
Real industrial case studies with graphical analysis,
By Mike Clayton "MikeClayton" (Mesa, AZ) - See all my reviews
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This review is from: Statistical Case Studies for Industrial Process Improvement (ASA-SIAM Series on Statistics and Applied Probability) (Paperback)
This classic set of case studies from manufacturing rather than services has become a proven way to trigger re-characterization of older factory machines, processes, metrology systems, (as well as newer factories,) using proven industrial methods. The software in those days (RS/1 from BBN) ran on old VMS operating system, but was years ahead of other statistical methods software in its use of Graphical Analysis Methods, with firm foundation in classical and robust industrial statistics.[Over time, the server operating systems have gotten less robust (VMS is alive and well) but personal computers were invented, then web-enabled analysis tools, and modern software can finally perform the best graphical methods, with higher speed, in spite of weaker OS's due to the micro-chip revolution that resulted from engineers using the methods found in this book! The "cloud" will take all that analysis capability to the next level.] These case studies will remain useful in learning how real factories can be created or made more robust, precise and efficient. Boring stuff for those that given up on manufacturing, perhaps. But amazingly, big banks are hiring statistically-trained engineers to design experiments and analyze data just like they did in these industrial cases. Manufacturing is now global, with serious supply chain logistical problems, but also serious sub-contractor manufacturing problems that require statistical methods like those in this book, with analysis shared globally. The only thing that has changed, is the productivity in engineering due to broader understanding of basic methods pioneered in high technology manufacturing of the 1980's and beyond, WHEREVER real work happens.
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Inside This Book
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First Sentence:
Two major concepts of measurement process characterization are the location of the measurements and the variability of the measurements. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Solution Strategy, Texas Instruments, Novellus Concept, James Buckner, Novellus Systems, Veronica Czitrom, Western Electric, Bell Laboratories, Jon Henri, University of California, University of Texas, Journal of Quality Technology, Lucent Technologies, Thk Var, Bell Labs, Digital Equipment Corporation, Error T-Value Signif, Term Coeff, Chapel Hill, Journal of Applied Statistics, Taguchi Method, University of North Carolina, Van Nostrand Reinhold, Analysis of Messy Data New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Two major concepts of measurement process characterization are the location of the measurements and the variability of the measurements. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Solution Strategy, Texas Instruments, Novellus Concept, James Buckner, Novellus Systems, Veronica Czitrom, Western Electric, Bell Laboratories, Jon Henri, University of California, University of Texas, Journal of Quality Technology, Lucent Technologies, Thk Var, Bell Labs, Digital Equipment Corporation, Error T-Value Signif, Term Coeff, Chapel Hill, Journal of Applied Statistics, Taguchi Method, University of North Carolina, Van Nostrand Reinhold, Analysis of Messy Data New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
10
books
cite this book:
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- Statistical Methods for Quality Improvement (Wiley Series in Probability and Statistics) by Thomas P. Ryan on 5 pages
- Measurement Theory for Engineers (Engineering Online Library) in Back Matter (1), and Back Matter (2)
- Six Sigma: Quality Improvement with MINITAB by G. Robin Henderson in Back Matter
- The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions (ASA-SIAM Series on Statistics and Applied Probability) by Peter R. Nelson in Front Matter
- Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed Anova Models (ASA-SIAM Series on Statistics and Applied Probability) by Richard K. Burdick in Front Matter
See all 10 books citing this book
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