Six Sigma with R

Statistical Engineering for Process Improvement
2012
ISBN: 978-1-4614-3651-5
Verlag: Springer

Buch, Englisch, Band 36, 284 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 482 g

Reihe: Use R!

Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. With Six Sigma, we are solving problems and improving processes using as a basis one of the most powerful tools of human development: the scientific method. For the analysis of data, Six Sigma requires the use of statistical software, being R an Open Source option that fulfills this requirement. R is a software system that includes a programming language widely used in academic and research departments. Nowadays, it is becoming a real alternative within corporate environments.

The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Cano, Emilio L.

Redchuk, Andrés

Martinez Moguerza, Javier

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Qualitätsmanagement, Qualitätssicherung (QS), Total Quality Management (TQM)
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

Part I Basics.- Six Sigma in a Nutshell.- R from the Beginning.- Part II R Tools for the Define phase.- Process Mapping with R.- Loss Function Analysis with R.- Part III R Tools for the Measure phase.- Measurement System Analysis with R.- Pareto Analysis with R.- Process Capability Analysis with R.- Part IV R Tools for the Analyze phase.- Charts with R.- Statistics and Probability with R.- Statistical Inference with R.- Part V R Tools for the Improve phase.- Design of Experiments with R.- Part VI R Tools for the Control phase.- Process Control with R.- Part VII Further and Beyond.- Other Tools and Methodologies.

Über Autor(innen)

Emilio L. Cano is Adjunct Lecturer at the Department of Mathematics at University of Castilla-La Mancha and Research Assistant Professor at the Department of Statistics and Operations Research at University Rey Juan Carlos.

Javier M. Moguerza is Associate Professor in Statistics and Operations Research at University Rey Juan Carlos and member of the Global Young Academy.

Andres Redchuk is Master Black Belt and Research Assistant Professor at the Department of Statistics and Operations Research at University Rey Juan Carlos.