Robust Quality

Powerful Integration of Data Science and Process Engineering
1. Auflage 2021
ISBN: 978-0-367-78097-5
Verlag: CRC Press

Buch, Englisch, 142 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 231 g

Reihe: Continuous Improvement Series

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.

Features:

- Integrates data science, analytics and process engineering concepts

- Discusses how to create value by considering data, analytics and processes

- Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches

- Reviews a structured approach for analytics execution

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Jugulum, Rajesh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Industrielle Qualitätskontrolle
• Technische Wissenschaften Technik Allgemein Industrial Engineering

Inhaltsverzeichnis

Chapter 1 The Importance of Data Quality and Process Quality Chapter 2 Data Science and Process Engineering Concepts Chapter 3 Building Data and Process Strategy and Metrics Management Chapter 4 Robust Quality—An Integrated Approach for Ensuring Overall Quality Chapter 5 Robust Quality for Analytics Chapter 6 Case Studies Appendix I: Control Chart Equations and Selection Approach Appendix II: Orthogonal Arrays Appendix III: Mean Square Deviation (MSD), Signal-to-Noise Ratio (SNR), and Robust Quality Index (RQI)

Über Autor(innen)

Rajesh Jugulum, PhD, is the Informatics Director at Cigna. Prior to joining Cigna, he held executive positions in the areas of process engineering and data science at Citi Group and Bank of America. Rajesh completed his PhD under the guidance of Dr. Genichi Taguchi. Before joining the financial industry, Rajesh was at Massachusetts Institute of Technology where he was involved in research and teaching. He currently teaches at Northeastern University in Boston. Rajesh is the author/co-author of several papers and four books including books on data quality and design for Six Sigma. Rajesh is an American Society for Quality (ASQ) Fellow and his other honors include ASQ’s Feigenbaum medal and International Technology Institute’s Rockwell medal. Rajesh has delivered talks as the keynote speaker at several conferences, symposiums, and events related to data analytics and process engineering. He has also delivered lectures in several universities/companies across the globe and participated as a judge in data-related competitions.