Abstract
I congratulate the authors for their interesting and insightful discussion on big data in production environments. Although problems with big data are trendy research topics, there are many practical opportunities and challenges for its use by quality practitioners for data-driven activities such as assurance, diagnosis, monitoring, and control. I appreciate the opportunity to discuss and provide my insights on the topic. In the following, I confine my assessments to data acquisition and preprocessing, and statistical methods for quality engineering.
Additional information
Notes on contributors
Murat Caner Testik
Murat Caner Testik is a professor and department chair in the Industrial Engineering Department at Hacettepe University. He has a PhD degree in Industrial Engineering from Arizona State University with a major in quality engineering. His research interests and publications are mainly in the area of quality engineering and data mining for quality and process improvement. He is currently the president of the European Network for Business and Industrial Statistics. He is a past editor-in-chief of Quality Engineering.