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This course, and the existing course at TUM, is based on the excellent textbook:

3rd Edition
A First Course in Quality Engineering
Integrating Statistical and Management Methods of Quality, Third Edition
By K.S. Krishnamoorthi, V. Ram Krishnamoorthi, Arunkumar Pennathur
Copyright Year 2019

which is available here:

K.S. Krishnamoorthi

Dr. K.S. Krishnamoorthi is a Professor of Industrial Engineering in the Industrial and Manufacturing Engineering & Technology Department of Bradley University in Peoria, Illinois.  He has a B.E. in mechanical engineering from University of Madras, India, an M.A. in Statistics and Ph.D. in industrial engineering from the University of Buffalo.  He teaches statistics and quality engineering among other industrial engineering subjects, and conducts research in the area of quality costs, process capability indices, and statistical thinking.  He has provided consulting to several small and large corporations in process and product quality improvement using statistical tools.  His book A First Course in Quality Engineering, in its second edition, has been published by the CRC Press.


V. Ram Krishnamoorthi, M.D., M.P.H. is an Attending Internist at the University of Chicago Medical Center and an Assistant Professor of Medicine of University of Chicago, Chicago, IL.  He obtained a bachelors degree in economics from Princeton University and his M.D. and M.P.H. from Northwestern University Feinberg School of Medicine in Chicago. He is involved in health services research and quality improvement initiatives at the U of C Hospitals, and is interested in problems of quality and access to affordable health care at the national level.

 Arunkumar Pennathur

Arunkumar Pennathur is an Associate Professor of Instruction, Industrial and Systems Engineering at the University of Iowa's College of Engineering. His areas of specialty are Research training, Healthcare Delivery in Low-Resource Settings, Designing for Older Adults, Training and Skills Acquisition, Human Performance Modeling, Mental and Physical Workload Modeling