Quality Engineering and Management

Dr. Holly Ott and Dr. Martin Grunow

Technische Universität München

Since the introduction of mass production, the concept of “quality” has evolved from simple assembly line inspections to a broad approach to production and management involving an entire corporation. Quality has become a critical driver for long-term success through continuous process improvement and customer satisfaction. Quality Management today concerns the entire value chain, encompassing multi-tiered supplier networks and customer service and returns.

This course balances the quantitative elements of quality engineering with a managerial approach to using quality in an organization to effect change. We cover the statistical basics needed for each of the well-known process-improvement cycle steps: Define, Measure, Analyze, Improve, and Control, covering the most important quality methods and techniques including sampling, statistical process control, process capability, regression analysis, and design of experiments. Quality assurance is examined, from the viewpoint of quality incorporated into product design, measuring and controlling quality in production and improving quality using quantitative problem-solving and interactive, guided exercises.

The contents of this course are essentially the same as those of the corresponding TUM class (Quality Engineering and Management) and will enable you to immediately understand and apply quality concepts in your work and research.

key words, tags

quality engineering, quality management, supply chain, DMAIC, production, process, customer


Course properties

Competition track
Science and engineering
Form of education
Informal
Learning language
English
Discipline
Manufacturing and processing, Engineering and engineering trades, Business and administration
Course authors
Dr. Holly Ott and Dr. Martin Grunow
Organization
Technische Universität München
Output knowledge, abilities, skills
Students will learn the fundamentals for quality engineering and management and the statistical basics to apply the DMAIC process-improvement cycle to your work and research.
Entrance test
Groups formation by readiness level
Teachers presence
Tutors presence
Facilitators presence
Training materials forms
multimedia, video lecture, presentation, quiz questions, case, professional software access
Interactivity in training materials
Collaborative learning presence
Practical activities
coursework, project
Discussions, forums presence
Webinars, video conferences presence
meetup presence
LMS integration
Learning Analytics
Certification presence
Certification types
edX Honor Code Certificate, edX Verified Certificate
Course time limits
Duration
10 (weeks)
Learning types (sync/async)
asynchronous
Assessment types
test
Personal learning path possibility, course individualization
Operating System
Mac, Windows, Linux
Supported browsers
All standard browsers (Safari, Firefox, Chrome, Internet Explorer)
Special needs support

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