INE-3600 Quality Management and Improvement - 5 stp

Introduction to quality management and Lean Six Sigma.

Definitions and terms.

Relationship between Lean Six Sigma and other forms of quality management. The Laws of Lean Six Sigma.

How to define value.

The five principles of "lean" manufacturing.

The Deming Cycle and continuous improvement.

DMAIC as systematic problem solving and improvement.

Measurements (five-step methodology).

Analysis of the data.

Performance measures, Cp, Cpk, DPMO.

Statistical process control and control charts.

Machine learning.

Multivariate anomaly detection.

Combination of visual control charts and process control by AI.

Bachelor degree in Engineering program in mechanical, electrical power, electronics, mechatronics, material science, industrial engineering, process engineering or other equivalent majors.

In addition, the following requirements must be met:

-minimum 25 credits in mathematics (equivalent to Mathematical Methods 1, 2 og 3), 5 credits in statistics and 7,5 ects i physics on a higher level is required.

Application code: 9371

After passing the course, students will have the following learning outcomes:

Knowledge and understanding:

- After finishing the course and passing the exam the students will have a broad understanding of the ideas and principles that the Lean Six Sigma concepts are based on.

- The students will acquire knowledge on the DMAIC methodology.

- The students will understand the need to control industrial processes by use of human and artificial intelligence.

Skills:

- The students will obtain a Six Sigma green belt certificate after passing the course.

- The students will be able to utilize Lean Six Sigma in order to manage and improve company operations as leaders for improvement projects.

- The students will be able to use the quality improvement tools and methods that are part of the Lean Six Sigma concept.

- The students will be able to use statistical control charts and machine learning tools for process control.

Concentrated lectures and exercises spread over two weeks. 25 - 28 lecture hours, 20 hours of exercises, plus self studies. Total workload is estimated to about 125 hours full time work. Both exercises and examination may independently be subject to continuation according to normal procedures for exam and project work.

Lectures are streamed online.

All course material is available from the online course portal.

Software to be used is available as UiT student: Spreadsheet such as Microsoft Excel. Simul8 process simulation (student license for free). Matlab and/or Python for machine learning.