Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2026 361881 Quality Management 1 3.0 3 ▲ Jay Huang 4IEM3A 43 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	361881	Quality Management	1	3.0	3	▲	Jay Huang	4IEM3A	43	0

Course Plan：

Instructor Jay Huang　Office Hours

Email jayhuang@ntut.edu.tw

Last Updated 2026-06-07 15:08:17

Course Objective This course enables students to understand the concepts, principles, and domains of quality management. It covers the organization and functions of the quality management department, how to manage the quality of products or services, quality management tools and methods, methods for continuous quality improvement, quality representation methods, how to evaluate the effectiveness of quality management, relevant regulations, and future trends in quality management. The course content includes quality responsibility, total quality management, basic statistics, control charts, probability theory, attribute control charts, acceptance sampling, reliability, and more.

Course Schedule Chapter 1- Modern Quality Management and Improvement Chapter 2- The DMAIC Problem Solving Process Chapter 3- Statistical Models or Quality Control and Improvement Chapter 4- Statistical Inference in Quality Control and Improvement Chapter 5- How SPC Works Chapter 6- Variables Control Charts Midterm exam Chapter 7- Attributes Control Charts Chapter 8- Determining Process and Measurement Systems Capability Chapter 9- Time-Weighted Control Charts Chapter 13- Basic Experimental Design for Process Improvement Chapter 15- Basic Acceptance sampling Procedures Final exam

Evaluation and grading policy midterm 30%, final exam 40%, attendance and particiation 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Statistical Quality Control – A Modern Introduction; by D. Montgomery 東華書局新月圖書

The access to curricular consultation class line group

Expanding teaching and resources ● 無 (None)

The course corresponds to the SDGs 無（None）

Does the course incorporate AI ● 無（None）

Note
　

Instructor	Jay Huang　Office Hours
Email	jayhuang@ntut.edu.tw
Last Updated	2026-06-07 15:08:17
Course Objective	This course enables students to understand the concepts, principles, and domains of quality management. It covers the organization and functions of the quality management department, how to manage the quality of products or services, quality management tools and methods, methods for continuous quality improvement, quality representation methods, how to evaluate the effectiveness of quality management, relevant regulations, and future trends in quality management. The course content includes quality responsibility, total quality management, basic statistics, control charts, probability theory, attribute control charts, acceptance sampling, reliability, and more.
Course Schedule	Chapter 1- Modern Quality Management and Improvement Chapter 2- The DMAIC Problem Solving Process Chapter 3- Statistical Models or Quality Control and Improvement Chapter 4- Statistical Inference in Quality Control and Improvement Chapter 5- How SPC Works Chapter 6- Variables Control Charts Midterm exam Chapter 7- Attributes Control Charts Chapter 8- Determining Process and Measurement Systems Capability Chapter 9- Time-Weighted Control Charts Chapter 13- Basic Experimental Design for Process Improvement Chapter 15- Basic Acceptance sampling Procedures Final exam
Evaluation and grading policy	midterm 30%, final exam 40%, attendance and particiation 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Statistical Quality Control – A Modern Introduction; by D. Montgomery 東華書局新月圖書
The access to curricular consultation	class line group
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 無（None）
Note