2021 Quality Management

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Miyakawa Masami 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Tue3-4(S223)  Fri3-4(S223)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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Course description and aims

This course focuses on the essence, principles and practices of quality management. Emphasis will be placed on both theory and implementation of quality management. Topics cover statistical quality control, total quality management, seven QC tools, multivariate analysis, design of experiments, quality engineering and reliability management. For the multivariate analysis, students will work in preparing at least one "deep" case analysis by deriving and checking own hypotheses as well as making a presentation. And as for the design of experiments, students are required to form teams to conduct several experiments and data analyses.
This course is aimed at learning fundamental knowledges and skills to achieve efficient and effective quality management.

Student learning outcomes

By the end of this course, students will be able to:
(1) Understand the basic concepts of quality management, multivariate analysis, design of experiments, quality engineering and reliability engineering.
(2) Utilize SQC methodology and tools in the engineering or quality problem-solving process.
(3) Derive and check the hypotheses by multivariate analysis.
(4) Compute and interpret the results of multivariate analysis.
(5) Design the efficient experiments and a way of data collection and analyses.


Statistical Quality Control, TQM, Control Chart, Multivariate analysis, Design of Experiments, Quality Engineering, Reliability Engineering

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Give a lecture and discussions on each topic.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Orientation Understand course overview
Class 2 Basics of Quality Management Understand basic concept and idea of quality management
Class 3 Statistical Quality Control and QC7 tools Understand basic concept of statistical quality control and QC7 tools
Class 4 Several Control Charts Understand the way of graphical summarization of data based on 3 sigma
Class 5 Multivariate analysis (1) Regression Analysis and Discriminant Analysis Understand regression analysis and discriminant analysis
Class 6 Multivariate analysis (2) Principal Component Analysis and Factor Analysis Understand principal component analysis and factor analysis
Class 7 Case study of Multivariate analysis (select a topic and data collection) Select a topic and data collection
Class 8 Case study of Multivariate analysis (data analysis) Deriving own hypetheses and data analysis
Class 9 Presentation of case study (1/2) Make a presentation on own case study
Class 10 Presentation of case study (2/2) Make a presentation on own case study
Class 11 Analysis of Variance and Structural Model Understand the relationship between the way of experiment and analysis
Class 12 Design of Experiments (1) Group work and data analysis Group work with paper made helicopter and data analysis
Class 13 Design of Experiments (2) Orthogonal array Experiment with orthogonal array
Class 14 Quality Engineering Understand basic concept of quality engineering

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.


Provide handouts on each topic.
Enkawa, Takao and Miyakawa, Masami. SQC Theory and Practice. Tokyo: Asakura Shoten; ISBN-13: 978-4254126075

Reference books, course materials, etc.

Nothing in particular.

Assessment criteria and methods

Final exam, exercise problems and presentation.

Related courses

  • IEE.A204 : Probability for Industrial Engineering and Economics
  • IEE.A205 : Statistics for Industrial Engineering and Economics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students must have successfully completed both "Probability for Industrial Engineering and Economics" and "Statistics for Industrial Engineering and Economics"or have equivalent knowledge.

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