2023 Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Quality Management
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Ryuji Uozumi
- Class Format
- Lecture/Exercise (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (WL2-301 (W631)) / 5-6 Fri (WL2-301 (W631))
- Class
- -
- Course Code
- IEE.C302
- Number of credits
- 110
- Course offered
- 2023
- Offered quarter
- 4Q
- Syllabus updated
- Jul 8, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course provides the essence, principles and practical applications of quality management including 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 derive and confirm their own hypotheses by working on data analysis projects and make a presentation. For the design of experiments, students are required to form teams to conduct several experiments and data analyses. This aim of this course is to develop and enrich fundamental knowledge and practical skills to achieve efficient and effective quality management.
Course description and aims
Students will be able to:
(1) Understand the basic concepts of quality management, multivariate analysis, design of experiments, quality engineering and reliability engineering.
(2) Use SQC methodology and tools in the engineering or quality problem-solving process.
(3) Derive the hypotheses and assess via multivariate analysis.
(4) Compute and interpret the multivariate analysis results.
(5) Design the efficient experiments, collect data, and perform data analysis.
Keywords
Statistical Quality Control, TQM, Control Chart, Multivariate Analysis, Design of Experiments, Quality Engineering, Reliability Engineering
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (preparation and review)
To enrich 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 materials.
Textbook(s)
Provide handouts on each topic.
Reference books, course materials, etc.
N/A
Evaluation methods and criteria
Final exam, problem sets and presentation.
Related courses
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites
Prior completion of "Probability for Industrial Engineering and Economics" and "Statistics for Industrial Engineering and Economics" or equivalent is highly recommended.