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.
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
|✔ Specialist skills||Intercultural skills||Communication skills||✔ Critical thinking skills||✔ Practical and/or problem-solving skills|
Give a lecture and discussions on each topic.
|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|
|Class 15||Reliability Engineering||Understand basic concept of reliability engineering|
Provide handouts on each topic.
Enkawa, Takao and Miyakawa, Masami. SQC Theory and Practice. Tokyo: Asakura Shoten; ISBN-13: 978-4254126075
Nothing in particular.
Final exam, exercise problems and presentation.
Students must have successfully completed both "Probability for Industrial Engineering and Economics" and "Statistics for Industrial Engineering and Economics"or have equivalent knowledge.