KA241501 Quality Management

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA241501 Subject Classification Specialized Education

Subject Name 品質管理

Subject Name
（Katakana）ヒンシツカンリ

Subject Name in
English Quality Management

Instructor ZHENG JUNJUN

Instructor
(Katakana) テイ　シュンシュン

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Tues7-10：ENG 218

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lecture-based

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Quality control, quality assurance, reliability engineering, seven QC tools, control chart, sampling inspection, FMEA/FTA, software & AI quality

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course provides fundamental knowledge essential for ensuring and improving quality in the development and operation of information systems and software. It serves as a practical foundation for designing and evaluating highly reliable systems. In particular, the course also addresses quality perspectives and uncertainties in AI systems, equipping students with the ability to respond to the evolving challenges of quality management in next-generation information technologies.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Comprehensive Abilities）
・D2. Ability to derive optimal system solutions based on abundant cutting-edge information technologies for cross-sectoral issues in a diversified and complicated information society.

Data Science Program
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.

Class Objectives
/Class Outline The aim of this course is to systematically learn the fundamental concepts and techniques of quality management from an information science perspective, and to develop both analytical and practical skills for continuous quality improvement in products and services. Topics include quality management methods such as TQM, the PDCA cycle, the Seven QC Tools, control charts, and sampling inspection. The course also covers reliability engineering and risk management techniques such as FMEA and fault tree analysis. In addition, students will explore quality characteristics and evaluation indicators in modern information systems, including software and AI, building a strong foundation to support next-generation quality assurance.

Class Schedule lesson1 What is quality?
(Definitions of quality, TQM, PDCA, overview of JIS/ISO, customer satisfaction and value)
lesson2 Quality data and statistical thinking
(Types of data, central tendency and variation, histograms, Pareto charts)
lesson3 Seven QC tools (1)
(Visualization of quality data: check sheets, graphs, scatter plots)
lesson4 Seven QC tools (2)
(Cause-and-effect diagrams, stratification, practical case-based usage)
lesson5 Basics of control charts
(Structure of X̄-R charts, evaluating process stability)
lesson6 Applications of control charts
(p and np charts, control limits, abnormal patterns and handling)
lesson7 Practice with QC tools and control charts
(Group exercises using real data, explanation of practice problems)
lesson8 Sampling inspection and OC curves
(Sampling inspection methods, acceptance criteria, OC curves, risks)
lesson9 Fundamentals of reliability engineering
(Failure rate, MTBF, bathtub curve, basic concepts of reliability)
lesson10 FMEA and fault tree analysis (FTA) + Exercise
(Preventive evaluation using FMEA, logical structure via FTA, simple exercise)
lesson11 Software quality and reliability
(ISO/IEC 25010 quality model, fault trends, reliability growth)
lesson12 Introduction to AI and quality perspective
(Machine learning structure, learning and generalization, uncertainty in AI)
lesson13 AI testing and quality evaluation
(Accuracy, recall, confusion matrix, connection to test design)
lesson14 Final practice session or review
(Applied exercises and group discussion based on course content)
lesson15 Course wrap-up and comprehension check
(Summary, short test or discussion for consolidation of learning)

Text/Reference
Books,etc. Textbook and References:
・「品質管理と品質保証，信頼性の基礎」，真壁肇，鈴木和幸共著，日科技連（2018）

PC or AV used in
Class,etc. Handouts, moodle

(More Details)

Learning techniques to be incorporated Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Fieldwork / Survey, Post-class Report

Suggestions on
Preparation and
Review Weeks 1–4 (Fundamentals and QC tools): Review definitions of quality and the basics of QC tools before class, and reinforce understanding by drawing charts and visualizing data after class.
Weeks 5–7 (Control charts and practice): Study the structure and usage of control charts in advance, and deepen practical understanding through group exercises during and after class.
Weeks 8–10 (Sampling inspection and reliability): Familiarize yourself with statistical decision-making and reliability indicators before class, and practice calculation-based problems for mastery.
Weeks 11–13 (Software and AI quality): Review the definitions of evaluation metrics such as accuracy and recall beforehand, and use test data and sample problems after class to build analytical skills.
Weeks 14–15 (Comprehensive practice and summary): Consolidate your knowledge by reviewing all topics and participating in quizzes or discussions to confirm overall understanding.

Others:
Students are encouraged to participate in the course evaluation surveys conducted for improving class quality. Feedback will be provided by the instructor to support continuous improvement.

Requirements

Grading Method Grading will be based on a combination of class participation (including attendance and attitude), exercises and reports, and tests.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA241501	Subject Classification	Specialized Education
Subject Name	品質管理
Subject Name （Katakana）	ヒンシツカンリ
Subject Name in English	Quality Management
Instructor	ZHENG JUNJUN
Instructor (Katakana)	テイ　シュンシュン
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Tues7-10：ENG 218
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture-based
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Quality control, quality assurance, reliability engineering, seven QC tools, control chart, sampling inspection, FMEA/FTA, software & AI quality
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course provides fundamental knowledge essential for ensuring and improving quality in the development and operation of information systems and software. It serves as a practical foundation for designing and evaluating highly reliable systems. In particular, the course also addresses quality perspectives and uncertainties in AI systems, equipping students with the ability to respond to the evolving challenges of quality management in next-generation information technologies.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Comprehensive Abilities）・D2. Ability to derive optimal system solutions based on abundant cutting-edge information technologies for cross-sectoral issues in a diversified and complicated information society. Data Science Program （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Abilities and Skills）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT. （Comprehensive Abilities）・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science.
Class Objectives /Class Outline	The aim of this course is to systematically learn the fundamental concepts and techniques of quality management from an information science perspective, and to develop both analytical and practical skills for continuous quality improvement in products and services. Topics include quality management methods such as TQM, the PDCA cycle, the Seven QC Tools, control charts, and sampling inspection. The course also covers reliability engineering and risk management techniques such as FMEA and fault tree analysis. In addition, students will explore quality characteristics and evaluation indicators in modern information systems, including software and AI, building a strong foundation to support next-generation quality assurance.
Class Schedule	lesson1 What is quality? (Definitions of quality, TQM, PDCA, overview of JIS/ISO, customer satisfaction and value) lesson2 Quality data and statistical thinking (Types of data, central tendency and variation, histograms, Pareto charts) lesson3 Seven QC tools (1) (Visualization of quality data: check sheets, graphs, scatter plots) lesson4 Seven QC tools (2) (Cause-and-effect diagrams, stratification, practical case-based usage) lesson5 Basics of control charts (Structure of X̄-R charts, evaluating process stability) lesson6 Applications of control charts (p and np charts, control limits, abnormal patterns and handling) lesson7 Practice with QC tools and control charts (Group exercises using real data, explanation of practice problems) lesson8 Sampling inspection and OC curves (Sampling inspection methods, acceptance criteria, OC curves, risks) lesson9 Fundamentals of reliability engineering (Failure rate, MTBF, bathtub curve, basic concepts of reliability) lesson10 FMEA and fault tree analysis (FTA) + Exercise (Preventive evaluation using FMEA, logical structure via FTA, simple exercise) lesson11 Software quality and reliability (ISO/IEC 25010 quality model, fault trends, reliability growth) lesson12 Introduction to AI and quality perspective (Machine learning structure, learning and generalization, uncertainty in AI) lesson13 AI testing and quality evaluation (Accuracy, recall, confusion matrix, connection to test design) lesson14 Final practice session or review (Applied exercises and group discussion based on course content) lesson15 Course wrap-up and comprehension check (Summary, short test or discussion for consolidation of learning)
Text/Reference Books,etc.	Textbook and References: ・「品質管理と品質保証，信頼性の基礎」，真壁肇，鈴木和幸共著，日科技連（2018）
PC or AV used in Class,etc.	Handouts, moodle
(More Details)
Learning techniques to be incorporated	Discussions, Quizzes/ Quiz format, PBL (Problem-based Learning)/ TBL (Team-based Learning), Fieldwork / Survey, Post-class Report
Suggestions on Preparation and Review	Weeks 1–4 (Fundamentals and QC tools): Review definitions of quality and the basics of QC tools before class, and reinforce understanding by drawing charts and visualizing data after class. Weeks 5–7 (Control charts and practice): Study the structure and usage of control charts in advance, and deepen practical understanding through group exercises during and after class. Weeks 8–10 (Sampling inspection and reliability): Familiarize yourself with statistical decision-making and reliability indicators before class, and practice calculation-based problems for mastery. Weeks 11–13 (Software and AI quality): Review the definitions of evaluation metrics such as accuracy and recall beforehand, and use test data and sample problems after class to build analytical skills. Weeks 14–15 (Comprehensive practice and summary): Consolidate your knowledge by reviewing all topics and participating in quizzes or discussions to confirm overall understanding. Others: Students are encouraged to participate in the course evaluation surveys conducted for improving class quality. Feedback will be provided by the instructor to support continuous improvement.
Requirements
Grading Method	Grading will be based on a combination of class participation (including attendance and attitude), exercises and reports, and tests.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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