[Thai] Course 4: Quality Management for Extended Enterprise

Course 4: Quality Management for Extended Enterprise

Syllabus:  Course 4_ Quality Management for Extended Enterprise

Last Update: February 1,  2020

Course Objective: The extended enterprise concept has been adopted to collaborate in the entire supply chain. Quality and efficiency issues, therefore, extend well beyond the traditional enterprise. This course constructs student competencies of management skills, particularly on how to define, develop, implement and manage the strategy to improve and build the quality system to align with the digital domains. Students will be trained on the modern quality management methods used in product design, product development ,and production planning, as well as, the quality management methods focused on statistical quality control methods and data analytics, under the context of the extended enterprise. This course will also develop a technical skill for students to implement quality control and monitoring system that covers both process operation and supply chain operations

Learning Outcomes: The students on the completion of this course would be able to:

  1. Understand the impact of digitalization on quality management system, particularly on processes and people.
  2. Identify the strategy to assess the need and define the suitable technologies in order to transform the production system and organization, quality culture and processes to maximize value
  3. Analyze operational quality-related data for sustaining the process and Enterprise as well as identify the improvement by using quality monitoring tools such as SPC and modern data analytic technique and be able to embed quality management principles and tools in the value chain of operations and integrate with business operation strategy.
  4. Design a data visualization platform and Create its system components based on operational data such as quality and productivity output characteristics as well as Enterprise data (Enterprise Quality Metrics visualization). 
  5. Design the proper quality management system for smart factories that can integrate the production and quality operations under the digital quality management concept

Prerequisite:  None

 Course Outline:

Week Topic Workshop Learning Materials Teaching Materials Note
1 Quality Management Concept under the Digital Era    MSIE-04-L-M1S1-TH
2 Organization Performances and Quality System Strategy for I4.0 MSIE-04-L-M1S2-TH
3 Quality Strategy for Digital Quality Management System MSIE-04-L-M1S3-TH
4 Quality System Structures for I4.0 MSIE-04-L-M1S4-TH
5 Quality Control Concept under Digital Data Collection MSIE-04-L-M2S1-01_TH

MSIE-04-L-M2S1-02_TH
6 Automated SPC Strategy MSIE-04-L-M2S2-TH
7 Multivariate SPC Strategy MSIE-04-L-M2S3_TH
8 Data Analytic for Quality Monitoring MSIE-04-L-M2S4_TH
9 Automated Quality Report Concept MSIE-04-L-M3S1-TH
10 Strategic Quality Improvement under I4.0 MSIE-04-L-M3S2-TH
11 Real-Time Quality Control MSIE-04-L-M3S3-TH
12 Quality System Transformation MSIE-04-L-M3S4-TH


Laboratory Sessions: None

Learning Resources:

Textbooks: No designated textbook, but class notes and handouts will be provided.

Reference Books:

  1. Juran’s Quality Handbook: The Complete Guide to Performance Excellence
  2. Montgomery, Douglas C. Introduction to statistical quality control. John Wiley & Sons, 2012.
  3. Luis Rocha-Lona, Jose A. Garza-Reyes, and Vikas Kumar. Building Quality Management Systems. CRC Press, 2013
  4. J.D.T. Tannock. Automating Quality Systems. Chapman & Hall, 1992.

Journals and Magazines:

  1. Germany Trade & Invest, “Smart manufacturing for the future,” http://www.gtai.de/GTAI/Content/EN/Invest/_SharedDocs/Downloads/GTAI/Brochures/Industries/industrie4.0-smart-manufacturing-for-the-future-en.pdf; National Academy of Science and Engineering, “Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative of Industry 4.0
  2. Forces of change: Industry 4.0
  3. A Deloitte series on Industry 4.0
  4. Case study: FANUC, the Japanese robotics company, https://www.cbinsights.com/research/future-factory-manufacturing-tech-trends/#quality
  5. 8 different steps of the manufacturing process to Future Factory, https://www.cbinsights.com/research/future-factory-manufacturing-tech-trends/#quality
  6. Nikon Strategic Focus on Quality 4.0, https://metrology.news/nikon-strategic-focus-on-quality-4-0/
  7. A strategist’s guide to Industry 4.0, https://www.strategy-business.com/article/A-Strategists-Guide-to-Industry-4.0?gko=a2260
  8. Suggested Indicators to Measure the Impact of Industry 4.0 on Total Quality Management, International Scientific Conference on Industry 4.0, At 3-16. DECEMBER 2017, BOROVETS, BULGARIA
  9. Developing a Kano-Based Evaluation Model for Innovation Design, Mathematical Problems in Engineering 2015(2):1-8 · October 2015
  10. The Complete Guide to the Kano Model: Prioritizing Customer Satisfaction and Delight https://foldingburritos.com/kano-model/
  11. Perdikis, Theodoros, and Stelios Psarakis. “A survey on multivariate adaptive control charts: Recent developments and extensions.” Quality and Reliability Engineering International 35.5 (2019): 1342-1362.
  12. Mason, Robert L., and John C. Young. Multivariate statistical process control with industrial applications. Vol. 9. Siam, 2002.

Teaching and Learning Methods:

This is an activity-based course. During lecture sessions, class discussion will be conducted. During workshop sessions, the students, to be active learners, will practice several skills including, but not limited to, decision making, problem-solving, critical thinking, written communication, oral communication, presentation, debate, and teamwork.

Time Distribution and Study Load:

Lectures: 45 hours

Workshop: 60 hours

Self-study: 45 hours

Evaluation Scheme:  

The final grade will be computed according to the following weight distribution:

Assessment (CLO1):  (20%)

  • LogBook/Journal + Cases Study (5%)
  • Oral Presentation (Individual Work Presentation & Report) (5%)
  • Open Exam  (10%)

Assessment (CLO2):  20%

  • Role Play + Cases Study (5%)
  • Extended Response Question  (5%)
  • Report of Strategy Plan and Analysis  (10%)

Assessment (CLO3):   (30%)

  • Professional Discussion (5%)
  • Cases Study +Simulation (10%)
  • Assignment  (15%)

Assessment (CLOs 4&5):  (30%)

  • Project  (20%)
  • Oral Presentation (5%)
  • Oral Question (5%)

 An “A” would be awarded if a student can design the proper quality management system for smart factories.

A “B” would be awarded if a student can evaluate the proper quality management system for smart factories.

A “C” would be given if a student can analyze the proper quality management system for smart factories.

A “D” would be given if a student can only remember some criteria for designing the proper quality management system for smart factories.

Developer: Wichai Chattinnawat (CMU), Suriya Jirasatitsin (PSU), Anintaya Khamkanya (TU)

[THAI] Course 15: Customer Experience-Driven Design

Course Objective: Economic offerings have progressed to the fourth evolution when products and services are used as props and stages for creating memorable experiences for customers. It is important for students to be able to support an industry with this change. This course aims to build student competence in design customer experience with knowledge on a concept of customer experience management (CEM) and on a systematic approach for an experience design process. In this course, the students will learn customer perception, customer involvement, and customer experience. Besides, they will learn and practice how to design a customer journey and to prevent failure of offering in a team environment.

Learning Outcomes: The students on the completion of this course would be able to:

    • Present entrepreneurial and creative attitude towards seeking various problem solutions (Apply)
    • Identify customer needs (Analyze)
    • Identify potential failure of offerings (Analyze)
    • Manage customer experience journey (Create)
    • Communicate effectively and work in an interdisciplinary team environment (Apply)
    • Design a pain point-free, memorable customer experience journey (Create)
    • Utilize Industry 4.0 technologies/applications to support the creation of a memorable customer experience journey (Apply)

Prerequisite:  None

 Course Outline:

Week Topic Learning Materials (Thai) Worshop(Thai)
I. Pain Point-Free Customer Experience Journey
1 1. Introduction to Experience Economy MSIE-15-L-M1S1-01-A1

 

 MSIE-15-L-M1S1-02-A
2  2. Customer Journey MSIE-15-L-M1S2-01-A1 MSIE-15-L-M1S2-02
3  3. Experience Clues MSIE-15-L-M1S3-01-A1M1S3-02 MSIE-15-L-M1S3-02
4 4. Customer Oriented-Failure Prevention -Part 1 MSIE-15-L-M1S4-01-A1 MSIE-15-L-M1S4-02
5 4. Customer Oriented-Failure Prevention -Part 2 MSIE-15-L-M1S4-03-A1 MSIE-15-L-M1S4-04
II Customer Experience Value Creation
6 1. Understanding Customers
7 2. Customer Perceived Value Model MSIE-15-L-M2S2-01-A1MSIE-15-L-M2S2-01-A
8 3. Product-Service Systems MSIE-15-L-M2S3-01-A
9 MSIE-15-L-M2S3-02-A
10 4. Co-Creation MSIE-15-L-M2S4-01A
III Memorable Customer Experience Design
11 1. Customer Experience Journey Design
12 2. Embedding Memorable Experience into Customer Experience Journey MSIE-15-L-M2S3-02-A
13 3. Customer Experience Co-Creation MSIE-15-L-M3S3-01-A
14 4. Industry 4.0 Technologies/Applications for the Creation of Customer Experience

Laboratory Sessions:

1. Customer Journey Creation
2. Embedding Clues into Customer Journey
3. Assessing Potential Failure in Customer Journey
4. Customers Need Identification
5. Customer Perception
6. Applying Product Service System for Customer Journey Design
7. Co-created Customer Experience
8. Customer Experience Journey Design
9. Customer Memorable Experience Journey Design
10. Co-created Customer Journey

Learning Resources:

Textbooks: No designated textbook, but class notes and handouts will be provided.

Reference Books:

1. Chavez, T., O’Hara, C. and Vaidya, V. Data Driven: Harnessing Data and AI to Reinvent Customer Engagement, McGraw-Hill Education, 2018
2. Goodman, J. Customer experience 3.0: High-profit strategies in the age of techno service, Amacom, 2014
3. Kalbach, J. Mapping experiences: A complete guide to creating value through journeys, blueprints, and diagrams, O’Reilly Media, Inc., 2016
4. Loeffler, B. and Church, B. The experience: The 5 principles of Disney service and relationship excellence, John Wiley & Son, 2015
5. Shaw, C. The DNA of Customer Experience, Palgrave Macmillan, 2007
6. Shep, H. The Cult of the Customer: Create an Amazing Customer Experience That Turns Satisfied Customers into Customer Evangelists, Wiley, 2009
7. Walters, D. Behavioral Marketing: Delivering Personalized Experiences at Scale, John Wiley & Sons, 2015
8. Weinschenk, S. 100 Things Every Designer Needs to Know About People, Pearson Education, 2011

Journals and Magazines:

• European Management Journal, Elsevier
• Journal of Business Research, Elsevier
• Journal of Engineering Design, Taylor and Francis
• Journal of Hospitality Management, Elsevier
• Journal of Interactive Marketing, Elsevier
• Journal of Services Marketing, Emerald Insight
• Journal of Service Theory and Practice, Emerald Insight
• Harvard Business Review
• MIT Sloan Management Review

Teaching and Learning Methods:

This is a participant-centered learning course that the students actively involve. Lecture materials include, but not limited to, slides, case study, games, interesting animations, and videos. Most of the lecture sessions contain discussion and students are encouraged to participate actively in the discussion. To increase understanding of the subject, the students are required to do literature reviews, group project, and presentations. The literature reviews are the individual assignments. The group project is for the students to develop and practice several skills including, but not limited to, decision making, problem-solving, communication, critical thinking, negotiation, conflict resolution, and teamwork. Presentations are a part of the project and assignments for personal development and knowledge sharing.

Time Distribution and Study Load:

Lectures and discussion: 30 hours
Presentations: 10 hours
Laboratory sessions: 35 hours
Group meeting outside classroom: 40 hours
Self-study: 20 hours

Evaluation Scheme:

The final grade will be computed according to the following weight distribution: Class discussions and participation (20%); Peer Assessment in-class activities
(10%); Individual assignments and presentations (10%); Project (40%); and Final Examination (20%)

An “A” would be awarded if a student can demonstrate a clear understanding of the knowledge learned in class as well as from literature reviews, can apply the knowledge appropriately in the project, and involve actively in class discussion.
A “B” would be awarded if a student can understand the basic principles of the knowledge learned in class and from literature reviews, can apply the knowledge in the project, and participate in class discussion.
A “C” would be given if a student shows partial understanding of the basic principles of the knowledge learned in class and from literature reviews, needs much guidance to apply the knowledge in the project, and is quiet during class discussion.
A “D” would be given if a student shows lack of understanding of the knowledge learned in class and from literature reviews, cannot apply the knowledge properly in the project and does not participate in class discussion.

Developer: Pisut Koomsap (AIT), Duangthida Hussadintorn Na Ayutthaya (AIT), Tomasz Nitkiewicz, Agnieszka.Ociepa-Kubicka (CUT), Apiwat Muttamara (TU)

[Thai] Course 10_ Cyber-Physical Industrial Systems

Course 10_ Cyber-Physical Industrial Systems

Course Objective:   Gaining knowledge about: the main characteristics of the Cyber-Physical Systems, their application areas, components selection rules, programming methodology, specific aspects related to different measured physical parameters, data storage, reporting and communications.

Learning Outcomes: The students on the completion of this course would be able to:

        • Identify links between industrial engineering knowledge and methods, on one side, and the design, modeling and management activities related to CPIS, on the other side (Analyze)
        • Implement smart production and co-created product design & development concepts in CPIS related activities (Create)
        • Identify use cases of big data and real time data analytics applied for CPIS, for supporting smart production, product design & development and advanced manufacturing process (Evaluate)
        • Exploit the CPIS online connectivity for strengthening business capability (Apply)
        • Applying CPIS related  knowledge and competences for improving sustainability (Apply)

Prerequisite:  None

Course Outline:

Module 1:  Sensors and Transducers Basics

        1. Introduction – concept of CPS, basics, model
        2. Identifying the physical quantities to be measured or the datasets to be acquired and computed
        3. Types of transducers, characteristics, application fields, selection criteria
        4. Choosing or designing the sensors and/or the transducers for measuring or for data collection

Module 2:  Data Acquisition Basics

        1. Signal conditioning basics
        2. Data acquisition basics
        3. Choosing or designing the needed electronics
        4. Connecting the CPS components
        5. Testing the CPS assembly

Module 3:  Programming for Data Acquisition, Processing and Communication

        1. Data acquisition programming basics
        2. Developing the CPS data acquisition software components
        3. Data processing basics
        4. Developing the CPS data processing software components
        5. Data communication basics
        6. IoT communication protocols basics
        7. Developing the CPS data communication software components

Module 4:  Advanced CPIS Topics

        1. Cloud computing and artificial intelligence basics
        2. Feeding artificial intelligence component with experimental data
Week Topic Workshop Learning material Teaching material
1 Introduction – concept of CPS, basics, model 01+MSIE-10-L-M1S1part1(แก้ไขแล้ว)
2 Establishing the projects’ subjects and forming the teams MSIE-10-T-M1S1-W01
2 Identify the physical quantities to be measured or the datasets to be acquired and computed MSIE-10-T-M1S1-W02
3 Types of transducers, characteristics, application fields, selection criteria M04+MSIE-10-L-M1S2แก้ไขแล้ว
4 Choose or design the sensors and/or the transducers for measuring or for data collection MSIE-10-T-M1S2
5 Signal conditioning basics 06+MSIE-10-L-M2S1-01(แก้ไขแล้ว)
5 Data acquisition basics 07+MSIE-10-L-M2S1-02(แก้ไขแล้ว)
6 Choose or design the needed electronics (power supplies, signal conditioning, analog to digital converters, multiplexers, communication subsystems) MSIE-10-T-M2S1-W01
7 Connect the CPS components MSIE-10-L-M2S1-W02
8 Test the CPS assembly MSIE-10-L-M2S1-W03
9 Data acquisition programming basics 11 MSIE-10-L-M3S1_thai_finish
9 Develop the CPS data acquisition software components MSIE-10-L-M3S1-W01
10 Data processing basics 13+MSIE-10-L-M3S2(แก้ไขแล้ว)
10 Develop the CPS data processing software components MSIE-10-T-M3S2
11 Data communication basics 15+MSIE-10-L-M3S3-01(แก้ไขแล้ว)
11 IoT communication protocols basics 16+MSIE-10-L-M3S3-02(แก้ไขแล้ว)
12 Develop the CPS data communication software components MSIE-10-L-M3S3-W01
13 Cloud computing and artificial intelligence basics 18+MSIE-10-L-M4S1(แก้ไขแล้ว)
14 Feed artificial intelligence component with experimental data MSIE-10-L-M4S1-W01
15 Final project presentation

Learning resources:

Textbooks: No designated textbook, but class notes and handouts will be provided.

Reference books:

  1. Wang, L. and Wang, X.V. (2018). Cloud-Based Cyber-Physical Systems in Manufacturing. Springer
  2. Markwedel, P. (2018). Embedded System Design: Embedded Systems, Foundations of Cyber-Physical Systems, and the Internet of Things. Springer.
  3. Brown, P. (Ed.) (2016). Sensors and Actuators: Technology and Applications. Library Press.
  4. Morris, A.S. and Langari, R. (2017). Measurement and Instrumentation: Theory and Application (Second Edition). Elsevier.
  5. Boyer, S.A. (2009). SCADA: Supervisory Control and Data Acquisition. ISA The Instrumentation, Systems and Automation Society
  6. Buyya, R. and Dastjerdi, A.V. (Eds.) (2016). Internet of Things: Principles and Paradigms. Morgan Kaufmann

Teaching and Learning Methods

The teaching / learning methodology is mainly student-centered (active learning) rather than teacher-centered. The course comprises lectures and laboratory sessions (for projects’ development). The lectures, besides the expositive part (teacher-centered), incorporate elements of active learning (e.g. small tasks to be solved individually or by teams in 5-10 minutes). The laboratory sessions adopt the project-based learning (PBL) approach. The projects are developed by teams and incorporate project management skills (e.g. time management and tasks’ distribution), problem solving, hands-on work (learning by doing), communications skills (project presentation and discussion) and peer assessment.

Time Distribution and Study Load:

  • Lectures: 15 hours
  • Laboratory sessions: 45 hours
  • Autonomous work (self-study): 60 hours

Organisational topics

  • One semester course
  • 15 – 20 students in a group, 3 – 4 students in a team
  • Different project for each team

Assessment

During lectures

  • Presence is compulsory
  • Students are graded according to their answers to questions addressed during the lecture

During teamwork lab activities

  • Each student continuously assessed during the lab works, individually graded every week regarding:
    • solutions correctness
    • volume of needed support
    • adopted approach
    • innovative solutions
  • Each student peer assessed, by the teammates, regarding:
    • contribution to the overall project objective achievement
    • Innovative solutions
  • Team graded every week regarding the alignment to the project plan and milestones achievement

During the final project presentation:

  • Each student individually graded regarding:
    • Solutions
    • Presentation skills (also peer assessed by other teams)
  • Team graded regarding:
    • Technical solutions (also peer assessed by questions from other teams)
    • Quality of the technical report
    • Quality of teamwork
    • Questions asked to other teams

Evaluation Scheme

The final grade will be computed according to the following weight distribution:

  • Assessment during lectures: 10 %
  • Assessment during teamwork lab activities:
    • Individuall student grade: 50 %
    • Peer assessment by teammates: 10 %
    • Team grading: 10 %
  • Assessment during final project presentation:
    • Individually: 10 %
    • Team assessment: 10 %