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 Learning Materials Teaching Materials Note
1 Quality Management Concept under the Digital Era   MSIE-04-L-M1S1 MSIE-04-T-M1S1-01

MSIE-04-T-M1S1-02

MSIE-04-T-M1S1-03

 Please see instruction in Teaching Materials
2 Organization Performances and Quality System Strategy for I4.0 MSIE-04-L-M1S2 MSIE-04-T-M1S2
3 Quality Strategy for Digital Quality Management System MSIE-04-L-M1S3  MSIE-04-T-M1S3-01

MSIE-04-T-M1S3-02

MSIE-04-T-M1S3-03
4 Quality System Structures for I4.0 MSIE-04-L-M1S4 MSIE-04-T-M1S4
5 Quality Control Concept under Digital Data Collection MSIE-04-L-M2S1-01
MSIE-04-L-M2S1-02		 MSIE-04-T-M2S1-01 MSIE-04-T-M2S1-02
6 Automated SPC Strategy MSIE-04-L-M2S2 MSIE-04-T-M2S2-01

MSIE-04-T-M2S2-02
7 Multivariate SPC Strategy MSIE-04-L-M2S3 MSIE-04-T-M2S3-01

MSIE-04-T-M2S3-02
8 Data Analytic for Quality Monitoring MSIE-04-L-M2S4 MSIE-04-T-M2S4-01

MSIE-04-T-M2S4-02
9 Automated Quality Report Concept MSIE-04-L-M3S1 MSIE-04-T-M3S1
10 Strategic Quality Improvement under I4.0 MSIE-04-L-M3S2 MSIE-04-T-M3S2
11 Real-Time Quality Control MSIE-04-L-M3S3 MSIE-04-T-M3S3
12 Quality System Transformation MSIE-04-L-M3S4  MSIE-04-T-M3S4

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 9: Applied Data Analytics

Course 9: Applied Data Analytics

Last Update: May 13,  2020
Course Objective:The objective of this course is to help students develop competences on statistical techniques needed for data analysis, and various data mining techniques and algorithms used in practical problems that require processing big data for decision making purpose.Learning Outcomes: The students on the completion of this course would be able to:

    • Apply various inferential statistical analysis techniques to describe data sets and withdraw useful conclusions from the data sets (e.g., confidence interval, hypothesis testing)
    • Apply data visualization techniques and key data mining techniques (e.g., classification analysis, associate rule learning, anomaly/outlier detection, clustering analysis, regression analysis) in dealing with big data sets
    • Implement the analytic algorithms for practical data sets
    • Perform large scale analytic projects in various industrial sectors

Prerequisite:  None

Week Topic Workshop Learning Materials Teaching Materials Note
1 Basic Concepts MSIE-09-L-M1S1-T
2 Basic Concepts (continued) MSIE-09-T-M1S1-W01MSIE-09-T-M1S1-W02
3 Statistical Inferences MSIE-09-T-M1S2-W01 MSIE-09-L-M1S2_T
4 Hypothesis Testing MSIE-09-L-M1S3-T
5 Hypothesis Testing (continued) MSIE-09-T-M1S3-W01
6 Data Visualization MSIE-09-L-M2S1-T
7 Data Dashboard MSIE-09-L-M2S2-T
8 Regression Analysis MSIE-09-T-M3S1-W01 MSIE-09-L-M3S1-T
9 Regression Analysis (cont.)
10 Data Classification MSIE-09-L-M3S2-T
11 Data Classification (cont.)
12 Data Clustering MSIE-09-L-M3S3-T
13 Data Clustering (cont.)
14 Association Rules MSIE-09-L-M3S4-T
15 Association Rules (cont.)


Laboratory Sessions: None

Learning Resources:

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

Reference Books:

    1. Larose, D.T. and Larose, C.D., Data Mining and Predictive Analytics, 2nd edition, Wiley, 2015
    2. Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr., K.C., Data Mining for Business Analytics – Concepts, Techniques, and Application in R, Wiley, 2018
    3. Ankam, V., Big Data Analytics, Packt, 2016
    4. Walkowiak, S., Big Data Analytics with R, Packt, 2016
    5. Grolemund, G., Hands-on Programming with R, O’Reilly, 2014
    6. Wickham, H. and Grolemund, G., R for Data Science, O’Reilly, 2017
    7. Wexler, S., Shaffer, J. and Cotgreave, A., The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios, Wiley, 2017
    8. O’Cornor, E., Microsoft Power BI Dashboards Step by Step, Practice Files, 2019

Journals and Magazines:

    1. Management Science, Informs
    2. Journal of Supply Chain Management, Wiley
    3. Computational Statistics & Data Analysis, Elsevier
    4. Advances in Data Analysis and Classification, Springer

Teaching and Learning Methods:

The teaching is done via lectures by the instructor. Tutorial/workshop sessions are conducted on the use of tools in each subject. The learning methods include group discussion, individual/group assignment and group project/case study.

Time Distribution and Study Load:

Lectures: 30 hours

Tutorials/Group Discussions: 30 hours

Self-study: 45 hours

Group project: 40 hours

Evaluation Scheme:  The final grade will be computed according to the following weight distribution: Mid-semester examination 20%, assignments and group projects 50%, final examination 30%. In final grading

An “A” would be awarded if a student shows a deep understanding of the knowledge learned through home assignments, project works, and exam results.

A “B” would be awarded if a student shows an overall understanding of all topics.

A “C” would be given if a student meets below average expectation in understanding and application of basic knowledge.

A “D” would be given if a student does not meet expectations in both understanding and application of the given knowledge.

Developer: Huynh Trung Luong (AIT), Sirorat Pattanapairoj (KKU); Komkrit Pitituek (KKU), Wimalin Laosiritaworn (CMU)