Course 3: Smart Operations Management

Course 3: Smart Operations Management 

(Syllabus)

Last Update: May 21th,  2020

Course Objective:

The objective of this course is to develop competences on design and implementation of continuous and efficient operations while creating a digital copy of the end-to-end process. The Internet of Thing (IoT) system to collect real time data need to be discovered. Real-time data analytics can help to evaluate, and simulate the end-to-end operation to improve and manage all operations efficiently. Emphasis is on cross-enterprise integration of the physical and virtual systems among various functions including operation strategy, process design, capacity planning, facility location and design, forecasting, production scheduling and inventory control.

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

    • apply knowledge and methods from the advanced science of industrial engineering to model, evaluate and improve industrial processes and systems in relation with company operating efficiency and customer service.
    • create smart production concepts in planning and controlling company’s operations.
    • design real time data analytics and software systems to support planning, scheduling and control of smart production processes and systems.
    • design smart production processes and systems to efficiently respond to changes in operating conditions.

Prerequisite:  None

 Course Outline:

Week Topic Workshop Learning Materials Teaching Materials Note
1 Module 1: Advanced science of industrial engineering to model, evaluate and improve industrial processes and systems MSIE-03-L-M1S1-01
2 Lesson 1-1: Operation management strategy in industry 4.0 context MSIE-03-L-M1S1-W01
3 Lesson 1-2: Smart product MSIE-03-L-M1S2-01
4 Lesson 1-3: Smart manufacturing concept MSIE-03-L-M1S3-01
5 Lesson 1-4: Smart operation concept MSIE-03-L-M1S4-W01 MSIE-03-L-M1S4-01
6 Module 2: Smart production in planning and controlling company’s
operations integrated production planning and shop-flow control
system concept		 MSIE-03-L-M2S1_W01 MSIE-03-L-M2S1_01 MSIE-03-T-M2S1_L01
7 Lesson 2-1:Implementation forecasting model under real-time
situation		 MSIE-03-L-M2S1_W02 MSIE-03-L-M2S1_02 MSIE-03-T-M2S1_L02
8 Lesson 2-2:Inventory management under real-time situation MSIE-03-L-M2S2_01 MSIE-03-T-M2S2_L01 MSIE-03-T-M2S2_L02 MSIE-03-T-M2S2_L03 MSIE-03-T-M2S2_L04 MSIE-03-T-M2S2_L05
9 Lesson 2-3:Advanced integrated production planning MSIE-03-L-M2S3-W01 MSIE-03-L-M2S3-01 MSIE-03-L-M2S3-02 MSIE-03-L-M2S3-03 MSIE-03-L-M2S3-04
10 Lesson 2-4:Advanced shop floor control MSIE-03-L-M2S4-W01 MSIE-03-L-M2S4-01 MSIE-03-L-M2S4-02 MSIE-03-L-M2S4-03 MSIE-03-L-M2S4-04
11 Module 3: Real time data analytics and software systems to support
planning, scheduling and control of smart production processes and
systems
12 Lesson 3-1: Real-time monitoring system MSIE-03-L-M3S1_01
13 Lesson 3-2: IoT system MSIE-03-L-M3S2_01
14 Lesson 3-3: Real-time data analytics MSIE-03-L-M3S3_W01
15 Lesson 3-4: Big data for predictive analytics, predictive modeling,
and forecasting		 MSIE-03-L-M3S4_W01

Laboratory Sessions: None

Learning Resources:

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

Reference Books:

  1. Ibrahim Garbie, Sustainability in Manufacturing Enterprises: Concepts, Analyses and Assessments for Industry 4.0, Springer International Publishing, 2016
  2. Klaus Schwab and Nicholas Davis, Shaping the Future of the Fourth Industrial Revolution, Crown Publishing Group, 2018
  3. Guilherme Frederico, Operations and Supply Chain Strategy in the Industry 4.0 Era, Independently Published, 2018
  4. Diego Galar Pascual, Pasquale Daponte and Uday Kumar, Handbook of Industry 4.0 and SMART Systems, CRC Press, 2018
  5. Alasdair Gilchrist, Industry 4.0: The Industrial Internet of Things, Apress, 2016

Journals and Magazines:

  1. Computers and Industrial Engineering
  2. Computers in Industry
  3. Engineering Science and Technology
  4. International Journal of Distributed Sensor Networks
  5. International Journal of Industrial Engineering Computations
  6. International Journal of Production Economics
  7. International Journal of Production Research
  8. Journal of Industrial and Production Engineering
  9. Journal of Manufacturing Systems
  10. Journal of Productivity Analysis
  11. Nature
  12. Smart and Sustainable Manufacturing Systems

Teaching and Learning Methods:

This is an activity-based course. During lecture sessions, class discussion will be conducted. During workshop sessions, active learning will be used. Students 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: 30 hours

Workshop: 30 hours

Self–study: 30 hours

Evaluation Scheme:

The final grade will be given according to the following weight evaluation:

Assessment (CLO1): 25%

  • Workshop 15%
  • Open Exam 10%

Assessment (CLO2): 25%

  • Case study 10%
  • Oral Presentation 5%
  • Open Exam 10%

 Assessment (CLO3): 25%

  • Class Project 15%
  • Workshop 10%

 Assessment (CLO4): 25%

  • Assignment 5%
  • Case Study 10%
  • Oral Presentation 5%
  • Report 5%

Developer: Wimalin Laosiritaworn (CMU), Anirut Chaijaruwanich (CMU), Chompoonoot Kasemset (CMU), Warisa Wisittipahich (CMU), Uttapol Smutkupt (CMU), Wasawat Nakkiew(CMU)

[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)

Surveys for pilot tested courses

Dear Colleagues
according to Luong Message:
“I would like to request all lecturers who are offering pilot courses and who used Vclass to help fill in the ” Evaluation form on Vclass for lecturers” and also request the students who register for your pilot course in this semester to fill in the other two forms, i.e., ” Student evaluation form for pilot courses” and ” Evaluation form on Vclass for student.”
we have prepared google drive versions of the surveys (see below). please use them to make the distribution and collection of results more convinient. If you need translated versions please try to use Google Drive feature to get your local language version of these forms
Student’s Evaluation Form on V-Class
Lecturer’s Evaluation Form on V-Class
COURSE EVALUATION FORM