Smart Operations Management

[Thai] Course 3: Smart Operations Management 

(Syllabus)

Last Update: May 25th,  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		[Thai]MSIE-03-L-M1S1-01
2	Lesson 1-1:Operation management strategy in industry 4.0 context	[Thai]MSIE-03-L-M1S1-W01
3	Lesson 1-2:Smart product		[Thai]MSIE-03-L-M1S2-01
4	Lesson 1-3:Smart manufacturing concept		[Thai]MSIE-03-L-M1S3-01
5	Lesson 1-4:Smart operation concept	[Thai]MSIE-03-T-M1S4-W01	[Thai]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		[Thai] MSIE-03-L-M2S1_01	[Thai] MSIE-03-T-M2S1_L01
7	Lesson 2-1:Implementation forecasting model under real-time situation	[Thai] MSIE-03-L-M2S1_W01	[Thai] MSIE-03-L-M2S1_02	[Thai] MSIE-03-T-M2S1_L02
8	Lesson 2-2:Inventory management under real-time situation		[Thai] MSIE-03-L-M2S2_01	[Thai] 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	[Thai]MSIE-03-L-M2S3-W01	[Thai]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	[Thai]MSIE-03-L-M2S4-W01	[Thai]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		[Thai] MSIE-03-L-M3S1_01
13	Lesson 3-2:IoT system		[Thai] 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)