Introduction to smart manufacturing and industrial data analytics
Capturing data from 4M+1E assets in a factory
Creating the value of quality, cost, and delivery from the captured data
Learning a formal industrial data analytics procedure

Understanding manufacturing processes

Homework 30%
Mid-Term Exam 30%
Final Project 40%

Use Cases

Week 1. Industry trends
§ Assignment 1: Draft a ChatGPT prompt on a topic of your interest. Due by Sunday.
Week 2. Product, process, and production engineering
§ Watch manufacturing process in Youtube in class
§ Assignment 2: Write 1-page essay for each Youtube. Due by Sunday.
Week 3. Productivity, quality
Week 4. Cost & delivery
§ Assignment 3: Write down the inter-relationship of productivity, quality, cost, and delivery. You may include some diagrams. Due by Sunday.
Week 5. Mass production, Lean production
§ Assignment 4: Perform a comparative analysis for mass production and lean production and then represent the result into a table (3 pages in total). Due by Sunday.
Week 6. Smart X, Data thinking
§ Assignment 5: Devise a Prompt similar to, but not the same as “What are the key differences among traditional, automated, smart, and autonomous manufacturing?,” and then summarize it.
Week 7. Practices with Python
Week 8. Review session (Monday) & Mid-term exam (Wednesday)
Week 9. Project team formation, Problems explanation, Proposal presentation
Week 10. DX process & success factors, Pain points definition
Week 11. Data preparation - Internet of Things (IoT) in manufacturing (Invited Lecturer)
Week 12. DataWare development, Operation and monitoring
Week 13. Storytelling with data
Week 14. Intermediate project presentations
Week 15. Use cases
§ DataWare in Manufacturing (SeAH Steel Co.’s traceability)
§ DataWare in Manufacturing (Changshin Inc.’s Inspection)
Week 16. Preparation session (Monday), Final project presentation (Wednesday)

Lectures, Discussions, Presentations