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

In-class participation 10%
Homework 20%
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
• Assignment 2: Watch manufacturing process youtube and write 1-page essay for each process (6 pages in total). Due by the next 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: Apply a prompt similar to, but not the same as “What are the key differences among traditional, automated, smart, and autonomous manufacturing?,” and then summarize the result.
Week 7: DX Process & Success Factors, Pain Points Definition
• Assignment 6: Find a use case for digital transformation and summarize it. Due by Sunday.
Week 8: (Wednesday) Mid-term exam
Week 9: (Wednesday) Project Team Formation & Proposal Presentation
Week 10: Data Preparation - Internet of Things (IoT) in Manufacturing (Invited Lecturer)
Week 11: DataWare Development, Operation and Monitoring
Week 12: Storytelling with Data
Week 13: (Wesdnesday) Intermediate Project Presentations
Week 14: Use Case of DataWare in Manufacturing (SeAH Steel Co.’s traceability)
Week 15: Use Case of DataWare in Manufacturing (Changshin Inc.’s Inspection)
Week 16: (Wednesday) Final Project Presentation

Lectures & Presentations