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Course Outline
Introduction to AI in Manufacturing
- Trends in smart manufacturing and Industry 4.0
- Overview of AI applications in operations
- Essential performance metrics and KPIs
Data Collection and Preparation
- Manufacturing data sources (sensors, PLC, MES)
- Processing and formatting time-series data
- Preprocessing techniques using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Data exploration and visualization techniques
- Correlation analysis and identifying root causes
- Creating custom dashboards with Power BI
Machine Learning for Process Optimization
- Supervised and unsupervised learning methods
- Clustering for pattern recognition
- Regression and classification for predictive insights
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive alert systems
- Models for failure prediction
- Enhancing product quality via model-derived insights
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing capabilities
- Integration with SCADA/MES systems
- Feedback mechanisms for automated process adjustments
Case Study and Capstone Project
- Practical analysis of real-world datasets
- Designing and validating an optimization model
- Presenting a final AI-driven improvement plan
Summary and Next Steps
Requirements
- Familiarity with manufacturing processes or operations management
- Background in data analysis or Excel-based reporting
- Foundational knowledge of programming or scripting
Target Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma practitioners
21 Hours
