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Course Outline
Introduction to AI in Manufacturing
- Emerging trends in smart manufacturing and Industry 4.0
- Overview of practical AI applications in operations
- Essential performance indicators and KPIs
Data Collection and Preparation
- Identifying manufacturing data sources (sensors, PLC, MES)
- Cleaning and structuring time-series data
- Preprocessing workflows using Pandas and Jupyter
Descriptive and Diagnostic Analytics
- Exploratory data analysis and visualization techniques
- Correlation analysis and root cause identification
- Building custom dashboards with Power BI
Machine Learning for Process Optimization
- Fundamentals of supervised and unsupervised learning
- Clustering techniques for pattern discovery
- Regression and classification methods for predictive modeling
AI for Predictive Maintenance and Quality
- Anomaly detection and predictive alert systems
- Developing failure prediction models
- Enhancing product quality through model-driven insights
Real-Time Analytics and Feedback Loops
- Streaming data and real-time processing strategies
- Integration with SCADA/MES systems
- Implementing feedback loops for automatic process adjustments
Case Study and Capstone Project
- Hands-on analysis of real-world datasets
- Designing and validating an optimization model
- Presenting a comprehensive AI-driven improvement plan
Summary and Future Directions
Requirements
- Proficiency in manufacturing processes or operations management
- Practical experience with data analysis or Excel-based reporting tools
- Foundational knowledge of programming or scripting languages
Target Audience
- Process Engineers
- Plant Supervisors
- Lean Six Sigma Practitioners
21 Hours
