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Course Outline
Introduction to Artificial Intelligence in Manufacturing for Government
- Trends in smart manufacturing and Industry 4.0 for government applications
- Overview of AI use cases in operations within the public sector
- Key performance metrics and KPIs relevant to government agencies
Data Collection and Preparation for Government
- Sources of manufacturing data (sensors, PLC, MES) in government facilities
- Cleaning and formatting time-series data for government systems
- Using Pandas and Jupyter for preprocessing in a public sector context
Descriptive and Diagnostic Analytics for Government
- Data exploration and visualization techniques suitable for government use
- Correlation analysis and root cause identification tailored to government operations
- Custom dashboards with Power BI for government decision-making
Machine Learning for Process Optimization in Government
- Supervised and unsupervised learning methods for government processes
- Clustering techniques for pattern discovery in government data
- Regression and classification models for predictive analysis in the public sector
AI for Predictive Maintenance and Quality in Government
- Anomaly detection and predictive alerts for government equipment
- Failure prediction models tailored to government assets
- Enhancing product quality through model insights for government products
Real-Time Analytics and Feedback Loops for Government
- Streaming data and real-time processing in government systems
- Integrating with SCADA/MES systems in the public sector
- Feedback mechanisms for automatic process adjustments in government operations
Case Study and Capstone Project for Government
- Hands-on analysis of real-world data sets relevant to government agencies
- Designing and validating an optimization model for government use
- Final presentation of an AI-driven improvement plan for government operations
Summary and Next Steps for Government
Requirements
- A comprehension of manufacturing processes or operations management for government
- Experience in data analysis or Excel-based reporting
- Fundamental knowledge of programming or scripting
Audience
- Process engineers
- Plant supervisors
- Lean Six Sigma professionals
21 Hours
