Course Outline
Course Program
Module 1: Fundamentals of AI and Machine Learning
• Key concepts
• Types of models
• Industrial case studies
Module 2: Predictive Maintenance
• Anomaly detection
• Supervised models
• Interpretation of results
Module 3: Production Optimization
• OEE analysis
• Bottleneck detection
• Resource optimization
Module 4: Quality Analytics
• Defect prediction
• CAPA analysis
• Risk assessment
Module 5: Implementation Roadmap
• Data architecture
• Project phases
• ROI measurement
Requirements
General Information
This course offers a practical and strategic perspective on how to implement Artificial Intelligence solutions in manufacturing environments.
It is aimed at companies seeking to apply AI for predictive maintenance, production optimization, quality improvement, and material planning. The training sessions are led by expert instructors in live format.
Available Delivery Modes:
- Online with a live instructor (1 to 10 participants)
- On-site at your facilities (1 to 15 participants)
After completing this training, participants will be able to:
• Identify viable AI use cases in their plant
• Assess data availability and quality
• Apply basic industrial prediction models
• Design an initial implementation roadmap
Duration
21 hours (3 days of 7 hours each, including breaks and lunch).
Target Audience
• Process engineers
• Maintenance engineers
• Quality personnel
• Production leaders
• IT and innovation teams
