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Course Outline
Day 1: 09:00 - 16:00 (7h)
Fundamentals of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Separating industry AI myths from reality.
AI within Smart Manufacturing
- Defining the characteristics of a "smart" factory.
- The role of AI in Industry 4.0 and industrial automation.
- An overview of enabling technologies, including IoT, edge computing, and digital twins.
Primary Manufacturing Use Cases
- Predictive maintenance and enhancing equipment reliability.
- Quality assurance and anomaly detection techniques.
- Process optimization and yield enhancement.
Navigating the Data Lifecycle
- Sensing and aggregating industrial data.
- Data preparation and addressing quality considerations.
- Core concepts in data-driven decision-making.
Day 2: 09:00 - 16:00 (7h)
Strategic AI Project Planning
- Identifying high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and implementing mitigation strategies.
Case Studies and Sector Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights gained from digital transformation journeys.
- Critical success factors and common pitfalls to avoid.
Getting Started: A Practical Roadmap
- Steps for successfully launching an AI initiative.
- Technology considerations and vendor selection criteria.
- Scalability, ethical considerations, and workforce adaptation.
Recap and Future Directions
Requirements
- A foundational understanding of basic industrial processes or plant operations.
- An interest in digital transformation or innovation strategy.
- Familiarity with discussions surrounding technology adoption.
Target Audience
- Operations managers.
- Plant executives.
- Technical leads.
14 Hours
Testimonials (2)
All in general
Daniele Donzelli - ITT ITALIA S.r.l.
Course - CANoe for CAN Compact Training
PLC basic knowledge
