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Course Outline
Day 1: 09:00 - 16:00 (7h)
Core Concepts of Artificial Intelligence
- Defining AI, machine learning, and deep learning
- Learning paradigms: supervised, unsupervised, and reinforcement
- Dispelling myths and understanding the realities of AI in industrial settings
AI within the Smart Manufacturing Landscape
- Defining the characteristics of a 'smart' factory
- The contribution of AI to Industry 4.0 and industrial automation
- Overview of supporting technologies (IoT, edge computing, digital twins)
Prominent AI Applications in Manufacturing
- Enhancing equipment reliability through predictive maintenance
- Improving quality assurance and detecting anomalies
- Optimizing processes and increasing yield
Navigating the Data Lifecycle
- Sensing and aggregating industrial data
- Data preparation and ensuring data quality
- Fundamental concepts in data-driven decision-making
Day 2: 09:00 - 16:00 (7h)
Strategic Planning for AI Projects
- Identifying use cases with high impact
- Assembling the right team and defining success metrics
- Addressing common challenges and developing mitigation strategies
Case Studies and Industry Insights
- Practical examples from automotive, food, pharmaceutical, and heavy industries
- Insights gained from digital transformation journeys
- Key success factors and common pitfalls to avoid
Roadmap for Implementation
- Action steps for launching an AI initiative
- Technical considerations and vendor evaluation
- Scalability, ethical considerations, and workforce adaptation
Conclusion and Future Directions
Requirements
- Foundational knowledge of basic industrial processes or plant operations
- Curiosity regarding digital transformation and innovation strategies
- Willingness to engage in discussions about 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