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Course Outline
Day 1: 09:00 - 16:00 (7h)
The Basics of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning methodologies: supervised, unsupervised, and reinforcement learning.
- Clarifying myths and realities of AI in the industry.
AI within Smart Manufacturing Frameworks
- Defining the characteristics of a “smart” factory.
- The role of AI in Industry 4.0 and industrial automation.
- Overview of supporting technologies such as IoT, edge computing, and digital twins.
Major Applications in Manufacturing
- Predictive maintenance and ensuring equipment reliability.
- Quality assurance and anomaly detection techniques.
- Optimizing processes and improving yield.
Grasping the Data Lifecycle
- Sensing and gathering industrial data.
- Data preparation and quality considerations.
- Fundamental concepts of data-driven decision-making.
Day 2: 09:00 - 16:00 (7h)
Planning and Strategy for AI Projects
- Identifying high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and mitigation strategies.
Case Studies and Sector-Specific Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights gained from digital transformation experiences.
- Key success factors and potential pitfalls to avoid.
Roadmap for Implementation
- Steps for launching an AI initiative.
- Technology considerations and vendor selection processes.
- Scalability, ethical considerations, and workforce adaptation.
Wrap-up and Next Steps
Requirements
- Familiarity with basic industrial processes or plant operations.
- Interest in digital transformation or innovation strategy.
- Comfort with discussions regarding 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
