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Course Outline
Day 1: 09:00 - 16:00 (7h)
Foundations of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Debunking myths and exploring the realities of AI in industry.
AI in the Context of Smart Manufacturing
- Defining the characteristics of a “smart” factory.
- The role of AI in Industry 4.0 and industrial automation.
- Overview of enabling technologies such as IoT, edge computing, and digital twins.
Key Manufacturing Use Cases
- Predictive maintenance and enhancing equipment reliability.
- Quality assurance and anomaly detection techniques.
- Process optimization and strategies for yield improvement.
Understanding the Data Lifecycle
- Sensing and acquiring industrial data.
- Data preparation and considerations for data quality.
- Foundational concepts in data-driven decision making.
Day 2: 09:00 - 16:00 (7h)
AI Project Planning and Strategy
- Identifying high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and mitigation strategies.
Case Studies and Industry Applications
- Real-world examples from automotive, food, pharma, and heavy industries.
- Insights gained from digital transformation journeys.
- Success factors and common pitfalls to avoid.
Roadmap for Getting Started
- Steps for launching an AI initiative.
- Technology considerations and vendor selection criteria.
- Scalability, ethics, and workforce adaptation.
Summary and Next Steps
Requirements
- Familiarity with basic industrial processes or plant operations
- Interest in digital transformation or innovation strategy
- Willingness to engage in discussions on 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
