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Course Outline
Day 1: 09:00 - 16:00 (7h)
The Fundamentals of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Distinguishing myths from realities of AI in industrial settings.
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 (IoT, edge computing, digital twins).
Primary Manufacturing Applications
- Predictive maintenance and enhancing equipment reliability.
- Quality assurance and anomaly identification.
- Process refinement and yield enhancement.
Navigating the Data Lifecycle
- Sensing techniques and industrial data collection.
- Data preparation and quality management considerations.
- Fundamental concepts in data-driven decision-making.
Day 2: 09:00 - 16:00 (7h)
Strategic Planning for AI Projects
- Pinpointing high-impact use cases.
- Assembling the appropriate team and defining success metrics.
- Addressing common challenges and mitigation strategies.
Case Studies and Industry Implementations
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights from digital transformation experiences.
- Key success factors and common pitfalls to circumvent.
Roadmap for Initiation
- Steps to launch an AI initiative.
- Technology assessment and vendor selection.
- Scalability, ethical considerations, and workforce adaptation.
Recap and Future Directions
Requirements
- Familiarity with fundamental industrial processes or plant operations
- A keen interest in digital transformation or innovation strategies
- Readiness to engage in 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
