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Duration 14 hours
Course Outline
Day 1: 09:00 - 16:00 (7h)
Fundamental Concepts of Artificial Intelligence
- Definitions of AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Addressing myths versus realities of AI in industrial settings.
AI within Smart Manufacturing Contexts
- Characteristics that define 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 detection techniques.
- Process optimization and yield enhancement strategies.
Navigating the Data Lifecycle
- Sensing and acquisition of industrial data.
- Data preparation and quality management considerations.
- Foundational concepts in data-driven decision making.
Day 2: 09:00 - 16:00 (7h)
Planning and Strategy for AI Projects
- Identifying high-impact use cases.
- Assembling appropriate teams and defining success metrics.
- Addressing common challenges and mitigation tactics.
Case Studies and Industrial Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights gained from digital transformation experiences.
- Key success factors and common pitfalls to avoid.
Getting Started Roadmap
- Steps to initiate an AI program.
- Technology evaluation and vendor selection.
- Scalability, ethical considerations, and workforce adaptation.
Recap and Forward-Looking 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
Testimonials (2)
All in general
Daniele Donzelli - ITT ITALIA S.r.l.
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PLC basic knowledge
