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Course Outline
Day 1: 09:00 - 16:00 (7h)
Fundamentals of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning paradigms: supervised, unsupervised, and reinforcement.
- Dispelling myths and clarifying realities of AI in industry.
AI in the Smart Manufacturing Context
- Defining the characteristics of a “smart” factory.
- The contribution of AI to Industry 4.0 and industrial automation.
- An overview of supporting technologies including IoT, edge computing, and digital twins.
Critical Manufacturing Use Cases
- Predictive maintenance and enhancing equipment reliability.
- Quality assurance and anomaly detection techniques.
- Process optimization and strategies for yield improvement.
Navigating the Data Lifecycle
- Sensing mechanisms and industrial data collection.
- Data preparation and quality assurance considerations.
- Fundamental concepts of data-driven decision-making.
Day 2: 09:00 - 16:00 (7h)
AI Project Planning and Strategy
- Identifying high-impact use cases.
- Assembling the appropriate team and defining success metrics.
- Common obstacles and strategies for mitigation.
Case Studies and Industry Applications
- Real-world 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
- Steps to initiate an AI initiative.
- Technology assessments and vendor selection.
- Scalability, ethical considerations, and workforce adaptation.
Summary and Future Actions
Requirements
- Familiarity with basic industrial processes or plant operations.
- A keen interest in digital transformation or innovation strategy.
- Ease in discussing 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
