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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
- Dispelling myths and exploring the realities of industrial AI
AI Within the Smart Manufacturing Landscape
- Defining what characterizes a “smart” factory
- The function of AI in Industry 4.0 and industrial automation
- An overview of supporting technologies (IoT, edge computing, digital twins)
Significant Manufacturing Applications
- Predictive maintenance and ensuring equipment reliability
- Quality assurance and identifying anomalies
- Refining processes and enhancing yield
Navigating the Data Lifecycle
- Capturing and gathering industrial data through sensing
- Preparing data and addressing quality standards
- Fundamental principles of data-driven decision making
Day 2: 09:00 - 16:00 (7h)
Planning and Strategy for AI Projects
- Pinpointing high-impact use cases
- Assembling the appropriate team and defining success metrics
- Addressing common obstacles and implementing mitigation strategies
Case Studies and Sector-Specific Applications
- Real-world insights from automotive, food, pharmaceutical, and heavy industries
- Key takeaways from various digital transformation experiences
- Identifying success drivers and avoiding common pitfalls
Establishing a Path Forward
- Initiating steps for an AI project
- Evaluating technology choices and selecting vendors
- Considering scalability, ethical implications, and workforce adaptation
Wrap-up and Future Actions
Requirements
- Familiarity with fundamental industrial workflows or plant operations
- A keen interest in digital transformation or innovation strategies
- An openness to discussing the adoption of new technologies
Target Audience
- Operations managers
- Plant-level executives
- Technical leaders
14 Hours
Testimonials (2)
All in general
Daniele Donzelli - ITT ITALIA S.r.l.
Course - CANoe for CAN Compact Training
PLC basic knowledge
