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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 methodologies: supervised, unsupervised, and reinforcement learning.
- Distinguishing between AI myths and industrial realities.
AI in the Context of Smart Manufacturing
- Characteristics that define 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 Use Cases in Manufacturing
- Predictive maintenance and ensuring equipment reliability.
- Quality assurance and detecting anomalies.
- Process optimization and improving yield.
Understanding the Data Lifecycle
- Sensing and gathering industrial data.
- Data preparation and quality assessment.
- Fundamental 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 establishing success metrics.
- Addressing common challenges and implementing mitigation strategies.
Case Studies and Industry Applications
- Real-world examples from automotive, food, pharma, and heavy industries.
- Insights gained from digital transformation journeys.
- Key success factors and common pitfalls to avoid.
Roadmap for Getting Started
- Steps to launch an AI initiative.
- Technology considerations and vendor selection.
- Scalability, ethics, and workforce adaptation.
Summary and Next Steps
Requirements
- Familiarity with basic industrial processes or plant operations.
- Interest in digital transformation or innovation strategies.
- Comfort 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
