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Course Outline
Foundations of Artificial Intelligence
- What is AI, machine learning, and deep learning?
- Types of learning: supervised, unsupervised, reinforcement
- Myths and realities of AI in industry
AI in the Context of Smart Manufacturing
- What makes a factory “smart”?
- AI’s role in Industry 4.0 and industrial automation
- Overview of enabling technologies (IoT, edge computing, digital twins)
Key Use Cases in Manufacturing
- Predictive maintenance and equipment reliability
- Quality assurance and anomaly detection
- Process optimization and yield improvement
Understanding the Data Lifecycle
- Sensing and collecting industrial data
- Data preparation and quality considerations
- Basic concepts in data-driven decision making
AI Project Planning and Strategy
- Identifying high-impact use cases
- Building the right team and setting success metrics
- Common challenges and mitigation strategies
Case Studies and Industry Applications
- Real-world examples from automotive, food, pharma, and heavy industries
- Lessons learned from digital transformation journeys
- Success factors and pitfalls to avoid
Roadmap for Getting Started
- Steps for launching an AI initiative
- Technology considerations and vendor selection
- Scalability, ethics, and workforce adaptation
Summary and Next Steps
Requirements
- An understanding of basic industrial processes or plant operations
- Interest in digital transformation or innovation strategy
- Comfort with technology adoption discussions
Audience
- Operations managers
- Plant executives
- Technical leads
14 Hours
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