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Course Outline
Day 1: 09:00 - 16:00 (7 hours)
Fundamental Principles of Artificial Intelligence
- Defining AI, machine learning, and deep learning.
- Learning methodologies: supervised, unsupervised, and reinforcement.
- Distinguishing myths from realities in industrial AI.
AI in the Context of Smart Manufacturing
- Defining the characteristics of a "smart" factory.
- The role of AI in Industry 4.0 and industrial automation.
- Overview of enabling technologies (IoT, edge computing, digital twins).
Major Applications in Manufacturing
- Predictive maintenance and equipment reliability.
- Quality assurance and anomaly detection.
- Process optimization and yield enhancement.
Navigating the Data Lifecycle
- Sensing and acquiring industrial data.
- Data preparation and quality management.
- Basic concepts in data-informed decision-making.
Day 2: 09:00 - 16:00 (7 hours)
AI Project Planning and Strategic Approach
- Identifying high-impact use cases.
- Assembling the right team and defining success metrics.
- Addressing common challenges and implementing mitigation strategies.
Case Studies and Sector-Specific Applications
- Real-world examples from automotive, food, pharmaceutical, and heavy industries.
- Insights from digital transformation journeys.
- Key success factors and common pitfalls to avoid.
Roadmap for Initiation
- Steps to launch an AI initiative.
- Technology evaluation and vendor selection.
- Scalability, ethics, and workforce adaptation.
Recap and Forward-Looking Actions
Requirements
- Basic familiarity with industrial workflows or plant operations.
- Interest in digital transformation or innovation strategies.
- Openness to discussions regarding technology adoption.
Target Audience
- Operations managers.
- Plant executives.
- Technical leads.
14 Hours
Testimonials (1)
PLC basic knowledge
