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Course Outline

Day 1: 09:00 - 16:00 (7h)

Core Concepts of Artificial Intelligence

  • Defining AI, machine learning, and deep learning
  • Learning paradigms: supervised, unsupervised, and reinforcement
  • Dispelling myths and understanding the realities of AI in industrial settings

AI within the Smart Manufacturing Landscape

  • Defining the characteristics of a 'smart' factory
  • The contribution of AI to Industry 4.0 and industrial automation
  • Overview of supporting technologies (IoT, edge computing, digital twins)

Prominent AI Applications in Manufacturing

  • Enhancing equipment reliability through predictive maintenance
  • Improving quality assurance and detecting anomalies
  • Optimizing processes and increasing yield

Navigating the Data Lifecycle

  • Sensing and aggregating industrial data
  • Data preparation and ensuring data quality
  • Fundamental concepts in data-driven decision-making

 

Day 2: 09:00 - 16:00 (7h)

Strategic Planning for AI Projects

  • Identifying use cases with high impact
  • Assembling the right team and defining success metrics
  • Addressing common challenges and developing mitigation strategies

Case Studies and Industry Insights

  • Practical 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

  • Action steps for launching an AI initiative
  • Technical considerations and vendor evaluation
  • Scalability, ethical considerations, and workforce adaptation

Conclusion and Future Directions

Requirements

  • Foundational knowledge of basic industrial processes or plant operations
  • Curiosity regarding digital transformation and innovation strategies
  • Willingness to engage in discussions about technology adoption

Target Audience

  • Operations managers
  • Plant executives
  • Technical leads
 14 Hours

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