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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
  • Separating AI myths from industrial realities

AI within Smart Manufacturing Contexts

  • Defining the characteristics of a "smart" factory
  • The role of AI in Industry 4.0 and industrial automation
  • Introduction to enabling technologies such as IoT, edge computing, and digital twins

Primary Applications in Manufacturing

  • Ensuring equipment reliability through predictive maintenance
  • Enhancing quality assurance and detecting anomalies
  • Improving yield through process optimization

Navigating the Data Lifecycle

  • Sensing and gathering industrial data
  • Addressing data preparation and quality standards
  • Fundamental concepts in data-informed 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 challenges with effective mitigation strategies

Industry Case Studies and Applications

  • Real-world examples from automotive, food, pharmaceutical, and heavy industries
  • Key takeaways from digital transformation experiences
  • Success factors and potential pitfalls to circumvent

Getting Started: A Roadmap

  • Steps to initiate an AI initiative
  • Considering technology choices and vendor selection
  • Addressing scalability, ethics, and workforce adaptation

Recap and Future Actions

Requirements

  • Familiarity with fundamental industrial processes or plant operations
  • A keen interest in digital transformation or innovation strategies
  • Ease in engaging with topics related to technology adoption

Target Audience

  • Operations managers
  • Plant executives
  • Technical leads
 14 Hours

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