Introduction to AI in Smart Factories and Industrial Automation

• 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

AI in Smart Factories is the application of artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led, live training (online or onsite) is aimed at beginner-level decision-makers and technical leads who wish to gain a strategic and practical introduction to how AI can be leveraged in smart factory environments.

By the end of this training, participants will be able to:

• Understand the core principles of AI and machine learning.
• Identify key AI use cases in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimization.
• Evaluate the steps involved in launching AI-driven initiatives.

Format of the Course

• Interactive lecture and discussion.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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