Improving Quality and Yield in Chemicals Manufacturing with AI

Overview

Discover how Rockwell's FactoryTalk DataMosaix combines IT, OT, and ET data in a single DataOps solution, helping manufacturers extract more value from complex industrial data at scale.

Syllabus

• Introduction to AI in Chemicals Manufacturing
Overview of AI and Machine Learning
Importance of AI in the Chemical Industry
Case Studies of AI Implementation
• Understanding Rockwell's FactoryTalk DataMosaix
Introduction to FactoryTalk DataMosaix
Features and Capabilities
Benefits for Industry Professionals
• DataOps in Manufacturing
Defining DataOps
Integration of IT, OT, and ET Data
Tools and Techniques
• Extracting Value from Industrial Data
Data Collection and Management
Data Analytics and Insights
Enhancing Decision-Making with AI
• Improving Quality in Chemical Manufacturing
Quality Control Processes
AI-Driven Quality Improvement Strategies
Case Studies and Best Practices
• Yield Optimization Techniques
Understanding Yield in Manufacturing
AI's Role in Process Optimization
Practical Applications and Impact
• Scaling AI Solutions in Manufacturing
Challenges and Solutions in Scaling AI
Infrastructure and Implementation
Measuring Success and Continuous Improvement
• Hands-on Workshop and Practical Applications
Analyzing Real-World Data with FactoryTalk DataMosaix
Interactive Case Study Exercises
Building a Roadmap for AI Implementation
• Future Trends and Innovations in AI for Manufacturing
Emerging Technologies
AI and Sustainability
Preparing for Future Developments
• Course Conclusion and Q&A
Summary of Key Concepts
Open Discussion and Feedback
Resources for Continued Learning

Subjects

Computer Science