AI for Process Optimization in Manufacturing Operations

• An understanding of manufacturing processes or operations management
• Experience with data analysis or Excel-based reporting
• Basic familiarity with programming or scripting

Audience

• Process engineers
• Plant supervisors
• Lean Six Sigma professionals

AI for Process Optimization is the application of machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing operations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level manufacturing professionals who wish to apply AI techniques to streamline operations, reduce downtime, and support continuous improvement initiatives.

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

• Understand AI concepts relevant to manufacturing optimization.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to support data-driven decisions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Introduction to AI in Manufacturing

• Trends in smart manufacturing and Industry 4.0
• Overview of AI use cases in operations
• Key performance metrics and KPIs

Data Collection and Preparation

• Sources of manufacturing data (sensors, PLC, MES)
• Cleaning and formatting time-series data
• Using Pandas and Jupyter for preprocessing

Descriptive and Diagnostic Analytics

• Data exploration and visualization
• Correlation analysis and root cause identification
• Custom dashboards with Power BI

Machine Learning for Process Optimization

• Supervised and unsupervised learning
• Clustering for pattern discovery
• Regression and classification for prediction

AI for Predictive Maintenance and Quality

• Anomaly detection and predictive alerts
• Failure prediction models
• Improving product quality through model insights

Real-Time Analytics and Feedback Loops

• Streaming data and real-time processing
• Integrating with SCADA/MES systems
• Feedback for automatic process adjustments

Case Study and Capstone Project

• Hands-on analysis of real-world data sets
• Designing and validating an optimization model
• Final presentation of AI-driven improvement plan

Summary and Next Steps