Applications and management of artificial intelligence to adjust and increase production #254018

Upon completion of this course, participants will be able to:

• Understand AI Fundamentals: Comprehend key AI technologies and their specific applications in industrial production environments

• Identify Optimization Opportunities: Recognize production bottlenecks and areas where AI can deliver significant improvements in efficiency and output

• Implement AI Solutions: Develop and execute strategies for integrating AI technologies into existing production systems

• Analyze Production Data: Utilize AI-powered analytics to extract actionable insights from production data

• Manage AI Projects: Lead AI implementation projects from conception to deployment, including change management and team training

• Measure ROI: Calculate and demonstrate the return on investment from AI implementations in production settings

• Ensure Sustainable Implementation: Address ethical considerations and maintain AI systems for long-term success

This course is designed for professionals involved in manufacturing, production management, and industrial operations:

• Production and Plant Managers

• Manufacturing Engineers and Process Engineers

• Operations Directors and Supply Chain Managers

• Quality Control and Assurance Managers

• Industrial Automation Specialists

• Continuous Improvement Professionals

• Technology Implementation Managers

• Industrial Data Analysts

• Operations Executives seeking to understand AI applications in production

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of AI in Industrial Production

• AM: Introduction to AI in Manufacturing

  • The evolution of smart manufacturing: From automation to AI-driven production

  • Key AI technologies relevant to production: Machine Learning, Computer Vision, Predictive Analytics

  • Current state and future trends of AI in industrial applications

• PM: Data Infrastructure for AI Implementation

  • Understanding industrial data sources: IoT sensors, PLCs, SCADA systems

  • Data collection, cleaning, and preparation strategies

  • Building a data-driven culture in production environments

  • Workshop: Assessing data readiness in production facilities

Day 2: AI for Process Optimization and Efficiency

• AM: Predictive Maintenance and Equipment Optimization

  • Implementing AI for predictive maintenance to reduce downtime

  • Optimizing equipment performance through machine learning algorithms

  • Case studies: AI-driven maintenance success stories in manufacturing

• PM: Production Line Optimization

  • Using AI to identify and eliminate production bottlenecks

  • Real-time production monitoring and adjustment systems

  • Implementing digital twins for production simulation and optimization

  • Hands-on exercise: Analyzing production line data for optimization opportunities

Day 3: Quality Control and Precision Manufacturing

• AM: AI-Powered Quality Assurance

  • Computer vision systems for automated quality inspection

  • Machine learning for defect prediction and prevention

  • Implementing real-time quality monitoring systems

• PM: Precision and Adaptive Manufacturing

  • AI for parameter optimization in complex manufacturing processes

  • Adaptive control systems that respond to changing conditions

  • Reducing material waste through AI-driven precision manufacturing

  • Workshop: Designing an AI-enhanced quality control system

Day 4: Supply Chain Integration and Scalability

• AM: AI in Production Planning and Scheduling

  • Demand forecasting and production planning using AI

  • Dynamic scheduling systems that adapt to changing conditions

  • Inventory optimization and material flow management

• PM: Scaling AI Solutions Across Operations

  • Strategies for scaling successful AI implementations

  • Integrating AI systems with existing ERP and MES platforms

  • Change management and workforce training for AI adoption

  • Case study: Enterprise-wide AI implementation in manufacturing

Day 5: Implementation Strategy and Future Outlook

• AM: Developing an AI Implementation Roadmap

  • Assessing organizational readiness for AI adoption

  • Building a business case for AI investments

  • Developing a phased implementation strategy

  • Measuring and demonstrating ROI from AI implementations

• PM: Capstone Project and Future Trends

  • Group exercise: Develop a comprehensive AI implementation plan for a production facility

  • Emerging trends in AI for manufacturing: Generative AI, advanced robotics, and IoT integration

  • Ethical considerations and sustainable AI practices in production

  • Final presentations and action planning session

  • Course wrap-up: Developing a personal implementation strategy