Course in Applications of Artificial Intelligence in Production Management

In today’s rapidly evolving industrial landscape, artificial intelligence (AI) has emerged as a transformative force that is reshaping production management and driving unprecedented levels of operational efficiency. No longer confined to research or software domains, AI has become a strategic tool within modern factories and production lines, enabling organizations to improve quality, reduce costs, and strengthen their competitive advantage. Geneva Institute of Business Administration proudly presents this specialized training program under the title “Applications of Artificial Intelligence in Production Management”, which is designed to equip participants with a thorough understanding of how AI technologies can be effectively integrated across different stages of production, from planning to monitoring, ensuring enhanced performance and sustainable operations.

Target Audience

• Senior executives and production managers in industrial companies who seek to leverage AI technologies to achieve improved efficiency and performance.

• Production and operations engineers aiming to upgrade their technical expertise and align their skills with modern intelligent manufacturing approaches.

• Quality control officers and continuous improvement specialists who wish to enhance standards of quality and minimize errors through advanced data analysis tools.

• Data analysts and technical professionals interested in applying artificial intelligence in production environments in a practical and effective manner.

• Decision-makers and industrial consultants responsible for designing institutional strategies to boost competitiveness through digital transformation.

Objectives

• Provide participants with comprehensive knowledge of the theoretical and practical foundations of AI applications in production management.

• Enable participants to understand modern tools and techniques that support automation of production and improve operational efficiency.

• Develop participants’ ability to apply machine learning algorithms and predictive models to optimize production planning and demand forecasting.

• Raise awareness among participants of the importance of AI in quality monitoring, minimizing waste, and reducing downtime across production lines.

• Strengthen institutional capabilities to design innovative digital strategies that achieve sustainability and ensure a lasting competitive edge in industry.

Course Outline 

• Introduction to Artificial Intelligence in Production

  • Definition of AI and its role in modern industry.

  • Evolution of production from traditional methods to intelligent automation.

  • Key benefits of applying AI in manufacturing environments.

  • Challenges of integrating AI into production systems.

• Digital Infrastructure for Smart Production

  • The role of big data in supporting AI applications.

  • Hardware and software requirements in industrial settings.

  • Integration of the Internet of Things (IoT) with AI systems.

  • Importance of cloud computing in managing production processes.

• AI in Production Planning

  • Demand forecasting using intelligent algorithms.

  • Optimization of schedules and resource allocation.

  • Reducing downtime through predictive planning.

  • Decision support tools for supply chain management.

• Enhancing Operational Efficiency with AI

  • Process analysis to identify bottlenecks and inefficiencies.

  • Techniques for minimizing waste and optimizing resources.

  • Role of smart robotics in production lines.

  • Improving system flexibility in dynamic industrial environments.

• AI for Quality Control

  • Use of computer vision to detect product defects.

  • Applications of deep learning in quality monitoring.

  • Reducing human errors in inspection activities.

  • Continuous monitoring and real-time data analysis.

• Predictive Maintenance and Fault Management

  • Predicting equipment failures using AI-driven models.

  • Reducing unplanned downtime through proactive maintenance.

  • Role of sensors integrated with AI systems.

  • Extending equipment lifespan and improving reliability.

• AI and Supply Chain Integration

  • Inventory management supported by predictive analytics.

  • Monitoring flow of raw materials and finished goods.

  • Reducing logistics and distribution costs with AI.

  • Enhancing responsiveness to market fluctuations.

• Energy Management and Sustainability in Production

  • AI applications in optimizing energy consumption.

  • Reducing emissions and waste through automation.

  • Aligning sustainability goals with industrial performance.

  • Monitoring environmental impact using intelligent models.

• Digital Transformation toward Smart Factories

  • Concept and components of the smart factory.

  • Transitioning from traditional systems to intelligent ones.

  • Role of AI in enhancing industrial resilience and adaptability.

  • Strategies for change management in digital environments.

• Future Outlook of AI in Production

  • Emerging trends in intelligent manufacturing.

  • Integration of AI with Industry 4.0 technologies.

  • Innovation in production models and resource management.

  • Long-term predictions for digital transformation in industry.