Course description

This course offers a comprehensive exploration of the vital role played by planning and scheduling in the automotive industry. By leveraging quantitative modeling techniques and artificial intelligence (AI) applications, students will gain insights into optimizing production processes and enhancing supply chain efficiency. The lecturer is Dr. Thomas Stäblein who is the Head of Data Ops for AI at Mercedes-Benz AG.

The course delves into the challenges faced by the automotive industry, including fluctuating demand, supply chain disruptions, and resource constraints. Students will learn how quantitative modeling utilizes mathematical and statistical methods to analyze historical data, allowing for informed decision-making and proactive planning.

Moreover, the course focuses on the transformative potential of AI technologies, such as machine learning and deep learning, to enable predictive maintenance, optimize inventory management, and facilitate dynamic production scheduling. By harnessing the power of AI, automotive companies can increase overall equipment effectiveness, reduce downtime, and enhance collaboration with suppliers for efficient supply chain management.

Throughout the course, students will engage in real-world case studies and hands-on exercises, applying the acquired knowledge to address practical challenges in the automotive industry. By the end of the course, participants will be equipped with the skills and understanding necessary to make data-driven decisions, adapt to market fluctuations, and ensure operational excellence in the rapidly evolving automotive sector.

Prerequisites: 

• Interest in planning and scheduling
• Basic knowledge in operations research
• Basic knowledge in production economics 
• Basic knowledge in operations management
• Basic knowledge in mathematics
• Good knowledge in statistics
• Passion for the automotive industry

Purpose

The course enhances participants' abilities in applying quantitative methods for planning and scheduling in the automotive industry. With practical examples, it covers new product development, production ramp-up, plant layout design, global supply chains, and scheduling methods. Through hands-on exercises and industry case studies, students will develop practical skills to optimize planning processes for enhanced efficiency.

Methodologies

Linear Optimization, Production planning and scheduling, Project planning and scheduling, Systematic Layout Planning, Capacity and investment planning, Production ramp-up management, Order-fulfillment, Supply chain coordination,  Mathematics

Learning Objectives

• Gain an overview and insights on different manufacturing environments for automotive production and supply chain management
• Review different planning and scheduling frameworks applied in the automotive industry
• Apply project planning and scheduling methodologies in the context of product development in the automotive industry
• Apply optimization techniques for capacity and investment planning, production network design and strategic planning
• Understand the concept of assembly lines and systematic layout planning. Be able to apply balancing and sequencing OR algorithms and the basic simulation principles for logistical processes
• Be able to calculate complex product launch and ramp-up scenarios for automotive production
• Understand order fulfilment strategies to match customer demand and automotive order-to-delivery processes
• Understand different supplier systems and coordination mechanisms
• Link product variety to order-fulfillment strategies and understand the impact of mass customization on operations