AI in Production Management

Abstract

This course provides the students insights on principles and practices for how AI can be used to enhance production management. Hands-on learning exercises shall deepen the understanding by providing real-world production challenges that can be solved through or supported by AI.

Objective

After taking this course, students will be able to: 1. Determine common challenges of production management where AI can be useful 2. Explain what AI is in the context of production management 3. Evaluate the value of an AI solution in production management 4. Collect data from the shop floor through various methods 5. Prepare and clean selected types of shop floor data 6. Evaluate, select and apply analytical models to analyze production data 7. Deploy selected AI models in a production environment 8. Identify and acknowledge specific hurdles for applying AI in a production setting

Content

Students will mainly come from D-MAVT, D-MTEC MSC/MAS, and D-BAUG who are considering pursuing a career in production management. Students should have visited the course 363-0445-00L Production and Operations Management, or comparable courses from other institutions. Preferably, but not necessarily, students already have had their first exposure in an industrial setting through internships. This course caters to students who want to deepen their knowledge of how AI methods could be used in production management. The course offers a focused perspective on production management, which complements existing ETH courses on manufacturing engineering and management on the one hand and computer science and data analytics on the other hand. This course emphasizes the specific challenges, practices, and translation skills needed for using AI in production management. It enables students to transfer state-of-the-art knowledge on leveraging AI to improve the productivity of manufacturing companies. The course will be structured as a series of lectures along a framework for developing AI use cases. It starts with a generic introduction to the topic of AI and discusses common challenges for production management. Then, it introduces different state-of-the-art frameworks and discusses their relevance for AI in production management. A specific framework is selected that spans the processes of analyzing the problem, understanding, and preparing the data, developing, and evaluating the model, and deploying the models in real manufacturing settings. Within each stage, the most critical aspects will be emphasized and trained. The focus lies on providing both theoretical and practical knowledge about necessary tools, frameworks, and concepts in each stage to successfully implement AI in production management. Additionally, the course discusses critical qualitative aspects such as regulatory hurdles, organizational and technical prerequisites, and the implications for the workforce. To support the learning provided through lectures, students will need to work in groups on assignments in which they solve real-world challenges. In the end-of-semester exam, the students will finally be required to show their comprehension of the topics of the course individually.

Resources