Data-driven methods for managing manufacturing processes

During the course, the students will analyze and redesign a manufacturing operation, based on data from practice. The students will learn about methods that are available to guide the redesign of an operation in a stepwise manner. Subsequently, each week they will study techniques that can be applied in the various steps and apply these techniques to the dataset. Students will be assisted in performing these tasks in an introductory lecture, working sessions, and through office hours that are kept by the lecturers.

Additional information tests Students can resit the project and final report by improving the previous work and implementing new tasks that are specified by lecturers. Students cannot resit the presentations.

Learning objectives:

After this course, students are able to:

• explain the relevant information systems for manufacturing operations;
• apply a suitable data-driven design methodology (e.g., CRISP-DM) to address a particular design problem in manufacturing;
• refine methods and techniques from scientific papers to solve parts of a design problem in manufacturing;
• improve a business operation from manufacturing practice, using the methodology, methods and techniques from AI, especially data mining, machine learning, and optimization;
• to report on a design and the procedure followed to arrive at that design, using both an oral and a written report.

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Completed none of the course modules listed below
• Design of AI-driven business operation (1BM130)
• Collection 2
• Completed Pre-Master
• Completed none of the course modules listed below
• Design of AI-driven business operation (1BM130)

• Selected scientific papers (accessible via TU/e library) to be made available in the course syllabus