Data-driven methods for managing manufacturing processes

​​​​During the course, students will learn which information systems are relevant in manufacturing contexts, which types of data they produce and how to use this data to generate insights to improve manufacturing processes. ​​​ ​​​​ ​​​

​​​​Moreover, students will learn:​​​

​​​​1) what kind of questions can be answered with these types of data, e.g., "How many products do not pass the quality end check?"​​​

​​​​2) how to use the results obtained through the data-driven analyses (including data mining, machine learning or AI techniques) to generate relevant insights, e.g., "10% of the products do not pass the final quality test which results in a loss of 10.000€ per week." ​​​

​​​​3) how to recommend based on generated insights, actions to improve manufacturing processes, e.g., "To ensure we detect faulty products earlier in the process, we will perform an additional test directly after the first welding step."​​​

​​​​Students will be given the means to acquire the required knowledge through lectures, working sessions, and office hours that are held by the lecturers. Students will apply their knowledge in two group assignments using data provided by the lecturers.​​​​ ​

After this course, students are able to:

• ​​​​explain based on well-known concepts like the manufacturing automation pyramid or similar which information systems are relevant in manufacturing contexts and which types of data they produce;​​​​ ​​  ​
• ​​​​select the relevant types of data suitable data mining, machine learning or AI techniques (for example process mining, time series data analysis, or similar) to address manufacturing problems;​​​​ ​​ ​
• ​​​​use the results obtained through these data-driven analyses to generate insights to be able to recommend actions to improve manufacturing processes​​​​ ​​​

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Collection 2
• Completed Pre-Master

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