Below, you find an overview of projects carried out in the group in which I am directly involved. For an overview of all projects of all group members, click here.



Certification of production process quality through Artificial Intelligence

Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production problems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production processes and techniques from artificial intelligence that can predict how the new process is likely to behave in practice in terms of data that its machines generate. This is especially important in mass customization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project.

Staff involved

  • Boudewijn van Dongen (professor)
  • Renato Calzone (program manager JADS)
  • Natalia Sidorova (assistant professor)
  • Dominique Sommers (PhD Candidate)
  • Remco Dijkman (professor at TU/e)
  • Eric Postma (professor at Tilburg University)
  • Jeroen Middelhuis (PhD Candidate TU/e)
  • Gabrial Raya (PhD Candidate Tilburg University)
  • Roland Bijvank (Lecturer Utrecht University of Applied Sciences)
  • and many others




Philips Flagship

The Data Science Centre Eindhoven (DSC/e) is TU/e's response to the growing volume and importance of data and the need for data & process scientists. The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting.

Staff involved

  • Boudewijn van Dongen (Associate professor)
  • Natalia Sidorova (Assistant professor)
  • Alok Dixit (PhD Candidate)
  • Bart Hompes (PhD Candidate)
  • Niek Tax (PhD Candidate)

Process Mining in Logistics at Vanderlande

Logistics processes are notoriously difficult to design, analyze, and to improve. Where classical processes are scoped around the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed together with other physical objects in one process at one or more physical locations, then distributed and later on re-aggregated with other physical objects in another process at other physical locations. In essence, logistics deals with numerous processes, cases, and objects that interact with each other in a multi-dimensional fashion. On one hand, this subjects logistics processes to many external influences which can have a negative impact on process outcomes and process performance. On the other hand, when analyzing the performance of flows across networks of logistics, the multi-dimensional nature is especially prevalent and existing data-driven process analysis techniques such as process mining which assume a single viewpoint cannot be applied.

Staff involved

  • Wil van der Aalst (Full professor)
  • Boudewijn van Dongen (Associate professor)
  • Dirk Fahland ((Assistant professor)
  • Vadim Denisov (PhD Candidate)