In today鈥檚 rapidly evolving digital landscape,鈥痮rganizations can transform the way they work by exploiting the information that is constantly being collected. We also call this digital process transformation.
To support digital process transformation, the Process Engineering cluster develops methods, tools, and techniques that help organizations use data to improve and optimize their business process landscape as a whole. This includes techniques for process monitoring, process mining, digital twinning, (goal-driven) modeling, and AI-driven optimization. The process engineering cluster has a specific focus on improvement of processes towards regulatory compliance, and personalized process execution.
The process engineering cluster specifically develops methods, tools, and techniques in three areas:
- Data-driven Process Optimization and Execution Guidance: Creating methods to optimize routine processes and non-routine, knowledge-intensive processes, including simulation-based methods, AI-driven methods, and decision support.
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Process Mining with Unstructured Data: Developing techniques to align unstructured process information, such as procedure descriptions, rules, and regulations, with process models in order to improve their performance and compliance.
- Process Pattern Mining and Explainable Process Prediction: Developing techniques to mine execution patterns within processes, using these patterns to explain decision-making and improve it accordingly.