Rule-based Decision Support for No-Code Digitalized Processes
Digitalized processes offer various advantages: uniform information flow, traceability of process progress, and enhanced analyzability. While these represent initial benefits from the digitalization of the process, a recurring evaluation and optimization of the process are required to sustain and improve process efficiency. For both non-digitalized and digitalized processes, the challenges for process owners are comparable. However, for digitalized processes, convenient tools facilitate process modifications. Particularly no-code digitalized processes exploit the opportunity for quick and agile process adaption with low efforts but require a structured and systematic method to ensure a quality-driven approach. This paper conceptualizes a rule-based decision support model for process improvement that takes advantage of digital traces recorded during process execution of digitalized processes. On the one hand, process mining techniques support data analysis and assist the extraction of process performance indicators as precursors to predict low process performance. On the other hand, process experts compile a list of recommendations for action in workshops and interviews to counteract low process efficiency. The approach to rule-based decision support developed in the scope of this research merges these two dimensions and constitutes an assistive tool for the continuous improvement of digitalized processes. Initial results of the model in practical application support this assessment and demonstrate further research needs.