Developing a Digital Twin for Discrete Event Processes Using Event Knowledge Graphs and Process Mining Techniques

This thesis describes the development of a discrete events digital twin and the evaluation of the twin on a medical kits sterilization process.

The research strategy that is considered for the thesis includes both primary and secondary research methodologies through investigating the available documentation at the company and the literature that is related to the topic of the thesis as well as by processing the required data from the sterilization process and the reports of the company and meeting the involved departments for a clearer understanding of the process. The methodology that is followed to achieve the target of the thesis includes analyzing the current sterilization process of the company and developing a digital twin model according to the process extracted from the event data that was provided by the company, then analyzing each activity in the sterilization process in order to replicate the process.

In order to evaluate the discrete events digital twin, a comparison between the results of the digital twin and the original event data provided by the company was done for key performance indicators that include the number of sterilized kits per day, the time required for each activity, and the average time required to complete sterilization.

According to the results of the evaluation, the developed digital twin shows a similarity with the original process and can be used as a digital replica for process planning and optimizations.