This post is by Mahsa Bafrani, Scientific Assistant in the Process and Data Science team at RWTH Aachen. Contact her via email for further inquiries.
Using process mining actionable insights can be extracted from the event data stored in information systems. The analysis of event data may reveal many performance and compliance problems, and generate ideas for performance improvements. This is valuable, however, process mining techniques tend to be backward-looking and provide little support for forward-looking approaches since potential process interventions are not assessed. System dynamics complements process mining since it aims to capture the relationships between different factors at a higher abstraction level, and uses simulation to predict the effects of process improvement actions. In this paper, we propose a new approach to support the design of system dynamics models using vent data. We extract a variety of performance parameters from the current state of the process using historical execution data and provide an interactive platform for modeling the performance metrics as system dynamics models. The generated models are able to answer “what-if” questions.
Figure 1: our proposed framework for using process mining and system dynamics together.
Our proposed framework for using process mining and system dynamics together in order to design valid models to support the scenario-based prediction of business processes shown in Fig. 1. The model creation steps is an important step which we are going to focus on, i.e., the highlighted step.
Figure 2: the main approach including the SD-log generation, relation detection, and the discovery of the type and direction of the relations.
Our approach, Fig. 2, continues with the automatic generation of causal-loop diagrams (CLD) and Stock-flow diagrams (SFD). The type of relationship is used to form the underlying equations in SFD and the effect and time directions are automatically used to design the CLD as a backbone of SFD.
In this work, we proposed a novel approach to support designing system dynamics models for simulation in the context of operational processes. Using our approach, the underlying effects and relations at the instance level can be detected and modeled in an aggregated manner. For instance, as we showed in the evaluation, the effects of the amount of workload on the speed of resources are of high importance in modeling the number of people waiting to be served per day. In the second scenario, we focused on assessing the accuracy and precision of our approach in designing a simulation model. As the evaluations show, our approach is capable of discovering hidden relations and automatically generates valid simulation models in which applying the domain knowledge is also possible. By extending the framework, we are looking to find the underlying equations between the parameters. The discovered equations help to obtain accurate simulation results in an automated fashion without user involvement. Moreover, we aim to apply the framework in case studies where we not only have the event data but can also influence the process.
Mahsa Pourbafrani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Supporting Automatic System Dynamics Model Generation for Simulation in the Context of Process Mining. BIS 2020: 249-263