A Comparative Study of Process Mining Software for Supporting Bottleneck Analysis of Production Systems

Process mining (PM) has already found its place in analyzing and visualizing business processes across different sectors such as banking, healthcare, insurance, and logistics. However, its application is very limited in production systems. Production systems are very complex and dynamic, hindering the accurate capture of different process chain events. Generating the event log data out of the recorded events requires a proper understanding of the processes. The results of the bottleneck analysis can help the production experts to optimize the process. This study analyzes three to four PM tools to select an appropriate tool that can perform bottleneck analysis. The initial research will focus on various features of the PM tools such as modeling support, user friendliness, flexibility, stability, animation support, and BPMN modeling support. The feature analysis uses the event-log data generated from simulating the process model created in a Simulation-based Optimization tool FACTS Analyzer. Some PM tools can generate the BPMN model from the process model; animation support and modeling support features are vital for the bottleneck analysis. The PM tool's ability to create the process model automatically from the event log data can help save time involved in data collection through a physical visit to the site, conducting interviews, observations, etc. In contrast, the bottleneck analysis used simulated event log data and an event log generated from a real-world production system. The study focuses on finding whether PM can support the bottleneck analysis of the real-world production systems and find the appropriate PM tool available in the market for the same application. The use of process mining tools in the bottleneck analysis requires human intervention in identifying the bottlenecks in the process as the PM tools are incapable of recognizing them on their own. But, appropriate use of the PM tools in the bottleneck analysis can help save a considerable amount of time in manually building the simulation model. Performing iterative optimization could lead to socio, economic, and environmental sustainability as it helps avoid wastages in time, material, and energy.