Analysis of Business Process Batching Using Causal Event Models

Process mining supports business process management with operational insights extracted from event logs. A key challenge for process mining is that operational processes in production and logistics often include batching and unbatching, e.g., to delivery several packages using one truck tour. Such n:m relations blur the notion of a process instance and make the causality between events difficult to trace. In this paper, we address this research problem by introducing causal event models that capture batching behavior accurately. To this end, we construct conflict-free prime event structures for event instances of the event log, and devise various analysis techniques on top of them. We implemented the techniques in a tool and run in real data of a manufacturing company with various 1:n and n:1 relations in their production process showing the potential of our approach.