Process mining is an analytical discipline that uses event log data from information systems to discover, monitor, and improve real-world business processes. Rather than relying on interviews or workshops to model how a process works, process mining reveals how processes actually execute based on recorded system data.

Origins and History

Process mining emerged from academic research in the early 2000s, primarily through the work of Wil van der Aalst at Eindhoven University of Technology (TU/e) in the Netherlands. Van der Aalst’s research group developed the alpha algorithm and other techniques for automatically constructing process models from event logs. The open-source ProM framework, first released in 2004, became the foundational research platform for the field. The IEEE Task Force on Process Mining, chaired by van der Aalst, published the Process Mining Manifesto in 2012, establishing guiding principles and challenges. Commercial adoption accelerated in the mid-2010s with vendors such as Celonis, Minit, and Disco (Fluxicon) bringing process mining to enterprise users.

Core Techniques

Process mining encompasses three main types of analysis. Process discovery automatically generates a process model from an event log without any prior model, revealing the actual flow of activities. Conformance checking compares a reference process model against event log data to identify deviations, bottlenecks, and compliance violations. Process enhancement extends or improves an existing model using additional event log information such as timestamps, resource assignments, or cost data to overlay performance metrics onto the discovered process.

Practical Applications

Organizations use process mining to audit purchase-to-pay and order-to-cash processes in ERP systems, to identify bottlenecks and rework loops in service delivery workflows, and to verify compliance with regulatory or internal process standards. Process mining is increasingly combined with robotic process automation to identify and prioritize automation candidates based on actual process data rather than subjective assessment.

Sources

  1. van der Aalst, W.M.P. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer.
  2. IEEE Task Force on Process Mining (2012). “Process Mining Manifesto.” Lecture Notes in Business Information Processing, Vol. 99, Springer.
  3. van der Aalst, W.M.P., Weijters, T., and Maruster, L. (2004). “Workflow Mining: Discovering Process Models from Event Logs.” IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142.