Process mining is the method for extracting knowledge from enterprise information systems to acquire actionable insights for improving operational processes. It is a data-driven approach that combines data science and process analytics to help organizations discover, monitor, and improve real processes rather than assumed processes. It can be used to identify patterns, trends and gain greater visibility into the performance of current processes.
Business process management in many organizations have long struggled with problems where understanding processes is not as accurate as they are made out to be. They tend to focus more on improvements or “to be” processes rather than analyzing the current “as is” processes. This lack of understanding of how the processes are performing currently, the problems plaguing them and variations in the processes across the organization not only makes efforts toward improvement and optimization difficult but makes it hard to argue if the proposed improvements justify the investment.
This gap between understanding “as is” processes and an organization’s understanding of the processes is a result of how the process analysis has been carried out. Often, current process analysis is based on interviews and notes that may be subjective and not provide an accurate representation of the processes as they currently perform. The problem is further aggravated by the disconnect between the enterprise information systems and business processes and even in instances where enterprise systems are process-oriented, hey do not provide complete visibility into the performance of the processes, issues, and bottlenecks. Process management in such circumstances is a difficult job and collection and synthesis of data for process optimization requires a lot of manual effort.
Process mining bridges this gap by bringing a data-driven, automated approach to understanding processes. It mines event logs data to identify trends and patterns to provide in depth insight into actual activities going on in the organization as opposed to presumptions of activities that may be going on. Organizations can use process mining software to capture data from enterprise transaction systems and create event logs to reflect work done which can include receipt of order, delivery of product, payment made, customer contacts and similar activities. Process analytics and artificial intelligence, when applied to process mining, can provide detailed and granular insights which can be useful for creating key performance indicators and detecting root causes of issues.
Professor Wil van der Aalst, Chief Scientist at Celonis is widely credited for inventing process mining. His research states that the main process mining techniques include:
Process Discovery – This refers to a basic method in process mining in which data-based visualization of a process is generated automatically from event logs data. This model, created independently and without influence of existing process models, provides more accurate and data-driven insights into the processes.
Conformance Checking – This technique utilizes the event log data to conduct comparisons between the actual process and the reference model or target model of the same process and identifies deviations. This helps ensure compliance and identification of unplanned process sequences and responds appropriately.
Model Enhancement – Model Enhancement can be used to improve and optimize the process model and the related process. It is an analysis of a data-driven process model that generates new information on deficiencies such as bottlenecks and unwanted process sequences that can be used to find more process optimization opportunities. Process mining techniques such as Process Discovery and Conformance Checking can use the results of Model Enhancement to continually optimize processes.
The transformative power of process mining in improving operational processes has caught the attention of business leaders. Gartner even published its Market Guide for Process Mining and identified lead vendors for process mining. Process mining also works well with other technologies such as RPA, Hyperautomation, artificial intelligence and machine learning, Process Analytics, and others to help organizations modernize and optimize their operational processes. When it comes to implementation of this new technology, V-Soft Digital’s partnerships with leading process mining vendors such as Celonis and Software AG and industry expertise is at the forefront in helping its clients adopt this transformative technology.