Process mining is an analytical discipline which benefits from specialized data mining methods. It reflects the actual performance of the business processes by a layer of software which sits on top of the company’s IT systems. The discrepancies give a full picture of where any bottlenecks, inefficiencies, or gaps exist in the company’s processes. As a result, companies can better focus their efforts for further improvement.
How does it work?
Process mining tools discover actual process models out of the raw event logs. By extracting event logs from each case and combining them, these tools show companies how their processes perform in reality.
For the detailed answer, you can find how process mining tools work step by step below:
- The process mining tools integrate with the related software to collect the event logs from the company’s system.
- These tools extract the activity sequence for each case from the event logs. In this step, variations between cases will become apparent. These variations occur because of manual changes or errors in the process.
- After deriving the activity sequence of each case, process mining tools start to “merge” these sequences. As variations occur, the actual process will be more complicated than the planned one. This output also enables the company to understand where its process has deviated.
Why process mining?
Even though most companies are not aware of this, business processes tend to have many exceptions and tend to be complex:
When there are bottlenecks, or when companies aim to find areas of automation, the companies need to have explicit knowledge. If not, they may take inaccurate actions which won’t provide any improvements. Thus, process mining becomes a prominent solution for such cases. With its support, companies can understand the actual processes and give data-driven decisions.
Process mining allows us to visualize what the actual process based on the event logs. After observing the discrepancies with the assumed process, the companies can come up with three outcomes.
- The company can identify the reasons for the discrepancies and take actions to fix them. As a result, the actual process will become more alike with the assumed process.
- The company might have constituted the assumed process wrong. The discrepancies might have happened because the company doesn’t follow its assumed process. In such a case, the company needs to revise the assumed process in the documentation.
- The discrepancies are so minor that the company doesn’t need to take action for those cases.
It will be useful to know about these discrepancies to have a complete picture. They may give insights to the company to identify process inefficiencies and areas for process automation. Without these insights, automation projects can focus on the wrong processes or operate inefficiently.
As a result, companies should mine their processes to perceive how they look like in real life. With the knowledge gained from the software, they can take proper actions for reducing inefficiencies.
You can learn more about the benefits of process mining tools from our related article.
If you want to see relevant process mining case studies, you can also read our article about process mining case studies.