What is Process Mining?

Process mining is a technique that facilitates the analysis of business processes by using event logs that can be extracted from computer systems. The objective is the acquisition of knowledge of real processes from the processing of a large amount of data, by automatic or semi-automatic methods.

The strong point of process mining is to be able to observe the behavior of the organization, based on facts and actual figures over a period of time. We therefore have a solid foundation for conducting a thorough study of business processes and their improvement. Nevertheless, the result obtained using process mining software would need to be combined with human investigation to fully understand the situation.

Main applications of process mining

Process Mining essentially supports three process management disciplines:

Business Process discovery: which consists of the graphic representation – thus the reconstruction based on event logs – of the current processes of the organization; including possible process variants. The result is a process model – or even an animated diagram.

Conformance checking: which is the comparison of an existing process model to an event log of the same process, in order to verify if the model corresponds to reality (as recorded in the log), or vice versa, if the Implementation of the process is well in line with the model. This makes it possible to check if the (company) rules have been respected or not.

Business Process enhancement & improvement: Thanks to the graphs and measurements obtained from Process Mining algorithms, it is easier to detect bottlenecks, to see which resources are underutilized, and so on. The many possible points of view of such an analysis facilitate the detection of the causes of inefficiency or errors within a process and thus the organization.

Advantages of process mining

Here are the main advantages of Process Mining, according to the study of J. Claes and G. Poels:

Objectivity: facts do not lie. The Event Logs (group of actions and date recorded by the system) reflect how a process is actually working. Models reconstructed using Process Mining are not influenced by a human perception that is often subjective.

This is the reason why you will have differences between models obtained from process mining and the ones obtained through interviews or workshops. But there are also information to learn here!!!

Speed: Even if you still need to do your homework and to have an understanding of the context in advance (to select which information are relevant and which Event Logs to use), it is obvious that process representation through Process Mining speeds up mapping and modeling activities.

Less effort (more efficiency): Process discovery through process mining is efficient. You will minimize the number of interviews or workshops with business people and they will be more efficient as you will review and discuss facts.

Full state: One very important point, the analysis of relevant event logs makes it easier to distinguish between the main process and other variants or exceptions if you run the analysis over a sufficient period of time. You can spot variants and exceptions that you might not discover without Process Mining because people forgot to mention them in the interviews

Transparency: we are looking at facts recorded by the system and if needed, we can zoom to details as who performed a transaction, at what time, and so on. This can help to get more transparency on how the organization really works.

Compliance: As explained in “full state”, process mining helps identifying variant, exceptions from the main process. Also this main process can be compared to the desired or defined process to obtain nonconformities.

Root-causes & bottlenecks: visualization of how the process is running helps discovering root causes, bottlenecks, etc . Our objective is now to look how to improve the processes.

Predictions & simulations: the facts helped us to build a model. Now we can modify the model and see how it will perform running the data. This simulation makes it possible to predict the future behavior of a process with the modification we are proposing.

Challenges of process mining

Despite the many benefits of Process Mining, it also presents challenges and you need to be aware of them. The challenges are very similar with the challenges encountered in data mining and business intelligences. Claes & Poels mention the following challenges:

Data quality: Data quality is a full subject in itself and it is not the subject of this post. Building the Events log might require the use of different sources which have different formats, contain errors, etc.  This bring us to the next point.

Cost of data preparation: it might take effort to clean the data and this represent a cost.

Access to data: It is not always possible to obtain valid data. Especially when dealing with ‘older’ computer systems that do not produce Event Logs.

Technology: algorithms have limitation and are not suitable to deal with all possibilities and situations which could occur during the process execution.

Usability of the tool: there are different providers and process mining software present different combination of flexibility and user-friendliness. More flexibility comes with more set up to be performed but you can performed more type of analysis.

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