Process mining for transparency in your processes

 Optimization of your processes with process mining

With Process Mining to Digital Transformation

Process mining represents a versatile and effective extension of established business process management methods. In addition to simplifying and accelerating the documentation of processes, process mining also offers optimization and analysis. In some cases, this is even done across multiple systems. Process mining represents an important step on the way to the digital transformation of your company. Read more about process ming and procedure of implementation by Bross & Partner Consulting Engineers.

How does process mining work?

Process mining compares real processes with the ideal processes of theory or best practice solutions. This provides insight and transparency. Especially in IT processes of indirect areas it is much harder to see if a process is running optimally compared to the physical processes in production. These IT processes are less tangible, which is why insight into the real process is needed. Without a process model, such an analysis is hardly conceivable. We obtain this process model by looking at all existing data of the IT systems. Data about business processes are stored in log files and databases. The large amount of data requires an approach similar to data analysis in data mining. The approach is first to prepare the data and visualize it in a process diagram. This enables the identification of, for example, additional work and sneak paths. In practice, the deviation of processes, e.g. in the lead time, is recognized by means of key performance indicators, so-called KPI. Process mining goes one step further and analyzes the process and identifies the reason for the delay and thus helps to eliminate it. Data mining thus goes beyond the mere visualization of bottlenecks.

How do deviations from the ideal process occur?

Process mining can be used to identify deviations from the ideal process. Reality often looks different from the ideal. This is also the case with business processes, e.g. in purchasing or sales. In reality, these are much more complex, unstructured and less clear than described in documented processes. The way employees think that processes run and how they run in reality also differ significantly. Misinformation can be a critical problem in organizations. Therefore, it is very important to understand the difference between reality and ideal. So how does this deviation between reality and ideal state occur:

Additional work

Employees spend a lot of time on rework and additional steps that are not foreseen in the described processes.

Exceptions

Processes are described as they should run. Exceptions are ignored, even if they occur more frequently.

Sneak peeks

Processes are abbreviated or skipped completely.

Definition

Often, processes are not clearly defined in the first place. Employees often perform steps differently. This creates a difference. In addition, each employee only sees his process steps and has no insight into the upstream or downstream processes.

Changes

Processes are frequently changed due to reorganization and restructuring and are therefore often not up-to-date.

What data is needed for process mining?

For the analysis of your business processes using process mining, at least the following data is required:

  • Transaction number (ID)
  • Timestamp
  • Status change
  • Other attributes (optional)

With this data, the actual process can be represented using process mining software such as Celonis and Signavio. As a rule, this crystallizes a main process, the so-called happy path, as well as other deviating processes as they run in reality. Process mining provides you not only with the transparency of the processes, but also with the visual preparation of key figures. This allows you to determine the percentage distribution, deviations and outliers of individual processes.

Do you want to optimize your processes using process mining?

Contact our process mining experts for a free & no obligation consultation.