Process Mining is a technology for process modelling, analysis, and optimization. Thanks to the overall visualization and evaluation of business processes, important data is no longer overlooked and can be used for process optimization. This helps companies to increase their productivity and profitability.
Despite highly developed and user-friendly Process Mining tools, the implementation and use of Process Mining is by no means simple. On this page, we explain the requirements and challenges for Process Mining. The information on this page applies to all Process Mining methods - both conventional Process Mining and Object Centric Process Mining (OCPM).
Digitalized Process Flow is the most important condition
As Process Mining tools work with digital data, digitized process flows are essential for their application. Company processes are digitized if process data is collected digitally via software applications (e.g. SAP) from start to finish and all process steps are recorded.
If companies have some analog business processes, it is still possible to use Process Mining. In this case, the analog processes are excluded from Process Mining and the application is limited only to the digital business processes.
Having digital processes is the fundamental criterion for whether Process Mining technology is applicable. Once you have digital processes in place, you must do the following things to use it efficiently and error-free:
- Install Process Mining software
- Prepare clean data files from the various system sources in your company
- Configure the software to establish the correct data connections and variables, based on the specific business process
- Train the front-end users and back-end IT specialists in the company
In summary, the core requirement is first and foremost that the processes run digitally (using IT systems). These IT systems are then connected the Process Mining software that is individually adapted to the company in order to reconstruct specific processes.
Clear identification and separation of processes
In order for processes to be correctly reconstructed in Process Mining and for qualitative data to be mapped, the processes must be clearly identified and separated. This requirement is fulfilled by having the following:
- An assigned identification category (e.g. workpiece number in the production process; order number in the ordering process)
- A set time stamp for the correct chronological mapping of the individual steps in the business process
- A clear name for the process (e.g. creation of an order; dispatch of the order)
These three attributes for the individual activities of a process can be used to create the event logs, which are then recorded by the Process Mining tool. The event logs are systematic tables that contain the three mandatory attributes for process activities and other optional categories.
As soon as business processes can be clearly separated from non-associated activities in the company, there is no longer any risk of incorrect data being collected. This would disrupt process analysis or even make it completely impossible.
The clearly defined start and end of business processes enables a comprehensive process analysis by allowing process lead times to be calculated. The collected data volumes promote the precise analysis of the actual state and contribute to the development of a greater amount of optimization potential.
Confidential handling of personal data in IT systems
Data protection is a legal sticking point when it comes to Process Mining. Without the consent of the persons involved, no data on person-specific services may be collected during Process Mining. If people and their services are involved in processes, their role must therefore be anonymous.
Possible data that may be collected includes personal data of individuals, data on their activities, and data on their interactions in the course of business processes. The staff representatives must be informed about which personal data is collected in Process Mining. The following solutions should be promoted:
- The analysis of personal data volumes must be limited to the relevant processes.
- In addition, an anonymous classification of the data should be aimed for. One method of anonymization would be, for example, to name roles or authorizations instead of employee names. Then data on roles or authorizations would be collected and analyzed.
- In addition, only the personal data that is really relevant to the processes should be collected.
In general, it should not be forgotten that Process Mining, as a component of digitalization and a method for process optimization, is accompanied by changes for the workforce. Process Mining can be viewed critically by some employees, not only from a data protection perspective, but also in view of the intrusion into previously familiar workflows.
The changes in the company resulting from Process Mining should be communicated at an early stage. Open change management, in the course of which employees are informed about upcoming changes and slowly introduced to them, creates the right personnel conditions for the use of Process Mining.
Empowering employees to take an analytical approach to processes and their optimization based on the data analysis from the Process Mining software is an important success factor. It is also beneficial if employees have direct access to the data. This increases transparency. Ideally, employees are given more authority to solve problems independently by being able to access the newly gained process knowledge from Process Mining.
User competences: Achieving the target process with Process Mining
In order to optimize actual processes and achieve the desired target processes with the help of Process Mining, certain user skills are required. We will discuss three key skills in detail below. It is important for process analyses and process improvements to combine these skills into a functioning whole in order to be able to optimize business processes as a whole.
Analytical skills
For a qualitative analysis, the user must be able to interpret the visualization and evaluate the generated data. A broad knowledge of statistical models is also beneficial.
Companies usually already have employees with the relevant specialist knowledge in their departments. These employees can access the detailed data from Process Mining. Together with technological skills, analytical skills are the key to process visualization and accurate evaluation of data volumes.
Technological skills
Furthermore, the successful application of Process Mining requires technological skills. These include skills such as modeling and providing data as well as understanding data flows within the entire IT landscape - this includes the ability to handle the new Process Mining tool.
Technological skills are often the sticking point where the implementation of Process Mining in companies fails. To overcome this problem, we at mpmX provide employees with comprehensive training. This includes training in data analysis using our software and in deriving the right measures for process improvement.
Organizational skills
Last but not least, it is essential to combine analytical and technological skills with the content-related and organizational aspects of the company. Process improvement can only be achieved through data analysis and process optimization that is individually tailored to the company.
Anyone who carries out an analysis and decides to make changes to the processes without a connection to the company risks failure. In all organizations, there are a large number of system components that are subject to constant interaction. The potential interactions caused by changes to processes must be taken into account.
Example of the implementation and application of Process Mining
Once a Process Mining software has been selected, the service provider integrates it into the business processes in alignment with the agreements with the company. All processes within a company that are to be modeled and analyzed using Process Mining are recorded in the software.
In order to check in advance whether Process Mining makes sense in relation to the individual processes in your own company, it is helpful to carry out pilot projects. In the pilot projects, the application and added value of Process Mining are evaluated. If the evaluation is positive, the processes are recorded in the Process Mining tool and the implementation of Process Mining begins.
In the course of recording the processes, the processes are clearly named so that all process data can be collected. A time window of around two weeks should be scheduled for connecting the software to the company's systems and determining and generating the relevant process data. During this period or even before, employees and IT specialists can be trained in the use of the software and prepared for the changes in the company.
FAQ: Questions and answers on the topic of “Process Mining and its requirements”
OCPM enables the cross-process modeling of business processes. For example, the potential for optimization at the interfaces of several processes is also uncovered. Traditional Process Mining, on the other hand, is only used to analyze and optimize individual processes with a clearly defined beginning and end.
Yes, we are happy to support you in upgrading your conventional Process Mining to OCPM. With mpmX, it is easy to switch from conventional Process Mining to the more advanced and comprehensive OCPM.
Business intelligence applications (BI tools) are used to measure the performance of the entire company. Similar to Process Mining, data records are automatically collected for this purpose. However, business intelligence assumes that there is nothing to optimize in the business processes themselves.
Process Mining offers the functionalities of BI and even more. Process Mining can also be used to measure all KPIs that measure the company's performance. In addition, Process Mining offers possibilities for visualizing and analyzing process data in order to identify the causes of poor KPIs and make more targeted process improvements.
Yes, it is possible to use mpmX even if you are already using another BI tool (e.g. Qlik; Power BI; Tableau).
Compared to data mining, Process Mining has several advantages. These consist in the fact that not only static data is used for analysis, but also how the data was created in the course of the processes. In contrast to data mining, deviations from the target process can be identified in real time using Process Mining.