SEO Glossary: Process Mining

 

Process mining: definition, types and how it works

Classic process mining runs from a specific starting point, which marks the beginning of a process, to the completion of the process at its end point. It can only be applied to clearly definable end-to-end processes. Various actions and activities take place in the course of the end-to-end process .

Process mining is used to achieve the efficient flow of all actions and activities required for the process. It helps to increase process efficiency by automatically collecting process data, analyzing processes and identifying opportunities to improve processes.

• Every action and action within a digital process leaves a digital footprint.
• Process mining software such as that from mpmX collects the digital footprints from various applications and combines them into an end-to-end process. The software provides data and visualizations of the process flow.
• In most cases, the process mining software shows that the actual process actually taking place deviates from the originally planned target process and its procedures. This enables companies to recognize that the process needs to be optimized.
• The data from the software can be used to extract unknown and inefficient process steps (bottlenecks), among other things. Other deficits in the process can also be identified. Furthermore, the software lists options for process automation, if available.

The process knowledge gained with the help of process mining tools not only helps to identify discrepancies between the actual and target processes. It also helps to optimize the originally planned target processes.

Ultimately, process mining is a technology that is used to reconstruct, evaluate, optimize and, if necessary, automate processes. Process mining is limited exclusively to fully digital processes that have a clearly definable beginning and end (end-to-end processes).

What are the four types of process mining?

The Institute of Electrical and Electronics Engineers (IEEE) has published a manifesto with three types of process mining. The Dutch scientist Wil van der Alst also mentions a fourth type of process mining in his work. This is an overview of the three types of process mining:

• Discovery: This type of process mining is used to identify, model and visualize processes based on the automatically collected data.
• Conformance: In this procedure, process mining tools check the conformity of the analyzed actual process with an existing process model. This is a target/actual comparison.
• Enhancement (improvement): This process mining variant is used to optimize the target process. For example, a process model developed by the company is improved.

Wil van der Alst mentions a fourth type of process mining, operational support. The software provides predictions, warnings and recommendations regarding company processes and thus supports process management.

How does process mining work?

First, a few words about the complexity of digital business processes: Modern business processes run through several IT systems. Different users from different departments are involved in the processes. In addition, the IT systems usually use conflicting file types.

The complexity of modern business processes is no obstacle to process mining.

• When a case (as a specific part of a process is called) moves through the information systems, the software registers the changes to the case. These changes are nothing more than individual process steps.
• The process mining software independently extracts the required data volumes for each case from the IT systems (e.g. ERP system, CRM system). It uses the so-called event logs.
• Event logs are data that is stored in the IT systems for a case. This event data contains the identification number for the case object (case ID), the respective activity and the timestamp as information about when the case object passed through the process.
• The software gradually collects process data across all systems in order to create an illustration and evaluation of the actual process . A common form of process visualization is the so-called direct follower graph.
• Special process mining algorithms are also able to identify potential for robotic process automation (RPA).

For process mining to be applicable, a process must be fully digitized . This is because the software cannot collect any information without a connection to IT.

If information from the systems is continuously fed into the process mining software, real-time business data analysis is possible. It is the key to permanent analysis and optimization of process flows, which leads to maximum process efficiency and greater business success.

What is the difference between data mining and process mining?

In contrast to process mining, data mining has no specific focus and generally concerns the collection of all types of data. Process mining is a special subcategory of data mining in which process data is collected and analyzed in a targeted manner .

In addition, process mining differs from general data mining in that it analyzes the creation of data and makes predictions in order to contribute to process improvement.

 

 

Why is process mining important for companies?

Digitalization has already made inroads into the corporate landscape. As non-digitalized companies are barely competitive these days, digitalization is being driven forward in all sectors.

Digital transformation goes hand in hand with digitalized processes. This is the basis for the applicability of process mining. With the help of process mining technology, companies can analyze their processes more efficiently than with almost any other method. This results in numerous advantages with a small number of challenges.

What are the advantages of process mining technology for business processes?

Process mining contributes to the objectivity and transparency of processes. Process flows are automatically visualized through the digital traces in the company's IT. The scope of the process analysis and visualization leads to an objectivity and transparency that would hardly be possible without process mining.

If companies apply the detailed knowledge gained about process flows to optimize and, if necessary, automate their processes, they achieve an increase in efficiency in the accomplishment of a wide variety of tasks in companies. This is another advantage of using process mining solutions.

In all of this, the process mining tools are easy to integrate into a company's existing IT landscape. They are quickly ready for use and their functions can be customized. For example, users can manually insert event logs into the data analysis or determine the type of process visualization.

These are some of the other advantages of process mining at a glance:

• Higher stakeholder satisfaction (i.e. the people entitled to receive benefits from a company) by improving processes and increasing the company's success
• Extensive support for process management by providing data that facilitates decision-making
• Shorter time-to-market phases thanks to a reduction in production time
• Promoting compliance in companies by uncovering legal violations in the course of process analysis
• Increasing the customer experience and user experience (UX) by applying process mining to processes related to customers and users

Challenges in the application of process mining tools

The challenges of process mining relate less to process mining itself than to its uptake and impact on employees. Among other things, the implementation of the technology could require a change management concept in order to introduce employees to the change in the company.

In addition, it must be ensured that the software is not used to measure person-specific performance, as this could be perceived by employees as a method of performance control and therefore negatively.

 

 

Areas of application: Examples for the application of process mining

Process mining can be applied to all digital processes. There are no limits to the technology, from controlling to healthcare to product development, as long as it is used for clearly definable end-to-end processes.

In addition to the classic process mining described above, there is also object-centered process mining (OCPM), which can be used across all processes and offers even more advantages. In the following list of application examples, we at Timo Specht focus on the areas of application of classic process mining.

• More efficient controlling and relief for responsible employees: Companies along the entire supply chain can use process mining to improve invoice verification and discover potential for reducing costs and increasing revenue.
• Clearer presentation of big data in the healthcare sector for better patient treatment: Process Mining can be used to summarize the different treatments of patients to increase the efficiency of medical treatment pathways and save patients unnecessary visits to the doctor.
• Efficient handling of production processes through detailed analysis of all steps in production: process mining provides insights into bottlenecks in the manufacturing of products and promotes knowledge of the possibilities for automation.
• Improving order processes for a higher conversion rate and improved online reputation management: thanks to process mining, it is easier to analyze product ordering processes and reduce the number of clicks required from the search for a product to the final order.
• Optimization of training courses by monitoring the performance of participants: If approved by the participants, process mining can be used to analyze performance and compare it with the curricula.

These are just a few examples of how process mining can be used. Process mining can be used to increase efficiency in almost every sector - from industry and gaming to public authorities and educational institutions.

Professional IT service providers who develop process mining tools and integrate them into companies advise interested parties on the individual implementation and use of process mining solutions.