Process Mining Definition

As the name suggests, process mining works primarily on the basis of specific process data. So-called cases, such as a specific order received by the sales department, are tracked through the process and the company using a unique case ID.

To make the idea behind process mining even more tangible, we would like to use an analogy. Similar to a well-logged train journey, each stop (process step) is documented with its name and the arrival time of the train (case). This log data provides the necessary database for a process mining system to automatically display and further analyse even the most complex train networks.

Additional information in any form, such as the number of passengers, the transport company responsible or the train type, can be linked to the cases. These important points of reference make it possible to identify systematic causes of errors with further and successive analyses (drilldowns and root causes). For example, after identifying regular train delays, it is possible to find out with just a few clicks that in 80% of these cases the number of passengers was greater than 100 on at least one leg of the journey. Presumably, increased crowding when boarding and alighting led to delays. Adjusting the number of doors or optimising the aisles could therefore be a solution here.

A detailed analysis of the train network using process mining could also show that certain sections of the route are regularly overloaded, while others are barely used. Based on these findings, transport companies could optimise their resources, for example by deploying additional trains on busy routes or reducing less-used routes. This would reduce capacity bottlenecks and enable more efficient operations.

Process mining could also be used to analyse data on the connections between different trains. If there are frequent transfer problems or delays with connections, appropriate optimisation measures could be taken. For example, the timetable could be adapted to allow sufficient time for changing trains or the platform layout could be improved to shorten the distances between trains.

Another option is to analyse the capacity utilisation of trains. With the help of process mining, transport companies could determine which trains are regularly overcrowded and which are less frequented. These findings could be used to adjust capacities, for example by using larger trains or increasing connections at peak times.

In addition to optimising operations, process mining technologies could also help to improve the customer experience. By analysing customer data and behaviour in conjunction with process data, personalised offers or recommendations could be developed. For example, if it is known that many passengers use a certain means of transport after arriving at their destination station, appropriate services or discounts could be offered to increase customer satisfaction.

Process mining therefore offers a wide range of possibilities for analysing complex processes such as train connections, identifying bottlenecks, optimising operations, improving the customer experience and ultimately making better decisions for process optimisation.

Process mining offers a wide range of possible applications, as it is neither limited to typical processes nor to specific specialist areas. Process mining can be used by companies of different sizes and in different industries. In principle, anyone with sufficient process data can use process mining as a valuable tool.

However, an important prerequisite for this is that detailed information about the individual steps of relevant business processes is recorded using IT systems and stored permanently, for example in the form of event logs. In concrete terms, this means that process mining is particularly suitable when transactions are processed via ERP systems, support requests are managed via a ticket system or workflows within the company are carried out via workflow management systems.

By using process mining, existing data can be utilised to make complex company processes tangible and measurable. With thorough preparation, similar to plug-and-play, process mining can be easily implemented.

A well thought-out data structure and high data quality are very important for the targeted use of process mining technologies. The process data should be complete, correct and consistent. Inconsistent or incorrect data can lead to inaccurate analyses and incorrect conclusions. It is therefore important to ensure that the data is prepared and cleansed before using process mining in order to minimise potential sources of error.

In addition, a basic understanding of the business processes to be analysed is required. This enables users to interpret the results of the analyses correctly and make well-founded decisions on process optimisation. Close cooperation between the specialist departments and the process mining experts is important in order to ensure a comprehensive understanding of the processes.

Process Mining is the technology for precisely and comprehensively analysing your business processes. With process mining, you can easily overcome company-specific hurdles, be it with virtual data in online trading or in historically grown productions. Careful consideration of systemic requirements and data quality guarantees reliable results.

You decide which process steps should be analysed. This opens up new possibilities for optimising and increasing the efficiency of your business processes. You achieve maximum added value with a minimum investment - uncover the potential of your company with process mining!