## 5 Discussion  
The presented case demonstrated that the use of a TMS in a transport company is often associated with problems caused by incorrect actions of managers. These actions can lead to various disruptions in the transport process, associated, for example, with both distortions in documents and deviations in the operation of vehicles. In our study, we showed that an approach based on Process Mining technologies and ontological modeling allows us to diagnose organizational problems and compare the actions of managers in the TMS with the data it contains, as well as the consequences of these actions on the  
Figure 10: Number of activities in TMS for setting the winter period.  
Figure 9: Fragment of the “Sorokina” dispatcher actions in TMS “Autobase” concerning the tracking of tires and battery units.  
transportation process and the influence of these data on management decision making. Before using procedures for analyzing user actions from the TMS event log, identifying deviations in business processes was a non-trivial task, which was also complicated by the need to have specialized knowledge from the transport industry. The use of an ontological model allows us to solve this problem, thanks to the generated thesaurus of entities and a graph of connections between them. Thanks to the developed ontological model, the cause-and-effect relationships between various business elements of the transport company and the external environment, as well as various factors affecting the reliability of the transport process, are clearly presented.  
Therefore, thanks to the digitalizing of all events that occur during the transport process, a digital transportation model (Figure 11) is created, which is now called a digital twin. The sources of data on weather and climatic conditions are daily weather reports and weather forecasts provided by meteorological services. The sources of vehicle operation data are GPS/GLONASS modules installed in the vehicles. The source of dispatcher and manager performance data is the TMS event log.  
One of the most important roles in our proposed architecture concept for supporting transportation process reliability is given to the subsystem analyzing dispatcher interaction with the TMS that is implemented by means of Process Mining technologies. In the road transportation industry, erroneous or inefficient actions of dispatchers often lead to deviations in the terms of transportation, which can be interpreted as a failure. It is also possible for critical operations to occur due to improper actions by the dispatcher. For example, before a trip, there is a TMS alert that a vehicle must undergo maintenance. However, the dispatcher still makes a decision to allow the vehicle to proceed on that trip believing that under favorable weather conditions the specified route will not cause difficulties. But this dispatcher's decision could result in an emergency situation due to an unexpected change in weather (snow and slush). The use of the digital twin concept implies that an information system uses the digitally modeled behavior of a given vehicle operated by a given driver to make a justified and correct decision ensuring reliable transportation.  
To form a digital twin of an organization, we propose the following algorithm:  
1. Determine the entities of the transport organization and the connections between them.
2. Form an ontological model of a transport organization.  
Figure 11: Digital twin model of the transportation process.  
From the TMS event log, select data related to the entities of the ontological model.
4. Use Process Mining procedures to reconstruct the company's business processes from the selected data.
5. Diagnose business processes; identify possible deviations; and, if necessary, optimize them.
6. Based on the developed ontological model and the model of optimized business processes, form a model of the organization's digital twin.