## 3 Results
### Creating Parametric Data Model of Production Process  
After creating the digital production model with the basic hierarchical arrangement, it is necessary to fulfil the individual objects and knots of the model with the data representation, i.e., to assign the time demandingness to the described activities of the production operators (see Figure 6). The data representation consists of two categories--the data unique from the point of view of the product (operation)--i.e., the measurement and set up activities and the data that are unique from the viewpoint of the activity realised by the operator on the engineering device. Figure 5: Resulting production capacity of engineering devices of the modelled production—an overview. (source: Authors). Figure 6: Stage of creating the data model design. (source: Authors). The data unique from the point of view of the product (operation) consists of two components--the measurement activities (\(M_{o}\)) and setup activities (\(S_{o}\)). The formulae for calculating individual components are as follows:  
Calculation \(S_{o}\):  
\[S_{o}=\frac{\sum_{i=1}^{i=n}A_{i}*\frac{Q_{s}}{L_{t}}*N_{ti}}{T_{A}} \tag{2}\]  
where \(n\) is the number of the entered setup activities, \(A\) is the time demandingness of the activities defined by the MTM UAS methodology, \(Q_{s}\) is the required amount of production per shift (according to the shift model), \(L_{t}\) is the defined life span of the tool, \(N_{t}\) is the number of the tools in the machine currently machining the same surface and \(T_{A}\) is the operator's available time (according to the shift model). Calculation \(M_{o}\):  
\[M_{o}=\frac{\sum_{i=1}^{i=n}A_{i}*R_{T_{i}}}{T_{A}} \tag{3}\]  
where \(n\) is the number of the entered setup activities, \(A\) is the time demandingness of the activities defined by the MTM UAS methodology, \(R_{T}\) is the repetition of the activities during the available time of the operator (\(R_{T}=R_{f}+R_{p}+R_{t}\)) and \(T_{A}\) is the operator's available time (according to the shift model). After entering and parameterising all activities, we will achieve a result of the partial operator's utilisation from the point of view of exchanging the tools on his/her machines and defined measurement activities necessary for maintaining the stability of the production process through the absolute cumulative number. These partial results can be continuously depicted during operation for following up the operators' capacity development by a statistical model. The data unique from the point of view of the activity carried out on the engineering device have to be divided into five categories. The goal of the classification into categories is to achieve a good overview of the operator's utilisation and we suggested dividing the activities into the following categories:  
* The regular activities (\(Ac_{R}\)), the activities bound with the production volume--loading/ unloading the line, clamping the piece to the machine, to the pallet; visual inspection of how the piece is machined. * The semi-regular activities (\(Ac_{S}\)). After a certain amount of the pieces checking a sample (e.g., the hardening process, etc.). * The irregular activities (\(Ac_{I}\)), the activities that are not changed with the production volume. The service activities at the workplace, morning meeting with the manager. * The SMED activities (\(Ac_{S_{O}}\)), activities of setting up the machine to another product. * The autonomous maintenance activities (\(Ac_{Ma}\)), maintenance time per shift. This categorisation method was chosen on the basis of a better possibility to search through a complex quantity of information when it is combined later and when variants of the employee's utilisation are created.