# An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0
## 5 Discussion  
The proposed approach makes it possible to identify the competences of maintenance department employees and it also makes it possible to give them validity to assign repair work correctly, depending on the type of failure.  
The model also assumes that data is obtained regarding the repair times of a given machine, from IT systems that the enterprise has implemented. Thanks to such defined elements, it is possible to define the so-called \"state of nature\" for each enterprise resource vis-a-vis each employee. The higher the value of a given natural state (max = 1.66), the better the matching of a given employee, to a given failure, on a given resource. In addition, formalisation rules were used for each element in the proposed approach in order to implement the IT system. The proposed solution allows for maintenance managers to increase the availability of the resources of the enterprise.  
Quantitatively defining the competences of employees in our research was a particularly difficult task.  
Therefore, formalisation rules were strictly defined for each type of competence, with the IT implementation of the given approach then being presented.  
The use of the proposed approach allows for decision making to be supported when selecting an expert with the best ratio of competences, in relation to the time that is required to repair a failure in a given resource. The use of the maintenance expert selection map is helpful in:  
* Selecting a maintenance expert, from among available employees, to repair a given resource.
* Selecting the scope of employee training, in order to improve the competences of employees in relation to the effective repair of resources, by shortening the elimination time of failures and by reducing the downtime of failures.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline  & R\({}_{1}\) & R\({}_{2}\) & R\({}_{3}\) & R\({}_{4}\) & R\({}_{5}\) & R\({}_{6}\) & R\({}_{7}\) & R\({}_{8}\) & R\({}_{9}\) & R\({}_{10}\) & R\({}_{11}\) & R\({}_{12}\) & R\({}_{13}\) & R\({}_{14}\) & R\({}_{15}\) & R\({}_{16}\) & R\({}_{17}\) & R\({}_{18}\) \\ \hline \(W_{1}\) & & & & & & & & & & & & & & & & & \\ \(W_{2}\) & & & & & & & & & & & & & & & & \\ \(W_{3}\) & & & & & & & & & & & & & & & & & \\ \(W_{4}\) & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: The “maintenance expert selection map”.  
* Defining a motivating system for all maintenance workers based on the value of natural states.  
The proposed approach is currently implemented in the form of a decision support system when selecting the most effective maintenance employees for repairing failures in manufacturing resources. The current implementation work has been partly completed in the form of a web-application, related to the definition of competences. The five competence questionnaires were defined and implemented. This application also implemented the FAHP method. Our web-application will be extended by the algorithm for automatic data, for the repair times of each failure and also extended by the acquisition and automatic creation of a maintenance expert selection map as part of further research.