## 3 Material and Methods
### Preparation of the Simulation Model for Research  
As part of the research problem, the production process of lighting poles was analyzed in terms of the selected parameter of the unit of time that should be spent on performing individual activities. The first step in building the model was to define the necessary elements and parameters of the analyzed production process. The analyzed production line consisted of the following elements:  
* HD-F \"Durma\" laser cutter,
* Sheet loading, unloading and transport system (part of the cutting machine),
* Pressing the brake of the \"Durma\" AD-R 30135,
* Integrated system for laser welding of lighting poles (welding cell),
* Cavity cutting station (Fanuc industrial robot),
* Manual (MIG--Metal Inert Gas, MAG--Metal Active Gas) welding machine. In addition to the above-mentioned machines, the use of human resources was essential. For the efficient operation of the line, the support of at least 5 employees was required to operate the cutting laser, hydraulic press, welding cell and pole foot welding. A round lighting pole with a length of 4 m was selected as the production object for the relevant analyses. The results of the measurements in the production environment are presented in Table 2.  
When a laser is used, 4 pieces of sheet metal with the given dimensions are obtained from one sheet. Therefore, for analysis, it was decided to divide the working time on this machine by 4 to obtain the processing time of one piece of product. After determining the implementation times of the individual process stages, it was decided to introduce information about all the events causing the operation of the individual machines to stop in the model. For this purpose, an analysis was performed using prediction tools. Among the many possible disruptions to the production process, an undoubtedly harmful phenomenon is the failure of the machines carrying out their production processes. Each defect causes the device to be excluded for a certain period. In the case of not very complicated damage, this time may range from a few to several minutes (repair by employees of the maintenance department). However, in the event of a serious fault, the machine's downtime may reach one or even several days (need to call the service center or order a damaged component or part). The fault causes an inability to perform a technological operation on a given machine, as well as a loss of smoothness of subsequent operations resulting from the technological route. This situation also affects the remaining tasks in the schedule. To conduct the presented research, it was necessary to collect historical data on failures and defects. The source of this data was service books for technological machines constituting part of the company's machinery. The books contained information on all activities carried out on 12 machines by employees of the maintenance department. However, data on failures were selected for research, and inspections (annual and periodic) were carried out (Table 3). Because most failure cases were recorded in 2017, data from this period were used for the analysis. The acquisition and appropriate processing of historical data allowed for their implementation in the STATISTICA 13.1 StatSoft Polska system. The collected data were saved in the form of a table using the appropriate variables:  
* Failure_1 and Failure_2--variables informing about a failure (value \"1\") or its absence (value \"0\"),
* Time_1 and Time_2--variables defining the number of days from the beginning of the observation to the occurrence of the event,
* Time_12--variable defining the time between the first and second failure,
* Complete_1 and Complete_2--variables informing about the observation status (complete--\"1\", abscissa--\"0\"),
* Previous--a variable defining whether the failure was related to the previous failure (\"1\"--yes, \"0\"--no),
* Time_review < 90--variable indicating whether the fault occurred within 90 days of an inspection (\"1\"--yes, \"0\"--no),