## 4 Results and Discussion
### Use of Tools Supporting the Improvement of Efficiency in the Analyzed Enterprise
#### 4.5.2 Method ABC (Pareto-Lorenza)  
As part of the optimization workshop, the enterprise used the ABC method based on the Pareto-Lorenz principle to identify the most important factors affecting the efficiency of production processes. This method enabled a detailed analysis to concentrate optimization activities in key areas generating the greatest losses (Table 7). The data were collected within a month from the production process, during which about 65 uses of the X machine were carried out. Here is a summary of the causes of losses, their value and percentage participation in total losses.  
The factors were assigned to ABC groups based on their impact on total time losses (Table 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disadvantages** & **Factor (Cause of Losses)** & **Start Time, Min** & **Relative Share, \%** & \
\begin{tabular}{c} **Cumulative** \\ **Percentage, \%** \\ \end{tabular} \\ \hline P\_1 &
\begin{tabular}{c} Tool setting is too \\ time-consuming \\ \end{tabular} & 190 & 42.60 & 42.60 \\ P\_2 & No precise instructions & 90 & 20.18 & 62.78 \\ P\_3 & Sensor problems & 78 & 17.49 & 80.27 \\ P\_4 & Operator errors & 29 & 6.50 & 86.77 \\ P\_5 & Logistic problems (no tools) & 24 & 5.38 & 92.15 \\ P\_6 & Deficiencies in the materials & 19 & 4.26 & 96.41 \\ P\_7 & Occasional machine failures & 11 & 2.47 & 98.88 \\ P\_8 & Other (sporadic) & 5 & 1.12 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Production losses.  
Analysis of data on losses in the process of moving machine X allowed for the assignment of individual factors to groups A, B and C, which enabled the determination of the priorities of optimization activities. Group A covered the most important causes of losses, responsible for 80.27% of the total loss time. It includes time-consuming tools, no precise instructions and sensor problems. These factors have been considered key to eliminate because their reduction would bring the greatest benefits for the effectiveness of the process. Group B covered reasons with an average impact on losses, which in total constituted 16.34% of losses. These were operators' mistakes, logistic problems related to the lack of tools and deficiencies in materials. These factors, although less important than those from Group A, still require corrective actions to minimize their impact on the downtime of the machine. In turn, Group C contained factors with the slightest impact on losses, responsible only for 3.59% of the total loss time. This group includes sporadic machine failures and other incidental problems. Due to their low participation in losses, they were considered the least important in the short-term perspective of optimization activities. As part of this research, a Pareto chart was developed, which illustrates the percentage distribution of individual losses and their cumulative impact. On this basis, optimization activities were focused on processes with the greatest impact on overall performance.  
Figure 12 shows that time-consuming tool settings, no precise instructions and sensor problems are responsible for most losses (Group A).