## 4 Results and Discussion  
### Collecting Data Before the Workshop: Measurement and Analysis of Production Indicators  
Before implementing optimization activities, the key stage was the collection and analysis of data on the functioning of the production process. The measurement of production indicators enabled the assessment of the initial state of process efficiency and the identification of areas requiring improvements. These data constituted a reference point for the assessment of the impact of implemented changes. Considering the specification of the production of the analyzed plant, in particular, the focus was placed on the analysis of three key indicators: reliability time, machine availability and the number of machine strokes in the time unit (productivity). During the analysis, before the implementation of the changes, data on the efficiency of the production process was collected, including rearranging times for products A and B on machine X and the productivity and availability of the machine time. The results have been combined and presented in tabular and graphic form. The measurement data presented in Figure 1 for medium-sized rearranging times over months indicate significant differences in work efficiency for the two analyzed products. Analysis of the data contained in Figure 1 shows the average calling time for the product and machine X in the period from January to May, where the average rearranging time for product A was 30.72 min. In January and February, the conversion time was on average 35 min, which suggests that this process was less effective during this period than in other months. Such a result may be the result of the lack of optimization of conversation procedures and insufficient staff training in the scope of rapid performance of this operation. It is possible that in the following months, some improvements were introduced at the stage of preparation for the workshop, such as better resource planning and more accurate preparation of tools, which could contribute to improving the effectiveness of the process. The average calling time for product B (Figure 2) is 35.12 min and it is more stable compared to product A, which may indicate a less complex process of reckoning in the case of product B. At the same time, the indicator showing the number of strokes in a minute of machine X was analyzed. The results of this analysis are shown schematically in Figure 3.  
Fluctuations in the number of strokes of machine X for product A before the workshop reflect the instability of the production process. The lack of uniform results and declines in February and April indicate the need to improve the procedures of conversion, reduce the amount of downtime and better machine management. The data presented in Figure 4 suggest that the production process of product B is less exposed to fluctuations, but still requires improvements, especially in the context of increasing maximum efficiency and the elimination of minor interference. Figure 5 and Table 1 show the machine's working time before the workshop. Presenting these detailed data allows for determining the challenges related to the effectiveness of production processes and potential areas for optimization. Figure 1: Average rearranging time of machine X before conducting the workshop. Figure 4: Number of machine X beats per minute for product B before conducting the workshop. Figure 3: Number of machine X beats per minute for the product before conducting the workshop. Figure 2: Average rearranging time of machine X for product B before conducting the workshop. The machine availability indicator increases successively in the analyzed period--from 74.6% in January to 79.9% in May. This means that despite the lack of direct workshop activities, some changes in the organization of work or operation of the machine could have a positive impact on the effective production time. The share of time intended for reliability decreased in the analyzed period--from 12.4% in January to 8.75% in May.