## 6 Verification of the Proposed Methodology
### Summary Verification of the Functionality of the Methodology  
Data from the companies can be analysed according to other aspects, and the resulting values of the levels can be compared according to criteria such as type of industry (automotive, mechanical engineering, electrical engineering), company size according to number of employees (small, medium, large) and according to production type according to repeatability (piece/small series, serial, large series). Within the data analysis, company output data were compared and on the basis of the outputs from the analyses using the methodology it is possible to state the following:  
The best rated industry is the automotive industry. The electrical engineering sector also shows a similar value; the worst rated is the mechanical engineering sector. The average value of readiness level for automotive companies is 2.59, companies in engineering have a value of 1.68. Enterprises in the field of electrical engineering have 2.55. As the number of enterprises is not the same in each sector, frequencies are based on the same base so that they can be compared. This comparison with the numerical data is shown in Figure 5. The red bars represent the field of engineering and it can be seen from the graph that this sector scores mainly at lower levels, especially at level 1, and then the number of occurrences decreases. Green bars are for the automotive industry, which scores mainly in level 2 and level 3. The blue bars represent the field of electrical engineering. Industries are shown in a group bar graph, including the interpolation of regression curves for all industries. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Number of Companies in the Level** & **Number of Companies in the Level** \\ \hline
0 & 2 & 6.9\% \\
1 & 9 & 31\% \\
2 & 9 & 31\% \\
3 & 8 & 27.6\% \\
4 & 1 & 3.4\% \\
5 & 0 & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary data on the number of enterprises and their levels in the current variant. Automotive companies have established certified process equipment, which contains several standards that they must meet for customers, and due to serial production, they also have basic automation in place, especially in the field of supply and storage. Automotive companies also have digitised data collection with their structuring and properly functioning supply chain operations. Across all five dimensions, preparedness levels are clearly better than companies in the engineering sector. * The level of readiness of internal logistics depends on the size of the company. The lowest levels of readiness were achieved by smaller companies. As the size of the company increases, the level of readiness of its internal logistics increases. The total average value of readiness for smaller companies with up to 250 employees is 1.42. Medium-sized companies with 250-900 employees have an average value of level of 2.23, and the best evaluation is, in line with our assumptions, for large companies, which have an average value for the entire area of internal logistics of 2.79. * The level of readiness of internal logistics depends on the type of production according to repeatability. At the same time, with increasing repeatability, the level of readiness of internal logistics increases, and companies with large-scale production have the highest readiness. The average value of the level of readiness of piece/small series production was 1.37 after analysis. Mass production has an average level of 2.47. Large-scale production has an average value of 2.77. * At present, companies in the Czech Republic are mainly in the lower levels of Industry 4.0 readiness assessment, which we consider to be level 0-3. The results of the verification of the methodology are compared with data from the Czech Statistical Office (CZSO).