# Simultaneous Use of Digital Technologies and Industrial Robots in Manufacturing Firms
## 4 Results and Discussion
### Statistical Tests  
The association and correlation between selected digital technologies, readiness levels and types of industrial robots were analyzed with the use of inferential statistics. The technologies were coded as T1: PLM systems, T2: ERP Systems, T3: digital exchange of data, T4: automation and management of internal logistics, T5: near-real-time production control systems, T6: mobile devices for programming and/or operating systems and/or machines, and T7: digital visualization (solutions) on the shop floor. Industrial robots were coded as R1: robots for manufacturing processes, R2: robots for handling processes, R3: mobile robots, R4: collaborative robots, and R5: autonomous robots. For analysis, IBM SPSS Statistics was used, and each technology and readiness level was tested with different types of industrial robots. Pearson's chi-square and its corresponding significance value were used when testing for the association. Values of Pearsons' chi-square significance below 0.05 indicate a significant association or relationship between the two variables, which implies that the observed enabling technology (or readiness level) affects the specific type of robots. If the value is above 0.05, then there is no significant relationship, and the enabling technology has no effect on the use of industrial robots, and the values can \begin{table}
\begin{tabular}{l c} \hline \hline
**Industrial Robot Type** & **Share [\%]** \\ \hline Industrial robots for manufacturing processes & 50.0\% \\ \hline Industrial robots for handling processes & 35.6\% \\ \hline Mobile industrial robots & 4.2\% \\ \hline Collaborating robots & 15.3\% \\ \hline Autonomous industrial robots & 19.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Industrial robot adoption in Slovenian manufacturing firms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Industry 4.0 Readiness Index Level** & **Share [\%]** \\ \hline Level 0 & 16.9\% \\ \hline Level 1 & 19.5\% \\ \hline Level 2 & 23.7\% \\ \hline Level 3 & 13.6\% \\ \hline Level 4 & 12.7\% \\ \hline Level 5 & 13.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Share of manufacturing firms in the Industry 4.0 Readiness Index levels. be ignored. Additionally, the Phi coefficient was used to gain a clearer understanding of the strength and direction of the (correlation) effect. As previously mentioned, the Phi correlation coefficient values between 0.1 and 0.3 were considered to represent a small effect size, whereas values ranging from 0.3 to 0.5 were considered to indicate a medium effect size. If the values exceed 0.5, it can be concluded that the effect size is large. Table 4 summarizes the results of the association and correlation analysis. As shown in the Table, industrial robots for handling processes have a positive association and correlation with each of the selected technologies. PLM systems, ERP systems, the digital exchange of data, and near-real-time production control systems have a medium correlation effect size, while the rest of the technologies have only a small effect size. Industrial robots for manufacturing processes are correlated with five out of the seven technologies. Although all five correlations are weak, the correlation between near-real-time production control systems and robots for manufacturing processes and mobile devices for programming and robots for manufacturing processes are very close to having a medium-sized effect. Collaborative robots are correlated weakly with the use of ERP systems, near-real-time production control systems and digital visualization (solutions) on the shop floor. No association or correlation was found between the selected digital technologies and mobile robots and autonomous robots. After conducting the initial association and correlation analysis, logistic regression was carried out to determine the likelihood of using a certain type of industrial robot based on the specific digital technology.