## 4 Discussion  
In the experiments, various PAs, except NNs, and data samples were used to train the models. The best-performing models, based on the highest achieved metric scores, were selected for evaluation, as shown in Table 6. However, a model's performance is only as good as the quality of the training data. In our research, we used the original dataset, assuming this would lead to greater acceptance by SMEs in applying ML. Therefore, at this stage, we chose not to apply data augmentation techniques to increase the dataset size. In some cases, no suitable ML model could be identified, suggesting that non-ML methods may be more appropriate, as shown in Tables 12 and 13. The methodology was applied to model the MJ/CT, manufacturing CT, and LT time estimations of product types A and B. The application of the estimation risk assessment identified 22 of 37 models of time estimation that are suitable or conditionally suitable for implementation, as shown in Table 14.  
The limited number of attributes combined with the low data rate resulted in inaccurate M/J CT estimation models. Estimating M/J CT based on the attribute count of the item alone is insufficient. Nevertheless, the choice of the decision tree algorithm is appropriate, the resulting models are easy to understand, and they provide an overview of the range in which the M/J CT varies for individual items. The modeling of manufacturing CT estimation has found accurate models for type A jobs, but their further application is conditioned by accurate M/J CT estimates. By modeling the manufacturing CT of type B jobs, which was performed on increased data, significantly less accurate models were found. This points to the insufficient representativeness of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Machine/Job/Item**} & \multirow{2}{*}{**Customer**} & \multicolumn{2}{c}{**Use**} & **Conditionally** & **Suitable Model** & **Suitable Model**, \\  & & \(\frac{\xi}{\mu}\) & **Risk** & **Another** & **Suitable** & **for** & **Simplifying** \\  & & & & **Method** & **Model** & **Implementation** & **Possible** \\ \hline \multicolumn{8}{c}{M/J CT models of type B item “Steel plate”} \\ \hline Cutting & D10183 & 0.51 & \(R^{ll}=0.66\) & & x & \\ Cutting & D10605 & 0.51 & \(R^{ll}=0.66\) & & x & \\ Shearing & D10206 & 0.54 & \(R^{l}=0.81\) & x & \\ Shearing & D10605 & 0.54 & \(R^{l}=0.75\) & x & \\ \hline \multicolumn{8}{c}{M/J CT models of type B item “Lasered and bent part”} \\ \hline Laser & D10216 & 1.14 & \(R^{l}=\)1.72 & x & \\ programming & D10607 & 1.14 & \(R^{ll}=\)1.43 & x & \\ Laser & D10605 & 1.14 & \(R^{ll}=1.43\) & x & \\ programming & D10605 & 1.14 & \(R^{ll}=1.43\) & x & \\ Laser & D10600 & 1.14 & \(R^{ll}=1.43\) & x & \\ Laser & D10600 & 1.14 & \(R^{ll}=1.43\) & x & \\ Programming & D10600 & 1.14 & \(R^{ll}=1.43\) & x & \\ Laser & D10600 & 1.44 & \(R^{l}=2.17\) & x & \\ Laser & D10607 & 1.44 & \(R^{ll}=1.63\) & & x \\ Laser & D10605 & 1.44 & \(R^{ll}=1.73\) & & x \\ Laser & D10600 & 1.44 & \(R^{ll}=1.63\) & & x \\ Bending & D10216 & 0.62 & \(R^{ll}=0.78\) & & x \\ Bending & D10607 & 0.62 & \(R^{ll}=0.78\) & & x \\ Bending & D10605 & 0.62 & \(R^{ll}=0.78\) & & x \\ Bending & D10600 & 0.62 & \(R^{ll}=0.83\) & x & \\ \hline \multicolumn{8}{c}{Manufacturing CT models of type B items} \\ \hline Steel plate & D10605 & 1.19 & \(R^{lll}=1.255\) & & x \\ Steel plate & D10216 & 1.19 & \(R^{ll}=1.484\) & & x \\ Steel plate & D10183 & 1.19 & Excessive risk & x & \\ Steel plate & D10176 & 1.19 & \(R^{l}=1.63\) & x & \\ \hline \multicolumn{8}{c}{LT models of type B items} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation of models of B type items. data of the modeled phenomenon. The selection of inappropriate data causes this, as the similarity criterion was only the same item term. Based on the training data, no suitable models for LT were found in the experiments because the LT estimation attribute values and the learning values showed a low dependence.