**6. Conclusions**

The developed model of the artificial neural network supporting the assembly sequence planning was positively verified using data not included in the training algorithm. The prediction results are characterized by good correlation coefficients R<sup>2</sup> > 0.9 for the group of verification data and an SOS error < 0.1. The predictive model presented in this publication is the beginning of work on the development of a universal tool for assessing the assembly sequence of various products, and thus obtaining a finished product in the shortest possible time. The use of the part DFA methodology to evaluate assembly sequences, which are the basis for network learning, is a novelty in this research area.

The effectiveness of the method depends mainly on the number of cases teaching the neural network that are able to generalize the knowledge and the neural network. Thus, a network constraint may be the presence of a large number of errors when predicting the assembly time on the base of DFA factors for other products.

The simulation results also sugges<sup>t</sup> that the proposed neural predictor could be used as a predictor for assembly sequence planning system. Further research will be aimed at extending the learning dataset and verifying the assumptions of the network model made for other products in a specific industrial plant.

The aim of the authors is to develop the conducted research and verify the operation of the network on a wide range of products.

**Author Contributions:** Conceptualization, M.S. (Marcin Suszy ´nski); methodology, M.S. (Marcin Suszy ´nski) and K.P.; software, M.S. (Marcin Suszy ´nski) and K.P.; validation, M.S. (Marcin Suszy ´nski) and K.P.; formal analysis, M.S. (Marcin Suszy ´nski) and K.P.; investigation, M.S. (Marcin Suszy ´nski) and K.P.; resources, M.S. (Marcin Suszy ´nski) and K.P.; data curation, M.S. (Marcin Suszy ´nski); writing—original draft preparation, M.S. (Marcin Suszy ´nski) and K.P.; writing—review and editing, M.S. (Marcin Suszy ´nski) and K.P.; visualization, M.S. (Marcin Suszy ´nski) and K.P.; supervision, M.S. (Marcin Suszy ´nski); project administration, M.S. (Marcin Suszy ´nski) and K.P.; funding acquisition; M.S. (Martin Svoboda) and V.C.; validation, M.S. (Martin Svoboda) and V. ˇ C.; review and editing. All ˇ authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Ministry of Science and Higher Education of Poland (No. 0614/SBAD/1547).

**Conflicts of Interest:** The authors declare no conflict of interest.
