**7. Conclusions**

The main research problem that was considered in the paper was an insufficient means of assessing the degree of the implementation of lean maintenance. To find the solution of identified problem, artificial intelligence methods such as decision trees and the rough set theory were used. When analyzing the results presented, it should be noted that the models generated with the rough set theory achieved much better results than in decision trees. The decision rules generated by DT showed better values for all indicators for the classifier for the class of 30–50%. However, better values for the class of 70–85% were achieved for RST, mainly for LEM2 algorithm. The number of rules generated by the LEM2 algorithm is the smallest compared to the other algorithms. This shows that a large number of rules is not needed to get good prediction results in the investigated problem.

The resulting indicators for testing the quality of classifiers confirmed the high usefulness of the generated decision rules, both those using decision trees and the rough set theory. The developed dependencies allow us to assess which results a given company can expect after the implementation of specific lean maintenance methods and tools, and which lean maintenance methods and tools should be used to achieve the intended goals. These dependencies may be the basis for determining the directions and effects of implementing lean maintenance in manufacturing companies. Additionally, an expert system in the form of a software application, developed on the basis of the generated dependencies (decision rules), allows for the selection of appropriate actions in order to obtain the best results after implementing lean maintenance.

The presented studies can be used by enterprises to build and organize maintenance processes, to select an appropriate action strategy, but above all, to improve already implemented activities in this area. Although the research was conducted in a limited area, it was based on common assumptions, principles, and objectives of implementing the lean maintenance concept in the enterprise. Therefore, the presented solutions are useful for practical use by all production companies for forecasting and assessing the effectiveness of implementing lean maintenance methods and tools, regardless of the region.

Moreover, the positive results obtained during the conduct of the described study lead to the conclusion that the activities in these areas should be continued. In particular, there ought to be studies considering the assessment of the effectiveness of using other methods and tools recommended in the literature within lean maintenance implementation; the possibility of extending functionality designed in a computer application; and the use of other methods of data exploration for generating decision rules and comparing their classification quality.

**Author Contributions:** K.A. gave the theoretical and substantive background for the provided research and conceived and designed the experiments; L.P. made an experimental verification of the proposed approach; A.G. provided technical guidance and gave critical review for this paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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