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Intelligent companies are able to become more and more advanced due to the accessible high-tech solutions, data collection and processing systems. Appropriate analysing and using of these large collections of data is significant in Industry 4.0 applications. The advancement of intelligent systems has a powerful effect on the progress of numerous innovative ideas considering the data analysis. They will make the choice of the proper data possible, but they will also suggest suitable methods for obtaining knowledge from these data. On that account, qualitative and quantitative research methods are employed in this paper to examine the outcomes on the product quality acquired from the case study company. The features which affect the product quality in a manufacturing process were recognized on the basis of statistical analysis of the acquired outcomes. Moreover, the work demonstrates that the machine learning method might be applied as a decision-supporting tool in a manufacturing process. The model created with the random forest method allowed to examine the influence of every single process parameter as well as the connection between them on the product quality. The acquired findings may be helpful while defining the most significant variables that might be considered to constitute the input data for developing an automated system for the uninterrupted monitoring of the process parameters, and, in consequence, for the product quality control.
