## 4 Conclusions  
This research was a continuation of the work started in [38]. The developments presented in this article are the basis for new knowledge in the field of the computational analysis of industrial data in the tire industry (both big data and small datasets) with the required accuracy and efficiency, creating the best fit components of complex systems (including computational models). These findings will be used in the further stages of our research, including building and testing prototypes of algorithms and systems in laboratory conditions and making improvements and corrections to them, maximizing the end result in industrial conditions. In the context of an industrial, commercialized solution, this will allow for an effective transition from TRL = 2-3 to TRL = 6. It will also allow for better harmonization and the interoperability of the proposed solutions with those already available on the market. In regression networks, the RMSE should not be automatically chosen as the only cost function, because the results may not be the most beneficial from a business, risk management, or other relevant perspective. A custom cost function (or a group of metrics) better reflects the requirements of the specific enterprise and may favor certain solutions. The structure of an error/accuracy interpretation (benchmarking) drives the form and capabilities of the model. The analyzed dataset was not obtained during an experiment; instead, it was generated from components that are used in the industrial environment under variable operating conditions. The main challenge when implementing the described model in factory settings involves defining the criteria used to verify the quality of its prediction abilities, because this will be the main basis for making decisions about rejecting a defective green tire. Every single incorrect decision in this case is equivalent to the loss of the cost of producing one tire. Therefore, it will be necessary to constantly adjust the hyperparameters and train the obtained model. In order to implement the best possible tool supporting the mass production of tires, our future research will focus on assessing the work of the model in various (local) weather conditions, considering factors such as the air temperature and humidity in the production hall and the temperature of tire components applied on a TAM (tire assembly machine). Measurements of these parameters are now recorded during mass production and will be correlated with the dependent variable (CON) in further research. The literature analysis shows that it is necessary to conduct research on a broader dataset for the best possible fitting of the model in the business environment. This will minimize the error (RMSE) and improve the prediction skill (R\({}^{2}\)) of the developed model. The expansion of the research will include the analysis of historical data on more tire sizes. This will verify the quality of the model's operations, which will likely affect the final structure of the model and the values of its parameters. A further advantage of the developed model, which makes it suitable for use in the mass production of tires, is its immediate response to the (out of range) values of the input variables. This feature can facilitate the immediate withdrawal of batches of faulty tire components, minimize material losses, and speed up the investigation of root causes of faults arising during the production process (leading to the elimination of time loss). The modifications of the machine settings suggested by the model in conjunction with expert verification can therefore stop a defective product (e.g., a green tire) from progressing to the next stages of the production process, facilitating the elimination of scrap. The developed solution will contribute to reducing the amount of tire waste by eliminatingpieces of products that do not meet the quality requirements during the earlier stages of the production process.