The paper considers the problem of studying the impact of key determinants on the industrial enterprise business model economic efficiency and aims to build an optimal model for predicting the industrial enterprise business model effectiveness using neural boundaries. A system of key determinants key factors has been developed. Significant factors were later used to build neural networks that characterize the studied resultant trait development vector. The procedure for constructing neural networks was performed in the STATISTICA Neural Networks environment. As input parameters, according to the previous analysis, 6 key factor indicators were selected. The initial parameter is determined by economic efficiency. According to the results of the neural network analysis, 100 neural networks were tested and the top 5 were saved. The following types of neural network architectures, multilayer perceptron, generalized regression network and linear network were used. Based on the results of the neural network modeling, 5 multilayer perceptrons of neural network architectures were proposed. According to descriptive statistics, the best model was a multilayer perceptron, with the MLP 6-10-1 architecture, which identifies a model with 6 input variables, one output variable and one hidden layer containing 10 hidden neurons. According to the analysis of the sensitivity of the network to input variables, it was determined that the network is the most sensitive to the variable the share of electricity costs in total costs. According to the results of selected neural networks standard prediction, the hypothesis of the best neural network was confirmed as Absolute res., Squared res, Std. Res for the neural network MLP 6-10-1 reached the optimal value and indicate that the selected model really has small residues, which indicates a fairly high accuracy of the forecast when using it.
The article proposes two methods for evaluating the financial efficiency of a business model of industrial enterprises. In order to evaluate the financial efficiency of the business model of an industrial enterprise, a system of single indicators for assessing the financial condition of the enterprise by such components as financial stability, liquidity and solvency, business activity and profitability was formed. Fishburne's rule weights the major components of an integral measure of an enterprise's business model financial performance. In addition, an integral measure of the financial performance of the business model is modeled using the fuzzy set method and taxonomic analysis, which will help to evaluate the financial performance level of the business model more objectively. The comparative analysis of the obtained results by different methods of calculation of integral indicators is carried out.
The article considers the problem of optimizing the financial condition of oil and gas companies. The offered methods of optimization of a financial condition by scientists from different countries are investigated. It is determined that the financial condition of the enterprise depends on the effectiveness of the risk management system of enterprises. It is proved that the enterprises of the oil and gas complex need to develop a system for risk management to ensure the appropriate financial condition. The financial condition is estimated according to the system of certain financial indicators, the integrated indicator of financial condition assessment is constructed using the method of taxonomy. According to the results of the calculation of the integrated indicator, it is concluded that this indicator does not have a stable trend. On the basis of the conducted researches it is offered to carry out optimization of an integral indicator of a financial condition with use of genetic algorithm in the Matlab environment. Based on the obtained results, recommendations of the management of the researched enterprises on increase of management efficiency are given.
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