Predicting the economic efficiency of the business model of an industrial enterprise using machine learning methods

Abstract

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.

Peculiarities of the Net Present Value and Profit Index Calculations Using Continious Functions and Differential Equations Models

“…The efficiency of the neural network approach in the STATISTICA package for forecasting the financial performance of the enterprise "Ukrtransnafta" is shown in the paper [8]. Based on the results of the regression neural network analysis, the key factors influencing the economic efficiency of this enterprise were identified and ranked, and the optimal neural network model was developed to predict the resulting indicator (efficiency of the enterprise business model).…”

“…Based on the results of the regression neural network analysis, the key factors influencing the economic efficiency of this enterprise were identified and ranked, and the optimal neural network model was developed to predict the resulting indicator (efficiency of the enterprise business model). According to [8], this can allow the company's management to rationally manage and increase the economic efficiency at relatively low costs. However, the application of such a methodology requires initial statistical data on financial and economic indicators for previous periods of the enterprise's activity.…”

“…According to [7] it has been introduced two thresholds for price (8). These thresholds mean that the price cannot be increased or decreased by more than 50 %.…”

Development of territorial communities: aspects of natural capital conservation and budget financing

“…I. Hryhoruk et al [3] and O. Panukhnyk et al [4] consider these issues based on the development of bioresources at the territories of the newly established communities and thus pay attention to the development of entrepreneurship. The detailed research of the methodological IOP Publishing doi:10.1088/1755-1315/1150/1/012004 3 framework for evaluation of natural resources capacity and losses from environmental pollution based on the ecosystem approach and budgetary funding is carried out in the studies of I Khvostina et al [5], N. Pylypiv et al [6], M. Stehnei et al [7], O. Dovgal et al [8], A. Sakhno et al [9]; B. Danylyshyn et al [10].…”

Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach

“…In this study, we propose a method of assessing the quality of educational programs and educational activities based on the neuro-fuzzy approach, due to the active development of analytical systems, based on the technology of artificial intelligence. The most popular and proven of these technologies are neural networks, which successfully solve a variety of "fuzzy" tasks -prediction, classification, recognition of handwritten text, language, images [14,17,22,28,35,37]. In such problems, where traditional technologies are powerless, neural networks often act as the only effective solution.…”