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. 1
The intensification of the digital transformation of industrial enterprises in general and oil transportation in particular necessitates the urgent introduction of digitalization tools and means, which should ultimately lead to an increase in the efficiency of economic entities. Given these circumstances, the issue of assessing the economic efficiency of the enterprise business model from the introduction of modern IT is relevant. Digitalization is one of the industrial enterprises’ development key determinants, it is a field for the formation of economic efficiency of the enterprise business model. Therefore, the success of companies, including their performance indicators, depends on the effective use of modern IT in the activities of business entities. In such conditions, it is important to model the enterprise business model effectiveness in the digital transformation of economic systems. 2
Examining the topic of modeling the effectiveness of economic entities, we can conclude that there are many methods, techniques, tools, methods of economic and mathematical modeling of the phenomenon. The difficulty of studying this phenomenon is that for different companies, the efficiency of activities can be affected by various factors. In addition, the concept of efficiency is not unambiguous, so it can be considered through the prism of various result indicators, influencing factors and their combinations. There is a huge variety of built efficiency models for different enterprises. Therefore, consider some of the proposed models of efficiency.
In particular, we can highlight economic production quantity model with learning
in production, quality, reliability and energy efficiency. The article [
A. Zakharov and S.-L. Jämsä-Jounela in [
In addition Agnieszka Bezat-Jarzębowska and Włodzimierz Rembisz proposed
efficiency-focused economic modeling of competitiveness in the agri-food sector [
Katarína Teplická in the article [
Mykola Havrylenko, Vira Shiyko, Liliana Horal, Inesa Khvostina, and Natalia
Yashcheritsyna [
When forming a business model of an enterprise, it is necessary to take into
account risks in order to minimize them. One of the methods for diversifying risks is
their insurance [
Entering the digital space of industrial enterprises, including oil transportation
companies, opens up new opportunities for these businesses, which are to use the
following technologies and approaches: artificial intelligence and advanced analytics
[
The process of assessing the impact of these IT on the efficiency of the oil
transportation company has remained unexplored [
These problems of digitalization of industrial enterprises require the transformation of production and organizational structures of economic entities for the effective implementation of ICT. The new revolution requires the formation of such a system of organization and management of activities, which will ensure, based on the analysis of performance, not only the elimination of the causes of existing discrepancies, but also help to identify and prevent their occurrence.
Thus, there is a need to assess the effectiveness and efficiency of activities “before” and “after” the introduction of ICT and to monitor them constantly. Evaluating the effectiveness of enterprises business processes digitalization will allow on the basis of the digitalized business model of the enterprise to monitor the activities in general, identify problem areas of the entity, make optimal management decisions and experiment by using a digitalized business model (digital twin of the enterprise) without intervention into the actual production process.
It should be noted that the indicators for assessing the effectiveness of digitization may be different for different processes and can be both generalized and detailed for each business process or function.
Due to the large number of the digitalization process influence area on the efficiency of the industrial enterprise digitalization effectiveness determining model will be: = ∑ ∑ (1) where Еd – the overall efficiency of the enterprise digitalization; еij – unit efficiency of the i-th business process (function) from the j-th IT.
The tasks performed by the procedure of evaluating of an industrial enterprise business processes digitalization effectiveness is that: ─ There is a search for problem areas during the interaction of employees of different departments in the process of information support and business processes; ─ The main, additional and auxiliary directions of economic entities activity for the purpose of decomposition on digital business processes are defined; ─ Prerequisites are formed for the construction of an orderly electronic document management system; ─ The regulation of enterprise activity is established; ─ The process of conducting experiments in the activities of the enterprise is possible in order to determine the strategies of enterprise development in the current conditions of enterprises operation.
To form a system for evaluating the effectiveness of digitalization of business processes of JSC “Ukrtransnafta” define its main stages, taking into account the peculiarities of the research enterprise. Figure 1 shows the stages of evaluating the JSC “Ukrtransnafta” business processes digitalization effectiveness.
1 2 3 4 5 • Construction of the enterprise virtual business model using the analysis of the work regulation information system and determination of the digital way of performing business processes. • Formation of quantitative indicators system for evaluating the effectiveness of each IT in relation to the business process on key performance indicators. • Calculation of parameters of quantitative indicators of business processes digitalization efficiency and formation of efficiency matrix in a ratio IT-business / business process. • Analysis of the obtained values of the business processes digitalization efficiency corfficients. • Forming conclusions about the business processes digitalization effectiveness. Development of business processes digitalization efficiency increase.
Given the peculiarities of the oil transportation companies activities, the business processes digitalization impact will be carried out directly on the tariffs. Thus, the model for determining the effectiveness of digitization must be built taking into account the sequence of the researched enterprise business processes. Figure 2 defines the sequence of JSC “Ukrtransnafta” business processes.
At each of the above stages of oil transportation in the conditions of digitalization it is necessary to enter the digitization coefficient, which will eventually be reduced to an integrated indicator of activities digitization, which will reflect the synergistic impact of each stage digitization coefficients. Thus, taking into account the peculiarities of the oil transportation company business processes, the integrated indicator of digitalization, which must be taken into account when forming the main performance indicator – the tariff will take the form: where kd – synergetic digitization factor; kі – digitization ratio on the i-th business process; і=1, …, n – the number of oil transportation business processes. (2) 4. Management of oil transportation systems • Operation of main and auxiliary equipment 1. Oil pumping station 2. Transportation by main pipeline • Storage of petroleum products for end use 3. Operation of the
tank farm
It should be noted that tariffs for transportation of oil and oil products are established by the general rules of “Tariffs formation methods”, which does not account for the development of alternative transportation markets, digitalization of the economy, accounting standards used time, resources, materials, regulated by the old technology, operating in the early 2000s.
Given that all of the above business processes form cost centers, it is necessary to determine the synergy of costs that arises from the digitalization of business processes. This synergetic effect of cost reduction will have a direct impact on the overall digitization ratio, which will increase economic efficiency. It is interesting for this company to form a correlation-regression model of the enterprise efficiency dependence through the introduction of ICT and reduce costs in each business process (fig. 2) of the studied enterprise. Based on the results of previous studies in the work, which indicate that for the investigated enterprise financial efficiency is at an average level and it increases at the end of the period, we consider it necessary to assess the economic efficiency of the oil company business model using computer economy methods to determine key success factors of the researched enterprise.
Therefore, the resulting indicator in the proposed correlation-regression model will be the efficiency as the ratio of net income from sales to the cost of goods sold. After all, when assessing the result of management must take into account not o nly the statement of the goal achievement, but also the optimal ways to achieve it. The choice of the regression or multifactor model type (analytical expression) depends on the type of factor features connection with the effective one. Thus, the initial form of the resulting function is reflected as a dependence: = ( ; ; … , ), 2.50 2.00 1.50 1.00 0.50 0.00 where Y – effective feature-function of the enterprise efficiency; х1, х2, х3, …, хn – factor features.
Figure 3 shows the dynamics of the JSC “Ukrtransnafta” business model efficiency and the volume of transported oil in UAH million for 2010-2019.
Thus, from the results of calculating the enterprise business model efficiency and its relationship with the dynamics of transported oil, we can conclude that the change of the first indicator does not have a stable trend, in addition, at the end of the period there is an increase in the efficiency of the business model production efficiency (volumes of transported oil). Therefore, it is necessary to investigate and identify the factors that affect the growth of the business model economic efficiency, along with the decline in production activity of the enterprise, which is associated with both the global crisis and unstable international economic relations. To further enhance the positive impact and level the negative, provided it is impossible to turn it into a positive impact.
To determine the main factors influencing the performance indicator, it is advisable to use the method of correlation-regression analysis. The indicator of enterprise activity efficiency was chosen as the resulting indicator (Y). As mentioned above, the key determinants that affect the efficiency of the investigated enterprise are costs, the cost of petroleum products transportation, the organizational structure of the volume in table 1 we will summarize the quantitative indicators of each of the identified key evaluation determinants and include their correlation-regression model of the studied enterprise business model economic efficiency.
Summary data for correlation-regression analysis of the influence of certain factors influence on the efficiency of the oil transportation company business model are given in table 2. Given the fact that all production and non-production areas of activity, including the pipeline system will consistently move to a digital display of activities, it is advisable to introduce methods of experiments in the study of improving the efficiency of the enterprise through optimization of input, significant parameters.
To build an adequate regression model of the studied enterprise business model economic efficiency, it is necessary to check the selected factors-indicators for the phenomenon of multicollinearity. Using the Data Analysis package in Excel, a correlation matrix was constructed, which demonstrates the strength of the relationship between the selected factor values and the performance indicator.
The calculation of the correlation matrix allows us to conclude that there is a close relationship between the performance indicator (Y) and the factor values, except for the pair (Y and x4, x5, x9, x10, x11, x12, x14). These factors will be excluded from the study when constructing the regression equation. In addition to the study of the correlation between the performance indicator and the factors, it is advisable to determine the interdependence (multicollinearity) of the factor quantities-features among themselves. The phenomenon of multicollinearity occurs when the coefficients of pairwise correlation of trait factors are equal to 0.7 or more according to the Chedok scale, which indicates a close and very close relationship between certain trait factors. In this case, those factor features between which the phenomenon of multicollinearity was established should be excluded from the correlation-regression model. Those indicators of the relationship between which remains weak, are included in the economic-mathematical model for further construction of the regression equation.
Further conducted regression analysis between the effective rate and factor variables x1, x2, x3, x6, x7, x8, x13 using the Regression tool of Data analysis package in MS Excel.
To verify the formed regression model for adequacy, significance in general and regression coefficients, in addition to identifying correlation-regression relationships between performance and factor values, the coefficient of determination, multiple regression, standard error, and Student’s t-test were calculated. The multiple regression coefficient R = 0.998 indicates a close relationship between the performance indicator and the factors. Regarding the value of the determination coefficient R2 of the obtained correlation-regression model R2 = 0.996, the dependence of the resulting feature by 99.6% is due to the selected factor values. The remaining 0.4% are due to other factors that affect the efficiency of the enterprise, but are not included in the regression model.
According to the results of the multiple regression coefficient calculation and determination, the relationship between the resulting indicator and the factors-features is quite natural. Analysis of the constructed model variance shows that the factors included in the regression model are significant, as the residual sum of squares, as it is indicated in table 2, is less than 1% of the total sum of squares, i.e. 99% of the resulting indicator variation is due to the studied factors and only 1% variation of the resulting indicator is caused by the action of random variables. Since F (71.37)>Significance of F (0.013) and F (0.013)<0.05, it can be stated that the results of the regression model correspond to the empirical data and the number of independent variables included in the regression model is sufficient to describe the dependent variable. The values of F and the significance of F indicate a sufficient level of the evaluation results reliability and the significance of the developed equation.
Table 3 shows the results of regression coefficients calculations and statistics of their significance. Based on the calculations, we can conclude that all factors included in the regression model are reliable, because tK>tcrit, where the critical value of tcrit at the set level of significance is 0.15 and was determined using the STEERING function of the MS Excel. The data in table 3 allow obtaining the following regression model:
Y=0.8843–0.0047x1–0.0013x2+0.000000078x3+0.9470x6+0.0394x7+0.0519x8–0.8867x13 Thus, based on the results of the correlation-regression analysis, it can be concluded that the greatest influence on the oil transportation company business model efficiency is exerted by the following factors: average tariff on the transit transportation of petroleum products by main pipelines per 1 ton net; average tariff on the transportation of petroleum products to refineries by main pipelines, UAH for 1 ton net; residual value of fixed assets, thousand UAH; level of fixed assets depreciation; proportion of wage costs in the total costs, %; proportion of electricity costs in the total amount of costs, %; proportion of capital investment in equipment repair.
Therefore, among the obtained results, the level of fixed assets depreciation has the greatest positive influence on the resulting indicator. The growth of this indicator by 1 unit will increase the resulting figure by 0.94 units. The biggest negative impact on the industrial enterprise business model efficiency has the proportion of capital investment in equipment repair, namely the growth of this factor by 1 will reduce the efficiency of the business model by 0.88 units. We calculated the elasticity for each factor by the formula: = , (4) where bj – the corresponding coefficient of the regression equation; – the arithmetic mean of the variable xj; – the arithmetic mean of the endogenous variable yj.
In practice, it is often necessary to compare the separate effect on the dependent variable of different explanatory variables, when the latter ones are expressed in different units. In this case, the coefficients of elasticity are used. The coefficient of elasticity Ej shows how many percent Y will change on the average if xj is increased by 1%. Since further calculations require additional statistical indicators for the resulting feature and factors, we will use the Statistics package of MS Excel, the obtained intermediate calculations for further analysis are summarized in table 3.
Thus, based on the results of the elasticity coefficient factors calculation of the regression model, we can conclude that the growth of the average tariff on the transit transportation of petroleum products by main pipelines, UAH per 1 ton of net, by 1% will reduce the economic efficiency of the business model by 0.51%, and an increase in the average tariff on the transportation of petroleum products to refineries by main pipelines, UAH for 1 ton net, by 1% will reduce the economic efficiency by 0.04%. In this case, the management of the investigated enterprise should take measures to reduce the tariffs, especially their cost. Another factor that reduces the economic efficiency of the business model of the studied enterprise is the proportion of capital investment in equipment repair, its increase by 1% will reduce the resulting indicator by 0.44%. Of the eight factors studied, four have a positive effect on increasing the economic efficiency of the oil transportation company business model. In particular, an increase in the residual value of fixed assets by 1% will lead to an increase in the resulting indicator by 0.38%, which shows the need to update the fixed assets base. An increase in the level of fixed assets depreciation by 1% will increase the economic efficiency by 0.19%. An increase in the proportion of wage costs by 1% will increase the resulting indicator by 0.56%, and an increase in the proportion of electricity costs in total costs by 1% will increase the economic efficiency of the business model by 0.29%. Thus, the increase in the studied costs leads to an increase in the efficiency of the business model to a greater extent than the increase in the tariff on transportation, as it is proved by the developed correlation-regression model.
To determine the factors that have the greatest reserve for improving the resulting indicator, taking into account the degrees of the factors-indicators variation, we use the calculation of standardized regression β coefficients by the formula: = ∆ = where – quadratic mean deviation of the i-th factor-indicator; – quadratic mean deviation of the resulting indicator.
The delta coefficient indicates what part of the contribution the studied factor makes in the total influence of all selected factors. It is determined by the formula: where – matching correlation coefficient; – coefficient of multiple determination.
It should be noted that the increase in the number of factors-indicators, which are included in the multifactor correlation-regression model, allows establishing the additional resources of the studied resulting indicator.
The results of the obtained calculations of ∆ and β coefficients are summarized in table 4. (5) (6)
deviation coefficient Y – the enterprise business model efficiency 0.4030771 0.8842506 х1 – average tariff for transit transportation of oil products by main pipe- 86.4343677 -0.0047326 -1.0148456 lines, UAH for 1 ton net х2 – average tariff for transportation of oil products to refineries by main 32.6194806 -0.0013603 -0.0005133 pipelines, UAH for 1 ton net х3 – fixed assets residual value, thousand UAH 6456617.74 0.0000001 0.0154706 х6 – the level of fixed assets depreciation 0.2879776 0.9470437 0.0000000 х7 – the share of wage costs in the total costs, % 7.3646677 0.0394198 1.0081115 х8 – share of electricity costs in the total amount 2.8927203 0.0519409 0.0204015 of costs, % х13 – the share of capital investment in equipment 0.2479886 repairs, %
0.0570191 coeffici- Paired corre
lation coeffients cient ∆ coefficients
Based on the obtained results of calculating ∆j and βj coefficients, it can be concluded that the vectors of the calculated βj and coefficients are proved by the results of elasticity coefficients calculation in table 4. However, according to the results of the latter calculations, the greatest negative impact on the studied enterprise business model efficiency by the standard deviation is the factor of the average tariff on the transit transportation of petroleum products by main pipelines, UAH for 1 ton net. The greatest positive impact on the resulting indicator by the standard deviation is exerted by the factor the proportion of wage costs in the total amount of costs, %. Based on the results of ∆j calculation and coefficients, it can be concluded that this statistics are confirmed by the results of βj calculation and characteristics.
Recently, along with traditional methods of analyzing the socio-economic indicators, the use of neural networks, which belong to artificial intelligence systems, is becoming more widespread. After all, their scope is extremely large: forecasting changes in the stock exchange, making credit plans, making decisions when landing a damaged aircraft, approximating functions, solving optimization problems, managing complex processes, forecasting, etc. That is why the use of neural networks is relevant for the analysis of factors influencing the efficiency of oil transportation companies, along with traditional methods. In a series of recent works [3; 1; 14; 13], the authors have demonstrated the possibility of using the theory of complex systems and a set of developed analysis tools to calculate the corresponding measures of system complexity. These complexity measures make it possible to differentiate the systems according to the degree of their functionality, to identify and prevent critical and crisis phenomena.
Let us move on to the development of the neural network. For this purpose, use the module Neural Networks of the Statistica software package.
In the Neural Networks window, we set the following parameters: ─ type of problem – “Regression”; ─ as input parameters, according to the correlation analysis, we use: the average tariff on the transit transportation of petroleum products by the main pipelines for 1 t. net; average tariff on the transportation of petroleum products to refineries by main pipelines, UAH for 1 ton net; residual value of the fixed assets, thousand UAH; level of fixed assets depreciation; proportion of wage costs in the total amount of costs, %; proportion of electricity costs in the total amount of costs, %; proportion of capital investment in equipment repairs; ─ the economic efficiency is taken as the input parameter (the resulting feature). Then, with the help of the Solution Wizard, we go to the window for building Neural Networks.
In the Solution Wizard window, we set the parameters for creating neural networks: ─ test 100 networks and save only the top 5; ─ the types of neural network architectures, used for modeling, are multilayer perceptron, generalized regression network and linear network; ─ run the analysis. We analyze the results of neural network modeling aimed at maintaining regression based on the detailed model results (table 5). According to the correlation indicators, shown in table 6, the best model is the first model – a multilayer perceptron, with the architecture MLP 6-10-1. The MLP 6-10-1 architecture identifies a model with 6 input variables, one output variable and one hidden layer containing 10 hidden neurons. Having investigated and analyzed the results obtained in table 6, we can make conclude that the constructed models work evenly as the correlation coefficients of the test sample are approximately at the same level. The Statistica Neural Networks program provides the ability to analyze the sensitivity of the network to input variables. This procedure allows us to make conclusions about the relative importance of the input variables for a particular neural network and, if necessary, to remove the inputs with low sensitivity. Sensitivity analysis can be used for purely informational purposes, or to remove entries.
Sensitivity analysis brings some clarity to the usefulness of the certain variables. It allows identifying the key variables, without which analysis is impossible, and identifying those that can be safely excluded from consideration.
Therefore, let us analyze the results of the input variable sensitivity to the output variable and see which variables are included in our model (table 7), and the standard prediction of the neural network, i.e. the predicted values for the resulting variables in the proposed models (table 8).
Thus, we can see that the chosen model has minor errors, and therefore it can be called reliable. The rest predicted results of the neural network models calculations are presented in tables 9-11. х7 - the share x6 - the level of х7 – the share х2 – average tariff for х13 – the share of of electricity depreciation of of salary costs transportation of oil capital costs in total fixed assets; in total costs,%; products to refineries investment in costs,%; by main pipelines, equipment
UAH for 1 t. netto; repairs;
Predictions spreadsheet for Y - efficiency of the enterprises business model (Spreadsheet1_(Recovered)) Samples: Train, Test, Validation
Y - efficiency of Y - efficiency of Y - efficiency of the enterprises the enterprises the enterprises business model - business model - business model
Abs. Res. Abs. Res. Abs. Res. 1. MLP 6-10-1 2. MLP 6-7-1 3. MLP 6-11-1
Y - efficiency of the enterprises business model
Abs. Res. 4. MLP 6-5-1
Y - efficiency of the enterprises business model
Abs. Res. 5. MLP 6-7-1
Y - efficiency of the
enterprises business model
Abs. Res.
Ensemble efficiency based on the various developed architectural neural networks.
Predictions spreadsheet for Y - efficiency of the enterprises business model (Spreadsheet1_(Recovered)) Samples: Train, Test, Validation
Sample Y - efficiency of the Y - efficiency of the Y - efficiency of the Y - efficiency of the Y - efficiency of the Y - efficiency of enterprises enterprises business enterprises business enterprises business enterprises the enterprises business model - model - Squared model - Squared model - Squared business model - business model Squared Res. Res. Res. Res. Squared Res. Squared Res. 1. MLP 6-10-1 2. MLP 6-7-1 3. MLP 6-11-1 4. MLP 6-5-1 5. MLP 6-7-1 Ensemble efficiency based on the various developed architectural neural networks. Predictions spreadsheet for Y - efficiency of the enterprises business model (Spreadsheet1_(Recovered)) Samples: Train, Test, Validation
Sample
Train
Test Validation
Test Train
Train Validation Validation
Train Test
Based on the investigation of JSC “Ukrtransnafta” business model economic efficiency it is possible to draw conclusions that the accompanying process of digitalization of production for the given enterprise is characterized by a number of problems which arise both at initial stages and in adaptation process. In addition, it has been proposed to use a synergetic coefficient of digitalization efficiency, which takes into account the sequence of the enterprise business processes and partial indicators of digitalization efficiency of each individual business process. However, it cannot be calculated for the enterprise under study, as the processes at JSC “Ukrtransnafta” production digitalization are currently underway. Taking into account these circumstances, a system of the previously determined key determinants factorsfeatures of the impact on the company business model economic efficiency of the oil transportation enterprise and the resulting features has been developed. According to the analysis of the impact levels of the selected factors, the main ones have been determined, which have been used to develop the neural network models of the enterprise’s business model economic efficiency. The main factors influencing the business model efficiency of the studied enterprise are the average tariff on the transit transportation of petroleum products by main pipelines, UAH for 1 t. net; average tariff on the transportation of petroleum products to refineries by main pipelines, UAH for 1 t. net; level of fixed assets depreciation; proportion of salary costs in total costs, %; proportion of electricity costs in total costs, %; proportion of capital investment in equipment repair. The regression neural network analysis has proved six factors that can be used to predict the economy of the oil transportation company business model economic efficiency. The performed analysis of the neural networks sensitivity, which indicates the direction and strength of the influence of factors, will allow the surveyed enterprise to manage the economic efficiency, and the state, in its turn, to find directions for industry development.
Line Plot of multiple variables Predictions spreadsheet for Y - efficiency of the enterprises business model (Spreadsheet1_(Recovered)) in Workbook21 8v*10c 2,4 2,2 2,0 1,8 1,6 1,4 1,2 1,0 0,8 1 2 3 4 5 6 7 8 9 10
Y - efficiency of the enterprises business model
Y - efficiency of the enterprises business model - Output
Y - efficiency of the enterprises business model - Output
Y - efficiency of the enterprises business model - Output
Y - efficiency of the enterprises business model - Output
Y - efficiency of the enterprises business model - Output
Y - efficiency of the enterprises
business model - Output
Thus, comparing the results of this research work and the previous work [