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https://doi.org/10.29013/EJEMS -20-4-104-109
Kondratenko Diana Volodymyrivna, PhD in Economics, the Faculty of Economics and Management Kharkiv national university of civil engineering and architecture,
E-mail: [email protected] Chuprin Yevhen Sergiyovych, postgraduate, the Faculty of Economics and Management Kharkiv national university of civil engineering and architecture,
E-mail: [email protected]
NORMALIZING THE LEVEL OF ECONOMIC SECURITY OF ENTERPRISES
Abstract. The scientific article stated that for effective decision-making under uncertain operating conditions of the system, it is necessary to apply methods based on the rules of fuzzy logic. The author built optimization models of economic security indicators for road transport enterprises in order to determine their sufficient values for further normalization of the level of economic security.
Keywords: economic security, enterprise management, road transport enterprises, fuzzy logic, optimization model.
The relevance and importance of the problem of Melnyk O., Nikitina A., Parfentiieva A., Pohodyna V.
ensuring the economic security of the enterprise is reinforced by the fact that it occupies a significant place in the algorithm of ensuring the economic security of Ukraine. Ensuring the reliability of economic security of the enterprise is possible only by means of complex and systematic management of it.
Research on the economic security of enterprises received considerable attention in the works of economist scientists. Having analyzed the publications [1-8] of Vovk V., Didenko E., Dub B., Zakha-rov O., Iliashenko S., V., Prokhorova V., Khryniuk O., Shulga I. on the issue of economic security, it can be concluded that scientists are sufficiently versatile to interpret terms and approaches to the definition of economic security.
The assessment of the level of economic security of enterprises, including of road transport, is the focus of attention of many domestic and foreign scientists. Among them: Blyzniuk A., Duleba N., Yendrychka V., Kudriavtseva O., Levkovets N.,
[22-30]. Analysing the work of economist scientists, there is a significant difference of opinions on the process of assessing the level of economic security and ambiguity in the effectiveness of existing approaches.
In particular, a number of the most common approaches to assessing the level of economic security of enterprises can be identified:
- Resource and functional;
- Indicator (threshold);
- Special-purpose programme;
- Based on economic risk theory;
- Expert;
- Mathematical modelling in economics;
- Accounting;
- Matrix.
By comparing and highlighting the main advantages and disadvantages of each approach, the best and most effective methodology for assessing the economic security of road enterprises through early
warning tests has been developed. According to this methodology, 20 leading road enterprises of Ukraine were evaluated [31].
In order to achieve the practical value of the results obtained, the development of methodological bases for normalization of the level of economic security in order to adopt measures to adjust the performance indicators becomes relevant.
The operation of the system of ensuring the economic security of road transport enterprises is intended to achieve normalization of the level of economic security, which depends on the features of activity and interacting subjects of the external environment. The lack of clarity of the relationship between qualitative and quantitative characteristics requires the application of fuzzy logic and fuzzy set theory.
Decision-making in problem-oriented information systems and management systems is carried out in conditions of an expected uncertainty caused by inaccuracy or incomplete initial data, stochastic nature of external impacts, lack of adequate mathematical model of functioning, unclear goal, human factor, etc. [32-34] Uncertainty of the system leads to increased risks from inefficient decisions, which can result in negative economic, technical and social consequences.
Uncertainties in decision-making systems are compensated by various artificial intelligence techniques. For efficient decision-making under uncertain operating conditions, fuzzy logic methods are used. Such methods are based on fuzzy sets and use linguistic quantities and expressions to describe decision-making strategies [35-37].
Fuzzy methods are particularly useful in the absence of an accurate mathematical model of system functioning. Fuzzy set theory makes it possible to apply inaccurate and subj ective expert knowledge of the subject area to decision-making without formalizing them in the form of traditional mathematical models.
Fuzzy set theory techniques are a convenient
means of designing interfaces for decision-making systems. Based on fuzzy logical inference, systems of
control, presentation of knowledge, support of decision-making, approximation, structural and parametric identification, recognition of images, optimization are built. Fuzzy logic finds application in household electronics, diagnostics, various expert systems. Fuzzy expert systems to support decision-making find widespread application in military affairs, medicine and economics. They provide business forecasting, risk assessment and return on investment projects. Based on fuzzy logic, global policy decisions are investigated and crisis situations are modeled [38; 39].
The fuzzy system selects solutions based on the dependence of the output value on several input values. Suppose that there is no mathematical model of output dependence on inputs and instead a base of expert rules is used in the form of fuzzy statements "if-then" in terms oflinguistic variables and fuzzy sets.
Then functionality of fuzzy decision-making system is determined by such steps [40]:
1) conversion of clear input variables to fuzzy ones, i.e. determination of degree of conformity of inputs of each of fuzzy sets;
2) calculation of rules based on use of fuzzy operators and application of implication to obtain initial values of rules;
3) aggregation of fuzzy outputs of rules to the total initial value;
4) converting the fuzzy output of the rules to a clear value.
The system is designed according to the scheme of multilayer artificial neural network, which consists of input, two hidden and output layers. The first layer represents system inputs, the second layer shows fuzzy linguistic variables, the third layer represents rules over fuzzy variables, and the fourth layer shows rule outputs.
In practice, a maximin composition is used for fuzzy inference, and fuzzy implication is realized by finding the minimum of membership functions.
Fuzzy logic and fuzzy set theory form the basis for many methods of investigating and modeling systems, belong to the field of artificial intelligence.
MATLAB uses the Fuzzy Logic Toolbox to implement fuzzy modeling.
In order to carry out optimization modelling of normalization of the level of economic security of road transport enterprises, RTE with the highest indicators of the level of economic security were selected, which was evaluated by means of early warning tests [31]. These RTE include:
- Gollner Expedition PJSC;
- Vneshtrans PJSC;
- Kirovohrad Transport Company PJSC.
The modeling includes economic security indicators for 2020 with a level below "normal" in order to determine their optimal level for normalization of economic security of road transport enterprises.
The next step is to define linguistic terms for selected indicators. For our modeling, the following linguistic terms (T) are adopted, which correspond to the levels of economic security of road transport enterprises:
- critical;
- unsatisfactory;
- satisfactory;
- normal;
- absolute.
Further, the membership function of linguistic terms are determined. The membership function represents elements from a universal set of a certain linguistic variable into a set of numbers in a given interval, indicating the degree of membership of each element of the universal set to a fuzzy term. In some cases, typical forms of membership functions (in parametric form) are used, then the task of construction is to determine its parameters [14].
Triangular, trapezoidal, Gaussian and sigmoidal membership functions were most common. The specific type of function is determined by the needs of the subject area under study.
In practice, it is convenient to use those membership functions that allow analytic representation in the form of some simple mathematical function. This simplifies not only the corresponding numeri-
cal calculations, but also reduces the computational resources required to store the individual values of these membership functions [41].
The output in the form of Mamdani 's algorithm takes into account the following parameters:
For the logical conjunction method (And method), which allows to specify one of the following methods for performing a logical conjunction under fuzzy rule conditions, the minimum value method (min) was selected.
For the logical disjunction method (Or method), which allows to specify one of the following methods for performing logical disjunction under fuzzy rules conditions, the maximum value method (max) was selected.
For the Implementation method, which allows to specify one of the following methods for executing (activating) a logical output in each of the fuzzy rules, a minimum value (min) method has been assigned.
For Aggregation method, which allows to specify one of the following methods for aggregation the membership function values of each of the output variables in the fuzzy rule outputs a method of algebraic sum has been assigned (probor).
For Defuzzification method, which allows to specify one of the following methods for defuzzification output variables in a Mamdani-type fuzzy system is selected the method mom middle of maximum, which is defined as the arithmetic mean of the left and right modal values;
For Mamdani 's fuzzy output, a plurality of rules are generated on the basis ofwhich the model will be built. To model the operation of the expert system according to the implication scheme, a number of fuzzy product rules are used, each ofwhich is built in the form of a conditional operator (if-then).
In total, rules can include all possible combinations of linguistic terms for all input variables combined by logical operations.
The output results are presented as a model that allows to determine whether the quantity of the
components is sufficient to achieve the required level of the resulting indicator.
The construction of an optimization model of the level of economic security for the road transport enterprises under study makes it possible to draw conclusions about the optimal indicators for normalization.
Thus, in order to normalize the level of economic security indicators of Gollner Expedition PJSC it is necessary: to achieve growth of profitability of transport from 0.04 to 0.37, to reduce the level of absolute liquidity from 1.43 to 0.227, to increase the value of profitability indicator of assets from 0.09 to 0.42, coefficient of coverage from 1.93 to 3.25, asset turnover ratio from 2.43 to 4.06.
For Vneshtrans PJSC in order to normalize the level of economic security indicators, it is necessary to increase the profitability of transportation from 0.07 to 0.38, achieve reduction of absolute liquidity from 1.15 to 0.229, achieve asset profitability growth from 0.09 to 0.42, asset turnover ratio from 1.83 to 4.05, reduce the wear rate of fixed assets from 0.68 to 0.211, increase the level of compliance with current legislation in the enterprise from 0.63 to 0.95.
To normalize the level of economic security indicators of Kirovohrad Transport Company PJSC it
is necessary: to increase the level of profitability of transportation from 0.07 to 0.41, to reduce the level of absolute liquidity from 1.36 to 0.226, increase the level of compliance with current legislation from 0.61 to 0.77, to achieve increase of level of work discipline from 0.65 to 0.95, to reduce wear rate of fixed assets from 0.87 to 0.4, to increase the level of ability to establish control of the enterprise by outsiders from 0.66 to 0.75, to achieve an increase in the level of state support for RTE from 0.59 to 0.95.
Thus, the study found that fuzzy logic-based methods were needed to make decisions effectively when the operating conditions of the system were uncertain. The most efficient and practical application of Mamdani 's algorithm is to obtain logical output. Optimization models of economic security indicators for road transport enterprises were constructed to determine their optimal values. Normalization of the level of economic security implies the development of a set of measures to optimize such indicators as profitability of transportation, absolute liquidity, the level of compliance with current legislation in the enterprise, level of work discipline, wear rate of fixed assets, ability to establish control of the enterprise by outsiders, level of state support of MTE.
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