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Assessment of the competitiveness of industrial enterprise activitie

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UDK 332.13:330.13

Cherep Ⱥ.

Doctor of Economics, Professor, Professor of Finance and Credit, Banking and Insurance,

Dean of the Faculty of Economics;

Zaporizhia National University, Ukraine;

ɟ-mail: [email protected]; ORCID ID: 0000-0001-5253-748 Beridze T.

Doctor of Economics, Associate Professor, Associate Professor of the Department of Automated Electromechanical Systems in Industry

and Transport Kryvyiy Rih National University, Ukraine;

ɟ-mail: ɜ[email protected]; ORCID ID: 0000-0003-2509-3242 Baranik Z.

Doctor of Economics, Professor, Professor of the Department of Mathematical Modeling and Statistics, Kyiv National Economics University named after Vadym Hetman, Ukraine;

ɟ-mail: [email protected]; ORCID ID 0000-0002-9753-4572 Korɟnyev V.

Doctor of Economics, Ɋrofessor, Professor of the Department of Personnel Management and Marketing, Zaporizhia National University, Ukraine;

ɟ-mail: [email protected]; ORCID ID: 0000-0003-4184-2573 Dashko I.

Candidate of Economic Sciences, Associate Professor, Associate Professor of the Department of Personnel Management and Marketing, Zaporizhia National University, Ukraine;

ɟ-mail: [email protected]; ORCID ID: 0000-0001-5784-4237 ASSESSMENT OF THE COMPETITIVENESS

OF INDUSTRIAL ENTERPRISE ACTIVITIE

Abstract. The purpose of the article is to study and analyze the competitive status of industrial enterprises (on the example of enterprises in the Kryvyi Rih region). Determining the long-term forecast of competitiveness on the basis of extrapolation of performance indicators of an industrial enterprise with the required accuracy. A methodological approach to long-term forecasting of competitiveness on the basis of extrapolation and the use of identification of discrete time series, which allowed to determine the predictive values of factors influencing the competitive status of the enterprise to make effective strategic management decisions. Modern methods of making effective strategic decisions are largely based on the use of forecasting methods, using appropriate statistical material. At the same time, such an approach requires the fulfillment of conditions, the neglect of which leads to the distortion of the obtained conclusions. In particular, this applies to the requirements relating to the identification of discrete time series. For the first time, the application of discrete time series identification is proposed, which is the basis for determining the forecast indicators of enterprise competitiveness on the basis of extrapolation.

Analytical dependences on the competitive position of the industrial enterprise of the Kryvyi Rih region and the corresponding factors of influence are constructed. The main components of the impact on competitiveness are analyzed: sales volume; net profit; market share in the product market; intensity of competition in the industry; the ratio of market share of the enterprise being analyzed to the market leader. The results of the study are used in the practice of managers of relevant enterprises in making effective decisions in the system of strategic management. The use of identification of discrete time series allowed to conduct an appropriate assessment of the competitive status of the enterprise and the relevant factors of influence. It is offered to consider the

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competitive status, to an information and analytical component of competitiveness of the industrial enterprise.

Keywords: enterprise, competitive status, forecast, identification, time series, extrapolation.

JEL Classification C19, D29

Formulas: 6; fig.: 4; tabl.: 2; bibl.: 31.

ɑɟɪɟɩ Ⱥ. ȼ.

ɞɨɤɬɨɪ ɟɤɨɧɨɦɿɱɧɢɯ ɧɚɭɤ, ɩɪɨɮɟɫɨɪ, ɩɪɨɮɟɫɨɪ ɤɚɮɟɞɪɢ ɮɿɧɚɧɫɿɜ ɬɚ ɤɪɟɞɢɬɭ, ɛɚɧɤɿɜɫɶɤɨʀ ɫɩɪɚɜɢ ɬɚ ɫɬɪɚɯɭɜɚɧɧɹ, ɞɟɤɚɧ ɟɤɨɧɨɦɿɱɧɨɝɨ ɮɚɤɭɥɶɬɟɬɭ, Ɂɚɩɨɪɿɡɶɤɢɣ ɧɚɰɿɨɧɚɥɶɧɢɣ ɭɧɿɜɟɪɫɢɬɟɬ, ɍɤɪɚʀɧɚ;

ɟ-mail: [email protected]; ORCID ID: 0000-0001-5253-7481 Ȼɟɪɿɞɡɟ Ɍ. Ɇ.

ɞɨɤɬɨɪ ɟɤɨɧɨɦɿɱɧɢɯ ɧɚɭɤ, ɞɨɰɟɧɬ, ɞɨɰɟɧɬ ɤɚɮɟɞɪɢ ɚɜɬɨɦɚɬɢɡɨɜɚɧɢɯ ɟɥɟɤɬɪɨɦɟɯɚɧɿɱɧɢɯ ɫɢɫɬɟɦ ɭ ɩɪɨɦɢɫɥɨɜɨɫɬɿ

ɬɚ ɬɪɚɧɫɩɨɪɬɿ, Ʉɪɢɜɨɪɿɡɶɤɢɣ ɧɚɰɿɨɧɚɥɶɧɢɣ ɭɧɿɜɟɪɫɢɬɟɬ, ɍɤɪɚʀɧɚ;

ɟ-mail: [email protected]; ORCID ID: 0000-0003-2509-3242 Ȼɚɪɚɧɢɤ Ɂ. ɉ.

ɞɨɤɬɨɪ ɟɤɨɧɨɦɿɱɧɢɯ ɧɚɭɤ, ɩɪɨɮɟɫɨɪ, ɩɪɨɮɟɫɨɪ ɤɚɮɟɞɪɢ ɦɚɬɟɦɚɬɢɱɧɨɝɨ ɦɨɞɟɥɸɜɚɧɧɹ ɿ ɫɬɚɬɢɫɬɢɤɢ, ȾȼɇɁ «Ʉɢʀɜɫɶɤɢɣ ɧɚɰɿɨɧɚɥɶɧɢɣ ɟɤɨɧɨɦɿɱɧɢɣ ɭɧɿɜɟɪɫɢɬɟɬ ɿɦɟɧɿ ȼɚɞɢɦɚ Ƚɟɬɶɦɚɧɚ», ɍɤɪɚʀɧɚ;

ɟ-mail: [email protected]; ORCID ID: 0000-0002-9753-4572 Ʉɨɪɿɧɽɜ ȼ. Ʌ.

ɞɨɤɬɨɪ ɟɤɨɧɨɦɿɱɧɢɯ ɧɚɭɤ, ɩɪɨɮɟɫɨɪ.

ɩɪɨɮɟɫɨɪ ɤɚɮɟɞɪɢ ɭɩɪɚɜɥɿɧɧɹ ɩɟɪɫɨɧɚɥɨɦ ɿ ɦɚɪɤɟɬɢɧɝɭ, Ɂɚɩɨɪɿɡɶɤɢɣ ɧɚɰɿɨɧɚɥɶɧɢɣ ɭɧɿɜɟɪɫɢɬɟɬ, ɍɤɪɚʀɧɚ;

ɟ-mail: [email protected]; ORCID ID: 0000-0003-4184-2573 Ⱦɚɲɤɨ ȱ. Ɇ.

ɤɚɧɞɢɞɚɬ ɟɤɨɧɨɦɿɱɧɢɯ ɧɚɭɤ, ɞɨɰɟɧɬ, ɞɨɰɟɧɬ ɤɚɮɟɞɪɢ ɭɩɪɚɜɥɿɧɧɹ ɩɟɪɫɨɧɚɥɨɦ ɿ ɦɚɪɤɟɬɢɧɝɭ,

Ɂɚɩɨɪɿɡɶɤɢɣ ɧɚɰɿɨɧɚɥɶɧɢɣ ɭɧɿɜɟɪɫɢɬɟɬ, ɍɤɪɚʀɧɚ;

ɟ-mail: [email protected]; ORCID ID: 0000-0001-5784-4237 ɈɐȱɇɘȼȺɇɇə ɄɈɇɄɍɊȿɇɌɈɋɉɊɈɆɈɀɇɈɋɌȱ ȾȱəɅɖɇɈɋɌȱ

ɉɊɈɆɂɋɅɈȼɈȽɈ ɉȱȾɉɊɂȯɆɋɌȼȺ

Ⱥɧɨɬɚɰɿɹ. Ɇɟɬɨɸ ɫɬɚɬɬɿ ɽ ɞɨɫɥɿɞɠɟɧɧɹ ɿ ɚɧɚɥɿɡ ɤɨɧɤɭɪɟɧɬɧɨɝɨ ɫɬɚɬɭɫɭ ɩɪɨɦɢɫɥɨɜɢɯ ɩɿɞɩɪɢɽɦɫɬɜ (ɧɚ ɩɪɢɤɥɚɞɿ ɩɿɞɩɪɢɽɦɫɬɜ Ʉɪɢɜɨɪɿɡɶɤɨɝɨ ɪɟɝɿɨɧɭ); ɜɢɡɧɚɱɟɧɧɹ ɩɟɪɫɩɟɤɬɢɜɧɨɝɨ ɩɪɨɝɧɨɡɭ ɤɨɧɤɭɪɟɧɬɨɫɩɪɨɦɨɠɧɨɫɬɿ ɧɚ ɡɚɫɚɞɚɯ ɟɤɫɬɪɚɩɨɥɹɰɿʀ ɩɨɤɚɡɧɢɤɿɜ ɞɿɹɥɶɧɨɫɬɿ ɩɪɨɦɢɫɥɨɜɨɝɨ ɩɿɞɩɪɢɽɦɫɬɜɚ ɡ ɧɟɨɛɯɿɞɧɨɸ ɬɨɱɧɿɫɬɸ. ɋɮɨɪɦɨɜɚɧɨ ɦɟɬɨɞɨɥɨɝɿɱɧɢɣ ɩɿɞɯɿɞ ɞɨ ɩɟɪɫɩɟɤɬɢɜɧɨɝɨ ɩɪɨɝɧɨɡɭɜɚɧɧɹ ɤɨɧɤɭɪɟɧɬɨɫɩɪɨɦɨɠɧɨɫɬɿ ɧɚ ɡɚɫɚɞɚɯ ɟɤɫɬɪɚɩɨɥɹɰɿʀ ɬɚ ɜɢɤɨɪɢɫɬɚɧɧɹ ɿɞɟɧɬɢɮɿɤɚɰɿʀ ɞɢɫɤɪɟɬɧɢɯ ɱɚɫɨɜɢɯ ɪɹɞɿɜ, ɳɨ ɞɨɡɜɨɥɢɥɨ ɜɢɡɧɚɱɢɬɢ ɩɪɨɝɧɨɡɧɿ ɡɧɚɱɟɧɧɹ ɱɢɧɧɢɤɿɜ ɜɩɥɢɜɭ ɧɚ ɤɨɧɤɭɪɟɧɬɧɢɣ ɫɬɚɬɭɫ ɩɿɞɩɪɢɽɦɫɬɜɚ ɡɚɞɥɹ ɭɯɜɚɥɟɧɧɹ ɟɮɟɤɬɢɜɧɢɯ ɫɬɪɚɬɟɝɿɱɧɢɯ ɭɩɪɚɜɥɿɧɫɶɤɢɯ ɪɿɲɟɧɶ. ɋɭɱɚɫɧɿ ɦɟɬɨɞɢ ɳɨɞɨ ɭɯɜɚɥɟɧɧɹ ɟɮɟɤɬɢɜɧɢɯ ɫɬɪɚɬɟɝɿɱɧɢɯ ɪɿɲɟɧɶ ɡɧɚɱɧɨɸ ɦɿɪɨɸ ɫɩɢɪɚɸɬɶɫɹ ɧɚ ɡɚɫɬɨɫɭɜɚɧɧɿ ɦɟɬɨɞɿɜ ɩɪɨɝɧɨɡɭɜɚɧɧɹ, ɤɨɪɢɫɬɭɸɱɢɫɶ ɜɿɞɩɨɜɿɞɧɢɦ ɫɬɚɬɢɫɬɢɱɧɢɦ ɦɚɬɟɪɿɚɥɨɦ. Ɋɚɡɨɦ ɡ ɰɢɦ ɬɚɤɢɣ ɩɿɞɯɿɞ ɩɨɬɪɟɛɭɽ ɜɢɤɨɧɚɧɧɹ ɭɦɨɜ, ɧɟɯɬɭɜɚɧɧɹ ɹɤɢɦɢ ɩɪɢɡɜɨɞɢɬɶ ɞɨ ɫɩɨɬɜɨɪɟɧɧɹ ɨɞɟɪɠɭɜɚɧɢɯ ɜɢɫɧɨɜɤɿɜ.

Ɂɨɤɪɟɦɚ, ɰɟ ɫɬɨɫɭɽɬɶɫɹ ɜɢɦɨɝ ɳɨɞɨ ɿɞɟɧɬɢɮɿɤɚɰɿʀ ɞɢɫɤɪɟɬɧɢɯ ɱɚɫɨɜɢɯ ɪɹɞɿɜ. ɍɩɟɪɲɟ ɡɚɩɪɨɩɨɧɨɜɚɧɨ ɡɚɫɬɨɫɭɜɚɧɧɹ ɿɞɟɧɬɢɮɿɤɚɰɿʀ ɞɢɫɤɪɟɬɧɢɯ ɱɚɫɨɜɢɯ ɪɹɞɿɜ, ɳɨ ɽ ɩɿɞʉɪɭɧɬɹɦ ɜɢɡɧɚɱɟɧɧɹ ɩɪɨɝɧɨɡɧɢɯ ɩɨɤɚɡɧɢɤɿɜ ɤɨɧɤɭɪɟɧɬɨɫɩɪɨɦɨɠɧɨɫɬɿ ɩɿɞɩɪɢɽɦɫɬɜɚ ɧɚ ɡɚɫɚɞɚɯ ɟɤɫɬɪɚɩɨɥɹɰɿʀ. ɉɨɛɭɞɨɜɚɧɨ ɚɧɚɥɿɬɢɱɧɿ ɡɚɥɟɠɧɨɫɬɿ ɳɨɞɨ ɤɨɧɤɭɪɟɧɬɧɨʀ ɩɨɡɢɰɿʀ ɩɪɨɦɢɫɥɨɜɨɝɨ ɩɿɞɩɪɢɽɦɫɬɜɚ Ʉɪɢɜɨɪɿɡɶɤɨɝɨ ɪɟɝɿɨɧɭ ɿ ɜɿɞɩɨɜɿɞɧɢɯ ɱɢɧɧɢɤɿɜ ɜɩɥɢɜɭ. ɉɪɨɚɧɚɥɿɡɨɜɚɧɨ ɨɫɧɨɜɧɿ

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ɫɤɥɚɞɨɜɿ ɜɩɥɢɜɭ ɧɚ ɤɨɧɤɭɪɟɧɬɨɫɩɪɨɦɨɠɧɿɫɬɶ: ɨɛɫɹɝ ɪɟɚɥɿɡɚɰɿʀ; ɱɢɫɬɢɣ ɩɪɢɛɭɬɨɤ; ɪɢɧɤɨɜɚ ɱɚɫɬɤɚ ɧɚ ɪɢɧɤɭ ɩɪɨɞɭɤɰɿʀ; ɿɧɬɟɧɫɢɜɧɿɫɬɶ ɤɨɧɤɭɪɟɧɰɿʀ ɜ ɝɚɥɭɡɿ; ɜɿɞɧɨɲɟɧɧɹ ɪɢɧɤɨɜɨʀ ɱɚɫɬɤɢ ɩɿɞɩɪɢɽɦɫɬɜɚ, ɳɨ ɚɧɚɥɿɡɭɽɬɶɫɹ? ɞɨ ɥɿɞɟɪɚ ɪɢɧɤɭ. Ɉɬɪɢɦɚɧɿ ɪɟɡɭɥɶɬɚɬɢ ɞɨɫɥɿɞɠɟɧɧɹ ɜɢɤɨɪɢɫɬɚɧɨ ɭ ɩɪɚɤɬɢɱɧɿɣ ɞɿɹɥɶɧɨɫɬɿ ɭɩɪɚɜɥɿɧɰɿɜ ɜɿɞɩɨɜɿɞɧɢɯ ɩɿɞɩɪɢɽɦɫɬɜ ɩɪɢ ɭɯɜɚɥɟɧɧɿ ɟɮɟɤɬɢɜɧɢɯ ɪɿɲɟɧɶ ɭ ɫɢɫɬɟɦɿ ɫɬɪɚɬɟɝɿɱɧɨɝɨ ɭɩɪɚɜɥɿɧɧɹ. ȼɢɤɨɪɢɫɬɚɧɧɹ ɿɞɟɧɬɢɮɿɤɚɰɿʀ ɞɢɫɤɪɟɬɧɢɯ ɱɚɫɨɜɢɯ ɪɹɞɿɜ ɞɨɡɜɨɥɢɥɨ ɩɪɨɜɟɫɬɢ ɜɿɞɩɨɜɿɞɧɟ ɨɰɿɧɸɜɚɧɧɹ ɤɨɧɤɭɪɟɧɬɧɨɝɨ ɫɬɚɬɭɫɭ ɩɿɞɩɪɢɽɦɫɬɜɚ ɿ ɜɿɞɩɨɜɿɞɧɢɯ ɮɚɤɬɨɪɿɜ ɜɩɥɢɜɭ. ɉɪɨɩɨɧɭɽɦɨ ɪɨɡɝɥɹɞɚɬɢ ɤɨɧɤɭɪɟɧɬɧɢɣ ɫɬɚɬɭɫ ɹɤ ɿɧɮɨɪɦɚɰɿɣɧɨ-ɚɧɚɥɿɬɢɱɧɭ ɫɤɥɚɞɨɜɭ ɤɨɧɤɭɪɟɧɬɨɫɩɪɨɦɨɠɧɨɫɬɿ ɩɪɨɦɢɫɥɨɜɨɝɨ ɩɿɞɩɪɢɽɦɫɬɜɚ

Ʉɥɸɱɨɜɿ ɫɥɨɜɚ: ɩɿɞɩɪɢɽɦɫɬɜɨ, ɤɨɧɤɭɪɟɧɬɧɢɣ ɫɬɚɬɭɫ, ɩɪɨɝɧɨɡ, ɿɞɟɧɬɢɮɿɤɚɰɿɹ, ɟɤɫɬɪɚɩɨɥɹɰɿɹ, ɱɚɫɨɜɢɣ ɪɹɞ.

Ɏɨɪɦɭɥ: 6; ɪɢɫ.: 4; ɬɚɛɥ.: 2; ɛɿɛɥ.: 31.

The problem statement. Current market conditions of industrial enterprises functioning require careful management as a means of not only surviving but also their further development. At the present stage of economic development, establishment and efficient use of certain competitive advantages facilitate accelerated development of production forces, scientific and technological progress, intensification of relations between countries’ economies. Extrapolation of the enterprise’s condition as «one of the most widely spread means of short-term forecasting of economic phenomena» (hereinafter courtesy translation) [1; 2] is one of pledges of achieving corresponding goals. In its wide meaning, extrapolation is known to be «a forecasting method that consists in studying stable trends of past and current processes and phenomena development and extending them into the future» [3—5]. Narrowly defined, extrapolation is determination of function values beyond the series using a series of the function data. In forecasting, extrapolation is applied to studying time series. There exist various methods of forecasting extrapolation which can be divided into complex and simple ones. Simple methods are based on the assumption that absolute values of levels, the average series level, the average absolute growth, the average growth rate are relatively stable in the future. Complex methods provide for spotting the main trend, i.e. application of analytical relations describing the trend. Methods of this group can be divided into analytical and adaptive [6—8].

The characteristic feature of extrapolation in the narrow meaning is that it can lean heavily on a rather small amount of data, this being often connected with properties of processes under study. When it comes to industrial enterprises, data volumes are not large as a rule, this being primarily explained by information being provided in the form of discrete time series with the time interval of one year. Due to this, application of mathematical statistics methods and conclusions while extrapolating is not reasonable and results in considerable errors. When making this kind of calculations, it is reasonable to base on point forecasts as determination of interval forecasts requires mathematical statistics methods. That is why, it is considered viable to extrapolate with the help of thorough studies of data features. Moreover, experts’ opinions must be taken into account while analyzing forecasting data. The Ukrainian society information processes require comprehensive forecasting of enterprises’ activity results in order to provide their corresponding competitiveness. Competitiveness of the enterprise potential provides for gaining profits due to new opportunities on the basis of studies of uncertainty factors and consideration of various risks.

Extrapolation allows for expansion of regularities of basic indicators formation beyond the current time interval and provision of scientific substantiation for efficient decision-making. The article aims to study and determine a competitive position and prospective forecasting of competitiveness on the basis of extrapolation of enterprise activity indicators with the necessary accuracy.

Analysis of the last research and publications. Processes of transformation of production activities at enterprises require forecasting competitiveness and development on the whole.

Economic policies of enterprises should be transparent and forecast.

In scientific literature there are many works devoted to forecasting results of enterprise activities. Forecasting methods and models are studied in works by L.I. Brovko, O.A. Chepiha [9]

where the essence of financial forecasting is substantiated and its methods and stages are

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determined. T. A. Vasylieva, V. O. Kasianenko and L. S. Zakharkina [10] analyze efficiency of measures for governmental stimulation of innovative activities and study dynamics of budgeting innovative activities and transfer of technologies by strategic priorities. Ɉ. Zhuk investigates issues of revenue forecasting and its place in activities of an enterprise and performs extrapolation-based forecasting of the enterprise’s earnings [11].

Works [12—14] demonstrate that calculating forecast financial and economic indicators is important for developing a scientifically substantiated strategy of providing competitiveness and enhancing the financial condition of an enterprise in the future [15] investigates the essence and importance of financial forecasting for establishing sustainable financial development of enterprises in Ukraine and determines priority factors of influence of forecast values on managerial decision- making to minimize financial risks of an enterprise. Forecasting modeling is dealt with in [16—18]

which consider main business trends in both Ukrainian and foreign markets [19; 20] pay attention to methods of analyzing competitive advantages that enable measuring and forecasting competitiveness of industrial enterprises, assessing their competitiveness increase potential, studying the most appropriate directions of its increase. The most common indicators used as the most important characteristics of the competitiveness level are dealt with and strategies for adequate response to market volatility are presented in [21]. The authors of [22] suggests the system for evaluating both the level of development and efficiency of a country’s industry and model their influence on the social and economic development. In [23], return on fixed assets of Ukraine’s agriculture is modeled applying extrapolation and forecast values of the indicator are calculated.

Research by Yu. A. Hajbura and L. A. Zahnitko determines prospective forecasting of economic indicators and the mechanism of using the obtained results in strategic management of an enterprise [24]. In [24—26], the authors state that economic forecasting consists in determining and studying principles, the structure, relations and mechanisms of social and economic processes. However, despite a great number of scientific works, the problem of analyzing and forecasting results of activities of industrial enterprises remains of relevance.

Purpose of the article. The purpose of the article is to study and determine the competitive position and long-term forecast of competitiveness on the basis of extrapolation of enterprise performance indicators with the required accuracy.

Results of a research. The actual market share in the total sales volume of particular products is one of the most common manifestations of the degree of competitive goals achievement.

It reflects important results of competition and shows the degree of the dominance of the enterprise on the market.

The competitive position of the j-th enterprise on the market ሾܯ௄௉ೕሺ೟ሻሿ at the time moment t = ti can be determined as follows [27; 28]:

ܯ௄௉ೕሼ೟ሻ ൌ ሼ݀ଵ௝ሺݐሻǡ ݀ଶ௝ሺݐሻǡ ݀ଷ௝ሺݐሻሽ t = t,

where ݀ଵ௝ሺݐሻ is the indicator describing a market share of the j-th enterprise in the strategic zone of economic activities at the time moment t = ti (formula 1);

݀ଶ௝ሺݐሻ is the indicator describing competitive intensity in the industry of the j-th enterprise (formula 2);

݀ଷ௝ሺݐሻ is the indicator describing the position of the j-th enterprise in relation to the leader of the industry (formula 3).

The market share of the j-th enterprise in the z-th industry

݀೔ೕ

೔೥, (1)

whereܸ௜௭ is the sales volume of the i-th products of all enterprises-competitors in the z-th industry.

Intensity of competitiveness of the i-th products in the z-th industry (݀):

݀೔భା௏೔మା௏೔యା௏೔ర

೔೥ , (2)

where ܸ௜ଵ௜ଶ௜ଷ, ܸ௜ସ are the sales volume of the i-th products of the first, second, third and fourth enterprises in the z-industry respectively.

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The market share of the enterprise under analysis to the z-th industry leader relation (݀):

݀೔ೕ

೔భ, (3) where ܸ௜ଵ is the sales volume of the i-th products of the enterprise-leader in z-th industry.

The status of the j-th enterprise is specified by a number of indicators characterizing this or that component. Thus, the next set is established in this way [28]:

ܸ ൌ ൫ܸଵ௝ǡ ܸଶ௝ǡ ǥ ǡ ܸ௡௝൯,

where ܸ௜௝ is the volume of the i-th products sold by the j-th enterprise, ݅ ൌ ͳǡ ݊തതതതത , ݊ is the number of product types, ݆ ൌ ͳǡ ݉തതതതതത, ݉ — is the number of enterprises.

The initial data is presented as the matrix ܸ= (ܸ௜௝). As the indicators under consideration may be of different dimensions, they should be standardized.

This indicator is a quantitative evaluation of the competitive status level for every enterprise by the given set of components. It enables determining the lifecycle stage of the enterprise at a particular time moment. Then the obtained data is ranked and interpreted.

Relevant studies and calculations have been performed for mining enterprises of Ukraine.

The status of enterprises was evaluated according to the suggested methods applying the following indicators [27]:

- the market share of the enterprise on the product market (d1);

- competitive intensity in the industry (d2);

- the market share of the enterprise under study to that of the market leader relation (d3) [27].

At the first stage of evaluating the competitive status at Ukrainian mining enterprises, the most important components of the earlier enterprise competitive status evaluation are analyzed.

The enterprises chosen are located in various parts of Ukraine. As enterprises of Kryvyi Rih dominate the industry, the priority is given to them. The following enterprises were chosen: the PJSC «Inhulets GZK», the PJSC «Central GZK», the OJSC «Southern GZK» the OJSC «Northern GZK», the PJSC «KZRK», the PJSC «Evraz Sukha Balka» and the OJSC «Poltava GZK».

Calculation of the integrated indicator d for each of the mentioned enterprises is the final step. This indicator is a quantitative evaluation of the competitive status level by the given set of components. Every enterprise has its own specific features without consideration of which the required accuracy of extrapolation cannot be achieved [29]. This refers to the full extent to mining and concentration enterprises of Ukraine.

Table 1 presents indicators of the competitive position of the PrJSC «KZRK» [30; 31].

Table 1 Competitive position indicators, PJSC «KZRK»

Years

Indicators 2014 2015 2016 2017 2018 2019

Sales volume, 000 UAH 2111434 1227289 3108511 4291290 3289691 3695740 Net profit, 000 UAH 1050379 200835 1196743 1743515 772156 819871 Share on product market

(d1) 0.073 0.072 0.105 0.070 0.060 0,027

Competitive intensity

in industry (d2) 0.862 0.937 0.687 0.819 0.894 0,694

Enterprise market share to that of market leader relation (d3)

0.222 0.161 0.370 0.265 0.162 0,098

Note: calculated on the basis of [30; 31].

To determine specificity of the indicators in Table 1 as time series, let us present them graphically. Analysis of the graphs in Fig. 1 demonstrates periodicity of sales volumes and net profits within the period under review. The graphs in Fig. 2a, 2b, 2c on time respectively present dependency of indicators d1, d2, d3 of the competitive position forecast of the PrJSC «KZRK».

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Fig. 1. S profit, 0

Note

Fig. 2b.

position i

Note

Ana are stocha deviation o

where ݔ෤௧ି

coefficient It is looks like Coe of the sum

Tab Table 1.

Sales volum 000 UAH —

e: developed by

. Dependen ndicator d2

on

e: developed by

alysis of the astic linear

of the time s

ି௞ ൌ ݔ௧ି௞െ ts; ܽȄis t

s observed t

efficients in of squared

ble 2 prese

me, 000 UA

— 2 (PrJSC

y the authors

ncy of the c

2 of the PrJ n time

y the authors

e graphs in processes.

series from

െ ݔҧǢݔҧ is th the residual

that for the

n formula (2 deviations

ܵሺ ents results

H — 1; net C «KZRK»)

competitive JSC «KZRK

Fig. 1 and This enabl the earlier d ݔ෤ ൌ ɀ൅ he time se

error.

graphs in F ݔ෤ ൌ 2) are determ by the coef ሺɀǡ ɀሻ ൌ σ

of identific

t )

Fig posi

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Fig. 2a, 2b les their pr deviation av

൅ ɀݔ෤௧ିଵ൅ ɀ eries averag

Fig. 1 and 2

ൌ ɀ൅ ߛݔ෤

mined by th fficients ߛǡ σ௞ୀଷ൫ݔ෤െ cation of d

g. 2. a) Dep ition indica

Note: de

ig. 2c. Depe tion indicat

Note: de

, 2c shows resentation verage (form

ɀݔ෤௧ିଶ൅ ڮ ge value; ߛ 2 the most a ݔ෤௧ିଷ൅ ܽǤ he least squ

ɀ (formula ߛെ ɀݔ෤ discrete tim

pendency o ator d1 of th

on time

eveloped by the

endency of tors d3 of th

on time

eveloped by the

that the giv as the regr mula 4).

ڮ ൅ ܽ, ߛ, ߛ, ߛǡ appropriate

uare method

a 6):

ିଷǤ me series ba

f the comp he PrJSC «K

e

e authors

f the compe he PrJSC « e

e authors

ven discrete ression of

…are the model of a

d through m

ased on the

etitive KZRK»

etitive

«KZRK»

time series the current (4) numerical time series (5) minimization

(6) e data from s

t ) l s ) n

) m

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Table 2 Results of discrete time series identification

Indicators of competitive position, PrJSC

«KZRK» Mathematical model of discrete time series

Sales volume, 000 UAH ݔൌ ͳͺͳͶͷʹͳ ൅ ͳǤͳ͹ͷ ή ݔିଷ

Net profit, 000 UAH ݔൌ ͺʹͶͻͶͻ ൅ ͲǤͺ͸ͺ ή ݔିଷ

Share on product market ሺ݀ ݔൌ ͲǤͳͷ െ ͳǤʹʹ ή ݔିଷ Competitive intensity in industry (d2) ݔൌ െ ͲǤͺͷͺ ൅ ͳǤͺ͸ͺ ή ݔିଷ Enterprise market share to that of market

leader relation (d3) ݔൌ ͲǤͶͲʹ െ ͲǤͺͷͺ ή ݔିଷ

Note: developed by the authors.

Fig. 1 and Fig. 2a, 2b, 2c present forecast results up to 2022 of indicators of the competitive position of the PrJSC «KZRK». Analysis of the given graphs only enables determining the qualitative character of the forecast due to limited data. Besides, it should be noted that the forecast for 2021 and up is based on the previous one, this affecting its accuracy. In its turn, the forecast structure remains unchanged and is proved by the wavelike character of the graphs.

Indicators of the other GZKs were calculated similarly to identification of discrete time series for the PrJSC «KZRK». Analysis of the presented mathematical models of discrete time series shows their similarity. This enables the conclusion about similar qualitative behavior of forecast indicators in terms of competitive positions of the other GZKs, their wavelike character in particular.

Conclusions. The obtained results enable the following conclusions: modeling and comparative analysis of the competitive position of an industrial enterprise, identification of discrete time series form the basis for determining forecast indicators of enterprises’

competitiveness applying extrapolation with the view of making efficient strategic managerial decisions. The work has analyzed main components of influence on competitiveness: the sales volume, the net profit, the market share, competitive intensity in the industry, the market share of the enterprise under study to that of the market leader relation. The work states that it is possible to obtain relevant forecasts of a qualitative character due to a limited volume of initial data.

Analytical dependences on the competitive position of the enterprise and the corresponding factors of influence for the enterprise are constructed. For the first time, the identification of discrete time series was obtained. The application of the extrapolation method allowed to determine the predicted values of factors influencing the competitive position of the enterprise. The results of the study were used in the practice of managers of relevant enterprises in making effective decisions in the system of strategic management. The use of identification of discrete time series will allow to carry out the corresponding estimation of a competitive position of the enterprise and the corresponding factors of influence. At present, the company needs significant investments in the renewal of fixed assets, introduction of modern technologies and so on.

Various disturbances of the internal and external environments of mining companies require appropriate adjustments to strategies. Transformations of the environment require editing and an appropriate strategic management process. That is, the process of strategic management is in constant dynamics. The enterprises of the Kryvyi Rih iron ore basin have real opportunities for further development of their economic potential. Thus, the process of strategic management is in constant dynamics. Economic and analytical assessment of competitive status is a tool, the use of which allows managers to obtain the basic characteristics of the production activities of the enterprise to invest in the most profitable or promising areas of development.

Ʌɿɬɟɪɚɬɭɪɚ

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27.ɉɨɧɨɦɚɪɟɧɤɨ ȼ. ɋ., Ƚɨɧɬɚɪɟɜɚ ȱ. ȼ. Ɇɟɬɨɞɨɥɨɝɿɹ ɤɨɦɩɥɟɤɫɧɨɝɨ ɨɰɿɧɸɜɚɧɧɹ ɟɮɟɤɬɢɜɧɨɫɬɿ ɪɨɡɜɢɬɤɭ ɩɿɞɩɪɢɽɦɫɬɜ : ɦɨɧɨɝɪɚɮɿɹ. ɏɚɪɤɿɜ : ɏɇȿɍ ɿɦ. ɋ. Ʉɭɡɧɟɰɹ, 2015. 404 ɫ.

28. Trydied O. M., Dziebko I. P. Implementation of strategic management accounting as a tool for increasing the company’s competitiveness. Problems of Theory and Methodology of Accounting, Control and Analysis. 2015. ʋ 1 (19). Ɋ. 376—382.

29. Astrom K. J. Lectures on the identification Problem — The Least Squares Method. Lund : Lund Institute of Technology, 1968.

30. SMIDA. URL : http://smida.gov.ua. (ɞɚɬɚ ɡɜɟɪɧɟɧɧɹ: 17.10.2020).

31.Ⱦɟɪɠɚɜɧɚ ɫɥɭɠɛɚ ɫɬɚɬɢɫɬɢɤɢ ɍɤɪɚʀɧɢ : ɨɮɿɰɿɣɧɢɣ ɫɚɣɬ. URL : http://www.ukrstat.gov.ua (ɞɚɬɚ ɡɜɟɪɧɟɧɧɹ: 17.10.2020).

ɋɬɚɬɬɸ ɪɟɤɨɦɟɧɞɨɜɚɧɨ ɞɨ ɞɪɭɤɭ 02.04.2021 © ɑɟɪɟɩ Ⱥ. ȼ., Ȼɟɪɿɞɡɟ Ɍ. Ɇ., Ȼɚɪɚɧɢɤ Ɂ. ɉ., Ʉɨɪɿɧɽɜ ȼ. Ʌ., Ⱦɚɲɤɨ ȱ. Ɇ.

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The article is recommended for printing 02.04.2021 © Cherep Ⱥ., Beridze T., Baranik Z., Korɟnyev V., Dashko I.

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