Methodological toolkit for assessing the level of stability of agricultural enterprises
Keywords:
enterprise, resulting parameter, stability, stochastic series of values.
Abstract
Purpose. The purpose of the study is to develop methodological toolkit for identifying the level of stability of agricultural enterprises based on the study of stochastic series of values of the resulting parameters.
Methodology / approach. During the study, a method of modeling stochastic quantities was used, in particular the Verhulst equation to study the dynamics of the values of the resulting parameter relative to a monotonically increasing series of values and different values of the coefficient of “density accumulation”. The GARCH model was used to predict the values of the variance of the resulting parameter, and regression analysis was used to determine the dependence of the state of enterprises on the factors of internal and external environments.
Results. It is substantiated that the values of the logistic function of Verhulst imitate the behavior of a stochastic quantity. Therefore, this function can be used to measure the transition point of the system from a stable state to a state of instability. It is proved that, taking into account the stochastic and reflective nature of the development of agricultural enterprises, it is expedient to forecast their state on the basis of comparing the variances of the values of the performance parameter. It is reasoned that the application of the function of changing the degree coefficients allows obtaining the most complete information about the influence of subjective factors of the internal environment of agricultural enterprises on their state.
Originality / scientific novelty. Methodological toolkit for identifying the level of stability of agricultural enterprises, which is based on the application of the Verhulst equation to study the dynamics of the values of the resulting parameter relative to the monotonically increasing series of values and different values of the coefficient of “density accumulation”, and, unlike existing parameter from the factors of internal and external environments of the agricultural enterprise.
Practical value / implications. The practical value of the developed methodological toolkit is in the possibility of its use by managers of agricultural enterprises to forecast their state, taking into account the influence of internal and external factors. Given that the prediction is reduced to one resulting parameter, the proposed toolkit is easy to use. It is advisable to use it to justify regulatory decisions, in particular in the process of adaptive management and streamlining of personnel management processes and intellectual potential of employees.
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2. Колещук О. Я. Інтелектуалізація концептуально-стратегічного мислення в управлінні машинобудівними підприємствами з використанням теорії хаосу. Бізнес-Інформ. 2019. № 11. С. 367–381. https://doi.org/10.32983/2222-4459-2019-11-376-381.
3. Пророк В. Теория хаоса и теория сетей – новые подходы к моделированию политической системы. PolitBook. 2016. № 1. С. 83–91. URL: https://cyberleninka.ru/article/n/teoriya-haosa-i-teoriya-setey-novye-podhody-k-modelirovaniyu-politicheskoy-sistemy/viewer.
4. Hayward T., Preston J. Chaos theory, economics and information: the implications for strategic decision-making. Journal of Information Science. 1999. Vol. 25. Is. 3. Рр. 173–182. https://doi.org/10.1177/016555159902500301.
5. Faggini M., Parziale A. The failure of economic theory. Lessons from chaos theory. Modern Economy. 2012. Vol. 3. No. 1. Рр. 1–10. https://doi.org/10.4236/me.2012.31001.
6. Xia Lu. A financial chaotic system control method based on intermittent controller. Mathematical Problems in Engineering. 2020. Vol. 2020. 5810707. https://doi.org/10.1155/2020/5810707.
7. Zhou W., Chu T., Wang X.-X. Stability, global dynamics, and social welfare of a two-stage game under R&D spillovers. Mathematical Problems in Engineering. 2021. Vol. 2021. 2096868. https://doi.org/10.1155/2021/2096868.
8. Кернасюк Ю. В. Прогноз розвитку аграрного сектору економіки з використанням штучних нейронних мереж. Вісник аграрної науки. 2019. № 6. С. 75–81. https://doi.org/10.31073/agrovisnyk201906-11.
9. Вєдєнєєв В. А. Оцінка ефективності довгострокових моделей прогнозування ціни реалізації продукції аграрного сектору в Україні. Економіка та держава. 2019. № 9. С. 46–51. https://doi.org/10.32702/2306-6806.2019.9.46.
10. Михайленко О. В., Хільченко І. Ю. Аграрний сектор України: сучасний стан, проблеми та перспективи реформування галузі. Modern Economics. 2019. № 15. С. 148–152. https://doi.org/10.31521/modecon.V15(2019)-21.
11. Kozlovskyi S., Khadzhynov I., Lavrov R., Skydan O., Ivanyuta N., Varshavska N. Economic-mathematical modeling and forecasting of competitiveness level of agricultural sector of Ukraine by means of theory of fuzzy sets under conditions of integration into European market. International Journal of Recent Technology and Engineering. 2019. Vol. 8. Is. 4. Рр. 5316–5323 https://doi.org/10.35940/ijrte.D7541.118419.
12. Akullo D., Maat H., Wals A. E. J. An institutional diagnostics of agricultural innovation; public-private partnerships and smallholder production in Uganda. NJAS: Wageningen Journal of Life Sciences. 2019. Vol. 84. Is. 1. Pр. 6–12 https://doi.org/10.1016/j.njas.2017.10.006.
13. Формування збалансованої системи оцінювання підприємств ринку харчової продукції на основі стандарту НАССР: моногр.; за наук. ред. С. В. Князя. Львів: Афіша, 2020. 114 с.
14. Maknickienė N., Stankevičienė J., Maknickas A. Comparison of Forex market forecasting tools based on Evolino ensemble and technical analysis indicators. Romanian Journal of Economic Forecasting. 2020. Vol. 23. Is. 3. Рр. 134–148. URL: https://ipe.ro/rjef/rjef3_20/rjef3_2020p134-148.pdf.
15. Lorenz E. N. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences. 1963. Vol. 20. Is. 2. Рр. 130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2.
16. May R. M. Simple mathematical models with very complicated dynamics. Nature. 1976. Vol. 261. Pр. 459–467. https://doi.org/10.1038/261459a0.
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Published
2022-03-20
How to Cite
Sumets, A., Kniaz, S., Heorhiadi, N., Skrynkovskyy, R., & Matsuk, V. (2022). Methodological toolkit for assessing the level of stability of agricultural enterprises. Agricultural and Resource Economics: International Scientific E-Journal, 8(1), 235-255. https://doi.org/10.51599/are.2022.08.01.12
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