Strategic management optimization of the regional agricultural sector by means of modern forecast modeling instruments

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Under the conditions of Ukraine’s integration into the world economic space, the agricultural sector is one of the priority and strategically important sectors of the national economy. The research objective is to substantiate the theoretical, methodological and methodical principles of strategic management of economic development of the regional agricultural sector and to solve actual problems in order to optimize strategic management based on cognitive scenarios of supply and demand balancing in the agrarian market, probabilistic modeling, which allows the regions to identify the “growth points”, to optimize the sectoral structures of the economy, to improve the quality and efficiency of the developed and implemented scenarios and the strategies of the agroindustrial production development in the region.
As a result of the research, a scenario-probabilistic model of economic development of the regional agrarian sector was proposed, which allows to identify the priority directions for the long-term perspective, to adjust the direction of development if necessary, to explore different scenarios of the development of events on the priorities change at the macro level in the conditions of uncertainty and risks.
Thus, the practical value of the research enables to predict the strategic development of the agricultural sector of the region and its individual areas by using a systematic approach and compositions of methodological approaches to analysis and forecasting, considering it as a complex and structured system.

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    • Table 1. Results of SWOT analysis of the regional socio-economic system development
    • Table 2. Grouping of factors influencing the development of agro-industrial production in the region
    • Table 3. Possible scenarios for the development of agro-industrial production
    • Bidiuk, P. I., Terentiev, O. M., Prosiankina-Zharova, T. I., Efendiiev, V. V. (2017). Прогнозне моделювання нелінійних нестаціонарних процесів у рослинництві з використанням інструментів SAS Enterprise Miner [Prohnozne modeliuvannia neliniinykh nestatsionarnykh protsesiv u roslynnytstvi z vykorystanniam instrumentiv SAS Enterprise Miner]. Kyiv: Scientific news of NTUU“KPI”, 1, 24-36.
    • Boks, Dz, Dzhenkins, G. (1974). Анализ временных рядов. Прогноз и управление [Analiz vremennykh ryadov. Prognoz i upravlenie]. Moscow: World.
    • Borg, A. (2016). 6 factors shaping the global economy in 2016. World Economic Forum.
    • Burstein, F., & Holsapple, C. W. (2008). Handbook of Decision Support Systems. Springer-Verlag Berlin Heidelberg.
    • Cummins, J. D. (2009, September). Convergence of Insurance and Financial Markets: Hybrid and Securitized Risk-Transfer Solutions. Journal of Risk and Insurance, 76(3).
    • Cummins, J. D., Grace, M. F., Phillips, R. D. et al. (1999). Regulatory solvency prediction in property-liability insurance: risk-based capital, audit ratios, and cash flow simulations. Journal of Risk and Insurance, 66, 417-458.
    • Dovhyi, S. O., Bidiuk, P. I., Trofymchuk, O. M. (2014). Системи підтримки прийняття рішень на основі статистично-ймовірнісних методів [Systemy pidtrymky pryiniattia rishennaosnovi statystychno-imovirnisnykh metodiv]. Kyiv: Lohos.
    • Gozhiy, A. P. (2011). Основные аспекты применения информационных технологий в задачах сценарного планирования [Osnovnye aspekty primeneniya informatsionnykh tekhnologiy v zadachakh stsenarnogo planirovaniya]. Mykolaiv: Naukovi pratsi ChDU im. Petra Mohyly, 148(160), 158-167.
    • Grünig, R., Kühn, R. (2011) Process-based Strategic Planning. Springer-Verlag Berlin Heidelberg.
    • Kozhukhіvska, R., Parubok, N., Petrenko, N. et al. (2017). Methods of assessment of efficiency of creating regional innovative clusters for dynamic development of economics. Investment Management and Financial Innovations, 14(3), 302-312.
    • Kussul, N. N., Kravchenko, A. N., Skakun et al. (2012). Регрессионные модели оценки урожайности сельскохозяйственных культур по данным MODIS [Regressionnye modeli otsenki urozhaynosti selskohozyaystvennykh kultur po dannym MODIS]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 1, 95-107.
    • Mamatova, N. (n.d.). A VAR Analysis of Electricity Consumption.
    • Maryuta, A. N., Redina, N. I., Dolgorukov, Yu. A. (2005). Экономико-математические модели производств и управление их запасами [Ekonomiko-matematicheskie modeli proizvodstv i upravlenie ikh zapasami]. Dnepropetrovsk: DDFA.
    • McAllester, D., Rosenblitt, D. (1991). Systematic Nonlinear Planning. In Proc. 9th National Conf. on AI (pp. 634-639).
    • Pankratova, N. D., Nedashkovskaya, N. I., Gorelovа, G. V. (2014). Гибридный метод многокритериального оценивания альтернатив принятия решений [Gibridnyy metod mnogokriterialnogo otsenivaniya alternativ prinyatiya resheniy]. Kibernetika i sistemnyy analiz, 50(5), 58-70.
    • SAS Institute Inc. (2013). Getting Started with SAS Enterprise Miner 12.3.
    • SAS Institute Inc. (n.d.). SAS Training and Bookstore.
    • Serhieieva, L. N., Bakurova, A. V. et al. (2009). Моделювання структури життєздатних соціально-економічних систем [Modeliuvannia struktury zhyttiezdatnykh sotsialno-ekonomichnykh system]. Zaporizhzhia: KPU.
    • Shvydenko, M. Z. (2013). Сучасні інформаційні технології моніторингу і аналізу стану інфраструктури аграрного ринку України [Suchasni informatsiini tekhnolohii monitorynhu i analizu stanu infrastruktury ahrarnoho rynku Ukrainy]. Kyiv: Naukovyi visnyk Natsionalnoho universytetu bioresursiv i pryrodokorystuvannia Ukrainy, 181(4), 350-359.
    • Smolin, I. V. (2013). Моделі стратегічного управління та умови їх застосування [Modeli stratehichnoho upravlinnia ta umovy yikh zastosuvannia]. Statystyka Ukrainy, 4, 52-55.
    • State Statistics Service of Ukraine (n.d.).
    • Talavyria, M. P., Pashchenko, O. V. (2012). Макроекономіка [Makroekonomika]. Kyiv: M. M. Lysenko.
    • Vector Autoregressive Models for Multivariate Time Series (2006). In E. Zivot & J. Wang (Eds.), Modeling Financial Time Series with S-PLUS® (pp. 385-429). Springer-Verlag Berlin Heidelberg.
    • World Bank Statistics (n.d.). GDP Statistics from the World Bank.
    • Zghurovskyi, M. Z. (2016). Форсайт та побудова стратегії соціально-економічного розвитку України на середньостроковому (до 2020 року) і довгостроковому (до 2030 року) часових горизонтах [Forsait ta pobudova stratehii sotsialno-ekonomichnoho rozvytku Ukrainy na seredno-strokovomu (do 2020 roku) i dovhostrokovomu (do 2030 roku) chasovykh horyzontakh]. Kyiv: Politekhnika.
    • Zghurovskyi, M. Z., Pankratova, N. D. (2007). Основи системно¬го аналізу [Osnovy systemnoho analizu]. Kyiv: VNU.
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