农业企业竞争力综合指标的建模:使用神经网络工具的方法论方法

Illia Chikov, Olha Khaietska, Okhota Yuliia, Denys Titov, Vyacheslav Prygotsky, Vitalii Nitsenko
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引用次数: 0

摘要

本文致力于开发一种系统的方法,利用神经网络工具对农业企业竞争力的综合指标进行建模。作者采用了专著、逻辑-理论、统计-经济-数学、可视化、系统分析、分类法和神经网络建模、泛化、逻辑抽象和结论生成等一般科学和特殊的研究方法。该研究基于乌克兰国家统计局的资料,国内外科学家对特定主题的科学发展,以及文尼察地区农业企业有限责任公司“Ahrokompleks”“Zelena dolyna”,PJSC“Dashkivtsi”,LLC“Selyshchanske”,PE“Dary sadi”,PE“Fortuna”的财务报表,其主要经济活动类型根据经济活动分类01.11 -种植谷物(水稻除外)。豆类和油籽。本文发展并提出了一种基于神经网络建模原理的农业企业竞争力评价的非经典方法。它可以获得一个有充分根据的定量指标,这可以很容易地解释为一个三级竞争力量表的语言评价,并用于比较,监测和制定合理的决策,以提高农业企业的竞争力。非经典方法补充了传统的竞争力评估方法,扩大了它们的能力并消除了某些局限性。在竞争力评估中使用神经网络建模,可以考虑到不同因素和指标之间复杂的非线性关系,这有助于提高竞争力评估的客观性和准确性,从而使企业能够更好地做出决策,改进战略,从而在市场中取得成功。研究结果可用于支持农业部门的战略决策,确定优先发展方向,改善企业的竞争战略和业务流程的功能。
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MODELING OF THE SYNTHETIC INDICATOR OF COMPETITIVENESS OF AGRICULTURAL ENTERPRISES: A METHODOLOGICAL APPROACH TO THE USE OF NEURAL NETWORK TOOLS
The article is devoted to the development of a methodical approach to modelling a synthetic indicator of the competitiveness of agricultural enterprises using the tools of neural networks.The authors used general scientific and special research methods, such as monographic, logical-theoretical, statistical and economic-mathematical, visualization, system analysis, taxonomy and neural network modelling, generalization, logical abstraction and conclusion generation. The study was based on materials from the State Statistics Service of Ukraine, scientific developments of foreign and domestic scientists on the defined topic, and financial statements of the agricultural enterprises of Vinnytsia region LLC «Ahrokompleks «Zelena dolyna», PJSC «Dashkivtsi», LLC «Selyshchanske», PE «Dary sadiv», PE «Fortuna» the main type of economic activity of which according to Classification of economic activities 01.11 – cultivation of cereals (except rice), legumes and oilseeds. The article develops and presents a non-classical approach to the assessment of the competitiveness of agricultural enterprises has been developed, which is based on the principles of neural network modelling. It allows to obtain a well-founded quantitative indicator, which can be easily interpreted into a linguistic evaluation on a three-level scale of competitiveness and used for comparison, monitoring and making sound decisions on improving the competitiveness of agricultural enterprises.The non-classical approach complements traditional methods of competitiveness assessment, expanding their capabilities and eliminating certain limitations. The use of neural network modelling in competitiveness assessment allows to take into account complex and non-linear relationships between different factors and indicators, which contributes to an increase in the objectivity and accuracy of competitiveness assessment, which in turn allows enterprises to make better decisions and improve their strategies to achieve success in the market.The results of the study can be used to support strategic decision-making in the agricultural sector, identify priority development directions, and improve the competitive strategies of enterprises and the functioning of business processes.
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0.60
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20.00%
发文量
268
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