Modelling the production and economic activities of an agricultural enterprise: the management aspect.

Анотація

Subject of study. Using management tools for modeling production and economic activities of agricultural enterprises. The aim of the study. The aim of this work is to study the theoretical and practical aspects of modelling the production and economic activities of agricultural enterprises in Ukraine in order to improve the efficiency of their management in conditions of martial law, economic instability and strategic integration into the European Union. Research methods. For the purposes of the study, the methods of system analysis, structural-functional analysis, comparative analysis, mathematical modelling, graphical analysis and generalisation were used. Results of work. It has been established that modelling of production and economic activities is a complex process of creating a simplified representation of real production and economic processes through mathematical, statistical and computer tools to optimise management decisions. Five main types of models have been identified: linear programming for optimising resource allocation, non-linear programming for complex agricultural interrelationships, dynamic optimisation for managing production over time, simulation models for operational decisions, and regression models for forecasting. Three groups of management methods have been identified: administrative methods based on AI and IoT technologies, economic methods using resource-efficient technologies, and socio-psychological methods with human-assisted automation. Specific modelling challenges have been identified: seasonality of production with yield variability of up to 20%, dependence on natural and climatic conditions with a 26% reduction in profitability, and the need for anti-fragility models. Five strategic development goals for 2030 have been systematised: sector competitiveness, food security, environmental protection, climate adaptation, and rural development. Critical external factors for the period 2024-2026 have been identified: military action with a 70% reduction in production capacity, contamination of 300,000 hectares of land with mines, a 3-5-fold increase in the cost of fertilisers, and volatility in export logistics. The need to develop comprehensive models combining traditional economic and mathematical approaches with scenario analysis, stochastic models, and adaptive algorithms has been established. The key role of AI-native innovations has been identified, with the AI market in agriculture.

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