Author
Listed:
- Alina R. SHAIKHISLAMOVA
- Natalia A. GASRATOVA
Abstract
The work analyzes the main trends in the import and export of medicines in Russia based on data obtained from open sources for the period from 2010 to 2021. The objective of this investigation is to conduct an analysis and construct a model for the import-export of pharmaceuticals in the Russian Federation. It is important to conduct a thorough examination of this matter in order to discern current trends. The development of a mathematical model can simplify forecasting changes and provide a more precise estimation of potential turnover in the area under consideration. This enables the making of more informed decisions when planning business strategies and developing measures for the advancement of the pharmaceutical industry. Particularly, a 3004 group of drugs was analyzed in accordance with the Harmonization Code System (HS code 3004 is used for therapeutic and prophylactic purposes and packaged in dosage forms). General data on imports and exports, share participation in the trade of medicinal products of importing and exporting countries are examined. Cumulative import-export curves were constructed based on the reference year, a mathematical model of sales volume in monetary terms under normal conditions was proposed, and a modified model for several inflection points was considered. The scientific novelty lies in the development of a new approach to modeling import and export processes based on differential equations. Unlike traditional economic models, the proposed model takes into account temporary changes and the dynamics of economic processes. The aforementioned methodology is founded on the usage of differential equations that describe the fluctuations in imports and exports in depending on other factors, such as economic growth and changes in exchange rates. The constructed model is capable of being used both within the framework of money and volume turnover, and can be applied to diverse product groups and countries. The resulting data can be effectively used to forecast import-export volumes, evaluate availability, pricing, risks, and safety, manage inventory and logistics, and forecasting crisis situations. The constructed model can be convenient for validating the outcomes of training neural networks during the analysis of the market over a long-term period.
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