Background and Purpose: The existing frameworks provide a superficial approach to the evaluation of product competitiveness which reveals the linkage between the level of product competitiveness and quantitative as well as qualitative factors that have the most significant impact thereon. Given this fact, the purpose of this paper is to elaborate a model for evaluating the competitiveness of sunflower packaged oil, considering both quantitative and qualitative factors that may alter it. Further, this model is being implemented to examine the most demanded Ukrainian sunflower oil brands in order to reveal possibilities for enhancing competitiveness. Design/Methodology/Approach: The general methodology of the research includes elements of theoretical, empirical, qualitative and quantitative analyses. The theoretical analysis aims to shed light upon a different understanding of “the evaluation of competitiveness”, as well as approaches and tools for analysing it. Empirical analysis focuses on observing official statistical data of the export of sunflower oil and future trends. Qualitative analysis consists in the identification, systematization and description of factors that affect the competitiveness of sunflower oil packaged. In turn, quantitative analysis is based on usage of the Fuzzy logic tool in order to evaluate the impact of complex and partial factors on the level of product competitiveness. Results: This paper provides a business case for product competitiveness evaluation of a particular sunflower oil brand. Based on usage of the Fuzzy logic toolkit, the impact of complex and partial factors on competitiveness level was analysed. As a result, simulation of the competitiveness sensitivity of a particular oil brand on relevant complex factors that determine it competitiveness level was presented. This business case may help managers to channel their efforts and resources in the proper particular direction to increase product competitiveness and product positioning on the market. Conclusion: The results of this research would be useful to practitioners in their assessment of product competitiveness, modelling future levels, and understanding hidden possibilities for enhancing product competitiveness. The framework offered might be adopted for other types of products.

In the conditions of a full-scale war with Russia and in connection with granting Ukraine the status of a candidate for EU accession in June 2022, the issue of liberalization of duty-free trade between Ukraine and the EU is urgent. In this context, it is important to form relevant information based on the volume and structure of trade in quota agricultural and food products with EU countries. For the study, a sample of data was formed based on official statistical information for 2021 on the export of the following products: honey; tomatoes; juices; starches; cereals; flour; poultry meat; eggs. The following priorities of the EU countries in the consumption of products of Ukrainian production were identified: honey; poultry meat; tomatoes; juices; starches; eggs; cereals; flour It was established that in 2021 Poland was the leader in terms of export volumes of the studied types of products, Germany, the Netherlands and Slovakia were quite close. The largest buyers of certain types of products were: honey and tomatoes – Poland; juice – Austria; starch – Malta; croup – the Netherlands; flour – Slovakia; poultry meat – the Netherlands; eggs – Latvia. The similarities and differences of the countries in terms of types and volumes of imports of Ukrainian products were identified and three clusters were identified: Germany and Poland; The Netherlands and Slovakia; all other countries. Poland and Germany buy honey, tomatoes, and juices the most. In the structure of product exports to the Netherlands and Slovakia, poultry meat occupies the largest share. To identify the similarity of countries in terms of types and volumes of export of products from Ukraine, the method of cluster analysis was used, in particular, the method of agglomerative procedure was used from hierarchical methods, and the k-means method was used from non-hierarchical methods. The agglomerative classification procedure made it possible to identify three clusters: Germany and Poland; The Netherlands and Slovakia; all other countries. The application of the k-means method gave the same clustering results as the method of agglomerative procedure, on the basis of which a conclusion was made about the correctness of the classification of objects. In addition, the structure of products imported to Germany, Poland, the Netherlands and Slovakia was analyzed and the similarity between Germany and Poland, the Netherlands and Slovakia was confirmed. The information obtained in the study can be used to develop recommendations regarding the policy of liberalization of duty-free tariff quotas for the products of Ukrainian manufacturers, as well as to optimize logistics routes.

Among the constituents of sustainable development of the enterprise is decisive economic. The enterprise is able to ensure sustainable development of other spheres only in case of stable economic activity. In order to make managerial decisions in the conditions of uncertainty and high level of dynamism of the business environment, it is important to be able to obtain an estimate of the trend of economic activity of the enterprise in a short time. The use of financial statement data will minimize the time required to form an information base. However, the dynamics of values of individual indicators may be different, which does not give grounds to conclude that there is a trend of sustainable economic development of the enterprise. In this regard, it is proposed to calculate the integral indicator-a taxonomic indicator of the level of development of the enterprise. On the basis of its values, it is possible to make a temporary ranking of the level of economic activity of the enterprise for the studied period. The object for research was selected by PJSC "Vinnytsia Oil and Fat Combine". According to the financial statements of the enterprise for 2013-2018 groups of financial and economic indicators have been formed and a taxonomic indicator of the level of enterprise development has been calculated. The obtained ratings reflect the instability of the enterprise's economic activity during the period under review. It is established that the lack of a trend of sustainable development of economic activity of the enterprise is due to internal and external factors. To ensure the sustainable development of economic activity, it is necessary to take into account the strengths and weaknesses of the enterprise, which are formed in its internal environment, and the opportunities and threats that are the source of the external environment. The proposed approach to estimating the trend of economic activity of an enterprise can be applied for different periods: short, medium and long term. The information base is formed on the basis of financial statements (quarterly or annual), which requires little time. Another advantage of this approach is that it can be used by external stakeholders.

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The world leaders in defense financing are the USA, China and Russia. The study of the EU countries was based on taking into account the relationship between the volume of military expenditures and the size of the countries (by population) and the level of their economic development. A comparison of EU countries was made according to the following indicators: GDP per capita (in purchasing power standards); military expenditure per capita. In most EU countries, these indicators are correlated. Exceptions are Ireland, Austria, Malta, Lithuania, Estonia and Greece. The first three countries, having high volumes of GDP per capita, direct significantly smaller amounts of funds (per capita) to finance the defense sector. The opposite is the situation in Lithuania, Estonia and Greece, where military expenditure per capita is quite high compared to the level of their economic development, which is undoubtedly due to their geographical location and predictions of possible threats from their neighbors. Cluster analysis methods were used to compare countries simultaneously for both indicators: agglomerative procedure according to the Ward method; k-means method. The clustering results were the same in both cases. Only Ireland was included in the first cluster. The second cluster contains 11 countries: Sweden; France; Germany; Finland; Lithuania; Belgium; Estonia; Italy; Netherlands; Greece; Denmark The third cluster includes 14 countries: Portugal; Slovenia; the Czech Republic; Croatia; Cyprus; Romania; Hungary; Spain; Poland; Slovakia; Latvia; Malta; Bulgaria; Austria. Ireland forms a separate cluster as the country with the largest GDP and one of the lowest military expenditure per capita. The second cluster is characterized by the largest values of statistical indicators. The corresponding indicators for the countries of the third cluster are the lowest. Innovations are intensifying in the defense sector. Military tech is actively developing in Ukraine. Military startups are divided into two groups: hardware; software. Innovations increase the attractiveness of the defense sector for private investors.