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The article is devoted to the analysis of the use of modern trends in the logistics of agricultural enterprises in Slovakia in order to achieve a competitive advantage and to ensure the improvement of the course of business activities. The research was carried out in 67 agricultural enterprises on the territory of Slovakia. The following methods of data collection were used: Computer-Assisted Web Interviewing, Computer-Assisted Telephone Interviewing and Computer-Assisted Personal Interviewing. When interpreting the results of research, descriptive statistics and hypothesis testing were used. Pearson’s chi-squared test, Pearson’s contingency coefficient and Tschuprow’s coefficient were applied as statistical tests. The result of the analysis is the need for digitisation and transfer of Big Data that will lead to better decisions, resulting in higher operational efficiency, cost reduction, risk reduction, flexibility, efficiency, and clarity of logistic processes. Agricultural enterprises in Slovakia will have to accept Industry 4.0. The results of the research revealed that 71.6% of agricultural enterprises strongly agrees with the usage of logistic technology. Price has a strong influence, as an important factor, on the implementation of a new trend in logistics in case of 80.6% agricultural enterprises. 29.8% of respondents accept logistic trends but do not have the funds to implement them in their enterprise.

Reiff M., Ivanicova Z., Surmanova K. (2018): Cluster analysis of selected world development indicators in the fields of agriculture and the food industry in European Union countries. . Abstract:The paper analyses the disparity in the performance of the agriculture and food industry sectors in EU countries during the period from 2002 to 2013, and identifies significant differences between countries as well as the dynamics of change. The individual countries are clustered according to the long-term average of the World Bank collection of development indicators such as agricultural raw materials exports, agricultural raw materials imports, crop production index, food production index, livestock production index, cereal yield, agriculture value added and agriculture value added per worker. The analysis reveals convergence in the given period and identifies significant differences in the indicator of agriculture value added per worker at the end of the analysed period.

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