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The purpose of this paper is to improve the commodity management of retail chains by developing theoretical provisions of decision support system of retail chain commodity management (DSS RCM). Trading networks buy goods directly from manufacturers or large wholesalers, place these goods in warehouses, organize their distribution to stores. Commodity managers should decide how to provide all stores of the network in timely manner with the most profitable product every day. The objective of the DSS RCM is to maximize the daily trading margin, per each euro invested in commodities taking into account restrictions on commodity resources and shelf space. The DSS RCM have to prepare bills of lading and orders for inbound logistics, distribution and re-distribution of commodities within the network. Decision support uses data available in any retail software. The calculation algorithms are simple and effective to ensure the necessary and sufficient accuracy with the time and hardware limitations and without significant investments in hardware and staff qualifications update. Algorithms calculate the optimal solution based on the objective function (for example, profit maximization) and restrictions (for example, inventory level, store sales, profit from sales of a given product). The paper systematized the approaches of other scientists to solve the problem of product management in retail. Author analyze the strengths and weaknesses of those approaches for purpose to find the optimal approach for retail chains. So the aim of this study is to develop theoretical basis of decision support system of retail chain commodity management (DSS RCM). The proposed method of optimization of commodity assets in retail based on their mathematical modeling and calculating of the consolidated profitability ratio. Research results are limited to homogeneous product retail chains (e.g., clothing or footwear). By using proposed algorithms calculations could be in real-time mode in the database for tens (hundreds) of thousands items in the product range (for example, when selling clothes and shoes, a unique combination of model, color and size forms a separate item in the product range) in hundreds (thousands) of stores. Therefore, it could be hundreds of millions of combinations items-stores (Big Data).

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Lithuanian school graduates wishing to be admitted to state-funded places at universities undergo a competitive selection based on their final school and state exam grades. The problem of organizing competitive selection is that in Lithuania there are different types and scales of school knowledge assessments. Algorithm developed by LAMA BPO address this problem by adjusting grades into a single scale. But choice of final arithmetic values into which pupil's grades are converted is not justified theoretically. Proposed by the authors algorithm is a development of the LAMA BPO algorithm and allows to achieve a consistently higher accuracy of predicting learning results at the university. The higher accuracy of the models indicates a better capture of the central trend: a positive correlation between the level of performance in individual school disciplines and the results of university education in certain study programs.

At the face of the COVID-19 pandemic, a lot of countries had to reorganise their education systems in a very short time by organising remote learning for students in general education schools. This change affected the working conditions of all participants in the learning process and presumably the learning outcomes of students. This paper examines the learning outcomes of students of Grades 5-12 during the COVID-19 lockdown. The research was carried out using the data from the electronic school diary �Mano Dienynas� of 22 schools: 11 schools from the favourable (high) SES municipal context and 11 schools from the unfavourable (low) SES municipal context. The total sample was 3747 students from high SES schools and 1942 students from low SES schools. The research results demonstrated that the learning results (marks) of both groups of students in maths and mother tongue (Lithuanian) were higher during the lockdown period than during the regular education that took place a year before. Moreover, the results in maths increased more than in Lithuanian, the learning results of lower grades (Grades 5-8) were higher than those of older grades (Grades 9-12), whereas the marks for tests were lower than those of independent work. The study encourages further investigation into remote learning and learning experiences, since scientific knowledge is of paramount importance in this special Covid-19 period with a fundamental change in educational practice.