CC BY
1758
ECONOMIC SCIENCES / «ШЦУШШУМ-ЛШШаИ» #22(ИШ, 2021
уменьшается на сумму уплаченных страховых взносов работников: 0,015 млн рублей * 12 месяцев = 0,18 млн рублей. В итоге, размер налога за год составляет: 0,72 млн рублей - 0,18 млн рублей = 0,54 млн рублей.
При упрощенной системы налогообложения «Доходы минус расходы» единый расчет годового налога можно рассчитать следующим образом (как правило, налоговая ставка составляет 15%): (1 млн рублей * 12 месяцев) - (0,75 млн рублей * 12 месяцев) * 15% = 0,45 млн рублей. Уменьшать сумму налогов из-за уплаченных страховых взносов в данном налоговом режиме предприниматели могут. Единственное - страховые взносы могут учитывать, как расходы предприятия.
Таким образом, при упрощенной системы налогообложения «Доходы» единый расчет годового налога составляет 0,54 млн рублей, а при упрощенной системы налогообложения «Доходы минус расходы» - 0,45 млн рублей.
В случае с общей системой налогового режима, необходимо посчитать следующие налоговые исчисления малого предприятия по разным объектам налогообложения:
- налог на прибыль можно рассчитать следующим образом: 1 млн рублей * 12 месяцев * 20% = 2,4 млн рублей;
- налог на НДС можно рассчитать следующим образом: 1 млн рублей * 12 месяцев * 20% = 2,4 млн рублей;
- налог на имущество организации - 2,2% от имущества, размер которого составляет условные 1 млн рублей = 0,022 млн рублей.
Суммарно налоговые выплаты предприятия за один календарный год составят примерно 5 млн рублей, что попросту не сравнимо с упрощенными системами налогообложения, входящих в специальные налоговые режимы.
Таким образом, при подробном анализе характеристики и условиях налогообложения, можно
установить следующее: что наиболее оптимальным выбором для малых предприятий будет упрощенная система налогообложения «Доходы минус расходы» (однако лишь в том случае, если лимиты предприятия не превышены).
Таким образом, создание и организация важнейшие этапы при создании нового малого бизнеса, и чтобы данный процесс был правильно организован и выполнен, необходимо соблюдение всех условий и стадий, в особенности, верного выбора режима налогообложения.
Список использованных источников
1. Базилевич А.Р. Проблемы и пути повышения финансовой устойчивости организации // Молодой ученый. 2019. № 37 (275). С. 116-118.
2. Китиева М.И., Орцханова М.А., Арчакова М.Б. Анализ и тенденции развития малого предпринимательства в Республике Ингушетия. Экономика и предпринимательство. 2015. № 10-2 (63). С. 966968.
3. Китиева М.И., Орцханова М.А., Полонкоева Ф.Я. Роль малого бизнеса в социально-экономическом развитии региона (на примере Республики Ингушетия). В сборнике: Вузовское образование и наука. Материалы Всероссийской научно-практической конференции. ФГБОУ ВО «Ингушский государственный университет». 2017. С. 82-86.
4. Кузнецова Ю.О., Сафарли М.С. Роль предприятий малого бизнеса в экономике Российской Федерации // Актуальные вопросы гуманитарных и естественных наук. 2019. С. 138-144.
5. Выбор режима налогообложения, или как же платить налоги. URL: https://www.nalog.rU/create_business/ip/in_progress/t axation_type_choice
6. Системы налогообложения: как сделать правильный выбор. URL: https://www.regberry.ru/nalo-gooblozhenie/sistemy-nalogooblozheniya.
УДК 338.001.36
Zalilova Z.A.
Bashkir State Agrarian University DOI: 10.24412/2520-6990-2021-22109-58-60 ABOUT WORKING WITH BIG DATA
Abstract.
The article is devoted to the fact that work with big data is constantly gaining momentum and the result of the analyzes and the collection of information depends on how to use them correctly.
Keywords: data, information, grouping, aggregation, result, analysis.
Today, when every member of the society has access to the media and the Internet, to which there are answers to almost any questions, there may arise problems in analysing information that was received. Often, information is given in long-term dynamics and in a wide spatial aspect, which in turn, makes it difficult for the users to draw the right conclusions on their own in short periods of time. To analyse the resulting array of information, we have to face a number of new issues that arise in the process of studying any question.
Large data sets are huge amounts of information
that are stored on any storage medium. At the same time, they are so large that it is impractical to process them using conventional software or hardware, and in some cases it is completely unrealistic.
An example of large data can be social networks -where each profile, or each user page, is a tiny drop in a huge ocean of information that is not structured in any way.
At the same time, we should not forget that, when each of us is submitting any documents at kindergartens, in school, at work, at the clinic, or anywhere else,
«ШУУШШШИМ-ЛШИГМаУ» #ШШ), 2021 / ECONOMIC SCIENCES
59
we always fill a document that gives permission to process personal data consequently sending all our information to the general information storage. This, too, is a real example of large data, which accumulate in virtually every sphere of human life.
In the agricultural sector, there is also a constant collection of information from agricultural producers of all forms of ownership. In some cases, researchers cannot just get exactly what interest them as sometimes the same information is fragmented and varies in the manner of submission by different opponents.
Day by day this process goes on continuously and thus the information around us and about us becomes big data.
In the educational process when teaching a number of economic disciplines, such as statistics, econometrics, methods of multidimensional analysis of statistical data, the lecturer's task is to teach students not only the calculation of the parameters using statistical and mathematical tools and the use of certain methods of the discipline, but first and foremost - the correct, competent search necessary for the analysis of information. In connection with the specifics of our agrarian university, lecturers bring their subjects as close as possible to the branches of agriculture, so that our graduates are ready to work in their specialty. The implementation of many calculations occurs in Microsoft Excel.
For example, a grouping of districts of the Republic of Bashkortostan by the number of bee colonies, in a process by which it is possible to identify which areas are leading this in this criteria, and in which areas have no bee colonies at all. Also, a simultaneous calculation of their productivity, gross output, marketable and fodder output is made, the average indicators are calculated by districts, by groups and in the whole republic. Then the results of the grouping are analysed and conclusions are formulated. When performing this study, students together with the teacher use in-depth methods of analysis, which involve the use of mathematical tools with advances of the field of information technology.
When conducting sample surveys of agricultural producers, students often use the split testing method, when a control population is selected from the available total data (by agricultural producers), which is alternately compared with other similar populations where a change is made. Conducting such tests helps to determine which of the parameters fluctuations have the greatest impact on the control population. a huge number of iterations can be carried out due to the large volumes of data, with each of them closer to the most reliable results.
The method of predictive analytics is also actively used in the educational process. Lecturers introduce students to various forecasting techniques in order to identify how a process or object of research will develop in the future, so that you can always apply leverage for successful development.
At the moment, the importance and value of processing large amounts of data is increasing every day. Leading information technology manufacturers are trying to develop new products in order to meet the demand of not only giant organizations, but also representatives of small and medium businesses. To do this,
storage is created in the form of clouds which are more financially beneficial; there is an active use of "dark data", where all non-digitized information about a particular object is stored despite not playing a key role in its direct use, but may serve as a reason for switching to a new information storage format; artificial intelligence technologies are being developed, etc.
All these, of course, will facilitate the process of collecting, storing and obtaining necessary information not only to University students, but also to all users. It will help to quickly create new projects that may become more popular in society; there will be an opportunity to correlate customer requirements with existing services and to receive as soon as possible all the necessary information or to correct it; it will be possible to assess the level of current satisfaction of all users, as well as each individual; will help in attracting the target audience to the Internet, as it will be able to control huge amounts of data.
References
1. Strategic Development and Use of Agro-Food Sector's Potential in Rural Areas / Z. Zalilova, M. Lukyanova, V. Kovshov, A. Sharafutdinov // Advances in Social Science, Education and Humanities Research: Proceedings of the Ecological-Socio-Economic Systems: Models of Competition and Cooperation (ESES 2019), Kurgan, Russia, 24 октября 2019 года. - Kurgan, Russia: Atlantis Press, 2020. - P. 156-161.
2. Scenario Method of Strategic Planning and Forecasting the Development of the Rural Economy in Agricultural Complex / M. T. Lukyanova, V. A. Kovshov, Z. A. Galin [et al.] // Scientifica. - 2020. -Vol. 2020. - P. 9124641. - DOI 10.1155/2020/9124641.
3. Залилова, З. А. Экономические факторы и организационные вопросы устойчивого развития отрасли пчеловодства / З. А. Залилова, А. Г. Манна-пов // Вестник Оренбургского государственного университета. - 2008. - № 8(90). - С. 123-127.
4. Лукьянова, М. Т. Практика стратегического планирования отрасли пчеловодства в Республике Башкортостан через маркетинговый и экономический анализ / М. Т. Лукьянова, В. А. Ков-шов, З. А. Залилова // Роль аграрной науки в устойчивом развитии сельских территорий: Сборник III Всероссийской (национальной) научной конференции, Новосибирск, 20 декабря 2018 года. - Новосибирск: Новосибирский государственный аграрный университет, 2018. - С. 1102-1107.
5. Маннапова, Р. А. Статистический анализ развития пчеловодства в разрезе категорий хозяйств / Р. А. Маннапова, З. А. Залилова // Международный журнал прикладных и фундаментальных исследований. - 2012. - № 7. - С. 92-93.
6. Analytical support of management accounting in managing sustainable development of agricultural organizations / A. Zakirova, G. Klychova, G. Ostaev [et al.] // E3S Web of Conferences: Topical Problems of Green Architecture, Civil and Environmental Engineering, TPACEE 2019, Moscow, 20-22 ноября 2019 года. - Moscow: EDP Sciences, 2020. - P. 10008. -DOI 10.1051/e3sconf/202016410008.
7. Improvement of the procedure for assessing
60
ECONOMIC SCIENCES / «ШЦУШШУМ-ЛШШаИ» #ШМЭ), 2021
the personnel of the agricultural organization / A. Za-kirova, G. Klychova, O. Doroshina [et al.] // E3S Web of Conferences: 2018 International Science Conference on Business Technologies for Sustainable Urban Development, SPbWOSCE 2018, St. Petersburg, 10-12
декабря 2018 года. - St. Petersburg: EDP Sciences, 2019. - P. 02073. - DOI 10.1051/e3sconf/201911002073.
УДК 338.001.36
Zalilova Z.A.
Bashkir State Agrarian University DOI: 10.24412/2520-6990-2021-22109-60-61 ABOUT THE FIXED ASSETS OF THE ORGANIZATION
Abstract.
The article describes the role offixed assets in the activities of the enterprise, their necessity and competent accounting in the process of use. The analysis was carried out on the basis of an operating shipyard in dynamics.
Keywords: fixed assets, organization, analysis, necessity, structure.
Fixed assets are labour resources that will take part in the production process, while maintaining the presence of their own natural form. They are intended for the purpose of the needs of the main activity of JSC Blagoveshchensk Shipbuilding and Ship Repair Plant (hereinafter referred to as JSC BSSZ) and should have a use period of over a year.
Fixed assets are material valuables that at the same time fulfill the following conditions: used in the manufacture of products, in the performance of work or the
provision of services or for the purpose of administrative needs of JSC BSSZ; used for a long period, that is, a useful life of more than 12 months or a normal operating cycle, if it exceeds twelve months; the organization does not plan a further resale of these assets; ready to deliver financial benefits (profits) to JSC BSSZ in the future.
Table-1
Fixed assets of JSC BSSZ
2019. Thousands. Roubles 2020. Thousands. Roubles
Types of fixed assets At the start of At the end of At the start of At the end of
the year the year the year the year
Buildings 33823 33823 33823 33823
Facilities and transfer units 22916 22916 22916 22916
Operating equipment 36561 36592 36592 32966
Transportation equipment 9221 9090 9090 8345
Production and household equipment 3221 3113 3113 3109
Other types 6742 6742 6742 6742
Land 1224 1224 1224 1224
Total 113708 113500 113500 109118
Analysing table 1, we can conclude that the largest share of fixed assets was for operating equipment which on average accounted for in 2018 -32.2%, in 2019 -31.2% and in 2020 - 30.2% of the total value of fixed assets. In close second place, are buildings, which on average accounted for in 2018 -29.8%, in 2019 -30.4% and in 2020 - 31.0% of the total value of fixed assets. In third place are facilities and transfer unit's devices, which account for just over 20.0% over the past three years.
Changes in the value of fixed assets increased in 2018 only in the category of operating equipment. There was a decrease in all other categories, except for the buildings category and the facilities and transfer unit's category.
Current assets - a complex enterprise funds required for the development and provision of the petty cash fund and circulating fund.
The current assets include: production reserves -raw materials, auxiliary materials, purchased semi-finished products, fuel, packaging, spare parts for equipment, as well as economic specialized inventory; unfinished goods - subjects of labour that are in production at various stages of processing in the divisions of the enterprise; the company's own semi-finished products - the subjects of labour, the processing of which is completed entirely in one of the divisions of the enterprise, but are available for post-processing in other units of the company; future expenses, which include costs of preparing and mastering the latest products, innovation and creativity.
