2782-6333 1 7 2023 1-94

RAR RUS 8-26 Central Russian Institute of Management Branch of RANEPA, Orel, Russian Federation Eremina Irina Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Degtereva Viktoriia Tashkent Institute of Finance, Tashkent, Republic of Uzbekistan Kobulov Khotamjon Tashkent Institute of Finance, Tashkent, Republic of Uzbekistan Yuldasheva Nadira Automation of investment and project management based on the introduction of an enterprise resource planning system in the power grid company Today, there is no universal software product that can fully cover the need for automation of management in all areas of a large company. The purpose of this study is to consider the automation of the asset management of a power grid company based on the introduction of an Enterprise Resource Planning (ERP) system. The subject of the study is the automation of investment and project management in a power grid company. This study uses abstract-logical and economic-statistical methods of information analysis. The automated system for managing investment activities and capital construction through the ERP system was introduced in the work of Rosseti Lenenergo PJSC. Here, the subsystems for project management and investment programme management are considered. The scientific novelty of the study is the fact that the results of the study provide insights for increasing the efficiency of the assets management processes of the power grid company, that is, the process of managing investment and project activities and the process of managing technical inspections and maintenance through automation based on the introduction of an ERP system. Of practical importance is the new integrated solution developed for the process of managing the investment activities of the power grid company, which takes into account the current requirements for project development. The results obtained show that the system developed involves the most ergonomic user interfaces that meet the requirements for convenience and speed. 10.48554/SDEE.2023.1.1 65.011.56 automation management process power grid company ERP system https://sustainable.spbstu.ru/article/2023.7.1/ 1_1.pdf

RAR RUS 28-44 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Pishchalkina Ilona Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia Tereshko Ekaterina

Polytechnicheskaya 29

Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia Suloeva Svetlana

Polytechnicheskaya 29

Application of self-organizing maps for risk assessment of mining and metallurgical enterprises Investigation of risk factor assessment and grouping is relevant because ranked risk groups help companies to navigate achieving their strategic development goals while minimizing the impact of external risk factors. Grouping, carried out with neural network modelling, enables the formation of a self-learning model that can be changed by rearranging the vectors of cluster groups under the influence of turbulent external factors. The aim of this research was to develop a risk factor-prioritizing neural network model for a vertically integrated mining and metallurgical company. To attain this goal, the authors identified risk factors by the mining and metallurgical enterprises’ key activities and allocated them into key groups by forming a risk register. In accordance with the risk register, the degree of influence and probability of each risk factor was assessed using the expert assessment method that allows for calculating the significance of each risk factor. The formation of risk factor groups by significance was carried out using the method of Kohonen self-organizing maps. The DataBase Deductor Studio Academic 5.3 software was used to simulate the results and build the artificial two-layer neural network. The study proved to be effective for (1) identifying the major risks and risk factors inherent in vertically integrated mining and metallurgical companies based on annual company reports; (2) assessing the impact and probability of risk factors using an expert computational method; (3) graphically presenting a two-layer neural network for further simulation; (4) forming five groups using neural simulation based on Kohonen networks; and (5) interpreting the simulation results, identifying the most significant risk in management decision-making and putting forth brief recommendations on using artificial neural networks for risk analysis and assessment. Based on the research results, recommendations on the use of artificial neural networks for risk analysis and assessment for vertically integrated mining and metallurgical companies are provided. The proposed algorithm allows large vertically integrated companies with a complex organizational structure and technological processes, as well as a wide list of risks affecting their activities, to quickly identify the most significant risks. 10.48554/SDEE.2023.1.2 65 risk management neural networks mining and metallurgical industry digital transformation risk analysis https://sustainable.spbstu.ru/article/2023.7.2/ 1_2.pdf

RAR RUS 45-62 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Dubolazov Viktor Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Simakova Zoya National University of Singapore, Singapore Chua Calvin Implementation of digital tools in the operational management of material procurement at machinery enterprises Operational management of material resources has become a major concern for machinery enterprises. Growing interest in this issue rests on multiple reasons, primarily increasing the costs of material resources, their significant effect on efficiency and competitiveness, the complexity of cutting-edge processes involved in resource management, and economic and political turbulence worldwide. In an era when new business processes are emerging, and the older ones are being improved and accelerated, digitalisation is becoming one of the major drivers for innovation in enterprise management. Readjustments also occur in the infrastructure and fabric of departments and staff, alongside the methods of motivation and performance assessment. This paper presents scientific findings from domestic and international research to consider the most urgent challenges that the machinery industry faces in its ongoing digitalisation. Specific attention is paid to the external and internal environment of machinery enterprises, their ability to adapt to dynamic fluctuations in demand, and unpredictable changes in supply and consumption. Further, the authors develop a range of methods and tools aimed at improving efficiency of calculation, and integrating a whole-scale approach to provide an enterprise with materials of the required quantity and quality in a timely manner, with the lowest costs and optimal reserves. 10.48554/SDEE.2023.1.3 338 material supply dynamic multi-type production digitalisation engineering https://sustainable.spbstu.ru/article/2023.7.3/ 1_3.pdf

RAR RUS 64-80 Peter the Great St.Petersburg Polytechnic University, Russia Victorova N. Natalia Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation Karpenko Pavel The Russian-Tajik (Slavonic) University, Dushanbe, Tajikistan Mirazizov Abdullo The Russian-Tajik (Slavonic) University, Dushanbe, Tajikistan Radzhabova Ilmira Holistic approach to managing socially secure development of a regional socio-economic system This paper considers the matters of regional governance and how it can be improved in social and economic aspects to ensure that people have a socially secure life. The insights highlighted here have been aggregated through a holistic research study that includes a theoretical framework and methodological results with regard to the posed problem and determines the goal setting. We clarified the concept of the socially secure development of a regional socio-economic system and elaborated a methodology for quantifying the state of human resources when the socially secure development of a regional socio-economic system is managed. We present an economic and mathematical description of a management model that ensures socially secure development of a regional socio-economic system, and developed and tested an algorithm for managing the development of a regional socio-economic system based on the proposed tools. In this study, we used general scientific methods, as well as economic and mathematical methods, including regression analysis. To quantify the unstructured information, we applied artificial intelligence technologies. The results of the study were tested on the case study of St. Petersburg, the federal city of the Russian Federation. In particular, we proved that the construction of a logistics hub as a major infrastructure project would influence the core of the social security of this region in the near future. 10.48554/SDEE.2023.1.4 338:657.6 management regional socio-economic system social security human resources https://sustainable.spbstu.ru/article/2023.7.4/ 1_4.pdf

RAR RUS 82-94 Volgograd State Technical University, Volgograd Lomakin Nikolai Ural State University of Economics, Yekaterinburg, Russia Maramygin Maxim Plekhanov Russian University of Economics, Moscow, Russia Kuzmina Tatyana 0000-0001-9239-0760 Chemnitz University of Technology, Chemnitz, Germany Tudevdagva Uranchimeg Volgograd State Technical University, Volgograd, Russia Kanchana Vimalarathne Volgograd State Technical University, Volgograd, Russia Lomakin Ivan The decision tree neural network as part of a cognitive model for forecasting the sustainability of the Russian economy This study addresses the problem of sustainable economic growth, a subject that is highly relevant in the current conditions of market uncertainty. Given the importance of having an accurate forecast of GDP in uncertain market conditions, this study proposes a digital cognitive model that includes an artificial intelligence (AI) system decision tree for forecasting GDP values. This study aims to test whether using a cognitive model with the application of the AI system decision tree can afford a more accurate forecast of GDP than known forecasting methods. To achieve this goal, this study: 1) investigated the theoretical fundamentals of sustainable economic growth; 2) identified the development trends of AI systems in economics and finance to create the model’s dataset; and 3) calculated the forecast value of GDP using the digital cognitive model that included the AI system decision tree. The methodology involves the formation and use of a cognitive model that uses a decision tree neural network based on the Python language in the Google Collab cloud environment. Further, monographic, analytical, and computational-constructive methods were used. The results showed that the developed digital cognitive model, which included an AI system decision tree, was capable of forming GDP forecast values under changing external parameters. 10.48554/SDEE.2023.1.5 338.266 digital cognitive model AI system decision tree GDP forecast https://sustainable.spbstu.ru/article/2023.7.5/ 1_5.pdf