Business Informatics

This article considers ways to improve the efficiency of the regulated procurement market by implementing recommender systems into the existing procurement IT infrastructure. Using state, municipal and commercial procurement of electric power products as an example, the article considers promising classes of recommender systems for implementation, proposes a methodology for developing such services, and discloses algorithms for processing, configuring and interpreting data necessary for their operation. The difference between the author’s approach to creating services and previously published works is substantiated, testing and A/B testing are carried out, and an assessment of the effectiveness is presented. The results obtained have scientific novelty (the methodology of using neural networks in relation to the procurement industry has been substantiated) and practical significance (the customer’s time saved on searching for suppliers by up to 40%; the pool of potential suppliers has been expanded; supplier risks have been diversified by selecting relevant procedures from new areas and from new customers; suppliers have been provided with the opportunity to find up to 2–3 new customers for 1 recommendation mailing with a frequency of 1–2 times a week). We proposed to implement the developments in the practice of the operator of public procurement tenders. The authors see further development of recommendation services and solutions for the procurement industry in improving the analysis of semantic (text, logical) content of procurement documents, as well as the behavioral strategies of suppliers. The risks and limitations are associated with the high cost of maintaining a staff of developers-practitioners in neural networks, possible hallucinations of neural networks and their high sensitivity to errors and original data sets.

Megaprojects represent large-scale investment programs with complex organizational structures, uniting a multitude of stakeholders whose interactions lead to the redistribution of power and the creation of temporary management centers. In conditions of unstable and uncertain external environments, such stakeholder behavior can result in the failure to achieve the set goals of the megaproject. An important scientific task is the development of mathematical models and methods for managing changes in megaprojects caused by the integrative actions of stakeholders under complex external conditions. The present study is aimed at creating a mathematical model and developing an information system for neural network analysis of the intensity of changes in megaprojects. Megaproject management is described using a vector-matrix model of a dynamic system with feedback based on the results of changes. To identify recurring patterns of negative events, the event-oriented analysis method was used. This allows for justifying new approaches to management aimed at reducing uncertainty and enhancing the effectiveness of megaproject implementation. Based on the proposed tools, a retrospective neural network analysis of the intensity of changes in the “Nord Stream 2” megaproject was conducted. Within the study, key groups of stakeholders were identified whose interactions significantly impacted the project’s implementation: Group 1 – Gazprom PJSC, European companies and the governments of Russia and Germany supporting the project; Group 2 – the governments of transit countries, the USA, environmental organizations and Baltic region countries opposing the project or expressing concern about its consequences. It was demonstrated that the integration of separate stakeholder groups contributes to the formation of temporary management centers with varying interests, leading to an increase in both positive and negative changes within the project. The outcome of the work was the development of an information system for analyzing the intensity of changes in megaprojects in the form of a prototype, which includes: a mathematical model for managing changes in megaprojects; a neural network analysis methodology based on the use of a large language model for processing textual information and generating quantitative assessments; as well as a software interface for uploading documents, automated data processing, and visualization of results. The primary neural network used was the large language model Qwen 2.5-Plus, which, while not specifically adapted for this task, had its parameters calibrated for analyzing the intensity of changes in megaprojects. The system prototype provides users with the ability to analyze stakeholder interactions, assess the intensity of changes and forecast potential risks based on historical data. A promising direction for further research involves applying the model we developed and neural network analysis methodology for comparative studies of various types of megaprojects.

This paper considers the transformation of methods and models of strategic management based on the ecosystem approach within the framework of the formation of a unified digital platform for management. The ecosystem approach to the socio-economic development of society is gaining popularity as a result of global social requirements for environmental protection and a careful attitude to use of limited natural resources. Environmental problems in the Russian agricultural sector are increasing, in particular due to the process of formation of agro-industrial associations, mainly in the form of agricultural holdings. In this case, there is a problem of a systematic approach to using of technologies for the integration of all types of resources involved in production, taking into account the growing number and importance of environmental factors. Mathematical modeling is proposed as the main method of strategic management research. Unlike most of the existing models, which are often iconographic, the model proposed allows us to consider a larger number of factors. This makes it possible to assess various options of the modeling objects development using a simulation approach. As a result of the research, a mathematical model of strategic management of agroholdings for sustainable development was developed. It is shown that the development strategy should be implemented considering an appropriate automated management information system. This will lead to a radical change in the whole system of management and production. It will allow the enterprise to apply strategic goal-setting, focusing first of all on quality, controllability and other components of competitiveness. The mathematical model proposed provides justification of unified methods of long-term digitalization applicable for large agricultural associations, as well as for small and medium-sized farms, which will be able to cooperate with agricultural holdings relying on the principles of outsourcing.