, 2022 (1 Vol 16) http://bijournal.hse.ru en-us Copyright 2022 Mon, 28 Mar 2022 13:46:29 +0300 Simulation of migration and demographic processes using FLAME GPU https://bijournal.hse.ru/en/2022--1 Vol 16/580904904.html       This article presents an approach to modeling migration and demographic processes using a framework designed for large-scale agent-based modeling – FLAME GPU. This approach is based on the previously developed simulation model of interaction between two communities: migrants and natives that is implemented in the AnyLogic simulation software. The model has had a low dimensionality of the discrete space representing the operating environment of the agent populations and a deterministic decision-making system of each agent. At the same time, the presence of multiple interactions between agents and transitions between their states determines a high computational complexity of such a model. The use of FLAME GPU makes it possible to conduct extensive simulation experiments with the model, mainly due to the parallelization of computational processes at the level of each agent, as well as the implementation of the mechanism of multiple computations using Monte Carlo techniques. The developed framework is used to study the impact of the most important parameters of the model (e.g., rate of migration, governmental expenditures on integration, frequency of creation of new workplaces, etc.) on the key outputs of the modeled socio-economic system (in particular, population size, share of migrants, number of assimilated migrants, GDP growth rate, etc.). The proposed approach can be used to develop decision-making systems for planning the hiring of new employees based on the forecast dynamics of migration and demographic processes. Analysing the firm failure process using Bayesian networks https://bijournal.hse.ru/en/2022--1 Vol 16/580905414.html       This work analyses the firm failure process stages using the Bayesian Network as a modelling tool because it allows us to identify causal relationships in the firm profile. We use publicly available data on French, Italian and Russian firms containing five samples corresponding to periods from one to five years before observation. Our results confirm that there is a difference between the stages of the failure process. For firms at the beginning of a lengthy process (3–5 years before observation), cumulative profitability is the key that determines liquidity. Then, as the process develops, leverage comes to the fore in the medium term (1–2 years before observation) for economies with more uncertainty. This factor limits the opportunities for making a profit, leading to further development of the failure. There are also national specifics that are caused, firstly, by the level of economic development and, secondly, economic policy uncertainty. Information-logical model of express analysis of the state of the enterprise that meets the requirements of standards and regulations, based on publicly available data https://bijournal.hse.ru/en/2022--1 Vol 16/580905966.html       The last 10 years have witnessed an explosive growth in the volume of information posted on the Internet and the digital economy, as well as the formation of official databases of various public authorities. The availability of a large information base open for research has facilitated the development of new methods and approaches to solving analytical problems. Building management and decision-making support systems based on the use of united disparate open data sources allows end users to make the most effective decisions. This is the approach that underpins business growth and managerial maturity at all levels – there is no alternative. Such an approach ultimately creates the conditions for further growth of the economy as a whole. This paper proposes the information and logical model of express analysis of compliance of socio-economic condition of the enterprise with the regulatory requirements of the control and supervisory authorities on the basis of open, publicly available information. The conclusions drawn on the basis of express analysis serve as a basis for deciding on the need for a more detailed, in-depth analysis of the state of individual enterprises. Digital transformation of music aggregation and distribution companies: The case of Russia https://bijournal.hse.ru/en/2022--1 Vol 16/580907858.html       Currently distributors ensure the operation of the whole value chain in the music industry, while most researchers focus on technological, streaming and copyright impact in light of digitalization. This paper tries to understand the influence of digitalization on business models and the role of music distributors in a value chain. Research identifies operational processes that were changed due to digitalization, barriers that arose, and actions taken to overcome them on the corporate level. Through retrospective case study based on an interview with the CEO, the experience of the Russian music distribution company Broma16 is analyzed. This paper describes the goals of business model transformation, drivers, performed activities and their results. The research derives four consequences of digitalization for the firm’s business model and role in a value chain: the firm can be considered as a technological company rather than a music company. Distribution relies on digital instruments for management and marketing. Local intermediaries get more opportunities to enter foreign markets, but they have to perform innovations in these markets. Music distributors operate at the complicated intersection of copyright and technological aspects. The research applies a general theoretical framework for the study of digital transformation of business models. A similar approach can be used to do research on companies in music and other creative industries, and to conduct workshops with industry representatives. The paper provides value for practitioners in emerging music markets, for example, Brazil, Argentina and Mexico, due to the presentation of management practices towards digitalization and the consequences of transformation for the distribution role. Customer segmentation using k-means clustering for developing sustainable marketing strategies https://bijournal.hse.ru/en/2022--1 Vol 16/580909688.html Sales and marketing is the indispensable department of an organization which leads to the generation of revenue and building customer relationship. Marketing is the process of finding the potential customers and sales is the process of converting those potential customers into real customers. Hence, it is imperative that marketing and sales go hand in hand. Developing marketing strategies needs proper market research which can cover the relevant pointers like demographics, culture, spending power, income and many more. The process of segmentation, targeting and positioning (STP) is carried out to develop marketing and sales strategies. STP is done by collection of the marketing intelligence. For this process, surveys are also used but data mining has far more effective and better results so far. Organizations tend to take risk because of the importance and relevance of the marketing and sales department. Most of the budget in the organizations is allocated for marketing and promotional activities. For making data-driven and accurate decisions, data mining is used in various fields to extract valuable information and patterns. This paper discusses the use of the data mining concept on marketing. This paper aims to analyze marketing data with k-means data mining clustering techniques and to find the relationship between marketing and k-means data mining clustering techniques Centralized resource allocation based on energy saving and environmental pollution reduction using data envelopment analysis models https://bijournal.hse.ru/en/2022--1 Vol 16/580915822.html       Environmental pollution has caused governments to be concerned about energy saving and the reduction of environmental pollution. Some researchers have presented resource allocation models as multi-objective linear programming (MOLP) in order to pay more attention to energy saving and environmental pollution reduction. Energy saving affects both desirable and undesirable outputs. In this paper, we argue for the inapplicability of the existing models for reducing the undesirable outputs through energy saving. The purpose of this paper is to design a model based on data envelopment analysis (DEA) that would result in reduced pollution through energy saving. Moreover, since an undesirable output is considered as a function of the total desirable outputs, if necessary, the changes should be applied to the total desirable outputs and there is no need to reduce each desirable output individually. Finally, the model proposed based on goal programming (GP) is used in 20 different regions in China. The results produced by this model indicate that the reduction proportion of total environmental pollution emissions per energy saving was larger than the reduction proportion of total desirable outputs.