METHODOLOGICAL APPROACHES TO THE INTEGRATION OF ECONOMIC AND MATHEMATICAL MODELS INTO DIGITAL MANAGEMENT SYSTEMS
Abstract
The article substantiates methodological approaches to the
integration of economic and mathematical models into digital enterprise management systems
under conditions of accelerated digital transformation and increasing uncertainty of the business
environment. The relevance of the study is determined by the need to transform large volumes of
data generated by modern digital information systems into analytically substantiated management
decisions. The purpose of the study is to develop a methodological approach to integrating
economic and mathematical models into the digital management environment and to substantiate
the architecture of a continuous analytical support system for managerial decision-making.
The methodological basis of the research comprises systemic, process, functional and economicmathematical approaches, as well as methods of analysis, synthesis, formalization, forecasting and
optimization. The study develops an authorial approach according to which an economic and
mathematical model is considered not as an isolated analytical instrument but as an integrated
functional component of the enterprise's digital management system. The proposed approach
provides a continuous transformation of primary data into analytical information, forecasts,
scenarios and management recommendations through the interaction of enterprise resource
planning systems, business intelligence technologies and economic-mathematical models.
The paper systematizes the principal types of economic and mathematical models suitable for
integration into digital management systems, including econometric, predictive, optimization,
simulation, scenario and risk models. A multilevel architecture of model integration is proposed,
comprising data sources, data integration and validation, data storage, the model layer, analytical
processing, visualization and the managerial decision-making layer. Particular attention is paid to
the feedback mechanism, which enables comparison of forecast and actual results, assessment of
the economic effect of managerial decisions and subsequent adjustment of model parameters.
The study proposes a sequence of integration stages, including formulation of the management
problem, identification of information requirements, formation and validation of the data set, model
selection and calibration, technological integration, generation of analytical results,
implementation of management decisions and evaluation of their economic effects. A system of
indicators for assessing the effectiveness of model integration is developed, covering forecasting
accuracy, decision-making speed, analytical resource efficiency, changes in financial performance
and reduction of economic risks.
The practical significance of the proposed methodological approach lies in its applicability to
the digital transformation of enterprise management systems and in its potential for integration
with ERP, BI and artificial intelligence technologies. The proposed approach creates a
methodological basis for developing adaptive digital decision-support systems capable of
combining quantitative modeling, predictive analytics and managerial expertise.
Keywords: economic and mathematical modeling, digital transformation, digital management
systems, enterprise resource planning, business intelligence, predictive analytics, management
decision-making, econometric modeling, optimization, artificial intelligence
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