Decision support systems and determination of the degree of determinism of information models in technical systems

Using AI to optimize management processes and decision-making in large organizations

The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.

THE ROLE OF DIGITAL COMPETENCE IN THE ENTERPRISE FINANCIAL MANAGEMENT SYSTEM

The article substantiates the role of digital competence in the enterprise financial management system under the active spread of analytical platforms, enterprise resource planning systems, cloud services, automated data exchange and artificial intelligence-based technologies. The purpose of the study is to deepen the theoretical understanding of the digital competence of a financial manager and to develop a structural and functional approach to determining its influence on planning, budgeting, cash flow management, evaluation of investment decisions, financial risk management, management reporting and forecasting. The methodology combines systems, competence-based and functional approaches, content analysis of scientific sources, structuring, comparison and an author-developed scoring assessment of the functional significance of individual components of digital competence. Seven interrelated components are identified, including data handling, proficiency in financial information systems, analytics and visualization, automation and the use of artificial intelligence-based systems, cybersecurity, digital communication, critical verification of algorithmic outputs and digital ethics. The study establishes that digital competence should be treated as a management resource that converts technological capabilities into high-quality financial decisions, while its insufficient level creates a gap between the availability of information infrastructure and the enterprise’s actual ability to use data for management. The author’s functional assessment produces an overall coefficient of 0.865 for the relationship between digital competence and the core functions of financial management. The strongest dependence is found for management reporting and financial control, with a coefficient of 0.952, followed by operational cash flow management, with a coefficient of 0.905. Among the competence components, the greatest functional significance belongs to data handling, with a coefficient of 1, and analytics and visualization, with a coefficient of 0.944. The scientific novelty lies in improving the theoretical approach to digital competence by directly linking its components with the functional tasks of financial management and determining a progression from a basic to an integrated level of competence development. The practical significance of the results lies in the possibility of applying the proposed approach to job profiling, professional development programmes, internal competence assessment and the identification of training priorities for financial managers at enterprises.

The Intelligent Decision Support System in Comprehensive Management of Enterprise Assortment and Inventory Under Rapid Market Transformations

The article explores the theoretical and practical aspects of managing enterprise assortment and inventory under fast-changing market conditions, which are characterized by high demand dynamics, external uncertainties, and growing data volumes. The feasibility of using intelligent decision support systems as a tool to improve the efficiency of assortment and inventory management is substantiated. A conceptual model of such a system is proposed, representing an integrated information-analytical environment that ensures a full data processing cycle – from collection to managerial decision-making. The system’s structure includes a number of interconnected functional modules. The data collection and integration module ensures the consolidation of information from internal and external sources, as well as its cleaning, normalization, aggregation, and structuring. The data storage and management module is responsible for organizing a unified information space, controlling data quality, keeping it up to date, indexing, and ensuring quick access to information. The analytics module implements a range of statistical analysis methods, including correlation, regression, and cluster analysis, as well as time series decomposition, which helps identify patterns and factors affecting demand. The forecasting subsystem is based on a combination of classic time series models and modern machine learning methods, including LSTM neural networks and ensemble approaches, which improves forecasting accuracy in conditions of unstable demand. The optimization and decision support module implements economic and mathematical models for inventory and assortment management, as well as multi-criteria optimization problems. An important feature is scenario modeling, which allows evaluating different market development options and choosing the most resilient managerial decisions considering risks and uncertainty. The user interface provides visualization of analysis results through dashboards, charts, and analytical reports, as well as recommendations for inventory management and assortment planning. The system’s interactivity makes it possible to run «what–if» scenarios and support real-time decision-making. The proposed conceptual model helps improve demand forecasting accuracy, optimize inventory levels, reduce costs, and minimize the risks of shortages or surpluses, which enhances the company’s operational efficiency and strengthens its economic security.

A Comprehensive Model for Assessing the Organizational and Economic Efficiency of Business Process Automation Using AI Agents

This article proposes a comprehensive model for assessing the organizational and economic efficiency of business process automation using AI agents. It substantiates the inadequacy of traditional labor cost savings calculations, as agent systems simultaneously generate direct, indirect, and risk-dependent effects, as well as require costs for integration, computing resources, data, monitoring, and managing organizational changes. A multi-level system of metrics has been developed, including economic, process, quality, risk-based, and organizational frameworks. A total cost of ownership model, a procedure for calculating risk-adjusted cash flow, and an integrated performance index for agent-based automation have been developed. The performance assessment model was applied to a simulation scenario for automating the end-to-end process of processing contractual applications at a defense industry enterprise in the Ryazan Region. It has been established that the positive local effect of an individual AI agent does not guarantee an increase in the efficiency of the overall process unless the assessment takes into account the costs of verifying results, handling exceptions, resolving errors, and redistributing personnel functions.

Features of time management application in digital companies in the system of productivity increase

The article reveals the features of time management application in digital companies in the system of productivity improvement. It is substantiated that in the conditions of active use of applied digital technologies, remote and hybrid employment, high speed of information exchange and constant change of market priorities, time acquires the importance of a strategic management resource. It is determined that digital companies operate in a specific internal organizational environment, where the effectiveness of activities depends not only on the professional competencies of the staff, but also on the ability of management to rationally organize work processes, set realistic deadlines, coordinate team actions, reduce information overload and maintain a balance between the speed of task completion and the quality of the final result. It is outlined that time management in digital companies cannot be reduced only to the personal self-organization of employees, as it encompasses a broader management area related to planning, prioritization, coordination of teamwork, regulation of digital communication, performance assessment and the use of automation tools. The emphasis is on the fact that project management systems, electronic calendars, corporate messengers, analytical platforms, customer relationship management systems, enterprise resource planning systems and systems based on artificial intelligence can significantly increase the effectiveness of activities only if they are combined with a well-thought-out organizational culture. At the same time, an excessive number of digital tools without uniform rules of use can lead to chaos, duplication of tasks, fragmentation of attention and a decrease in staff productivity. It is proven that effective time management helps reduce downtime, reduce loss of working time, improve the quality of team interaction, form responsibility for the result and prevent professional burnout. Special attention is paid to the need to evaluate productivity not only by the number of actions performed or the duration of an employee's online stay, but by the real contribution to achieving the company's goals. It is concluded that time management in digital companies is an important factor in increasing productivity, as it ensures consistency between technological capabilities, human potential, the quality of management decisions and strategic guidelines for business development.

Design of a decision-making system for monitoring and early identification of underperformance risks in companies with multiple subsidiaries