INTELLIGENT INFORMATION SYSTEMS FOR SUPPORTING MANAGERIAL DECISIONS
DOI:
https://doi.org/10.32782/2412-9208-2026-2-209-221Keywords:
intelligent information systems, decision support systems, managerial decisions, data analysis, big data processing, data mining, research and development; educational management.Abstract
The article develops a technical approach to designing intelligent information systems for supporting managerial decisions under conditions of large-scale heterogeneous data, multiple criteria, and the need for rapid choice among alternatives. The relevance of the topic is driven by the transition of organizations toward data-centric management, the spread of predictive analytics, knowledge discovery, large-scale data processing, and explainable artificial intelligence. The aim of the study is to formalize the architecture of an intelligent information system, propose a hybrid decision-support model that combines predictive analytics with multi-criteria ranking, and verify its performance through a simulation experiment on research and development portfolio prioritization. The study applies system analysis, structural and functional modeling, data normalization, machine learning, multi-criteria evaluation, scenario-based experimentation, and comparative assessment using ranking quality metrics. The proposed model includes data integration and validation loops, a knowledge base, an analytical core, an explanation subsystem, and a feedback mechanism. Within the experiment, a synthetic dataset of 600 alternatives across 120 decision sessions was generated, which enabled the comparison of rule- based selection, static ranking, gradient boosting, and a hybrid system. The hybrid model improved Top-1 accuracy to 0.867 versus 0.742 for rules and reduced mean regret to 0.0022. The scientific novelty lies in combining predicted utility with integrated multi-criteria scoring and in building a reproducible simulation bench for evaluating decision support quality. The practical significance lies in the ability to apply the proposed approach to project selection, resource allocation, risk management, and priority setting in corporate, research-intensive, and educational management environments.
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