Experiment shows visualization methods improve decision-making accuracy by 18% in project organizations, indicating efficiency in multi-criteria analysis under uncertainty.
The article examines the potential of data visualization methods to enhance decision-making efficiency in selecting design strategies within project organizations under conditions of multi-criteria alternative strategy selection and uncertainty. A comprehensive approach to multi-criteria analysis result visualizing is presented, based on the integration of Analytic Hierarchy Process, PROMETHEE, and TOPSIS methods. The paper explores the capabilities of primary visualization methods applicable to various data types generated during multi-criteria analysis of alternative strategies, including radar charts, parallel coordinate method, heat maps, and graphs. A modular software product structure is proposed, enabling flexible system adaptation to various tasks and application conditions, incorporating components for project management, criteria and strategies, multi-criteria analysis modules, efficiency assessment, visualization, and sensitivity analysis. Based on the developed decision support software product, specific visualization examples are demonstrated, contributing to improved analysis result interpretation. An experimental study was conducted at the project organization 'Application Integration Center' LLC with 20 experts, which established that the application of the developed visualization methods complex leads to a 27% analysis time reduction, an 18% increase in decision accuracy, and a 23.5% increase in confidence in decisions made. The study demonstrates how the purposeful use of visual representations can significantly improve the perception of complex multidimensional information by decision-makers and strengthen the validity of optimal design strategy selection. The research results contribute to expanding decision support capabilities through new technical solutions to enhance the efficiency of modern project organizations under conditions of complexity and uncertainty.
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Borzykh et al. (2025) studied this question.
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