METHODOLOGY FOR AUTOMATING DECISION-MAKING STAGES UNDER
CONDITIONS OF UNCERTAINTY
https://doi.org /10.59982/18294359-24.2-mr-20
Abstract
Intelligent Decision Making Support Systems (IDMSS) have become essential tools for managing complex systems, particularly in conditions of uncertainty, large volumes of data, and constantly changing external environments. This paper proposes the use of the ASSA (Automation of System Analysis Stages) methodology to significantly improve the structure and enhance the efficiency of IDMSS, which will lead to better decision-making quality and reduced risks associated with uncertainty. In the adaptation process, the stages of IDMSS have been expanded and aligned with the key stages of the ASSA methodology, such as “Problem Definition,” “System Goals Definition,” “System Analysis,” “System Synthesis,” and “System Implementation.” This ensures the systematization of the decision-making process, facilitating a more in-depth and comprehensive analysis of various aspects of the issues organizations face under high uncertainty.
Additionally, new technologies have been applied for each stage of the IDMSS, including modern data analysis methods, machine learning, modeling, and forecasting techniques, making the system more flexible, accurate, and adaptive. The implementation of these technologies enhances the quality, speed, adaptability, and rationale behind decisions, which is especially crucial in a rapidly changing environment and the increasing demands on management. This approach will significantly improve the decision-making process at all levels of the organization, ensuring its long-term sustainability and competitiveness.
Keywords: monitoring system, digital transformation, decision support, systems analysis, information systems
PAGES: 218-224