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Aggregated decision system for financial resilience and performance enhancement
1School of Business Administration, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China.
Scientific Reports
|October 24, 2025
Summary
This study introduces a novel decision-making framework combining fuzzy logic and multi-criteria decision-making to enhance organizational financial resilience and operational performance in uncertain environments. It offers a reliable approach for complex strategic evaluations.
Area of Science:
- Operations Research
- Decision Science
- Fuzzy Logic Systems
Background:
- Organizations face challenges in unpredictable financial and regulatory landscapes.
- Balancing financial resilience with operational performance amid uncertainty is critical.
- Existing decision-making models may not adequately capture complex interactions and expert ambiguity.
Purpose of the Study:
- To develop an integrated decision-making framework for strategic evaluation.
- To enhance organizational resilience and operational performance under uncertainty.
- To propose novel methods for handling ambiguity and interactions in decision criteria.
Main Methods:
- Integration of fuzzy logic with multi-criteria decision-making (MCDM) techniques.
- Development of novel aggregation operators (AOs), including q-rung orthopair fuzzy softmax-Einstein interactive weighted geometric (q-ROFSEIWG) operators.
- Application of the logarithmic percentage change-driven objective weighting (LOPCOW) method for criterion significance.
- Utilization of the MARCOS method for ranking strategic alternatives.
Main Results:
- Proposed AOs effectively capture expert hesitation, ambiguity, and nonlinear interactions.
- LOPCOW method provides a scale-sensitive, data-driven approach to objective weighting.
- MARCOS method enables thorough utility-based evaluation of strategic options.
- The framework demonstrates flexibility and reliability in dynamic, multi-dimensional environments.
Conclusions:
- The integrated framework supports consistent, data-driven, and uncertainty-aware strategic decision-making.
- The novel AOs and weighting methods improve the accuracy of complex evaluations.
- This approach aids organizations in navigating unpredictable financial and regulatory conditions.
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