Aggregated decision system for financial resilience and performance enhancement

Abstract

Organisations functioning in the current unpredictable financial and regulatory environment must formulate strategies that bolster financial resilience while simultaneously improving operational performance amid uncertainty. This study presents an integrated decision-making framework that merges advanced fuzzy logic with robust multi-criteria decision-making techniques to facilitate complex strategic evaluations. Several novel aggregation operators (AOs) are proposed to effectively capture expert hesitation, ambiguity, and nonlinear interactions among criteria. These include the q-rung orthopair fuzzy softmax-Einstein interactive weighted geometric (q-ROFSEIWG) operator and its other types of operators. The operators utilise softmax functions and Einstein interaction to more accurately represent real-world decision-making behaviour. The significance of each evaluation criterion is determined through the logarithmic percentage change-driven objective weighting (LOPCOW) method, which assesses variability in normalised data to facilitate a scale-sensitive and data-driven weighting approach. The MARCOS method is employed to rank strategic alternatives by measuring each option against ideal and anti-ideal solutions, facilitating a thorough utility-based evaluation. The proposed approach offers a flexible and reliable framework for the evaluation of complex decision problems, yielding insights that support consistent, data-driven, and uncertainty-aware decision-making in dynamic, multi-dimensional environments.