<p>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.</p>