From Uncertainty to Decisions: A Fuzzy Logic–Based Theoretical Framework for Intelligent Systems

Organizational decision-making frequently occurs in environments characterized by uncertainty, heterogeneous information, and qualitative expert judgment, which classical quantitative models cannot adequately capture. This article develops a mathematically grounded, fuzzy-logic–based theoretical decision architecture to enhance robustness, interpretability, and scalability in complex organizational systems. Building on an integrative synthesis of recent advances in fuzzy MCDM methods, AI-enhanced fuzzy inference, and sustainability-oriented performance modeling, three dominant research clusters are identified and consolidated into a unified multilayer framework. The proposed model is structured around four interdependent components—contextual conditions, technical fuzzy mechanisms, moderating structures, and observable outcomes—linked through an explicit feedback process formalized via composite fuzzy operators. Rather than introducing new algorithms, the framework specifies how established fuzzy components are functionally differentiated, constrained, and coordinated at the system level. It explains how expert-judgment quality, membership-function calibration, inference engines, interoperability with enterprise systems, and validation and traceability mechanisms jointly determine decision stability and transparency. The model further establishes key formal properties, including monotonicity, boundedness, adaptive stability, and traceable reproducibility, ensuring internal coherence and well-behaved system dynamics. By addressing the lack of unified, reproducible, and scalable architectures in the fuzzy decision-making literature, this study provides a generalizable theoretical foundation for explainable, sustainability-aligned intelligent decision systems under organizational uncertainty.