NeutroStructured Supply Chains Performance Evaluation: A Neutrosophic Cubic Spherical Framework for Intelligent Information Processing under Indeterminacy and Partial Validity

Modern supply chains are increasingly characterized by uncertainty, incomplete data, and conflicting constraints. Traditional decision-making models often fail to effectively capture such complexity. This paper proposes a novel framework termed the NeutroStructured Supply Chain—which leverages a hybrid extension of neutrosophic theory for intelligent information processing under indeterminacy and partial validity. At its core, the framework integrates two key components: a Neutrosophic Cubic Spherical Fuzzy Set (NCSFS) and NeutroGeometry. The NCSFS unifies point-valued and interval-based membership degrees constrained by a spherical boundary, enabling the simultaneous modeling of precise and imprecise information. NeutroGeometry, on the other hand, generalizes traditional structures by introducing partially valid and indeterminate axioms, thus capturing the ambiguity inherent in complex operational systems. An exponential aggregation operator is developed to synthesize multi-criteria evaluations while preserving neutrosophic characteristics. A case study on supplier selection illustrates the model’s practical utility under uncertain conditions. Results confirm that the proposed approach enhances decision robustness and better reflects real-world constraints compared to classical methods.