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

Abstract

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. 

DOI: 10.5281/zenodo.16183029