A Confidence-Interval Circular Intuitionistic Fuzzy Zero Point Model for Optimizing Spare Parts Transfer in Smart Manufacturing Environments

In Industry 4.0 systems, timely delivery of critical components to maintenance points is essential for continuous operation.This paper introduces a novel Confidence-Interval Circular Intuitionistic Fuzzy Zero Point Method (CIC-IFZPM) to optimize the transfer of spare parts in a smart factory setting.The method addresses uncertainty in transfer cost, delivery time, and priority assessment through circular intuitionistic fuzzy sets (C-IFS), which reflect both membership and hesitancy with geometric interpretation.A customized version of the index matrix algorithm integrates transportation constraints, expert confidence intervals, and machine availability limitations.The model is validated through a simulated industrial scenario, where production cells request components dynamically, and a central warehouse must allocate them optimally.Compared to classical fuzzy optimization approaches, the proposed method ensures more robust decision-making under incomplete or imprecise data, offering better performance in real-time control environments.The framework is applicable to predictive maintenance logistics, autonomous scheduling, and industrial resilience planning.