Impact of Supply Chain Collaboration on the Operational Performance of Textile Enterprises
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Abstract
This study investigates the impact mechanism of supply chain collaboration on the operational performance of textile enterprises through a hybrid modeling framework integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). A textile-oriented latent variable index system is established to characterize collaboration strength, information sharing degree, and operational performance. The PLS-SEM component is employed to quantify the structural relationships among latent variables, while the ANFIS component captures nonlinear dynamic interactions through a Takagi-Sugeno fuzzy inference mechanism and hybrid learning strategy. Monte Carlo simulation is further introduced to evaluate model robustness and parameter reliability. Experimental results demonstrate that the proposed framework effectively reveals the transmission pathways through which collaborative behaviors influence enterprise performance. When collaboration strength increases from 0.2 to 0.6, inventory turnover optimized by the ANFIS module rises from 1.98 to 2.80, indicating a significant nonlinear response to collaborative activities. The proposed approach provides an effective analytical framework for complex industrial systems characterized by intensive information interaction, dynamic decision processes, and distributed operational structures. The study offers a quantitative methodology for understanding collaboration-driven performance evolution and supports intelligent operational optimization in modern manufacturing environments.
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