Fuzzy-Integrated Modular Neural Networks for Accurate Prediction of On-Time-In-Full Supply Chain Performance

Accurate prediction of supply chain performance is essential for improving operational efficiency and enabling proactive, data-driven decision-making under dynamic and uncertain conditions. Conventional forecasting methods often struggle to capture the nonlinear relationships between operational factors and performance outcomes. This paper proposes an improved neural modeling framework for predicting supply chain performance based on artificial neural networks (ANNs). The proposed approach compares a mono-network model (global ANN) with a modular multi-network architecture composed of several local neural models integrated through a fuzzy fusion mechanism. Unlike existing studies that focus on isolated performance metrics, this work targets the prediction of the key composite indicator On-Time-In-Full (OTIF). Simulation experiments conducted on a nonlinear dynamic supply chain system demonstrate that the modular ANN approach achieves a significant reduction in learning error, dropping from 0.0223 in the global model to as low as 0.0004 in local modules. Furthermore, the total training time was reduced from 1631.58 seconds to an average of approximately 311 seconds per module. These results confirm that fuzzy-integrated modular architectures offer superior generalization and computational efficiency for advanced predictive analytics in complex supply chain management (SCM) environments.