Performance Evaluation of Cold Chain Logistics using a Backpropagation Neural Network-Based Predictive Framework

This study introduces a performance evaluation model for Cold Chain Logistics (CCL) enterprises utilizing the Backpropagation (BP) Neural Network algorithm. Cold Chain Logistics (CCL) plays a vital role in maintaining the safety and quality of perishable goods, but current performance assessment systems tend to be inefficient, have poor scalability, and cannot identify intricate nonlinear patterns in business data. Conventional methods largely depend on statistical models or heuristics, which cannot guarantee accurate, adaptive performance measurements under dynamic conditions. To address these challenges, this study introduces a framework based on a Backpropagation Neural Network for evaluating the operational efficiency and performance of cold chain logistics enterprises. The proposed model uses historical information and key performance indicators (KPIs) to train the neural network so that more accurate and dynamic performance measurements can be made. Comparison with current models reveals that the BP-based model performs better than conventional systems as far as accuracy and F1-score are concerned, with a higher predictive ability. The experimental results demonstrate enhanced model performance, achieving 98% accuracy and an impressive F1-score of 97.2%, underscoring its capability to effectively manage the complex challenges of cold chain logistics.