Dynamical system modeling for disruption in supply chain and its detection using a data-driven deep learning-based architecture

Background: The COVID-19 was a determining factor in the disruption of supply chains in the automotive industry, exacerbating material shortages. This led to increased supplier order cancelations, longer lead times, and reduced safety inventory levels. Methods: This study analyzes and models supply chain disruptions using system dynamics as a key tool, focusing on the disruptions caused by delays in scheduled orders and their impact on service levels within automotive supply chains in Mexico. This approach allowed us to capture the dynamic relationships and cascading effects associated with inventory shrinkage at Tier 2 suppliers, highlighting how these delays affect the chain's overall performance. In addition to modeling using system dynamics, a deep-learning-based network was proposed to detect disruptions using the data generated by the dynamic model. The network architecture integrates convolutional layers for feature extraction and dense layers for classification, thereby enhancing its ability to identify disruption-related patterns. Results: The performance of the proposed model was evaluated using the AUC metric and compared with alternative methods. The proposed network achieved an AUC of 0.87, outperforming the multilayer perceptron model (AUC = 0.76) and a Neyman-Pearson-based model (AUC = 0.63). These results confirm the superior discriminatory ability of our approach, demonstrating higher accuracy and reliability in detecting disruptions. Furthermore, the dynamical models reveal that the domino effect increases delays in order reception due to the reduction in raw material inventories at Tier 2 suppliers. Conclusions: This paper effectively evaluates the impact of disruptions by demonstrating how reduced service levels propagate through the supply chain.