Unveiling Structural Dynamics in Supply Chains' Resilience: A Multi-Effect High-Frequency Panel Analysis
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i4.1317Keywords:
Supply Chain Resilience, Hierarchical Bayesian Models, Particle Filtering, Resilience Dynamics, Panel Data, Exogenous Shocks, Structural Nonlinearity, Transfer Entropy, Resiliencia de las cadenas de suministro, modelos bayesianos jerárquicos, filtrado de partículas, dinámica de la resilienciaAbstract
This study introduces a new way to predict Supply Chain Resilience (SCR) by combining resilience stages, logistical multi-effects (like Synchronization, Spillover, Bullwhip, Ripple, and Economic effects), and strategic decision-making scenarios. A hierarchical nonlinear state-space model assesses resilience velocity in panel data by integrating structural dynamics, memory effects, and latent exogenous shocks. A Bayesian hierarchical particle filter with partial pooling is used to find the parameters. This method looks at changes at both the unit level and the global level. The framework includes nonlinear relative-change dynamics, an autoregressive memory part, and a latent exogenous variable found through principal component analysis. It also uses methods for updating strong likelihoods and resampling in a way that adapts. Transfer Entropy shows how things are connected in a dynamic way. The model is used to look at the Mexican maritime port system from 2008 to 2023, which includes the COVID-19 pandemic and the global financial crisis. The results indicate that Synchronization serves as the primary resilience driver, Bullwhip functions as an early-warning signal, persistent negative Spillovers occur, and Ripple effects are context-dependent. We use evaluation metrics and visualizations to check how well the model works.
Spanish-language metadata / Metadatos en español
Título en español:
Revelando la dinámica estructural de la resiliencia de las cadenas de suministro: un análisis de panel de alta frecuencia con múltiples efectos
Resumen:
Este estudio introduce una nueva forma de predecir la resiliencia de las cadenas de suministro mediante la combinación de las etapas de resiliencia, múltiples efectos logísticos —como los efectos de sincronización, desbordamiento, látigo, propagación y económico— y escenarios estratégicos de toma de decisiones. Un modelo jerárquico no lineal de espacio de estados evalúa la velocidad de resiliencia en datos de panel mediante la integración de dinámicas estructurales, efectos de memoria y perturbaciones exógenas latentes.
Para estimar los parámetros se utiliza un filtro bayesiano jerárquico de partículas con agrupamiento parcial. Este método analiza los cambios tanto en el nivel de cada unidad como en el nivel global. El marco incorpora dinámicas no lineales de cambio relativo, un componente autorregresivo de memoria y una variable exógena latente identificada mediante análisis de componentes principales. Asimismo, utiliza métodos robustos de actualización de la verosimilitud y procedimientos adaptativos de remuestreo. La entropía de transferencia permite identificar relaciones dinámicas entre las variables.
El modelo se aplica al sistema portuario marítimo mexicano durante el periodo 2008–2023, que incluye la pandemia de COVID-19 y la crisis financiera mundial. Los resultados indican que la sincronización actúa como el principal impulsor de la resiliencia, el efecto látigo funciona como una señal de alerta temprana, persisten efectos negativos de desbordamiento y los efectos de propagación dependen del contexto. El desempeño del modelo se evalúa mediante métricas y visualizaciones.
Palabras Claves:
Resiliencia de las cadenas de suministro; modelos bayesianos jerárquicos; filtrado de partículas; dinámica de la resiliencia; datos de panel; perturbaciones exógenas; no linealidad estructural; entropía de transferencia.
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