AI-based evaluation system for supply chain vulnerabilities and resilience amidst external shocks: An empirical approach

Abstract

The study focuses on the intricacies and vulnerabilities inherent in supply chains, which are often influenced by external disruptions such as pandemics, conflict scenarios, and inflation. The aim is to devise an AI-driven system that can accurately appraise these intricacies within the domain and mitigate their vulnerabilities effectively. The work employs an empirical approach utilizing datasets from various studies for developing Machine Learning (ML) and Deep Learning (DL) models. These include linear regression, deep learning, CNN networks are designed to predict supply chain risks and enhance the overall stability and performance of an industrial supply chain system. The Deep CNN regression model outperforms as compared to the other models in predicting supply chain risks, achieving an accuracy rate of approximately 90%. The developed model will be more proficient in dealing with complex and nonlinear relationships among the variables. The study introduces a novel approach to data augmentation using Fuzzy C-means in conjunction with a Deep Convolution network model. This approach expands the data size, reduces the forecast errors, and reduces the computational of the given model. The results highlight the potential of ML and DL in enhancing predictability and resilience in the face of escalating risks within supply chain networks. The findings of the work will significantly offer insights for strategists and planners in the supply chain sector to enhance their operational effectiveness and efficiency.