Artificial Neural Network for Enhancing Supply Chain Risk Management

The importance of supply chain (SC) resilience and performance has been underscored in response to recent disruptions caused by pandemics and other crises. Furthermore, there is a growing recognition of the importance of developments in information processing techniques, notably artificial intelligence (AI), in supporting supply chain risk management (SCRM) as the SC continues to expand towards digitalization, integration, and globalization. Modern organizations need to handle risks within the SC effectively. By examining and utilizing this information, companies can pinpoint potential risks and opportunities for improvement. Consequently, an effective approach is necessary for managing SC data and providing precise predictions to support decision-making. This paper introduces an artificial neural network (ANN) as a predictive tool for risk management within the SC. The ANN model is trained using real-world data obtained from a global automotive manufacturer. Demonstrating strong performance on a dataset comprising 9200 samples, the model exhibits notable accuracy in risk prediction, as indicated by the low mean square error (MSE). The proposed model excels in identifying potential risks within the SC and provides excellent decision support by effectively recognizing potential dangers within the SC. These results significantly improve the SC's capacity for risk prediction, which provides invaluable support for business operations decision-making.