Investigating the Potential of Machine Learning and Deep Learning Models in Probabilistic Supply Risk Forecasting: A Case Study in the Automotive Sector

In recent years, the growing number of disruptions across industries has driven researchers to explore the potential of artificial intelligence tools in proactively predicting supply chain risks. A key area of focus has been the use of machine learning and deep learning algorithms to predict supplier punctuality, particularly given the importance of anticipating late deliveries for companies that implement just-in-time or lean manufacturing strategies. However, existing studies have primarily examined the ability of these tools to make deterministic predictions, leaving a gap in understanding their capacity to provide probabilistic predictions in this domain. This paper addresses this gap through a case study investigation in the automotive sector, where the performance of traditional, machine learning, and deep learning models in making probabilistic predictions have been compared. Specifically, accuracy metrics such as coverage probability, sharpness, and interval score have been computed for the different class of models in the examined case study for short term and long-term forecasting horizons. Additionally, the models were assessed in terms of training time and storage requirements, providing a comprehensive comparison of their practical implementation.