Foundation Models for Supply Chain Risk Forecasting: A Case Study

These days, supply chains are complex, exposed to a growing number of risks and vulnerable to unexpected disruptions. In such context, forecasting risk factors is vitally important to improve the level of resilience, and customer demand fluctuation stands out as a critical information for decision makers in supply chain. Besides, inspired by the success of large language models, foundation models pre-trained on extensive datasets have emerged as a novel approach for time series forecasting. However, the impact of these novel forecasting methods in supply chain risk management remains unclear. The objective of this work is to evaluate the feasibility of using foundation models to forecast customer demand in supply chain. We tested foundation models on real-world data from an Italian automotive company, considering multiple levels of aggregation, forecasting horizons, and rolling window lengths. We then compared the results against traditional forecasting approaches, both in terms of execution time and accuracy. Results show that the models achieve good accuracy without needing to be trained on specific scenarios.