A Framework for Aggregating Timely and Potential Risks to Create Robust Supply Chains

Timely evaluation of risk factors is essential for building resilient supply chains and requires continuous monitoring and updating. This paper proposes a conceptual model for supply chain risk management that uses large language models (LLMs) to detect a disruption trigger event from news data, determines its impact, and develops a proper risk mitigation strategy. Even though the conceptual model can accommodate many kinds of supply chain risks, this study focuses on natural hazard risks affecting global supply chains. Because natural hazards are related to the locations of the suppliers, the model uses country level natural hazard risk scores to evaluate suppliers. Here, a country’s risk score determined by the Index for Risk Management (INFORM) is used as a baseline risk score for each country, which are then, updated by the disruption trigger event’s features captured by the LLM. Lastly, a supply chain optimization model is used to determine alternative suppliers to mitigate the impact of risk on disruption. The conceptual model is demonstrated through the case study of a bicycle company. Preliminary results indicate that the proposed model can be used to improve the resilience of supply chains against natural hazards.