Supply chain risk management is a critical challenge in today’s increasingly complex and interconnected global markets, particularly within specific supply chains where disruptions can have far-reaching consequences. Generative Artificial Intelligence (GAI) transformer models have emerged as powerful tools for effectively managing these risks. However, selecting the most suitable GAI model for specific supply chain contexts remains a significant challenge due to the diverse range of available models and the complex interplay of risk factors involved. This challenge is further compounded by the necessity of considering human-centric criteria to ensure that the chosen model aligns with ethical standards and practical needs. This paper addresses this challenge by introducing an enhanced multi-criteria decision-making (MCDM) framework that refines the Evaluation based on Distance from Average Solution (EDAS) method. Our approach first improves the logical structure of the EDAS method and then incorporates the interactions and interdependencies between criteria, thereby overcoming key limitations of traditional MCDM methods and providing a more accurate and comprehensive evaluation process. We applied this improved EDAS model to the task of selecting the best GAI transformer model for risk management in the food supply chain. Through a systematic evaluation of various GAI models, considering their performance across multiple risk factors, our study identified GPT (Generative Pre-trained Transformer) as the most suitable model for this context, demonstrating superior capabilities in addressing the complex challenges associated with food supply chain risks. This research not only advances the theoretical foundation of MCDM techniques but also offers practical insights into the application of AI in supply chain management, highlighting the importance of human-centric AI approaches that prioritize transparency, ethical alignment, and effective decision-making.