In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments.

Extraordinary emergencies trigger disruptive demand surges that frequently exceed the operational limits of existing supply chains. While traditional studies focus on optimizing stock resource efficiency, the mobilization of emergency production to generate incremental resources is critical under extreme shocks. However, a standardized methodology for assessing the “stress tolerance limit” of emergency material supply chains (EMSCs) remains lacking. This paper establishes a theoretical framework for EMSC stress testing, integrating conceptual definitions, operational mechanisms, and a standardized implementation procedure. To demonstrate its practical applicability, a multi-objective mathematical model is developed and applied to a case study of ibuprofen production during a sudden crisis. By identifying structural bottlenecks such as production latency and supply lead-time gaps, the results validate that the proposed framework provides a reproducible quantitative approach for evaluating EMSC supply capacity. This study offers guidance for prepositioned inventory and dynamic capacity reserve, fundamentally enhancing societal risk mitigation capabilities under extreme stress.