Behavior-Informed Disaster Preparedness: Data Engineering Systems for Optimizing Critical Stock and Emergency Supply Chains

Abstract

This article is a research examination of the use of behavior-informed data engineering system as a way of disaster preparedness and response to supply chain. The study propositions are tested by using quantitative data on mobility pattern, purchasing predictors, hazard predictors, and inventory storage models and simulation scenarios. The findings indicate that behavioral signals can evoke prediction of demand surges earlier and decrease stock shortages as well as enhance delivery coverage to impacted areas. Monte Carlo and time series models verify that improvements in performance would be consistent as opposed to traditional systems. The results indicate that the collaboration between behavioral information and real-time data stream can implement considerable resilience to emergency planning, resources, and operational decisions in the event of a disaster.