Digital Twin Simulation for Optimizing Emergency Response and Evacuation Protocols in Large-Scale Manufacturing Environments

Abstract

This study examined how digital twin simulation optimizes emergency response and evacuation protocols in large-scale manufacturing environments where static safety plans often fail to reflect dynamic hazards, blocked routes, worker-density variation, machinery risks, and real-time coordination needs. The problem addressed was the limited adaptability of traditional emergency planning in complex industrial facilities, where emergencies such as fire, chemical leakage, machinery explosion, blocked exits, and power disruption can rapidly affect evacuation safety and response efficiency. The purpose was to quantify the influence of digital twin simulation on five key emergency-management outcomes: emergency response efficiency, evacuation route optimization, bottleneck detection, coordination and decision-making, and preparedness confidence. A quantitative, cross-sectional, case-based design was adopted, using structured 5-point Likert-scale survey data from cloud-enabled and enterprise-oriented manufacturing safety cases involving plant engineers, safety officers, supervisors, emergency response personnel, and operations managers. From 230 distributed questionnaires, 214 valid responses were analyzed, producing a usable response rate of 93.0%. The analysis plan included descriptive statistics, reliability testing, correlation analysis, and regression modeling using SPSS, with Excel used for data preparation. Findings showed high perceived digital twin capability with a mean of 4.18 and strong ratings for emergency response efficiency (M = 4.09), evacuation route optimization (M = 4.14), bottleneck detection (M = 4.11), coordination and decision-making (M = 4.07), and preparedness confidence (M = 4.16). Reliability was strong, with Cronbach’s alpha values ranging from 0.84 to 0.89 and an overall scale alpha of 0.91. Correlation results showed significant positive relationships between digital twin simulation and all outcome variables, ranging from r = .67 to r = .76. Regression results confirmed that digital twin simulation significantly predicted emergency response efficiency (R² = .504), route optimization (R² = .548), bottleneck detection (R² = .476), coordination and decision-making (R² = .449), and preparedness confidence (R² = .578). The headline finding is that digital twin simulation most strongly improved preparedness confidence and evacuation route optimization, implying that manufacturing firms can strengthen worker safety, hazard anticipation, route intelligence, and organizational resilience by integrating digital twins into emergency planning systems.