Predictive Enterprise Resilience using Advanced Digital Twin Technology and Cloud-Native Machine Learning
DOI:
https://doi.org/10.64235/3g7dtx76Keywords:
Enterprise resilience, digital twin, cloud-native computing, machine learning, predictive analytics, Internet of Things, anomaly detection, disruption prediction, resilience management, cloud infrastructureAbstract
Enterprise resilience has become a critical organizational capability as businesses increasingly operate within interconnected
digital, physical, and cloud-based environments exposed to cyber threats, supply-chain disruptions, infrastructure failures,
market volatility, and operational uncertainty. Advanced digital twin technology combined with cloud-native machine learning
offers a promising approach for predicting disruptions and supporting proactive resilience management. This study examines
how digital twins can provide dynamic representations of enterprise assets, processes, dependencies, and operational states,
while cloud-native machine-learning models analyze real-time and historical data to identify emerging risks and predict potential
disruptions. The proposed approach integrates Internet of Things data, enterprise applications, cloud infrastructure, simulation
models, and machine-learning pipelines into a continuously updated resilience framework. The methodology emphasizes data
acquisition, digital-twin construction, predictive model development, scenario simulation, and resilience evaluation. Machinelearning
techniques can estimate disruption probabilities, detect anomalies, forecast resource requirements, and evaluate alternative
recovery strategies. Cloud-native architecture enables scalable model training, distributed processing, automated deployment,
and continuous learning. The study proposes that integrating these technologies can improve early-warning capabilities, decision
support, adaptive resource allocation, and recovery planning. The resulting framework provides a foundation for enterprises
seeking to move from reactive continuity management toward predictive and continuously adaptive resilience.
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