A Digital Twin Framework for Proactive Enterprise Management: Research on Operational Decision-making Integrating IoT...

Digital twin technology provides a powerful computational approach for representing and controlling complex enterprise systems by continuously integrating real-time sensing with dynamic modeling and predictive analytics. The main aim of this study is to develop and demonstrate a reproducible protocol for constructing an IoT-enabled digital twin that enables proactive, data-driven enterprise management. The methodology integrates nonlinear dynamic modeling, observer-based state estimation, uncertainty propagation, risk quantification, and stochastic model predictive control into a unified cyber-physical decision framework. Simulated sensor measurements are assimilated by a state estimator to synchronize the digital twin with the modeled enterprise system, enabling simulation-based prediction of future operational states within a defined computational environment. Uncertainty and risk measures are propagated through the dynamic model to identify emerging instabilities before they escalate into critical failures. A predictive control layer then optimizes multi-objective performance by generating control actions intended to reduce operational risk, stabilize system behavior, and evaluate potential long-term operational efficiency improvements within the simulated scenario. Representative results obtained from a simplified production-inventory-energy simulation model demonstrate the potential advantages of the digital-twin-driven method compared with conventional reactive strategies. The computational example shows smoother operational trajectories, lower cumulative risk, reduced uncertainty, and improved control efficiency across several evaluated performance dimensions. However, these findings are based on a defined simulation workflow and should be interpreted as a proof-of-concept demonstration rather than direct evidence of universal real-world performance. Although the protocol is computational, platform-independent, and potentially adaptable to enterprise domains such as manufacturing, logistics, supply-chain operations, and energy-intensive systems, broader deployment in practical industrial environments would require additional validation using real operational datasets and large-scale implementation studies. By following this procedure, researchers and practitioners can reproduce and further evaluate a scalable, data-driven, and risk-aware digital twin framework for enterprise decision-support applications.