Lacking information challenges the managementof port operational uncertainty in estimating the situation tosupport a decision on reactivity planning. This paper applies Digital Twin (DT) to model a replicated virtual port operationfrom a real-world port of Thailand. The proposed DT modeloffers a tool to accelerate generating data of the port operation with configurable uncertainty. The model is validated by using generated data from the DT model compared with the real-world data. The result shows that the DT model produces the same behaviour as the real-world system. An outcome of this paper is a DT model eligible to generate port operation data for later applying with a machine learning to predict the port capacity under uncertainty to support reactivity planning.