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International logistics is becoming increasingly active. Marine transportation, in particular, accounts for approximately 90% of the total volume managed in international logistics and plays a vital role in the supply chains of many companies. However, en route factors, such as weather conditions, often delay scheduled arrivals at destination ports, and an accurate prediction of the arrival time is required for supply chain efficiency. The arrival time has been predicted in previous studies by calculating the route to the destination port and the en route voyage speed without considering the influence of future weather conditions. Hence, the prediction accuracy may decrease when weather conditions change. In this study, we propose a prediction method that identifies the route from the voyage results of vessels whose weather condition is similar to the future one and uses Bayesian learning to calculate the voyage speed in consideration of future weather conditions. Consequently, future changes in weather conditions are reflected in the prediction results. The prediction accuracy of the proposed method is projected to be 28% higher than that from previous studies based on historical operational data of vessels carrying home appliance and automobile industry cargoes.

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