Optimizing Shipping Operations through Real-Time Monitoring and Control

Abstract

The shipping industry plays a vital role in the global economy, with container shipping being one of the critical components. Shipping companies outline the time for customer stripping days in its contracts. The availability of the containers depends on the stripping days. The stripping days’ tardiness will hinder the availability of the containers. Therefore, it is fundamental for shipping companies to monitor both the actual condition and the contract condition of stripping days to estimate container availability and prompt customers to expedite the unloading process. However, there has yet to be a tool for monitoring the actual and the contract conditions. In this study, we used the recorded container stripping data to analyze container stripping days, tardiness, and other important parameters that indicate the performance and reliability of stripping containers. These data were post-processed and analyzed using data mining methods, and the resulting information was visualized using a dashboard to facilitate quick and effortless monitoring the dashboard in this study depicts post-processed data on container stripping days and tardiness for each port of discharge, cargo, customer, and other parameters. The dashboard was constructed using Google Data Studio. As a result, the dashboard is expected to help companies monitor, control, and analyze customers with high tardiness, allowing companies to act and ensure that the number of available containers after stripping meets demand at a given time.