CJ Logistics Introduces Industry-First AI System for Predicting Cargo Ship Arrival Schedules

[Asia Economy Reporter Dongwoo Lee] CJ Logistics announced on the 1st that it has become the first company in the domestic comprehensive logistics industry to operate the ‘CJ Logistics Cargo Tracking’ system, which can predict the arrival time of cargo ships using artificial intelligence (AI) technology.

CJ Logistics explained that the accuracy of arrival date identification from shipping companies operating cargo ships was about 40%, but after introducing the Cargo Tracking system, the accuracy improved more than twofold to 85%. It is possible to identify the arrival date as well as whether it will be in the morning or afternoon.

The system developed by CJ Logistics predicts the date and time when a cargo ship arrives at an overseas local port through AI technology. To this end, the company developed 18 machine learning-based prediction models. These models analyze and predict the arrival date and time by applying variables such as navigation information, routes, weather, the presence or absence of satellite positioning system (GPS) information along the cargo ship’s route, and the cargo ship’s travel distance.

The company conducted a pilot test for about a month starting in November last year and began full-scale operation of the system this year. Customers who entrust their cargo to CJ Logistics can also check the arrival time through this system.

By being able to know the arrival date of cargo ships more accurately, companies exporting raw materials overseas can reduce so-called safety stock, which is held with a margin to prevent factory shutdowns caused by late arrival of cargo ships. CJ Logistics expects that the introduction of the system will reduce safety stock by about 30 to 40% compared to before.

Reducing safety stock can cut logistics costs related to storage, such as rent and labor costs. It is also expected to have effects such as more accurate manufacturing schedule establishment at production plants and prevention of overproduction. In case the arrival of urgent cargo loaded on cargo ships is delayed, it will be possible to identify this in advance and respond with emergency air transport as a substitute.

In particular, CJ Logistics explained that the arrival time prediction system is receiving great responses from customers amid the surge in demand for cargo ships due to the recent explosion in maritime transport caused by the COVID-19 pandemic.

The company expects that it will be able to improve prediction accuracy by an additional 10 to 15% based on big data accumulated through future system operation. A CJ Logistics official said, “We recognize technological competitiveness as logistics competitiveness and are actively striving to secure logistics super-gap capabilities through the development and introduction of advanced future logistics technologies.”

This content was produced with the assistance of AI translation services.