Leveraging automation and machine learning to ease port

In an increasingly fragmented global supply chain, ongoing port congestion has reached a tipping point, with many major ports struggling to handle incoming and outgoing cargo. Highlight The congestion level of each port is as follows: in the first half of 2024, the average waiting time at Durban Port is 8 days; the average waiting time at Ningbo-Zhoushan Port is 6.1 days; the average waiting time at Vancouver Port is 4.28 days; the average waiting time at Los Angeles Port is 3.6 days; and the average waiting time at Chittagong Port is 3.4 days. In addition, Estimated At risk is $131 billion in trade at ports such as Singapore, Tanjung Pelepas and Port Klang, where cargo has been severely backlogged in recent months, largely because ships have been diverted around the Red Sea.

See also: Riding the wave: Examining the looming threat of port congestion

As congestion periods become more frequent, costly and prolonged, targeted deployment of automation can streamline cargo handling, reduce manual errors and mitigate the risk of delays. However, even when ports come to a standstill, the risk-averse tendencies of logistics operators can hinder the rational adoption of automation. Often, concerns about disrupting established processes and uncertainty about the return on investment keep the status quo in place.

Automation and machine learning in action

Automated systems, such as AI-driven predictive analytics, real-time tracking, and robotic process automation (RPA), can mitigate the risk of port congestion by improving operational throughput and decision-making capabilities. As ports struggle to handle the increasing influx of cargo, automated cranes, loaders, and container handling systems can be used to speed up the loading and unloading process, thereby reducing vessel turnaround times. Amid labor shortages, automated guided vehicles (AGVs) can be used to efficiently transport containers within ports, reducing reliance on manually operated vehicles. These automated systems can work around the clock to ensure the continuous movement of cargo, while RFID tags, sensors, and cameras can verify and process trucks entering and leaving the port.

To increase efforts to reduce port delays, AI deployments can be complemented with machine learning innovations to enhance real-time data analysis while enabling predictive maintenance and more efficient resource allocation. Machine learning algorithms can also analyze data from a variety of sources, including shipping schedules, historical trends, and market conditions, making it easier to predict future cargo flows. Crucially, machine learning models can predict equipment failures by analyzing historical data and identifying patterns, enabling more proactive maintenance, reducing downtime, and ensuring equipment is always functioning properly. Machine learning-driven demand forecasting can also help ports prepare for upcoming cargo volumes, optimize resource allocation, and minimize congestion.

A look at China’s smart ports

There are some high-profile examples in global supply chains demonstrating significant efficiency gains from “smart ports,” which often combine artificial intelligence, machine learning, and cloud computing technologies. Reported China currently has 18 automated container terminals in operation, with another 27 under construction or being upgraded. By integrating artificial intelligence, the Internet of Things, and automation, Chinese smart ports such as Shanghai and Ningbo-Zhoushan have achieved significant improvements in cargo handling efficiency, reduced turnaround times, and overall port operations. Specifically, Tianjin Port The operating efficiency of a single gantry crane can be increased by more than 40%, and labor costs can be reduced by 60%. Although delays will still occur in the smart port during peak trade periods, the degree of congestion can be greatly reduced.

Given China’s dominance in smart ports, other regions must recognize that they could be at a significant disadvantage if they do not adopt automated container cranes, smart logistics, and driverless transport vehicles. As global trade continues to accelerate and the demand for faster and more efficient logistics grows, the adoption of smart port technologies driven by artificial intelligence and machine learning can help alleviate the ubiquitous threat of port congestion and relieve bottlenecks in international trade.