Leveraging Automation & Machine Learning to Alleviate Port Congestion

Across an increasingly fragmented global supply chain, ongoing port congestion has reached a tipping point, with many major ports struggling to process incoming and outgoing shipments. Supply chain visibility platform Beacon recently highlighted the scale of congestion across a range of ports, with an average wait time of 8 days over the first half of 2024 at Durban; wait times averaging 6.1 days at Ningbo-Zhoushan; 4.28 days at Vancouver; 3.6 days in Los Angeles; and an average wait time of 3.4 days at Chittagong. Additionally, it is estimated that $131 billion in trade is at risk of being disrupted at the ports of Singapore, Tanjung Pelepas and Port Klang, which have all been badly backlogged in recent months, mainly due to vessels bypassing the Red Sea. 

Read also: Navigating the Waves: Examining the Looming Threat of Port Congestion

With periods of congestion becoming increasingly frequent, costly and protracted, targeted deployments 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 logical adoption of automation. Often the fear of disrupting established processes and uncertainty about the return on investment keep the status quo modus operandi in place.

Automation & 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. As ports struggle to handle the growing influx of cargo, automated cranes, loaders, and container handling systems can be leveraged to expedite the loading and unloading processes, reducing the turnaround time for ships. Against a backdrop of labour shortages, Automated Guided Vehicles (AGVs) can be used to transport containers within the port efficiently, reducing the reliance on human-operated vehicles. These automated systems can work around the clock, ensuring continuous movement of cargo, while RFID tags, sensors and cameras can verify and process trucks entering and leaving ports.

In order to boost efforts to reduce port delays, AI deployments can be complemented by 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 various sources, including shipping schedules, historical trends, and market conditions, making it easier to predict future cargo flows. Crucially, ML models can predict equipment failures before they occur by analyzing historical data and identifying patterns, enabling more proactive maintenance, reducing downtime and ensuring that equipment is always operational. ML-driven demand forecasting can also help ports prepare for incoming cargo volumes, optimizing resource allocation and minimizing congestion. 

A Look at China’s Smart Ports

There are some compelling examples across the global supply chain that demonstrate the significant efficiency gains associated with ‘smart ports’, which typically combine AI, ML and cloud computing technologies. As of January 2024, it was reported that China had 18 automated container terminals in operation with an additional 27 under construction or being upgraded. By integrating AI, IoT, and automation, Chinese smart ports like Shanghai and Ningbo-Zhoushan have achieved remarkable improvements in cargo handling efficiency, reduced turnaround times, and enhanced overall port operations. Specifically, Tianjin Port has managed to increase the operating efficiency of a single gantry crane by over 40%, while reducing labor costs by 60%. Even though smart ports can still incur delays during intense periods of trade, the degree of congestion can be much more manageable. 

Given China’s dominance in spearheading smart ports, other regions must recognize that failing to adopt automated container cranes, smart logistics, and driverless transport vehicles could leave them at a marked disadvantage. As global trade continues to accelerate and the demand for faster, more efficient logistics grows, embracing AI and ML powered smart port technology can help mitigate the omnipresent threat of port congestion and alleviate bottlenecks in international trade. 

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