Artificial intelligence and digital air cargo booking

Artificial intelligence is playing an increasingly important role in digital air cargo booking, as companies look to improve efficiencies, reduce costs and enhance the customer experience. 

Improved pricing  

Artificial intelligence (AI) collects and analyzes data on factors such as demand, supply and cost to provide shippers with real-time pricing, allowing them to make more informed decisions about when and where to ship their cargo and to get the best price. 

Maersk, a global logistics company, is using AI technology for digital air cargo booking. Maersk has developed an AI-powered digital platform, Maersk Spot, that enables shippers to book air cargo space online in real-time. 

The platform uses algorithms and machine learning to provide shippers with instant quotes and a simplified booking process. Maersk Spot can also help shippers identify the most efficient and cost-effective routes for cargo.

Meanwhile, global logistics company UPS is using artificial intelligence to develop a tool that will help shippers get the best price on shipping. The tool will consider factors such as weight and size of the shipment, destination and time of year. 

In addition, e-commerce giant Amazon is using artificial intelligence to develop a pricing model for air cargo shipping.  

The company’s dynamic cost optimization (DCO) system uses AI and machine learning to analyze historical shipping data, forecast demand and optimize pricing for cargo shipping. The system can quickly adjust prices in response to changes in supply and demand, ensuring that Amazon remains competitive in the market. 

In addition to DCO, Amazon also uses AI-powered predictive analytics to forecast demand for cargo shipping services, enabling the company to better manage inventory and reduce shipping costs. These analytics use a variety of data sources, such as weather patterns, customer order histories and market trends, to make accurate predictions about future demand. 

Predictive analytics 

Predictive analytics involves the use of machine learning algorithms and other AI technologies to analyze large volumes of historical and real-time data on air cargo shipments and market trends. The goal is to identify patterns, correlations and additional insights that can help shippers and carriers optimize routing and scheduling, improve efficiencies and reduce costs. 

For example, predictive analytics can help carriers to adjust pricing and capacity to meet expected demand, reducing the risk of underutilized capacity or overbooking. 

Predictive analytics can also be used to optimize shipment routing and scheduling based on factors such as the size, weight and destination of shipments, as well as other logistical considerations such as customs requirements and delivery deadlines. 

Another application of predictive analytics is in risk management. By analyzing historical and real-time data on factors such as weather patterns, political instability and other risks, carriers can identify potential issues and take proactive measures to minimize disruptions and ensure the timely and secure delivery of shipments. 

Real-time tracking 

Artificial intelligence-powered tracking systems can provide real-time updates on the location and status of air cargo, helping shippers and carriers to better manage their inventories and reduce the risk of lost or damaged shipments. 

Carriers are using real-time tracking to improve the efficiency of their operations. By tracking the location of cargo in real-time, carriers can identify potential delays and problems early on, and take steps to mitigate them. This can lead to faster and more reliable shipping times, which can save carriers money. 

Improved customer service is a draw of real-time updates for shippers, allowing them to reduce customer anxiety and improve customer satisfaction, leading to repeat business and increased profits.  

In addition, third-party logistics providers (3PLs) are using real-time tracking to improve their value proposition by demonstrating their commitment to efficiency and transparency, which can help win new business and retain existing customers. 

AI-powered customer service  

Artificial intelligence-powered chatbots and virtual assistants can provide 24/7 customer service support for digital air cargo booking, answering frequently asked questions while providing assistance with bookings, tracking and payments. This frees up human agents to focus more on complex tasks, further improving efficiencies.  

Self-service portals — or websites or applications that allow customers to find answers to questions and resolve issues on their own — that use AI can include a knowledge base, FAQs and troubleshooting guides.