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Let’s imagine an air cargo warehouse that runs on its own with minimal human monitoring. Stock inputs would be captured with drone-fitted cameras and machine learning algorithms.

Having this type of “smart-warehouse” isn’t that far off in future. We are on the verge of using the next evolution of artificial intelligence for air cargo supply chain management. Machine learning will enable the intelligent value chain. Read on to learn more about machine learning and the implications it has for the air cargo industry.

Analytics 3.0 and Machine Learning

Analytics has been on a high evolution curve over the past few years. There has been talk around the dawn of Analytics 3.0, marked by an era where data analytics can be applied not only to internal operations but to its portfolio of offerings. This is accomplished with new techniques and methods that gather insights from big data at faster speeds than ever before.

Alibaba is on the forefront of this trend with their recent launch of the PAI 2.0 on the Alibaba Cloud. Using machine learning, the artificial intelligence (AI) software can learn and predict the locations and timing of airfreight shipping each day for various supply chain operators around the world. Not only is it pragmatic, but the user-friendly design aims to lower the barrier of entry for managing AI.

That being said, none of this is inherently “new” to the air cargo industry. Recently, Swiss WorldCargo said that automation, along with standardization and digitalization have been elements of the air cargo industry for years. But what we’re moving towards is being able to culminate the total knowledge across channels and produce powerful predictive models, thanks to machine learning and AI. This will allow us to further refine the already robust nature of automation in the industry and move forward with leaps and bounds..

Machine Learning Implications for Air Cargo

Air Cargo

Machine learning algorithms can be used to predict the on-time delivery rates of packages, taking into account factors like weather at all points in the supply chain. The algorithm analyzes the company’s data to create a predictive model. By predicting shifts in delivery dates, carriers could notify customers of changes in real time.

Air Traffic Management

Predictive analytics can help airports and air cargo carriers improve the use of runway space. Airports can predict peak traffic times and anticipate the demand on their staff and resources. Benefits can also extend to last-minute modifications to shipping routes.

Algorithms could also be used to route specialized or high-value products in the most efficient way possible. Knowing the fastest routes could allow for higher rates, as carriers could now quantify the values of each route.


Apart from improving the efficiency of the supply chain, machine learning can also help protect the chain’s growing stores of data. Algorithms can learn payment relationships and patterns, and detect when something seems off. This learning of relationships can also be helpful for detecting access anomalies and suspicious activity.

This level of learning can prove invaluable for air cargo carriers. Prevention against payment issues can improve relationships between stakeholders across the supply chain. Protection from security breaches can save carriers from costs for data recovery and security system overhauls.

Moving Forward with Machine Learning

Machine learning will only continue to transform industries like air cargo over the years to come, touching processes from inventory management to warehouse management to transportation. We are sure to see a significant improvement in the accuracy and efficiency of these process. We will also see this being taken a step forward in 2017 – and perhaps a few more years to come – to automate processes.

Explore how data science and predictive intelligence can be leveraged by air cargo carriers to enable proactive decisions and increased efficiency across the supply chain.

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