27 May 2017

Machine Learning in Air Cargo: 3 Potential Applications

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..

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Full Article: Mercator News Room

Source : Mercator News Room

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