According to the International Chamber of Shipping, 90% of world commerce happens at sea. Vessels are transporting every possible kind of commodity, including raw materials and semi-finished and finished goods, making ocean transportation a key component of the global supply chain. Manufacturers, retailers, and the end consumer are reliant on hundreds of thousands of ships carrying freight across the globe, delivering their precious cargo at the port of discharge after navigating for days or weeks.
As soon as a vessel arrives at its port of call, off-loading operations begin. Bulk cargo, containers, and vehicles are discharged, depending on the kind of vessel. Complex landside operations are triggered by cargo off-loading, involving multiple actors. Terminal operators, trucking companies, railways, customs, and logistic service providers work together to make sure that goods are delivered according to a specific SLA to the consignee in the most efficient way.
Shipping companies publicly advertise their vessels’ estimated time of arrival (ETA) in port, and downstream supply chain activities are planned accordingly. However, delays often occur, and the ETA might differ from the vessel’s actual time of arrival (ATA), for instance due to technical or weather-related issues. This impacts the entire supply chain, in many instances reducing productivity and increasing waste and inefficiencies.
Predicting the exact time a vessel arrives in a port and starts off-loading operations poses remarkable challenges. Today, a majority of companies rely on experience and improvisation to respectively guess ATA and cope with its fluctuations. Very few providers are leveraging machine learning (ML) techniques to scientifically predict ETA and help companies create better planning for their supply chain. In this post, we’ll show how to use Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly, to predict the arrival time of vessels.
Vessel ETA prediction is a very complex problem. It involves a huge number of variables and a lot of uncertainty. So when you decide to apply a technique like ML on a problem like that, it’s crucial to have a baseline (such as an expert user or a rule-based engine) to compare the performance and understand if your model is good enough.
This work is a study of the challenge of accurately predicting the vessel ETA. It’s not a complete solution, but it can be seen as a reference for you to implement your own sound and complete model, based on your data and expertise. The solution includes the following high-level steps:
- Reduce the problem to a single vessel voyage (when the vessel departs from one given port and ge
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/using-machine-learning-to-predict-vessel-time-of-arrival-with-amazon-sagemaker/