Maximized Asset Utilization for a Container Shipping Company

Impact by the NUMBERS

Translating Technology into a Positive Impact

86%

accuracy in predicting the number of empty containers at the port

11%

increase in previous average precision after adding just 20 days of more data

Challenge

The shipping company encountered a significant challenge concerning the inefficient utilization of dock space, primarily due to the uncertainty surrounding the availability of empty containers on any given day.

The Solution

A machine learning solution was created to predict the number of empty containers arriving at the port at any given date in the next month. It consisted of the following features:

ETL Implementation: Developed an automated ETL pipeline for migration of tables to the cloud, thereby reducing the cost and time needed to upload data.
AI/ML Use Case Implementation: Designed an advanced AI/ML model for precise prediction of the destination port for container returns.