Aachen, August 11, 2020 Inform and HPC Hamburg Port Consulting have collaborated to implement Inform’s Syncrotess Machine Learning (ML) Module at HHLA Container Terminal Burchardkai (CTB) to deliver an innovative solution for improving CTB’s container handling operations.

Dr. Alexis Pangalos, Partner at HPC said, “It was a productive implementation of Inform’s AI solution for the choice of container storage positions at CTB. The ML Module was trained with data from CTB’s container handling operations and is therefore tailor-made for their operations.”

Inform together with HPC have integrated the Syncrotess ML Module into the slot allocation algorithms already running within CTB’s terminal control system, ITS.
To mitigate this operational inefficiency, the joint project bringing together the terminal operator HHLA, the software specialist Inform and logistics consultant HPC utilizes machine learning technology to predict the individual container dwell time aiming a reduction of container rehandling for import containers at terminals.

Inform’s AI solution predicts the dwell time (i.e., the time period the container is expected to be stored in the yard) and the outbound mode of transport (e.g., rail, truck, vessel) – both of which are crucial criteria for selecting an optimized container storage location within the yard that avoids unnecessary rehandles.

“Utilizing machine learning and artificial intelligence and integrating these technologies in existing IT infrastructure are the success factors for reaching the next level of optimizations”, says Jens Hansen, as Executive Board Member responsible for IT at HHLA. “A detailed analysis, and a smooth interconnectivity between all different systems enable the value of the improved safety while reducing costs and greenhouse gas emissions.”

“Data availability and data processing is an important key when it comes to utilising AI technology”, says Alexis Pangalos, Head of Software Engineering at HPC. “It requires a detailed domain knowledge of terminal operations to unlock greater productivity of the terminal equipment and connected processes.”

The implementation is based on a machine learning assessment Inform undertook in 2018 whereby they set out to determine if they could improve optimization and operational outcomes using Inform’s broader ML algorithms developed for use in other industries such as finance and aviation.

In 2019, Inform reported the findings of that assessment in Port Technologies Edition 83, Machine Learning in Terminal Operations: A Practical Review of Impacts on Yard Optimization where they estimated that the Syncrotess ML Module could result in a relative improvement in prediction accuracy of 26% for dwell time predictions and 33% for outbound mode of transport predictions.

Dr. Eva Savelsberg, SVP of Inform’s Logistic Division and co-author of the original assessment paper said, “AI and machine learning allows us to leverage data from our past performance to inform us about how best to approach our future operations – our ML Module gives our Operations Research based algorithms the best footing for making complex decisions about what to do in the future. Inform’s Machine Learning Module allows CTB to leverage insights generated from algorithms that continuously learn from historical data.”