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terminal med europe

How to optimize terminal’s get out container operations by means of data available?

Why this challenge?

In smart port approach the operational objectives are to improve the fluidity of terminal outbound container traffic, truck traffic and contributing to the terminals performance.

The shipping sector is a very competitive industry and one of the major challenges for key players is at containers flow optimizing. As in the law of ‘connected vessels', by optimizing flow from arrival until exit of the terminal, all the actors of the port supply chain will be impacted positively.

Description

Thus, in these supply chain of containers several steps are inescapable to release container from terminal in an optimal delay. So far, these steps remain manual in some instances and related data sharing insufficient to optimize the container release and logistical operations needed.

Example situation

The challenge is about optimizing terminal processes; therefore, it will be limited as follows:  to define the challenge’s objectives you will find below two brief explanations about the activities of a terminal.

Get In / out of container

The terminal arrival of container can be carried out by different transport modes, but the case studied today is truck transportation. After having crossed terminal gate the truck drops the container on the yard and leaves with an outgoing container or with nothing.

Thus, Terminal equipment will take over deposited container: by vehicles, forklifts, cranes, gantries etc. and will be routed to the vessel or stored in a yard with restricted access. The reverse process is done to drive the outgoing containers into the yard with non-restricted access.

 

Get In/ out of container

The terminal arrival of container can be carried out by different transport modes, but the case studied today is truck transportation. After having crossed terminal gate the truck drops the container on the yard and leaves with an outgoing container or with nothing.

Thus, Terminal equipment will take over deposited container: by vehicles, forklifts, cranes, gantries etc. and will be routed to the vessel or stored in a yard with restricted access. The reverse process is done to drive the outgoing containers into the yard with non-restricted access.

Problematic

The major issue faced by this sector is given by the growth of global maritime traffic which has induced terminals’ saturation. Due to the growth in volumes of containers being traded, congestion and waiting at the gates of terminals are dramatically believing. The impacting factors are numerous:

  • Have a crane broken down and therefore not finish the operations on a ship in the expected time. In this case he must be able to give information very early the new estimated time of end of operations (for example as soon as the crane breaks down).
  • Perform operations on the weekend. It must be possible to notify the receiver of goods as soon as possible.
  • Having the information that a container has been just unloaded but no way to communicate it in real time to the customer.
  • A ship may have a problem while its transit between two terminals. It is necessary that the terminal (next port call) can be alerted as soon as possible to allow another ship to berth instead.

Expected benefits

  • Improvement of the customer supply chain

 

Trial, resources and co‑innovation

Experimentation, resources and co-innovation

Scope: The experiment will consist of

  • A proposal / test of a data exchange process between the terminal, the port authorities and the carrier.
  • The test of an algorithm to predict (plan) all the operations necessary for the exit of a container.
    • A priori
    • A posteriori
    • In real time

 

Resources made available

Datasets from CMA-CGM (loading plan, ship plan), GPMM (crane capacity, history) and public data, CMA container terminal of Fos. 

Co-innovation

Accompaniment from CMA CGM on the creation of the algorithm. 

 

Profile for the expected startup

Oriented data science and flow analysis (dataflow), and ability to analyze existing data and create optimization models.