The Challenge

The role of port authorities has evolved in recent decades from owner to orchestrator of the port ecosystem. Nowadays, the different actors involved in the maritime-port logistics chain often share data one at a time, with no real-time information, duplicated information, many radio or email communications, and a paper-intensive administrative process. In addition, the reaction of agents to congestion situations or exceptions is reactive, as there is no reliable and correlatable information base between planning processes. In this context, data platforms receive more attention and are needed to share data between the different agents.

EDI messaging remains the most reliable and consistent source for supply chain planning and cargo status, being also the official means in Spain for the exchange of data between the Maritime Single Window and the Port Authorities. This EDI data, together with the information coming from port calls and AIS data – which provide real-time information on the vessels during their navigation, both dynamic (speed, direction, etc.) and static (size, weight, type of cargo, etc.) characteristics- allow inferring the timestamps involved in the maritime-port logistics chain, significantly improving efficiency and safety. According to the European STM Validation Project, the impact of a platform based on shared data that guarantees Just In Time in port operations, supported by Artificial Intelligence applied to estimated timestamps and real-time data from port agents, will improve waiting times by 30% and reduce accidents by 50%.

The Solution

The SmartEDIPort experiment arises from the real need of the Port Authority of Huelva to optimize the maritime-port logistics chain, through JIT methods to improve the efficiency, safety, and coordination of port operations, positively impacting the coordination and efficiency also of its stakeholders. The objective of the SmartEDIPort Experiment is the creation of a data space oriented to the ingestion, sharing, and exploitation of data related to the maritime-port logistics chain for the optimization of the time forecasts of the operations of berthing, loading-unloading-departure of vessels through JIT and thus improve the efficiency, safety, and coordination of the Port Authority and all the agents involved in them.

The Data Space will receive 3 types of datasets:

1. the Port Calls data;
2. the raw EDI messages data, used by maritime agents as terminals, shipping agencies, and public administration, and;
3. the AIS data.

Business Projections – Scalability

The business model will be based on two revenues:

• The sale of data packages available on the Marketplace;
• New products/services created from them, or value-added data packages.

Several end-users have been identified: Port authorities, terminals, shipping agencies, nautical services operators (maritime pilots, tugs, moorers), and other agents, such as R&D groups. Prioritized markets are Spain, and South and Central America, since HIADES already has a solid commercial network, and also the USA, where it is currently opening this market with an international partner. We have a total TAM (Total Addressable Market) of more than a thousand ports.

In addition, we plan to improve the accuracy of the inferred ETS, ETC and introduce new datasets to infer other timestamps involved in the maritime-ports logistics chain.