The successful operation of a port requires its independent parties to share data to ensure efficient episodic tight coupling, but, to move beyond an operational perspective to long-term resource management, a greater level of data exchange is required, according to the EU-funded STM Validation project in a publication explaining the concept of Port Collaborative Decision Making (PortCDM).
The main objective of PortCDM is to enhance coordination among port call actors. By sharing their time stamp data related to port calls, information will be available for actors to utilize in real time. This will facilitate just-in-time arrivals, increase predictability, berth productivity, punctuality, reduce waiting and anchoring times and boost resource utilization. Moreover, the number of phone calls will be significantly reduced, resulting in reduced administrative burden. In the STM Validation Project, the PortCDM concept will be tested and validated in 13 ports throughout Europe.
For PortCDM, STM Validation finds two aspects currently important:
1. The sharing of accurate and detailed real-time data
2. Alignment of each actor’s intention to enable effective and efficient episodic tight coupling in a timely manner
These two ingredients create a “system” of data sharing, giving rise to well-coordinated port call processes based on high degrees of predictability and situational awareness. PortCDM brings collaborative dimensions into port call coordination (port internal collaboration) and synchronization (port external collaboration) into focus.
This does not mean that the port call process is optimized, but it does mean that some, though incomplete, data are available for others to start an optimization process for port calls and other operational services. PortCDM creates a system of records, based on a port’s actors’ systems of production, which can fuel data analytics, artificial intelligence, and machine learning.
However, planning a port call cannot solely rely on the situation in the port. It builds upon insights on the status of related actors, such as the previous port, and movement of ships, and hinterland transports. Today, the system of record is often incomplete, so not well-suited for learning through data analytics.
The issue can be addressed by an implemented system building upon PortCDM according to its objectives. It will require standardized message formats and interfaces for a port’s systems of production. In this way, the system building upon PortCDM, would enable each actor to plan what resources need to be available in different future coupling episodes contributing to an optimized port. The concept of PortCDM’s current system of record reveals what operations were planned for and conducted during in a visit, but it has no details of what resources in total were available at the time of the visit or the pattern of resource usage over an extended period. This requires additional details of resource utilization to be aligned with PortCDM.
Port call processes build upon patterns of activities among actors. There are at least three simultaneous ecosystem-oriented systems of production processes that needs to be inter-lined:
- Sailing ships berth-to-berth
- Serving ships according to their needs to realize the purpose of call
- Bringing goods from a hinterland hub to the terminal, or vice versa.
A port’s operations rely on insights from all these three production processes since each production process is dependent on consuming data from all three systems of records and about the progress in other production processes. PortCDM must provide the logic for identifying data sharing needs for each situation by
- providing situational awareness and
- aligning different actors by revealing diversities and unrealities in their timing of episodic tight coupling and ensuring that essential data for the coordination of a port call are shared in a timely manner.
STM note the prime goal of minimizing ship turnaround is limited by insufficient data resources.
It is therefore necessary that PortCDM works in alignment with integrating additional types of data from systems of production to ensure a port has the right long-term mix of resources to minimize ship turnaround. For example, data analytics might reveal that the presence of an additional pilot would over a year reduced ship turnaround by 10% due to the ability to conduct more parallel assignments of pilotage and thereby avoiding unnecessary waiting times. Planning for one visit can improve efficiency, as PortCDM has shown, and planning for hundreds of visits will give efficiency another major boost.
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