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This document was written by Julian Neugebauer from the University of Hamburg in Cooperation with the TIC Comittee and is currently in Version 0.8. The last revision was made on the . |
Management Summary
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How can one increase the transparency of operations and predict throughput or lead-times? How can impacts of disruptions or new strategies be simulated? To these business problems not only applicable to the port domain a real-time digital twin can be the answer. A digital twin collects all information resulting from sensors and systems across a system or container terminal for instance and includes models of these systems. These models can be used to implement decision-logic, visualizations or train advanced AI-based algorithms. Leanings and decisions of these parts of the digital twin can then be re-implemented into the real-worl operations.
When initiating a digital twin, which is a near real-time model of a physical asset with an automated two-way connection, it's crucial to define the scope. Decide whether the twin will represent the entire container terminal or specific container handling equipment (CHE). The model's complexity should be carefully planned, as it needs to include both real-time asset representation and decision-making capabilities. Often, multiple systems will need to be interconnected. In this context, TIC4.0 can significantly streamline the process of creating a digital twin.
The first steps involve defining the digital twin’s scope, complexity and required outcomes. This typically starts with integrating existing sensory data into a centralized database, enhancing it with additional information like the GPS positions of CHEs, and utilizing the standards and guides provided with TIC4.0. This will ensure readabile data is provided to all stakeholder without having to document each interface. Consideration of visualization, simulation tools, and the integration of IoT-enabled devices is critical. The twin’s complexity will vary based on the number of connected devices and the objectives it aims to achieve. Starting with a limited number of CHEs is advisable, focusing initially on straightforward visualizations and simple models such as travel-time analysis or predictions, which can be expanded later. Clear communication of the project's goals to all stakeholders is crucial, along with explanations of the digital twin concept and the integration of data sources and components. Stakeholders should then identify use cases by answering the following question: What current challenges could be solved, by applying the functions of a digital twin? Documenting these scenarios comprehensively, including their scope, applicability, expected results, and benefits, should be done together with the stakeholders. Following this, IT and other relevant stakeholders should evaluate these use cases to identify the required resources.
Management can then assess these defined use cases based on technical complexity and potential benefits. Tools such as cost-benefit analysis or the analytical hierarchy process can aid in this evaluation. It's also beneficial to consider combining use cases to leverage synergies. The most promising use cases can then guide the development of technical components and the realization of the digital twin, following the TIC4.0 documentation as a guideline.
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How can organizations improve transparency, predict throughput, or estimate lead-times in complex operations like those at a container terminal? How can the effects of disruptions or the impact of new strategies be effectively simulated? A real-time digital twin offers answers to these questions, applicable across industries. By integrating data from sensors and systems, a digital twin mirrors real-time operations, incorporates decision logic, visualizations, and supports AI-based learning. Insights can then be applied directly to optimize real-world processes.
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