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This document was written by Julian Neugebauer from the University of Hamburg in Cooperation with the TIC Comittee Committee and is currently in Version 0.8. The last revision was made on the 22 . |
Management Summary
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How can one increase the transparency of operations and organizations improve transparency, predict throughput, or estimate lead-times in complex operations like those at a container terminal? How can impacts the effects of disruptions or the impact of new strategies be effectively simulated? To these business problems not only applicable to the port domain a A real-time digital twin can be the answer. A digital twin collects all information resulting offers answers to these questions, applicable across industries. By integrating data 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.
Management Summary NEW
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.
Purpose and Scope
, 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.
Purpose and Scope
This document guides management on implementing a digital twin effectively, addressing organizational needs such as replaying historical processes, testing improvements, or enhancing decision-making. Success requires clear objectives, defined KPIs, and standardized data formats to deliver actionable insights. Standardization is especially important, and TIC4.0 plays a critical role in ensuring stakeholders speak the same language throughout the implementation process.
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The digital twin does more than just monitor—it can predict outcomes, optimize processes, and autonomously take or suggest actions to improve efficiency, reduce downtime, and enhance overall performance. Common applications include improving operational transparency, enabling predictive maintenance, optimizing resource use, and mitigating risks through simulation. For a better understanding of the functions, equipment, and systems that can be included in a digital twin setup for a container terminal, see the visualization to the rigthright.
To ensure that a digital twin meets the operational demands and strategic goals of a container terminal, engaging all stakeholders is crucial. The complex nature of digital twins adds layers of complexity to the information systems and decision-support tools they enhance. However, the maritime industry, particularly in the realm of container terminals, currently lacks detailed models for conducting requirement analyses for digital twins and deriving specific use cases.
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As we venture deeper into the creation of a digital twin for container terminals, it becomes imperative to engage in rigorous requirements engineering. This process is crucial in translating high-level operational needs into specific, actionable use cases that the digital twin will address. By focusing on this structured approach, we ensure that the digital twin aligns with both the TIC4.0 standards and the specific demands of the terminal's operations.
The first step in requirements engineering is the identification of suitable use cases where digital twins can significantly enhance operational efficiency and decision-making. For container terminals which are planning to adhere to TIC standards, this involves examining the current processes and pinpointing areas where digital solutions can provide substantial improvements. The potential functions of a digital twin include:
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Data Analytics: By leveraging data collected from various sources across the terminal, digital twins can offer insights into operations, identifying bottlenecks and opportunities for process optimization.
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Optimization: Digital twins can simulate different operational scenarios to find the most efficient approaches, reducing costs and improving service quality.
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of the terminal's operations.
The first step in requirements engineering is the identification of suitable use cases where digital twins can significantly enhance operational efficiency and decision-making. For container terminals which are planning to adhere to TIC standards, this involves examining the current processes and pinpointing areas where digital solutions can provide substantial improvements. The potential functions of a digital twin include:
Monitoring: Continuous monitoring of equipment and operations helps maintain high standards of operational efficiency and safety.
Reporting: Automated and enhanced reporting capabilities allow for more accurate and timely information dissemination, aiding decision-makers at all levels.
Data Analytics: By leveraging data collected from various sources across the terminal, digital twins can offer insights into operations, identifying bottlenecks and opportunities for process optimization.
Simulation: Complex simulations can test responses to hypothetical situations without risking actual resources, providing a valuable tool for strategic planning and training.Monitoring: Continuous monitoring of equipment and operations helps maintain high standards of operational efficiency and safetywithout risking actual resources, providing a valuable tool for strategic planning and training.
Optimization: Digital twins can simulate different operational scenarios to find the most efficient approaches, reducing costs and improving service quality.
Predictions: Utilizing historical data, digital twins can forecast future conditions and outcomes, enabling proactive management of resources and better handling of potential disruptions.
Each of these functions serves as a foundation for use case ideation. By examining existing challenges within the terminal's operations and considering how these digital twin functions can address them, stakeholders can develop a robust list of potential use cases. This ideation process should involve a diverse group of stakeholders, ensuring that the use cases cover a wide range of needs and opportunities within the terminal environment. But how can the use case be documented?
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The Utility Analysis method is a valuable tool for exatly exactly this comparison and decision-making process. It is often chosen for its simplicity and ability to include a broad range of evaluators, and both qualitative and quantitative criteria. It is described below. Beforehand, stakeholders should ensure that the significance of the use case in relation to these criteria is well-documented using a use case template developed in earlier phases.
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Lets look at an example of the use case of monitoring the tire pressure of CHE to look atof how this can be done practically:
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After all use cases have been evaluated we would suggest choosing a limited amount of the best-rated use cases (this ensures that the digital twin's scope is not too large to begin with) and evaluating them even more in-depth. For doing so the following questions can be very helpfulbeneficial:
Which technical solutions need to be implemented for each use case? Each new technical part should be listed and a table could be created listing each use case and its technical parts. An example would be a function for straddle carrier data ingest or a weather data API for providing additional information. Multiple use cases might include weather data or would need data of the straddle carrier to be send sent to the data lake or some other form of centralized data storage. This will quickly show synergies and the list of different technical functions also shows in which parts TIC4.0 should be included.
What use cases are absolutely necessary because of stakeholder or project requirements?
Which use cases need to be done in sequence? Some use cases might be depended on each other. A waiting-time analysis for example might only be possible once the operational data is fully integrated and enriched with vessel information. Thus the use cases should be done in sequence which will also support the structure of the project timeframe at a latter later stage.
Based on these questions and the previous evaluation the best X use cases can be selected by management and the digital twin build can be started.
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These use cases represent a balanced mix of quick wins and strategic projects that will provide significant benefits in both the short and long term. Their implementation will not only demonstrate the value of the digital twin but also set a solid foundation for future expansions and many other use cases. The documentation provided with TIC4.0 served us both to understand the to-be-expected data (e.g., for use cases regarding CHE) and to integrate it into further processing, such as machine learning or visualizations. An example of this would be that the data science department already knows which values will be sent as soon as the data infrastructure department provides the data. Generally, most, if not all use cases as well as departments, benefit from the implementation of data standards and easy integration of data sources, as is the case when using TIC4.0.
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The consistent application and integration of TIC4.0 standards throughout these developments are crucial. These standards ensure that the digital twin aligns with industry best practices and facilitates interoperability across systems and stakeholders. By adhering to TIC4.0, the digital twin initiative not only enhances its efficiency and effectiveness but also contributes to the broader industry goal of standardizing operations and data usage within container terminals and beyond. This strategic alignment with TIC4.0 ensures that the digital twin remains future-proof, scalable, and ready for further expansion , while delivering long-term value across the terminal’s operations.
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