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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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