According to the International Chamber of Shipping, the maritime industry has increased fourfold in the last four decades. As the complexity of marine trade increases, ports and shipping companies need to have access to the appropriate data. Through maritime zone tracking, important players in the shipping sector may see every vessel as it enters and exits ports, anchorage regions, and zones (maritime zones). Having this information at your fingertips is crucial because it enables you to monitor vessel traffic and effectively address safety, security, and compliance issues with changing conditions at sea, from bad weather to port congestion.
Data stream processing is useful in this situation. Instead of waiting for daily or weekly reports, it enables businesses to receive and process massive amounts of data in real time, enabling prompt decisions and optimizations. There are various types of streaming data in the cargo delivery ecosystem, and these data sources typically produce a large, continuous stream of messages. Here are the very high-level snapshots of a rich stream of data that is continuously in the entire cycle of maritime logistics
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| Data Source Type | Nature of Data |
| Ship location and AIS (Automatic Identification System) signals | GPS signals and Automatic Identification System messages provide latitude, longitude, speed, and direction of the ship in real time. |
| Satellites, buoys, weather stations, and ships | Real-time weather updates and current conditions Data API, IoT sensors track wind speeds, wave heights, etc |
| Port Data Management Portal, Electronic Logbooks, Smart Berth Allocation Tools, Advanced Mooring Systems, etc. | RFID and IoT sensors feed data, Sensors integrated into advanced systems that ensure seamless data transmission, enabling efficient real-time monitoring |
| Transportation management systems (TMS), Voyage Data Recorder, Electronic Data Interchange (EDI), Telematics Devices | Origin, destination, weight, dimensions, On-time delivery rates, dwell times at ports, route optimisation metrics, and shipment details, etc. |
Commercial value
Real-time insights, operational efficiency, and cost savings are gained for maritime logistics business operations using data stream processing. Data from ships, ports, sensors, and logistics systems can be utilized by seamlessly analyzing it. By relying continuously on processed stream data, the maritime groups optimize routing, save fuel, and proactively handle maintenance and supply chain interruptions.
By leveraging this live action, options are open for wiser decision making, less downtime, and by providing correct times when deliveries will be made, as well as better standard of customer service. In addition, it is more conducive to improved regulatory compliance and the implementation of new features in an established and profitable service in the industrial output, such as monitoring better energy conservation along with airborne emissions. Finally, by turning maritime logistics from track to pre-engineer, data stream processing offers a competitive edge for a modern, worldwide industry like no other.
Bird’s-eye view of implementation
We have seen above the list of very high-level source of real-time streaming data sources, where data is continuously generated due to the execution of multi-level operations across the port. These sources include inbound and outbound cargo ship data, weather data, oceanographic data, and more. Keeping all of these in mind, the overall streaming data processing architecture can be divided into five layers: the source layer, stream ingestion layer, stream processing layer, storage layer, and the querying or visualization layer.
The source layer comprises all the sensors, IoT devices, satellite data, weather data feeds, oceanic data, logistics data, and more.
The stream injection layer would hold the responsibility of receiving data from the source layer. In this layer, we could deploy a cloud-based or on-premises multi-broker Apache Kafka cluster to ingest all this real-time streaming data from the source layer to the stream processing layer.
In the stream processing layer, the actual transformation of the data stream takes place to make it consumable through data validation, cleanup, normalization, transformation, and enrichment. Multiple popular frameworks, such as Apache Flink, Apache Spark, and Kafka Streams, can be leveraged here to process the data.
The storage of streaming data is accomplished using a stream storage layer. In this layer, we can combine two types of storage, the streaming databases as well as the HDFS-based data lakes, or any commercial vendor-specific cloud data lakes for historical and archived data. The streaming databases are optimized for real-time analysis, such as anomaly detection, alerting, or running continuous queries, and the HDFS-based data lakes for long-term storage and batch processing. The multi-node cluster of Apache Druid or RisingWave can be effectively deployed here as streaming databases. Similarly, for historical and archiving for long periods, data can be shipped from streaming databases to cloud-based data lakes or Apache Iceberg.
In the querying + visualization layer, we can get real-time oversight. Also, we can generate reports or dashboards based on predefined metrics or queries. Some data visualization and reporting tools like Grafana, Kibana, Tableau, Power BI, etc. can be used in this layer, and subsequently, all business decisions can be taken. Besides, in this layer, we could integrate ML models for prediction using processed stream data as well as AI-driven systems to enhance mooring stability, improving safety and efficiency during loading/unloading and many port-related operations.
Conclusion
Briskly processing data streams can really give shipping a shot in the arm, useful for live monitoring, analysis, and decision-making within its intricate operations. With data constantly being processed from vessels, ports, weather systems, and cargo sensors, it is possible for stakeholders to immediately ascertain the best routes as well as performance of equipment, as well as any potential delays. This results in better fuel economy, reduced downtime, and superior supply chain coordination.
Additionally, streaming data also allows the for prediction maintenance and proactive risk management for the safety and reliability at sea. Finally, through data streaming technologies, maritime logistics becomes a more agile and data-driven environment that dynamically reacts to continuous operations and environmental change. I’ve described the concepts at a high level, but there will be many steps between where you are and the conditions you are hoping to reach. There are numerous technical problems to solve starting from data cleansing to the deployment strategy.
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Written by
Gautam Goswami
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