How RightShip Leverages Machine Learning and AI for Proactive Risk Management

The maritime industry faces challenges in safety, compliance, and operational efficiency. Traditional methods of risk assessment and safety management are time-consuming, manual, and subjective. RightShip recognized the opportunity to leverage ML and AI technologies to revolutionize these processes and drive positive change. The problem their models aim to solve is the identification and mitigation of risks in the maritime industry, empowering stakeholders to make informed decisions and take proactive measures.

RightShip has developed several key offerings that utilize machine learning and AI to address the challenges in the maritime industry. These offerings include the Detention Predictor, Incident Risk Classification Algorithm, and Vessel Trading Pattern Recognition.

Detention Predictor

RightShip’s Detention Predictor utilizes supervised machine learning algorithms to forecast the likelihood of vessel getting detained at the next port of call. By analyzing data from vessel inspections, historical detention records and movements data, the Detention Predictor generates the probability of a vessel getting detained on its next PSC inspection. This solution improves safety and risk management.

Incident Risk Classification Algorithm

RightShip has collaborated with leading research institutes to develop an Incident Risk Classification Algorithm. This algorithm aims to classify vessels based on their potential for getting into an incident, both in ports and open oceans. By incorporating cutting-edge research findings, RightShip enhances their risk assessments and contributes to improving safety practices.

Vessel Trading Pattern Recognition

RightShip’s Vessel Trading Pattern Recognition model analyzes historical AIS data and employs spatial modeling techniques to identify anomalies and patterns in vessel trading behaviors. By detecting potential risks and generating alerts for proactive risk mitigation, this model enhances the accuracy and explainability of predictive models. It contributes to improved safety, compliance, and operational efficiency.

Utilization of Spire Maritime’s Historical AIS Data

To ensure comprehensive and accurate data, RightShip formed a collaboration with Spire Maritime. Spire’s data coverage offers a wealth of information on vessel activities, enabling RightShip to analyze up-to-date and reliable data for their risk management solutions. Leveraging the high-quality AIS data, RightShip gains accurate assessments and actionable insights, enhancing the effectiveness of their solutions.

The utilization of historical AIS data plays a crucial role in RightShip’s Vessel Trading Pattern Recognition model. By examining the historical movements of vessels recorded in the AIS data, the model learns and identifies various trading patterns, routes, and behaviors exhibited by vessels over time. This historical data serves as a valuable training resource for the machine learning model, enabling it to accurately classify and identify different trading patterns. Through the application of spatial modeling techniques, anomalies and deviations from normal trading behavior are detected, providing insights into potential risks or non-compliance in vessel operations.

Results and Impact

The utilization of Spire Maritime’s historical AIS data has had a significant impact on RightShip’s risk management solutions. By incorporating vessel trading patterns and movement tracking into their models, RightShip has enhanced the explainability and interpretability of the results. The Vessel Trading Pattern Recognition model provides insights into different trading behaviors and identifies distinct groups within the data, enabling stakeholders to understand the factors contributing to risk assessments and decision-making processes. The model’s use of historical AIS data and advanced ML/AI techniques improves the accuracy and effectiveness of risk assessment and proactive risk mitigation.

Visual Cluster Definitions

RightShip has successfully clustered vessels based on their trading patterns using historical AIS data. These clusters provide valuable insights into vessel behavior and risk profiles. The clusters are defined as follows:

• Cluster 1: Vessels in this cluster travel across multiple continents and oceans, indicating their capability to traverse various regions. They have a medium distance traveled and a high proportion of bulk carriers, suggesting their capacity to transport large quantities of goods.

• Cluster 2: Vessels in this cluster travel medium distances but are primarily observed in one ocean, indicating more concentrated travels within a specific region. They visit a relatively low number of unique continents but have a higher frequency of stops at unique ports and countries. The vessel types with the highest occurrence in this cluster are general cargo ships, chemical/products tankers, and bulk carriers.

• Cluster 3: Vessels in this cluster undertake long-distance voyages, covering a high number of continents, countries, and ports. They are observed across multiple oceans, suggesting travels that encompass more than one region. The vessel types with the highest occurrence in this cluster are vehicle carriers, chemical/products tankers, and bulk carriers

• Cluster 4: Vessels in this cluster embark on long-distance journeys and are frequently observed in major waterways. They have a global reach, operating across various oceans, and visit numerous unique continents and waterways. The primary vessel type in this cluster is container ships, indicating their high deadweight tonnage and prevalent transshipment activities

• Cluster 5: Vessels in this cluster engage in shorter journeys within one ocean, with operations primarily focused within a specific region. They visit a relatively low number of unique countries and continents, indicating localized voyages. The vessel types with the highest occurrence in this cluster are bulk carriers, container ships (Fully Cellular), and general cargo ships.

The cluster analysis based on historical AIS data has provided valuable insights into vessel trading patterns and behaviors. This information helps stakeholders in the maritime industry identify and understand different trading dynamics, optimize operations, and enhance safety practices. By leveraging Spire Maritime’s historical AIS data, RightShip has been able to develop a robust Vessel Trading Pattern Recognition model that contributes to risk mitigation, safety improvement, and operational efficiency.

Hurdles and Difficulties Faced

During the development of their ML and AI solutions, RightShip encountered a significant challenge regarding the explainability and transparency of their models. Without leveraging vessel movement data and trading patterns, the models heavily relied on regulatory authorities, limiting the transparency and interpretability of the results. To address this challenge, RightShip recognized the importance of incorporating vessel trading patterns and movement tracking into their models.

By integrating Spire’s AIS data, which includes terrestrial and satellite feeds, RightShip overcame this challenge and enhanced the explainability and accuracy of their models. The historical AIS data allowed them to capture a comprehensive view of vessel activities, including routes, frequency of visits to specific ports, and duration of stays. These features became vital inputs in their models, enabling stakeholders to understand and trust the results.

Future of Machine Learning and AI in the Maritime Industry

According to Anshit Malik, Data Science Lead of RightShip, advancements in technology and the availability of more data will further enhance the role of ML and AI in optimizing operations, improving safety, and managing risks. Some key aspects of the future include:

• Advanced Predictive Models: ML and AI will enable the development of more advanced predictive models, incorporating a broader range of data sources. These models will facilitate proactive risk mitigation, improved decision-making, and optimized operations.

• Intelligent Port Operations: ML and AI will optimize port operations, including automated container handling, efficient berth scheduling, predictive maintenance, and intelligent logistics management. These technologies will streamline processes, reduce congestion, and improve port efficiency.

• Enhanced Safety and Risk Management: ML and AI will continue to enhance safety practices and risk management. Advanced algorithms will analyze data to identify risks, predict incidents, and provide recommendations for preventive actions. This will lead to a safer and more secure maritime environment.

• Sustainability and Environmental Impact: ML and AI can contribute to addressing sustainability challenges in the maritime industry. These technologies can optimize vessel routes, reduce fuel consumption, minimize emissions, and support environmentally friendly practices.

• Autonomous Vessels: ML and AI will contribute to the development and deployment of autonomous vessels. These vessels will leverage advanced algorithms and real-time data analysis to navigate, make decisions, and adapt autonomously. This will increase efficiency, reduce human error, and enhance safety.

RightShip’s Role as an Innovator

RightShip is committed to staying at the forefront of innovation in the maritime industry. They continuously engage in research and development efforts, collaborate with research institutes, and integrate new data sources. By exploring new possibilities, leveraging cutting-edge techniques, and incorporating customer feedback, RightShip aims to deliver tailored and effective solutions that meet the specific needs of the industry.

Upcoming Projects and Initiatives

RightShip has several upcoming projects and initiatives in the pipeline to further leverage ML and AI in the maritime industry. These include the launch of the Detention Predictor, the development of the Incident Risk Classification Algorithm, expansion of the Vessel Trading Pattern Recognition model, integration of new data sources, and continuous research and development. These initiatives will enhance safety, improve risk management, and optimize operations in the maritime industry.

RightShip’s collaboration with Spire Maritime and utilization of historical AIS data have significantly improved their risk management solutions. By leveraging Spire’s comprehensive and accurate data, RightShip has enhanced the accuracy, explainability, and effectiveness of their predictive models. Through the development of the Detention Predictor, Incident Risk Classification Algorithm, and Vessel Trading Pattern Recognition, RightShip empowers stakeholders in the maritime industry to make informed decisions, optimize operations, and enhance safety practices. The partnership between RightShip and Spire Maritime demonstrates the value of data-driven solutions and paves the way for continued innovation in the maritime industry.

Anshit Malik, Data Science Lead at RightShip, has been a professional in the maritime industry for over 6 years. With an exceptional background in data science, Anshit possesses extensive knowledge and proficiency in the fields of machine learning and artificial intelligence. Throughout his distinguished career spanning 9 years, he has garnered invaluable experience, leveraging his expertise to drive innovation and deliver impactful solutions in the maritime domain.

Originally posted by Spire: https://spire.com/case-study/maritime/how-rightship-leverages-machine-learning-and-ai-for-proactive-risk-management/