Trade Automation

What is Trade Automation in the Maritime Industry?

Trade automation in the maritime industry is a tool for fusing unstructured data, structured data, and domain knowledge. It uses Generative AI (Gen AI) to present its findings in a meaningful way to stakeholders. This smart data provides meaningful insights and guidance to improve efficiencies and forms the basis for strategic actions.

What is the Difference Between Structured Data, Unstructured Data, and Domain Knowledge? 

The following table shows the differences between the types of data fused together through trade automation.

How is Trade Automation Used?

Trade automation holds tremendous potential for a number of use cases in the maritime industry. One workflow that is available today is detention and demurrage automation.    

Detention and demurrage automation leverages Gen AI to proactively manage and reduce detention and demurrage costs. This kind of tool addresses the primary challenge of the lack of integration between the different data sets, which makes it difficult for stakeholders to manage detention and demurrage efficiently. 

Windward’s trade automation solution provides operators with tools for enhanced visibility and exception management around detention and demurrage buy and sell rates. Key features include:

• Full visibility of detention and demurrage buy and sell rates: operators can access comprehensive information on both purchase and sale rates for detention and demurrage
• Exception management: the system generates alerts for risks related to detention and demurrage, loss-making files, and rescheduling haulage, allowing operators to manage exceptions effectively
• Empowered operators: by integrating domain knowledge, operators are better equipped to handle the complexities of detention and demurrage management

Detention and demurrage trade automation empowers maritime operators by providing the necessary tools for proactive and efficient management of detention and demurrage costs.

What Role Does Data Curation, Data Governance, and Data Management Play in Trade Automation?

Data curation involves collecting, organizing, and maintaining data so that it can be easily accessed and used by stakeholders. In trade automation, data curation plays a crucial role in ensuring that the data from various sources (structured and unstructured) is accurate, relevant, and up-to-date. Proper data curation enables the effective use of generative AI and other analytical tools, leading to meaningful insights and informed decision-making.

Key aspects of data curation for trade automation include:

• Collection: gather data from multiple sources such as carrier contracts, port schedules, and shipment tracking systems
• Organization: structuring data in a consistent format, making it easier to analyze and integrate
• Maintenance: updating data to reflect the most current information, ensuring ongoing relevance and accuracy

Data governance refers to the overall management of the availability, usability, and security of an organization’s data. In the context of trade automation, data governance ensures that data is managed consistently and responsibly across the entire trade process. Effective data governance establishes clear policies, procedures, and standards for data usage, helping to maintain data quality and compliance with regulations.

Key aspects of data governance for trade automation include:

• Policies and standards: develop and enforce policies and standards for data management, ensuring consistency and reliability
• Compliance: ensure that data practices comply with international trade regulations and industry standards
• Security: implement measures to protect data from unauthorized access and breaches 
• Data stewardship: assign roles and responsibilities for data management, ensuring accountability and proper oversight.

Data management encompasses all the practices and processes involved in acquiring, storing, processing, and utilizing data. In trade automation, effective data management ensures that data is available when needed, properly processed, and used to drive automation and decision-making processes. Good data management practices enable seamless integration of various data sources, facilitating the smooth operation of automated systems.

Key aspects of data management for trade automation include:

• Acquisition: efficiently gathering data from diverse sources, including operational data, unstructured data, and domain knowledge
• Storage: securely storing data in a way that allows easy retrieval and processing, using databases and data warehouses
• Processing: transform raw data into usable formats through cleaning, integration, and analysis, preparing it for automation tools
• Utilization: leverage data to drive trade automation processes, such as predictive maintenance, route optimization, and exception management

Data curation, data governance, and data management are foundational elements in the successful implementation of trade automation. Together, these practices enable the effective integration and analysis of data, leading to enhanced efficiencies, informed decision-making, and streamlined trade operations.