Maritime - Case study

Upcoming Webinar : Leveraging Web Data For Advanced Analytics

On 6th Dec, 11.00 AM to 12.00 PM ( EST) 4.00 PM to 5.00 PM ( GMT )

Data for the Maritime Industry

Web Data Automation Case Study

Client

A leading source of critical maritime & trade insight.

The Business Need

Employing data automation, our main goal is to combat trade-based money laundering for various commodities in marine shipping. This involves automated identification of commodities from bills of lading, seamless integration into the client’s database, historical data analysis to estimate pricing based on shipping details and commodity information, and continuous updates to maintain real-time accuracy. Through this process, we guaranteed fair pricing for our client, ensuring transparency and integrity in their marine shipping operations.

Challenges

It is interpreted that the commodity and quantity associated are determined through handwritten bills of lading. In such circumstances, incorrect or missing information on these forms limits the solution. Based on the facts from these forms, currency and unit measure would differ, and they are the major elements establishing the fair price. Also, when gathering commodity price data, the most frequent goods in each category are selected for pricing input into the data lake.

Contact us for a solutions demo:

Our Solution

We came up with the approach to provide an end-to-end automated solution employing Text engineering, Machine learning models, Data Integration through API and ETL and a web component for display of the same.
● Integrating Data Sources: Building integrations from warehouse systems to consolidate data onto a cloud-based processing model.
● Predictive Pricing Model: Developing a pricing model using historical data of HS codes to anticipate commodity prices based on source, destination, and quantity.
● Real-Time Price Updates: Extracting latest commodity prices from the repository for accurate and up-to-date pricing information.
● Custom User Interface: Designing and deploying a user-friendly interface for consumers to query pricing data and explore results.
● Component Decoupling and API Integration: Separating components for flexibility and providing API services for seamless integration with other systems.
● Currency Conversion and Pricing Computations: Creating a generic component to handle currency conversions, unit conversions, and perform
complex pricing computations.

Highlights

Price Trend: Visualizing the trend of monthly average price in a graphical form which helps the user to compare the input price against the historical data.
Efficiency and Time Savings: We significantly reduced manual effort and improved efficiency. This led us to a time saving of 80%.
ML Based HS Code Identification: Deployed an ML based HS code identification component which helps to identify the HS code from the bills of
lading description.