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Cargo Classification

Mojo Case Study

Problem Statement

A global leader in information services faces challenges in accurately classifying approximately 40 million cargos with appropriate HS codes, distinguishing between dual-use and individual-use commodities. Manual processes are inefficient and error-prone. We propose a comprehensive, intuitive, and scalable model trained using Stochastic Gradient Descent (SGD) to automate classification and data cleansing, seamlessly integrating into existing workflows to enhance efficiency, accuracy, and regulatory compliance.

Our solution

The solution encompassed the following steps:

  • Automated HS Code Assignment : Develop a machine learning model to accurately assign HS codes to shipping cargo commodities.Utilize Stochastic Gradient Descent (SGD) for training the model to enhance prediction accuracy.
  • Classification of Dual-Use and Individual-Use Commodities : Implement classification mechanisms to distinguish between dual-use and individual-use items.Ensure compliance with international trade regulations and enhance security measures.
  • Scalability for Large Volumes : Design the model to handle the classification of approximately 40 million cargos efficiently.Ensure the solution remains effective and responsive as cargo volumes grow.
  • Data Cleansing Integration : Incorporate robust data cleansing processes within the model to address inconsistencies and improve data quality. Enhance overall classification accuracy and reliability through continuous data quality management.
  • Comprehensive and Intuitive Design : Ensure the model is user-friendly and integrates seamlessly into existing workflows.Provide a comprehensive solution that requires minimal disruption to current operations.
  • Enhanced Efficiency and Compliance : Automate the classification process to significantly reduce manual effort and errors. Achieve greater operational efficiency, cost savings, and adherence to regulatory standards.

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    Benefits

    • Increased Accuracy: The machine learning model provides accurate HS code assignment, enhancing compliance and reducing the likelihood of errors. Employing Stochastic Gradient Descent (SGD) improves the model’s performance, resulting in more precise predictions.
    • Regulatory Compliance: Accurate classification of dual-use and individual-use items ensures adherence to international trade regulations. 
    • Efficiency at Scale: The model is designed to handle approximately 40 million cargos, ensuring efficient classification even as volumes increase. The solution remains responsive and effective as cargo volumes grow, supporting long-term scalability.
    • Higher Data Quality: Robust data cleansing processes address inconsistencies, improving data quality. Continuous data quality management enhances overall classification accuracy and reliability.
    • Reduced Manual Effort: Automation significantly decreases the need for manual classification, reducing labor costs and errors. Greater operational efficiency and cost savings are achieved through streamlined processes.                   

    Contact us for a solutions demo:

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