Transport & Logistics — Logistics Feature Engineering Pipeline for Predictive Modeling

Overview

The primary purpose of this DAG is to create robust feature pipelines from logistics data to support the training of predictive machine learning models. It ingests multiple data sources, including ERP transaction logs, shipment tracking data, and inventory records, to build a comprehensive dataset for analysis. The ingestion pipeline begins with data extraction, followed by feature selection, handling of missing values, and normalization to ensure consistency across the dataset. Quality controls

The primary purpose of this DAG is to create robust feature pipelines from logistics data to support the training of predictive machine learning models. It ingests multiple data sources, including ERP transaction logs, shipment tracking data, and inventory records, to build a comprehensive dataset for analysis. The ingestion pipeline begins with data extraction, followed by feature selection, handling of missing values, and normalization to ensure consistency across the dataset. Quality controls are implemented at each processing step to monitor data integrity and accuracy. The outputs of this DAG include engineered feature sets ready for model training, performance metrics, and logs for troubleshooting in case of failures. Key performance indicators (KPIs) such as processing time and model accuracy are tracked to assess the efficiency of the pipeline. This DAG delivers significant business value by enabling data-driven decisions, improving operational efficiency, and enhancing predictive capabilities in the transport and logistics industry.

Part of the Scientific ML & Discovery solution for the Transport & Logistics industry.

Use cases

• Enhances predictive accuracy for logistics operations
• Reduces manual data processing efforts
• Improves operational efficiency through data-driven insights
• Facilitates faster decision-making in transport logistics
• Supports compliance with data quality standards