Data Science
Supply Chain Anomaly Detection
An anonymized case study on detecting revenue loss within complex warehouse-to-retail data pipelines.
Technical Diagram: Data Sequence 041-A
The Problem
A logistics firm was losing 4% of revenue to undetected data discrepancies in their warehouse-to-retail pipeline. Manual auditing was slow, reactive, and incapable of catching errors as they occurred.
The Tech
- Python: Core logic and data transformation.
- AWS Lambda: Serverless execution for real-time processing.
- Snowflake: Data warehousing and high-speed querying.
- Scikit-Learn: Machine learning models for outlier detection.
The RX (The Solution)
We built a real-time monitoring engine that flags data outliers in under 500ms. By implementing a proprietary anomaly detection algorithm, the system triggers automated alerts directly to the operations team the moment a discrepancy is detected.
The Result
- 85% reduction in manual auditing time.
- 3% recovery of annual lost revenue within the first 6 months.
- Improved data hygiene across the entire logistics network.
#Python
#AWS Lambda
#Snowflake
#Scikit-Learn