Logistics Supplier Ranker
Logistics networks struggle to dynamically evaluate and rank cargo providers, often relying on static, outdated spreadsheets that fail to capture real-time risks like defect ratios and late deliveries.
The Solution
I constructed a decision intelligence agent that automates risk profiling. By ingesting sparse logistical records using Pandas, the system fits a customized multi-variable regression model weighting late-deliveries, defect logs, and cost metrics to score each provider. I visualized the anomalies in a Streamlit prototype.
- Fitted a custom multi-variable regression algorithm weighting late-deliveries, defect logs, and cost metrics
- Designed a data validation and cleansing pipeline to ingest sparse logistical records using pandas
- Built an analytical prototype dashboard in Streamlit to visualize ratings and flag outlier suppliers
- Earned direct commendation from the CEO of Mesh Works during the hackathon presentation for ranking accuracy
System Pipeline
Engineering Trade-offs
Why?Due to the 48-hour constraint, I prioritized a robust data cleaning pipeline and a fast, interpretable multi-variable regression model over a complex, black-box deep learning approach. Transparency was critical for the judges to understand the scoring.
Proven Impact
The regression coefficients successfully highlighted anomalous suppliers, earning direct commendation from the CEO of Mesh Works during the hackathon presentation for its immediate practical applicability.