Logistics Supplier Ranker — Multi-Variable Decision Intelligence
Logistics managers often rely on static spreadsheets that fail to dynamically synthesize sparse operational metrics into an interpretable risk scorecard.
My Role & Contribution
Solo Hackathon Developer: Built the data cleaning pipeline in Pandas, trained multivariable regression models, and deployed a Streamlit dashboard for dynamic carrier scoring.
Proposed Solution
Developed an automated risk profiling tool using Pandas and Scikit-Learn. The system ingests shipment telemetry, computes normalized risk coefficients, and outputs an interactive supplier scorecard.
- Engineered multivariable regression model weighting late shipments, defect rates, and freight expense
- Robust automated data cleaning pipeline handling missing and sparse logistics logs
- Streamlit interactive dashboard visualizing supplier risk tiers and outlier carriers
- Earned personal commendation from the CEO of Mesh Works for ranking methodology and practical utility
Architecture & Dataflow
Engineering Trade-offs & Limitations
Rationale:Selected an interpretable multivariable regression approach over black-box deep learning, ensuring supply chain operators could clearly audit the exact factors driving supplier scores.
Results & Evaluation
Accurately flagged anomalous carriers during evaluation and received personal commendation from the CEO of Mesh Works during project presentation.