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The VIP Paradox: Decoding Supply Chain Chaos with Python & Power BI
The VIP Paradox: Decoding Supply Chain Chaos with Python & Power BI
Read Full Article (Medium) -> Link to Medium article
View Source Code (GitHub) -> Link to GitHub Repository
A predictive analysis project addressing the counterintuitive high delay rates in priority shipments.An e-commerce giant faced a silent crisis: nearly 60% of shipments were delayed. My goal was to build a predictive ML model, but the data revealed a fractured operation where operations defied mathematics.
- The ML Diagnosis: After testing 5 algorithms (including XGBoost and Gradient Boosting), the ~67% accuracy ceiling proved that delays were chronic and chaotic, not logically patterned.
- The VIP Paradox (Power BI): Interactive dashboards revealed that 'High Importance' products and expensive Air Freight suffered the highest delay rates (>60%).
- Business Impact: Shifted the strategy from deploying a predictive algorithm to conducting an emergency warehouse audit and renegotiating SLAs, saving premium costs on failing services.
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