A supplier scorecard analysis evaluating cost, quality and lead time across five suppliers in a manufacturing/retail supply chain dataset. Built to identify whether spend concentration aligns with supplier performance; a core procurement question when deciding where to consolidate or renegotiate.
Note: This uses a synthetic Kaggle dataset (100 SKUs, 5 suppliers) for methodology demonstration. Supplier names are anonymized placeholders, not real companies. The value here is the analytical framework, which applies directly to real procurement spend data.
Determine whether high-spend suppliers are also high-performing suppliers or whether cost concentration is masking quality/delivery risk.
- Source: Supply Chain Analysis dataset, Kaggle
- 100 rows (SKU-level), 24 columns including cost, defect rate, lead time and supplier fields
- No missing values; used as-is after supplier-level aggregation
- Aggregated SKU-level data to supplier-level scorecard: total cost, cost share %, average defect rate, average lead time, average shipping cost, SKU count
- Ranked suppliers by cost and by defect rate to identify misalignment between spend and performance
- Visualized cost distribution (bar chart) and cost-vs-quality positioning (scatter, bubble-sized by SKU count)
Supplier 1 holds the largest cost share (29.3%) but the lowest defect rate (1.80%) and fastest lead time (14.78 days); spend concentration here is performance-justified.
Supplier 5 shows a cost-performance mismatch: mid-tier spend (18.2%) paired with the highest defect rate (2.67%) and a slower-than-average lead time; a candidate for supplier review or renegotiation.
Maintain or grow allocation to Supplier 1. Open a performance conversation with Supplier 5 ahead of contract renewal.
Python (pandas, matplotlib), Google Colab
supplier_analysis.ipynb— full notebooksupplier_scorecard.csv— aggregated outputcost_by_supplier.png,supplier_risk_map.png— visuals