Efficiency and consistent quality are cornerstones of successful sourcing. The MuleBuy Spreadsheet automates tracking by logging key timestamps for Quality Control (QC) and delivery. This guide walks you through analyzing this data to identify your top-performing suppliers.
Understanding the Key Data Points
The MuleBuy Spreadsheet typically generates or records the following critical timestamps for each order:
- Order Date:
- QC Check Date:
- Ship Date:
- Delivery Date:
- QC Result:
- QC Check Date:
Step-by-Step Analysis for Supplier Comparison
Step 1: Calculate Core Performance Metrics
Create new columns in your spreadsheet to calculate these key intervals:
| Metric | Calculation | What It Measures |
|---|---|---|
| QC Processing Speed | QC Check Date - Order Date | Supplier's efficiency in preparing items for inspection. |
| Total Production Lead Time | Ship Date - Order Date | Total time from order to shipment readiness. |
| Shipping Transit Time | Delivery Date - Ship Date | Carrier/logistics performance (can be supplier-dependent). |
| Total End-to-End Time | Delivery Date - Order Date | The overall timeline experienced by the buyer. |
Step 2: Segment Data by Supplier and Product Category
Filter or pivot your data to view metrics grouped by Supplier Name. For fair comparison, also consider filtering by Product Type, as complex items may naturally have longer lead times.
Step 3: Assess Consistency (Quality + Speed)
Speed means little without quality. Integrate the QC result into your analysis:
- Calculate the QC Pass Rate %
- Compare the average Lead Timepassed QC. A supplier with fast but failed QC creates delays from reworks.
- Look for correlation: Do QC failures cause specific suppliers' lead times to vary wildly?
Step 4: Visualize and Rank Performance
Create simple charts or a dashboard view:
- Bar Chart:
- Scatter Plot:
- Line Chart:
- Scatter Plot:
Rank suppliers by a composite score that weights both speedquality pass rate
Pro Tips for Actionable Insights
- Benchmark:
- Flag Outliers:
- Communicate with Data:
- Factor in Seasonality:
- Flag Outliers: