# Purchasing Cycle-Time Scorecard

- **Categories analyzed:** 5
- **Cross-category median (Request → PO):** 6.0 days
- **Bottleneck threshold (2× median):** 12.0 days

## Per-category cycle times

| Category | N | Req→PO median | Req→PO P90 | PO→Pay median | PO→Pay P90 | Hops | Bottleneck? |
|---|---:|---:|---:|---:|---:|---:|---|
| Outside Counsel | 2 | 30.0 | 34.0 | 30.0 | 30.0 | 4.0 | **BOTTLENECK** |
| AWS | 2 | 7.0 | 7.8 | 30.0 | 30.0 | 2.0 | — |
| Monitoring / Observability | 2 | 6.0 | 6.8 | 30.0 | 30.0 | 2.0 | — |
| Expense / Spend Management | 2 | 5.0 | 5.8 | 30.0 | 30.0 | 1.0 | — |
| Email Marketing Platform | 2 | 4.0 | 4.8 | 30.0 | 30.0 | 1.0 | — |

## Goldratt — find the constraint

**1** category exceed the 2× median bottleneck threshold:

- **Outside Counsel** — Request→PO median 30.0d, P90 34.0d, approver hops median 4.0

Improving any non-bottleneck category does not change overall throughput. Focus on the constraint first — typical fixes per stage:

- **Long Request→Approval:** approval routing, parallel review, raise auto-approve threshold for low-risk categories.
- **Long Approval→PO:** PO creation friction; consider catalog buys for repeating categories.
- **High approver hops:** collapse routing tiers; one approver per $-band, not three.
- **Long PO→Pay:** AP cycle (3-way match, batch runs); negotiate net-terms only after measuring.