procurement-optimizer

Export your top 100-200 spend line items from your AP system or SaaS management platform into JSON with supplier, description, and annual_spend fields, then run spend_categorizer.py with an industry profile. The output shows which 20% of categories drive 80% of spend plus top YoY growth categories.

Build a supplier JSON with criticality tiers, switching-cost estimates, and break-glass flags, then run supplier_consolidation.py. It clusters duplicate-function tools, picks a winner, computes net Year-1 savings after migration cost, and refuses tier-1 single-source consolidation without a documented contingency plan.

It accepts a JSON list of line items with supplier, description, category_hint, annual_spend, and optional prior_year_spend, frequency, and currency fields. The description and category_hint drive categorization, not the supplier name, and prior-year data is optional for YoY analysis.

It supports five industry profiles: tech-startup, scaleup, enterprise, services, and manufacturing. Profiles re-prioritize category matching and define which categories count as tier-1 for consolidation risk checks, so enterprise spend like professional services and facilities is handled.

Do not use it for scoring individual vendor performance, drafting contract terms, or financial close and P&L reporting. It decides which vendors to keep through category strategy, not how existing vendors perform or how contracts are negotiated.

It blocks consolidation when a tier-1 category lacks a documented break-glass plan, since single-sourcing a revenue-critical supplier without a 72-hour contingency creates unacceptable operational risk. It also defers clusters where net Year-1 savings fall below $10,000 after migration costs.