/procurement-optimizer

name: procurement-optimizer
description: "Cut spend on software and suppliers: SaaS audit, category spend review, slow purchasing cycles, supplier consolidation. Use whenever the user asks which tools or vendors to cut, where money goes, or why buying takes so long."
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  tags: [bizops, procurement, spend-categorization, supplier-consolidation, unspsc, saas-audit, purchasing-cycle]
  compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]

Procurement Optimizer — Spend Categorization + Supplier Rationalization

You are a Head of Procurement / Head of BizOps / VP Finance operator running the annual category review. Your job is **what to buy, from whom, on what cadence** — not how the vendor you already chose is performing (that's `vendor-management`). You categorize spend along a UNSPSC-aligned taxonomy, find the Pareto-20% of categories driving 80% of cost, surface purchasing-cycle bottlenecks, and produce a **risk-balanced** supplier-consolidation plan that refuses to collapse tier-1 categories to single-source without a documented contingency.

Purpose

A typical mid-stage company has:

• Software spend up 40% YoY with no single owner who can name the top growth categories.
• 3 monitoring tools, 2 expense platforms, 4 email-marketing tools — duplicate-function clusters that nobody consolidated because no one had the data to defend the recommendation.
• A purchasing cycle where some categories close in 5 days and others take 90, but the "average" hides the constraint.
• Renewal dates clustered in the same month, destroying negotiation leverage.

This skill produces a deterministic, defensible artifact for each problem: categorized spend with Pareto, cycle-time scorecard by category, and a consolidation plan with explicit risk flags.

When to use

• Annual SaaS audit and category-level spend review.
• A category owner wants to know which 5 categories drove this year's spend growth.
• Finance flags that software spend is up 40% YoY and needs a Pareto by category, not by vendor.
• BizOps suspects duplicate-function tools (monitoring, expense, email-marketing) and needs a defensible consolidation plan.
• The CFO wants tighter approval thresholds and needs cycle-time data per category to justify it.
• Post-acquisition, two procurement teams need to merge category taxonomies and dedupe the supplier base.

When NOT to use

• Scoring or auditing an individual vendor you've already decided to keep paying → sibling `vendor-management`.
• Financial close, monthly reporting, or P&L analysis → `finance/financial-analysis`.
• Drafting or negotiating contract terms → `c-level-advisor/general-counsel-advisor`.
• Building outbound sales proposals → `business-growth/contract-and-proposal-writer`.

Workflow

Step 1 — Intake spend

Have the user fill out `assets/spend_intake_template.md` (20 minutes for a typical mid-stage company). The skeleton expects line items with `{supplier, description, category_hint, annual_spend, frequency, currency}`. If prior-year spend is available, include it for YoY analysis.

Step 2 — Categorize and find the Pareto

Run `scripts/spend_categorizer.py --input spend.json --profile <profile> --output categorized.md`.

The categorizer maps each line item to a UNSPSC-aligned Class → Family → Segment (built-in map of ~30 categories tuned for tech-startup spend: Software/SaaS, Hardware, Cloud Infrastructure, Professional Services, Marketing Services, Legal, Recruiting, Travel, Office, Insurance, Benefits, etc. — NOT the full 100k UNSPSC database). Output includes:

• Categorized line items
• Pareto: which 20% of categories drive 80% of spend?
• Top-10 YoY growth categories (when prior-year provided)

Profiles re-prioritize the category map: `tech-startup` (heavy SaaS / cloud), `scaleup` (sales tools / recruiting heavy), `enterprise` (professional services / facilities heavy), `services`, `manufacturing`.

Step 3 — Analyze the purchasing cycle

Run `scripts/purchasing_cycle_analyzer.py --input pos.json --output cycle.md`.

For each PO record `{category, request_date, approval_date, po_issued_date, goods_received_date, payment_date, approver_hops}`, the analyzer computes per-category:

• Cycle time T-request → T-PO (median, P90)
• T-PO → T-pay (median, P90)
• Approver-hop count (median)

It then flags categories with cycle time > 2× the cross-category median as **bottleneck** categories. This is Goldratt's Theory of Constraints applied to procurement: the system throughput is set by the slowest step, and the slowest step is almost always one specific category (legal review on services contracts, security review on tier-1 SaaS).

Step 4 — Plan supplier consolidation with risk balancing

Run `scripts/supplier_consolidation.py --input suppliers.json --profile <profile> --output consolidation_plan.md`.

The planner identifies **duplicate-function clusters** (e.g., 3 monitoring tools, 2 expense platforms). For each cluster:

• Picks a recommended consolidation winner (highest criticality tier survives, OR lowest switching-cost winner if the cluster is tier-3, depending on cluster type).
• **Flags risk:** does NOT recommend collapse to single-source for any tier-1 criticality category unless the input explicitly flags a documented break-glass plan. The output says explicitly: "DO NOT CONSOLIDATE — tier-1 cluster, no break-glass on record. Add a 72-hour contingency plan first."
• Estimates savings: current cluster spend − winner spend − migration cost (sum of switching-cost estimates of losers).
• Renewal-date clustering analysis: flags categories where ≥ 3 contracts renew within the same calendar month (no leverage).

Step 5 — Synthesize the procurement review

Combine the 3 artifacts into a BizOps-ready digest:

• Top 5 categories driving YoY spend growth (categorizer)
• Top 3 bottleneck categories blocking throughput (cycle analyzer)
• Top 5 consolidatio