/procurement-optimizer

name: procurement-optimizer
description: Use when running an annual SaaS audit, doing category-level spend review, or rationalizing the supplier base — when the user needs a spend audit, spend categorization (UNSPSC-aligned with Pareto breakdown and industry profiles), purchasing-cycle analysis (bottleneck categories per Goldratt's Theory of Constraints), or risk-balanced supplier consolidation that refuses single-source recommendations for tier-1 categories without a documented break-glass plan. Triggers on "spend audit", "SaaS audit", "spend categorization", "supplier rationalization", "supplier consolidation", "category strategy", "duplicate SaaS", "renewal cluster".
version: 2.8.0
author: claude-code-skills
license: MIT
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 e