quantitative-management

# Quantitative Management

## Purpose & Context

Implements CMMI **MA** (Measurement & Analysis), **QPM** (Quantitative Project Management), and **OPP** (Organizational Process Performance) through data-driven decision making, statistical process control, and predictive analytics.

**Core Principle**: Measure what matters, use data to drive decisions, distinguish signal from noise.

**Avoid**: Measurement theater (tracking without action), vanity metrics (looks good but no value), gaming metrics (optimizing numbers vs processes).

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## When to Use This Skill

**Triggers**:
- "What should we measure?"
- "How do I know if this variation is a problem?"
- "How do I establish process baselines?"
- "What's the difference between Level 2, 3, and 4 measurement?"
- "How do I use DORA metrics?"
- "How do I implement statistical process control?"
- "How do I use metrics for project decisions?"

**Use this when**:
- Establishing a measurement program
- Moving from Level 2 → 3 → 4
- Implementing DORA metrics
- Analyzing process performance
- Detecting process instability
- Predicting project outcomes
- Making data-driven decisions

**Do NOT use for**:
- Platform-specific metric collection → See `platform-integration` skill
- One-time reporting → Just write the report
- Metrics mandated by compliance → See `governance-and-risk` skill

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## Quick Reference: Finding What You Need

| You Want To... | Reference Sheet | Key Content |
|----------------|-----------------|-------------|
| **Plan what to measure** | measurement-planning.md | GQM methodology, cost/value analysis, anti-patterns |
| **Choose metrics** | key-metrics-by-domain.md | Quality, velocity, stability, deployment metrics |
| **Implement DORA** | dora-metrics.md | 4 DORA metrics, collection automation, baselines |
| **Analyze variation** | statistical-analysis.md | Control charts, SPC, trend detection |
| **Establish baselines** | process-baselines.md | Historical data analysis, baseline maintenance |
| **Make da