2nd/10_Wiki/Topics/Education/Management Consulting.md

---
id: wiki-2026-0508-management-consulting
title: Management Consulting
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [경영 컨설팅, Strategy Consulting, Mgmt Consulting]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [education, consulting, strategy, business]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: english
  framework: business
---

# Management Consulting

## 매 한 줄
> **"매 management consulting 은 hypothesis-driven problem solving as a service"**. McKinsey/BCG/Bain (MBB) 의 1960s codification — pyramid principle, MECE, issue tree, hypothesis-driven 의 four pillars. 매 2026 modern state: AI augmentation (Claude Opus 4.7, GPT-5) 으로 research/synthesis 의 80% acceleration, but human judgment + executive trust 가 still core.

## 매 핵심

### 매 four pillars
- **Pyramid principle (Minto)**: top answer first, supporting reasons next, evidence below.
- **MECE**: Mutually Exclusive, Collectively Exhaustive — partition framework.
- **Issue tree**: top question → sub-questions, recursively.
- **Hypothesis-driven**: form answer first, test against data — not bottom-up boil-the-ocean.

### 매 typical engagement structure
- Week 1-2: scoping, interviews, hypothesis tree.
- Week 3-6: data gathering, model building, expert calls.
- Week 7-9: synthesis, slide drafting, partner reviews.
- Week 10-12: client workshops, final readout, implementation roadmap.

### 매 modern (2026) augmentation
- **AI research**: Claude/GPT for industry primers, expert call prep, public filings synthesis.
- **AI modeling**: code-interpreter for forecasts, sensitivity tables.
- **AI slide drafting**: rough layout from issue tree + key numbers; human polish.
- **Still human**: client relationship, executive trust, judgment under ambiguity, internal politics navigation.

### 매 firm tiers
1. **MBB**: McKinsey, BCG, Bain.
2. **Tier 2**: Strategy&, Oliver Wyman, LEK, Roland Berger, Kearney.
3. **Big 4 strategy**: Deloitte Monitor, EY-Parthenon, PwC Strategy&, KPMG.
4. **Boutique**: Veritas, Putnam, Analysis Group (specialized).

## 💻 패턴

### Issue tree as data
```typescript
interface IssueNode {
  question: string;
  hypothesis?: string;
  children: IssueNode[];
  evidence: Evidence[];
  status: "open" | "supported" | "refuted";
}
function leaves(node: IssueNode): IssueNode[] {
  return node.children.length === 0 ? [node] : node.children.flatMap(leaves);
}
```

### MECE check
```typescript
function isMECE<T>(partition: T[][], universe: Set<T>): { mutually: boolean; exhaustive: boolean } {
  const flat = partition.flat();
  const mutually = flat.length === new Set(flat).size;
  const exhaustive = [...universe].every((x) => flat.includes(x));
  return { mutually, exhaustive };
}
```

### Pyramid principle slide skeleton
```markdown
# [Action title: the answer in one sentence]
- Reason 1: [supporting argument]
  - Evidence A
  - Evidence B
- Reason 2: [supporting argument]
- Reason 3: [supporting argument]
```

### Profitability tree (canonical)
```
Profit
├── Revenue
│   ├── Volume × Price
│   │   ├── Market size × Share
│   │   └── Mix × Discount
└── Cost
    ├── COGS (variable)
    └── SG&A (fixed)
```

### Expert call synthesis prompt (Claude Opus 4.7)
```typescript
const prompt = `
You are a research analyst. Given these 5 expert call transcripts on [TOPIC],
extract:
1. Areas of consensus (≥3 experts agree)
2. Areas of disagreement
3. Quantitative anchors (market size, growth, margin)
4. Open questions for further research
Output as MECE bullets, max 300 words.
`;
```

### 2x2 framework template
```markdown
|              | High Impact | Low Impact |
|--------------|-------------|------------|
| Easy to do   | DO NOW      | Quick wins |
| Hard to do   | Strategic   | DROP       |
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| C-suite strategy refresh | MBB or Tier 2 strategy boutique |
| Operational turnaround | Big 4 + ops specialists (AlixPartners) |
| M&A due diligence | Bain (PE focus), strategy boutiques |
| Digital/AI transformation | McKinsey QuantumBlack, BCG X, Bain Vector |
| In-house build | Hire ex-consultant + AI tooling |

**기본값**: Hypothesis-driven + issue tree + MBB-style synthesis. AI augmentation for research/modeling. Human for trust/judgment.

## 🔗 Graph
- 부모: [[Business Strategy]]
- 변형: [[Strategy Consulting]]
- 응용: [[Pyramid Principle]] · [[Issue Tree]]

## 🤖 LLM 활용
**언제**: industry primer, expert call prep, slide drafting, financial modeling, synthesis.
**언제 X**: client relationship building, executive trust, internal politics, judgment calls under deep ambiguity.

## ❌ 안티패턴
- **Boil the ocean**: hypothesis 없이 모든 data 모음 → time/budget overrun.
- **Pretty slides, weak answer**: aesthetics > insight 의 trap.
- **Recommendation without data**: "we believe" without grounding.
- **AI hallucination unchecked**: AI 의 fabricated stats 의 client-facing slide 의 disaster.

## 🧪 검증 / 중복
- Verified (Minto's Pyramid Principle, McKinsey/BCG/Bain public materials, 2026 industry observation).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — FULL spec rewrite with 2026 AI augmentation context |