refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-scm-supply-chain-management
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title: SCM (Supply Chain Management)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Supply Chain Management, Supply Chain]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [scm, supply-chain, logistics, optimization, operations-research]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: OR-Tools / Pyomo
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---
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# SCM (Supply Chain Management)
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## 매 한 줄
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> **"매 plan → source → make → deliver → return 의 end-to-end flow 최적화"**. 매 SCOR 모델 (1996) 의 frame 위에서 매 ERP/APS (SAP IBP, Oracle, o9, Kinaxis) 가 운영. 매 2020 COVID, 2022 우크라이나, 2024 홍해 등 매 black-swan 연쇄 이후 매 resilience + AI-driven autonomous SCM 이 핵심 의제 (Gartner: 50%+ 기업 by 2026).
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## 매 핵심
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### 매 SCOR 5단계
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- **Plan**: demand forecast, S&OP, inventory policy.
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- **Source**: supplier selection, procurement, contracts.
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- **Make**: production scheduling, MRP, capacity.
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- **Deliver**: warehousing, transportation, last-mile.
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- **Return**: reverse logistics, refurb, recycling.
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### 매 KPI
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- **OTIF** (On-Time-In-Full): delivery reliability.
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- **Cash-to-Cash cycle**: working capital efficiency.
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- **Forecast accuracy** (MAPE/WAPE).
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- **Perfect Order Rate**: error-free fulfillment.
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- **Inventory turnover** = COGS / avg inventory.
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### 매 응용
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1. Demand forecasting (TFT, N-BEATS, Chronos foundation model).
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2. VRP / route optimization (last-mile, mid-mile).
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3. Supplier risk monitoring (Resilinc, Everstream).
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4. Inventory optimization (multi-echelon, newsvendor).
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5. Digital twin of supply network.
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## 💻 패턴
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### Demand forecast (Chronos foundation model, 2026)
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```python
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from chronos import ChronosPipeline
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import torch, pandas as pd
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pipeline = ChronosPipeline.from_pretrained(
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"amazon/chronos-bolt-base", torch_dtype=torch.bfloat16
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)
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history = pd.read_csv("sales.csv")["units"].values
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forecast = pipeline.predict(
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context=torch.tensor(history),
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prediction_length=28, # 28-day horizon
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num_samples=100,
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) # quantile-aware probabilistic
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```
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### Multi-echelon inventory (newsvendor + safety stock)
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```python
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from scipy.stats import norm
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import numpy as np
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def reorder_point(mu_d, sigma_d, lead_time, service_level=0.95):
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z = norm.ppf(service_level)
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mean_lt_demand = mu_d * lead_time
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sigma_lt = sigma_d * np.sqrt(lead_time)
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safety_stock = z * sigma_lt
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return mean_lt_demand + safety_stock, safety_stock
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rop, ss = reorder_point(mu_d=120, sigma_d=30, lead_time=7)
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```
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### VRP (OR-Tools)
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```python
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from ortools.constraint_solver import pywrapcp, routing_enums_pb2
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def solve_vrp(distance_matrix, demands, vehicle_capacities, depot=0):
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mgr = pywrapcp.RoutingIndexManager(
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len(distance_matrix), len(vehicle_capacities), depot
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)
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routing = pywrapcp.RoutingModel(mgr)
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def dist_cb(i, j):
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return distance_matrix[mgr.IndexToNode(i)][mgr.IndexToNode(j)]
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routing.SetArcCostEvaluatorOfAllVehicles(
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routing.RegisterTransitCallback(dist_cb)
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)
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def demand_cb(i):
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return demands[mgr.IndexToNode(i)]
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routing.AddDimensionWithVehicleCapacity(
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routing.RegisterUnaryTransitCallback(demand_cb),
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0, vehicle_capacities, True, "Capacity"
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)
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params = pywrapcp.DefaultRoutingSearchParameters()
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params.first_solution_strategy = (
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routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
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)
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return routing.SolveWithParameters(params)
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```
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### Supplier risk score (LLM + structured)
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```python
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import anthropic, json
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client = anthropic.Anthropic()
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news = fetch_news(supplier="Foxconn Zhengzhou", days=7)
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=512,
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messages=[{"role": "user", "content": f"""
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Score supplier risk 0-100 based on news. Output JSON.
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News: {news}
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Schema: {{"score": int, "drivers": [str], "recommended_action": str}}
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"""}],
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)
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risk = json.loads(resp.content[0].text)
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```
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### Bullwhip effect simulation
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```python
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# Beer game style — order amplification upstream
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def bullwhip(retail_demand, smoothing=0.3, levels=4):
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orders = [retail_demand]
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for _ in range(levels):
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prev = orders[-1]
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next_orders = [prev[0]]
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for d in prev[1:]:
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next_orders.append(
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smoothing * d + (1 - smoothing) * next_orders[-1] + 0.5 * (d - next_orders[-1])
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)
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orders.append(next_orders)
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return orders # variance grows upstream
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```
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### Digital twin (NetworkX flow)
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```python
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import networkx as nx
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G = nx.DiGraph()
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G.add_edge("Supplier_TW", "Port_Kaohsiung", capacity=500, cost=10)
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G.add_edge("Port_Kaohsiung", "Port_LA", capacity=400, cost=80, lead=14)
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G.add_edge("Port_LA", "DC_Dallas", capacity=350, cost=20)
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flow_cost, flow_dict = nx.network_simplex(G, demand={"DC_Dallas": 300, "Supplier_TW": -300})
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| New SKU, no history | Foundation model (Chronos, TimeGPT) zero-shot |
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| Mature SKU, seasonal | TFT / Prophet + holiday regressors |
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| Sub-day routing | OR-Tools VRP / Hexaly |
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| Supplier disruption | Multi-source + safety stock + risk score |
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| Network redesign | MILP (Pyomo + Gurobi) |
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**기본값**: Chronos-bolt forecasting + multi-echelon safety stock + OR-Tools VRP.
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## 🔗 Graph
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- 부모: [[Operations-Research]]
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- 변형: [[S&OP]]
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- 응용: [[ERP]]
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- Adjacent: [[Time-Series-Analysis|Time-Series-Forecasting]] · [[Digital Twin]]
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## 🤖 LLM 활용
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**언제**: supplier risk synthesis from news, RFP analysis, procurement copilot, demand sensing from external signals (weather, social).
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**언제 X**: numerical optimization (VRP, MILP) — use OR solvers, not LLM.
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## ❌ 안티패턴
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- **Spreadsheet hell**: critical S&OP in Excel without version control.
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- **Single forecast number**: ignore quantiles → systematic stockouts or overstocks.
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- **Bullwhip ignored**: each tier optimizes locally, variance amplifies upstream.
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- **Supplier concentration**: 80% volume from one country/factory.
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## 🧪 검증 / 중복
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- Verified (APICS SCOR, Gartner Magic Quadrant 2025, MIT CTL research).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — SCM SCOR, forecasting, VRP, risk patterns |
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