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-supply-chain
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title: Supply Chain
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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: [SCM, Supply Chain Management, 공급망]
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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: [supply-chain, logistics, security, sbom, ai-optimization]
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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: ortools
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---
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# Supply Chain
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## 매 한 줄
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> **"매 supply chain 의 end-to-end network — raw material부터 end customer까지의 flow"**. 매 2026 supply chain 의 두 축: AI-driven optimization (demand forecast, route, inventory) 와 security (SBOM, supply chain attack defense). 매 SolarWinds·xz-utils 사건 이후 software supply chain 의 first-class 보안 concern.
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## 매 핵심
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### 매 5 components
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- **Plan**: demand forecast, capacity planning, S&OP.
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- **Source**: supplier selection, contract, procurement.
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- **Make**: production, quality, scheduling.
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- **Deliver**: warehousing, transportation, last-mile.
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- **Return**: reverse logistics, recycling, RMA.
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### 매 AI 적용 영역
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- **Demand forecasting**: Transformer-based time series (TimesFM, Chronos), LSTM 의 retire.
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- **Route optimization**: OR-Tools VRP + RL hybrid.
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- **Inventory**: (s,S) policy + safety stock dynamic adjustment.
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- **Anomaly detection**: shipment delay prediction, fraud.
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- **Supplier risk**: graph neural network on supplier dependency graph.
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### 매 software supply chain security
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- **SBOM** (Software Bill of Materials): SPDX, CycloneDX format.
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- **Sigstore**: keyless signing, transparency log.
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- **SLSA** (Supply-chain Levels for Software Artifacts): level 1-4 framework.
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- **Attack surface**: dependency confusion, typosquatting, malicious maintainer.
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### 매 응용
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1. **E-commerce**: Amazon FBA — AI demand forecast → DC pre-positioning.
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2. **Manufacturing**: Toyota JIT 의 AI evolve — predictive lead time.
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3. **Software security**: GitHub Dependabot + Sigstore + SLSA Level 3.
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## 💻 패턴
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### 1. Demand forecast (Chronos)
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```python
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from chronos import ChronosPipeline
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import torch
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import pandas as pd
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pipe = ChronosPipeline.from_pretrained(
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"amazon/chronos-bolt-base", torch_dtype=torch.bfloat16
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)
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# historical daily sales
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ts = pd.read_csv("sales.csv")["units"].values
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context = torch.tensor(ts[-365:])
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forecast = pipe.predict(context, prediction_length=30, num_samples=100)
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median = forecast.median(dim=1).values # 30-day median forecast
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p90 = forecast.quantile(0.9, dim=1) # safety stock upper bound
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```
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### 2. VRP (Vehicle Routing Problem)
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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, num_vehicles, depot):
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manager = pywrapcp.RoutingIndexManager(
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len(distance_matrix), num_vehicles, depot
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)
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routing = pywrapcp.RoutingModel(manager)
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def dist_cb(i, j):
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return distance_matrix[manager.IndexToNode(i)][manager.IndexToNode(j)]
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transit_idx = routing.RegisterTransitCallback(dist_cb)
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routing.SetArcCostEvaluatorOfAllVehicles(transit_idx)
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params = pywrapcp.DefaultRoutingSearchParameters()
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params.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
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return routing.SolveWithParameters(params)
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```
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### 3. (s,S) inventory policy
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```python
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import numpy as np
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def reorder(stock, s, S, demand_forecast, lead_time_days):
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# s = reorder point, S = order-up-to level
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expected_demand_during_lead = demand_forecast.mean() * lead_time_days
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safety = 1.65 * demand_forecast.std() * np.sqrt(lead_time_days)
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s_dynamic = expected_demand_during_lead + safety
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if stock <= s_dynamic:
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return S - stock
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return 0
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```
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### 4. SBOM generation (CycloneDX)
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```bash
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# Python project
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pip install cyclonedx-bom
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cyclonedx-py -o sbom.json --format json
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# Node project
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npx @cyclonedx/cyclonedx-npm --output-file sbom.json
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# Container
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syft packages docker:myimage:latest -o cyclonedx-json > sbom.json
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```
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### 5. Sigstore keyless signing
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```bash
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# Sign artifact (uses OIDC identity, no long-lived keys)
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cosign sign-blob --yes ./release.tar.gz \
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--output-signature release.sig \
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--output-certificate release.crt
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# Verify
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cosign verify-blob ./release.tar.gz \
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--signature release.sig \
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--certificate release.crt \
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--certificate-identity user@example.com \
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--certificate-oidc-issuer https://github.com/login/oauth
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```
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### 6. SLSA provenance (GitHub Actions)
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```yaml
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name: build
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on: [push]
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permissions:
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id-token: write
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contents: read
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jobs:
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build:
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uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v2.0.0
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with:
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base64-subjects: ${{ needs.hash.outputs.digests }}
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```
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### 7. Supplier risk GNN
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```python
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import torch
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from torch_geometric.nn import GraphSAGE
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# nodes = suppliers, edges = dependency
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model = GraphSAGE(in_channels=16, hidden_channels=64,
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num_layers=3, out_channels=2) # risk score
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# message passing: tier-1 supplier 의 risk → tier-2 propagation
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risk_scores = model(node_features, edge_index)
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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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| Demand forecast (long horizon) | Chronos / TimesFM |
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| Route opt (small, hard) | OR-Tools exact |
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| Route opt (large, soft) | RL + heuristic |
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| SBOM | CycloneDX (broader) or SPDX |
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| Signing | Sigstore (keyless, modern) |
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**기본값**: Chronos forecast + OR-Tools VRP + CycloneDX SBOM + Sigstore.
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## 🔗 Graph
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- 부모: [[Operations-Research]]
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- 변형: [[SBOM]] · [[SLSA]] · [[Sigstore]]
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## 🤖 LLM 활용
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**언제**: Demand pattern 의 explain, anomaly 의 root-cause analysis, SBOM 의 vulnerability summary.
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**언제 X**: Real-time route decision (latency), exact optimization (LLM 의 hallucinate cost).
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## ❌ 안티패턴
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- **Forecast 없는 inventory**: 매 lead time × demand 의 rough-cut estimate → stockout 의 cycle.
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- **SBOM 의 build 후 generation**: 매 reproducibility 의 lose. Build 시 generate.
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- **Long-lived signing keys**: 매 leak 의 catastrophic. Sigstore keyless 의 use.
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- **Dependency 의 pin without lock**: 매 supply-chain attack vector. lockfile + hash check.
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- **Tier-1 supplier 의 only monitor**: 매 cascade failure 의 ignore. Multi-tier visibility.
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## 🧪 검증 / 중복
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- Verified (CSCMP definitions, NIST SSDF SP800-218, SLSA spec v1.0).
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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 — supply chain (logistics + software security) full canonical |
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