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koriweb d8a80f6272 chore(wiki): dangling 링크 canonical 정규화 (768파일/1200건)

이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해
끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은
과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업.
도구: Datacollect/scripts/link_reconcile_apply.mjs

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-06-08 12:24:15 +09:00

6.3 KiB

Raw Blame History

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title

SCM (Supply Chain Management)

매 한 줄

"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).

매 핵심

매 SCOR 5단계

Plan: demand forecast, S&OP, inventory policy.
Source: supplier selection, procurement, contracts.
Make: production scheduling, MRP, capacity.
Deliver: warehousing, transportation, last-mile.
Return: reverse logistics, refurb, recycling.

매 KPI

OTIF (On-Time-In-Full): delivery reliability.
Cash-to-Cash cycle: working capital efficiency.
Forecast accuracy (MAPE/WAPE).
Perfect Order Rate: error-free fulfillment.
Inventory turnover = COGS / avg inventory.

매 응용

Demand forecasting (TFT, N-BEATS, Chronos foundation model).
VRP / route optimization (last-mile, mid-mile).
Supplier risk monitoring (Resilinc, Everstream).
Inventory optimization (multi-echelon, newsvendor).
Digital twin of supply network.

💻 패턴

Demand forecast (Chronos foundation model, 2026)

from chronos import ChronosPipeline
import torch, pandas as pd

pipeline = ChronosPipeline.from_pretrained(
    "amazon/chronos-bolt-base", torch_dtype=torch.bfloat16
)
history = pd.read_csv("sales.csv")["units"].values
forecast = pipeline.predict(
    context=torch.tensor(history),
    prediction_length=28,  # 28-day horizon
    num_samples=100,
)  # quantile-aware probabilistic

Multi-echelon inventory (newsvendor + safety stock)

from scipy.stats import norm
import numpy as np

def reorder_point(mu_d, sigma_d, lead_time, service_level=0.95):
    z = norm.ppf(service_level)
    mean_lt_demand = mu_d * lead_time
    sigma_lt = sigma_d * np.sqrt(lead_time)
    safety_stock = z * sigma_lt
    return mean_lt_demand + safety_stock, safety_stock

rop, ss = reorder_point(mu_d=120, sigma_d=30, lead_time=7)

VRP (OR-Tools)

from ortools.constraint_solver import pywrapcp, routing_enums_pb2

def solve_vrp(distance_matrix, demands, vehicle_capacities, depot=0):
    mgr = pywrapcp.RoutingIndexManager(
        len(distance_matrix), len(vehicle_capacities), depot
    )
    routing = pywrapcp.RoutingModel(mgr)

    def dist_cb(i, j):
        return distance_matrix[mgr.IndexToNode(i)][mgr.IndexToNode(j)]
    routing.SetArcCostEvaluatorOfAllVehicles(
        routing.RegisterTransitCallback(dist_cb)
    )

    def demand_cb(i):
        return demands[mgr.IndexToNode(i)]
    routing.AddDimensionWithVehicleCapacity(
        routing.RegisterUnaryTransitCallback(demand_cb),
        0, vehicle_capacities, True, "Capacity"
    )
    params = pywrapcp.DefaultRoutingSearchParameters()
    params.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
    )
    return routing.SolveWithParameters(params)

Supplier risk score (LLM + structured)

import anthropic, json

client = anthropic.Anthropic()
news = fetch_news(supplier="Foxconn Zhengzhou", days=7)

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=512,
    messages=[{"role": "user", "content": f"""
Score supplier risk 0-100 based on news. Output JSON.
News: {news}
Schema: {{"score": int, "drivers": [str], "recommended_action": str}}
"""}],
)
risk = json.loads(resp.content[0].text)

Bullwhip effect simulation

# Beer game style — order amplification upstream
def bullwhip(retail_demand, smoothing=0.3, levels=4):
    orders = [retail_demand]
    for _ in range(levels):
        prev = orders[-1]
        next_orders = [prev[0]]
        for d in prev[1:]:
            next_orders.append(
                smoothing * d + (1 - smoothing) * next_orders[-1] + 0.5 * (d - next_orders[-1])
            )
        orders.append(next_orders)
    return orders  # variance grows upstream

Digital twin (NetworkX flow)

import networkx as nx

G = nx.DiGraph()
G.add_edge("Supplier_TW", "Port_Kaohsiung", capacity=500, cost=10)
G.add_edge("Port_Kaohsiung", "Port_LA", capacity=400, cost=80, lead=14)
G.add_edge("Port_LA", "DC_Dallas", capacity=350, cost=20)
flow_cost, flow_dict = nx.network_simplex(G, demand={"DC_Dallas": 300, "Supplier_TW": -300})

매 결정 기준

상황	Approach
New SKU, no history	Foundation model (Chronos, TimeGPT) zero-shot
Mature SKU, seasonal	TFT / Prophet + holiday regressors
Sub-day routing	OR-Tools VRP / Hexaly
Supplier disruption	Multi-source + safety stock + risk score
Network redesign	MILP (Pyomo + Gurobi)

기본값: Chronos-bolt forecasting + multi-echelon safety stock + OR-Tools VRP.

🔗 Graph

부모: Operations-Research
변형: S&OP
응용: ERP
Adjacent: Time-Series-Analysis · Digital Twin

🤖 LLM 활용

언제: supplier risk synthesis from news, RFP analysis, procurement copilot, demand sensing from external signals (weather, social). 언제 X: numerical optimization (VRP, MILP) — use OR solvers, not LLM.

❌ 안티패턴

Spreadsheet hell: critical S&OP in Excel without version control.
Single forecast number: ignore quantiles → systematic stockouts or overstocks.
Bullwhip ignored: each tier optimizes locally, variance amplifies upstream.
Supplier concentration: 80% volume from one country/factory.

🧪 검증 / 중복

Verified (APICS SCOR, Gartner Magic Quadrant 2025, MIT CTL research).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — SCM SCOR, forecasting, VRP, risk patterns

6.3 KiB Raw Blame History