Case · Public Sector
Metro city — youth unemployment policy simulation
// PERSONA
Park, Senior Officer — metro city employment policy
// INDUSTRY
Public Sector
// DATASET
case_03 · regional_youth_employment.csv · 60-month series
PROBLEM
The Problem
To hit the 4.0% youth unemployment target, which policy variable — wage-subsidy ratio, startup grants, training seats, foreign-company relocations — should be moved by how much? No historical simulation existed, only after-the-fact analysis.
// YOUTH UNEMPLOYMENT
4.9%
매번 사후 분석만 가능 · 정책 시뮬레이션 부재
APPROACH
XimTier Approach
Municipal admin data is combined with public statistics, and Reverse What-If outputs the optimal combination of four policy variables to hit a target unemployment rate. The NIPA SaaS support program enables fast pilot-to-production conversion.
// POLICY VARIABLES
인건비 지원율
32%
→
48%
+16%
창업 지원금
₩50B
→
₩72B
+22B
직업 훈련 인원
8K
→
14K
+6K
기업 유치 수
12
→
21
+9
✓ 청년 실업률 4.0% 달성 시뮬레이션
OUTCOMES
Outcomes
OUT / 01
Policy scenario comparison time −70% (vs manual)
OUT / 02
Justification report auto-generated
OUT / 03
Eligible for NIPA SaaS support program
OUT / 04
PoC → production in 6 weeks (vs 6 months legacy SI)
⚠ Example data — fine-tuned on customer data at deployment.