A

University Enrollment Reduction Impact Simulator

3.85

Derivation Chain

Step 1 Local university restructuring and Dong-eui University personnel changes
→
Step 2 Consulting for university enrollment reduction response
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Step 3 Simulation tool for financial and staffing impact of department-level enrollment cuts

Problem

Planning and admissions offices at local private universities (5-15 staff) lack tools to simulate which departments and by how much to reduce enrollment to maintain university finances under the Ministry of Education's enrollment reduction pressure, spending 2-3 months on manual Excel analysis. The combined impact of tuition revenue, faculty salaries, and facility maintenance costs per department is hard to grasp at a glance, delaying decision-making.

Solution

Inputting department-level data on enrollment quotas, tuition, faculty numbers, and facility costs automatically simulates financial impacts (revenue decline, labor cost savings, facility operating cost changes) for enrollment reduction scenarios (5%-30%). It also predicts changes in diagnostic grades by linking to the Ministry of Education's University Basic Competency Diagnosis indicators.

Target: Planning and admissions offices at local private universities (enrollment 3,000-15,000 students, approximately 150 institutions nationwide)
Revenue Model: SaaS annual subscription: 3.6 million KRW per university (approx. $2,700) (equivalent to 300,000 KRW/month). Short-term license for the Ministry of Education diagnosis period (March-May): 1.5 million KRW for 3 months (approx. $1,125).
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
4.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (60/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
16.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Frontend [medium] Data Pipeline [low]
Dashboard