A

University Enrollment Reduction Impact Simulator

3.85

Derivation Chain

Step 1 Local university restructuring and Dong-eui University personnel changes
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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

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

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