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Industry-Academia Workforce Development Outcome Tracker

2.60

Derivation Chain

Step 1 Kunsan National University RISE program industry-academia workforce development
→
Step 2 Surge in customized workforce programs between regional universities and companies
→
Step 3 Employment outcome tracking and reporting tool for industry-academia workforce programs

Problem

Government-funded industry-academia workforce programs like RISE allocate trillions of won annually, yet each program office manually tracks graduate employment and retention rates to compile Ministry of Education performance reports. Program coordinators spend 40+ hours per month individually confirming hiring status across 10–30 partner companies via phone calls and emails. Over 20% of program offices fail to secure next-year funding due to inaccurate data.

Solution

Automatically sends streamlined survey links to partner companies and graduates to collect employment, job changes, and salary data, then auto-generates reports aligned with Ministry of Education RISE performance templates. Visualizes graduate career paths by cohort and analyzes gaps between industry demand and curriculum to provide course improvement insights.

Target: Industry-academia cooperation offices and RISE program coordinators at national and private universities; LINC3.0 program directors
Revenue Model: SaaS Annual Subscription: ~$2,700/year per program office (up to 200 graduates), ~$4,500/year (up to 500 graduates); Ministry of Education integration module add-on at ~$900/year
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
2.0/5
M Market
2.0/5
R Realizability
4.0/5
V Validation
2.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.6/25
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 (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
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