B

Airport Flow Predictor

3.05

Derivation Chain

Step 1 Airport zero-loss baggage systems
→
Step 2 Airport passenger flow optimization
→
Step 3 Terminal congestion real-time prediction SaaS

Problem

Duty-free and F&B tenants at major airports like Incheon cannot predict hourly passenger flow patterns, making staff allocation and inventory preparation inefficient. When departure gates change or flights are delayed, expected foot traffic shifts dramatically, but stores only feel the impact 30-60 minutes later. This causes peak-hour revenue losses of 10-15% of daily sales.

Solution

A SaaS that collects real-time flight schedules, gate assignments, and delay information to predict terminal zone congestion 30 minutes ahead. Provides store-specific alerts ('foot traffic near your store expected to double in 30 minutes'), staff deployment recommendations, and weekly/monthly trend Reports.

Target: Incheon Airport duty-free operators (Lotte/Shilla/Shinsegae store management teams), airport F&B franchise managers, airport retail consultancies
Revenue Model: SaaS monthly flat rate $217.50/store (290K KRW), $142.50/store (190K KRW) for 10+ stores. API-only plan $742.50/month (990K KRW) with unlimited calls
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
2.0/5
V Validation
3.0/5
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 (68%)

Tech Complexity
29.3/40
Data Availability
18.8/25
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 (52/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
3.0/10
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

Data Pipeline [medium] AI/ML [medium] Frontend [low]
Dashboard