Reusable Cup Return Rate Analyzer

2.70

Derivation Chain

Step 1 Starbucks reusable cup trend

→

Step 2 Franchise reusable cup operational efficiency demand

→

Step 3 Real-time reusable cup return/loss rate analytics

Problem

Major franchises including Starbucks are expanding reusable cup programs, but store-level return rates vary widely (40-80%), with cup losses costing $375-$750 (500,000-1,000,000 KRW) per store per month. HQ operations teams manually compile return data in Excel, spending 5-8 hours per week determining optimal inventory levels and washing schedules per store.

Solution

A SaaS that collects real-time QR code/RFID scan data from reusable cups to analyze per-store return rates, loss rates, and optimal inventory levels, and auto-generates washing schedules. Core features: (1) Real-time dashboard of per-store return/loss rates, (2) Demand forecast-based inventory optimization, (3) Automated washing schedule generation.

Target: Franchise HQ operations teams running reusable cup programs (100+ stores), reusable cup service providers

Revenue Model: SaaS Monthly Subscription at ~$22/store/month (29,000 KRW), 20% volume discount for 100+ stores. Initial integration setup fee ~$2,250 (3,000,000 KRW).

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

2.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (64%)

Tech Complexity

29.3/40

Data Availability

15.0/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

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	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

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

Dashboard