Oil Refiner Earnings Subcontract Impact Translator

3.30

Derivation Chain

Step 1 Iran risk and oil price surge

→

Step 2 Fluctuations in oil refining and petrochemical earnings

→

Step 3 Early diagnosis of earnings impact for subcontractor SMEs supplying oil refiners

Problem

SME subcontractors with 5-30 employees supplying parts and services to major oil refining and petrochemical companies find it difficult to understand how the prime contractor's facility investment and maintenance budgets change during oil price surges. The prime contractor's IR materials are mixed with English and full of technical jargon, taking 1-2 days to decipher, and failing to detect budget cuts in advance leads to order gaps (average 2-3 months).

Solution

Automatically collects IR materials and earnings announcements from the top 4 domestic oil refiners and major petrochemical companies, and translates 'facility investment budget changes → impact on our supplies' into Korean briefings from the subcontractor's perspective. Key features: (1) automatic collection of IR materials from oil refining and petrochemical companies and extraction of key metrics, (2) impact analysis by subcontractor industry (equipment, instrumentation, piping, painting, etc.), (3) order outlook scenario report.

Target: Sales managers/representatives of SMEs with 5-30 employees that are subcontractors to major oil refining and petrochemical companies

Revenue Model: SaaS monthly subscription at 39,000 KRW/month ($29.25) per account. Quarterly in-depth report at 9,900 KRW ($7.43) per report. 25% discount for annual billing.

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

2.0/5

R Realizability

4.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (78%)

Tech Complexity

34.7/40

Data Availability

23.3/25

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

9.0/15

Solo Buildability

6.0/10

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

AI/ML [medium] Backend [low] Frontend [low]

Dashboard