Methodology & Framework

Overview

The Global Trade & Industry Analysis Scorecard (GTIAS) is a comprehensive framework for evaluating industries across multiple strategic dimensions.

Our methodology combines structured data collection, AI-assisted analysis, and expert validation to produce consistent, comparable industry profiles that support strategic decision-making.

Each industry profile contains scores across 81 attributes, organized into 11 strategic pillars, with classification into 7 operational archetypes.

GTIAS Framework

The framework provides a standardized approach to industry analysis, enabling apples-to-apples comparisons across diverse sectors.

81 Attributes

11 Pillars

7 Archetypes

Framework Components

Attributes — Individual metrics measuring specific industry characteristics
Pillars — Thematic groupings of related attributes
Archetypes — Operational patterns defining how industries create value
Strategies — Frameworks for analyzing and responding to industry dynamics

Scoring System

Each attribute is scored on a 0-5 scale, where the interpretation depends on the specific attribute. Generally:

0 N/A

1 Very Low

2 Low

3 Moderate

4 High

5 Very High

Scores are accompanied by rationale text explaining the assessment and, where applicable, citations to supporting data sources.

Note: Some attributes use inverted scales where lower scores indicate higher risk. Always refer to the attribute definition for proper interpretation.

The 11 Pillars

Attributes are organized into 11 thematic pillars, each representing a distinct analytical dimension:

MD

Market & Trade Dynamics Supply, demand elasticity, pricing volatility, and competitive rivalry.

ER

Functional & Economic Role Structural factors: capital intensity, cost ratios, barriers to entry, and value chain role.

RP

Regulatory & Policy Environment Political stability, intervention, tariffs, strategic importance, sanctions, and IP rights.

SC

Standards, Compliance & Controls Technical standards, safety regimes, certifications, and fraud/adulteration risks.

SU

Sustainability & Resource Efficiency Environmental footprint, carbon/water intensity, and circular economy potential.

LI

Logistics, Infrastructure & Energy Supply chain complexity, transport modes, storage, security, and energy availability.

FR

Finance & Risk Financial access, FX exposure, insurance, credit risk, and price formation.

CS

Cultural & Social Consumer acceptance, sentiment, labor relations, and social impact.

DT

Data, Technology & Intelligence Digital maturity, data transparency, traceability, and interoperability.

PM

Product Definition & Measurement Master data regarding units, physical handling, and tangibility.

IN

Innovation & Development Potential R&D intensity, tech adoption, and substitution potential.

Explore All Pillars

Industry Archetypes

Every industry is classified by its dominant operational archetype — the fundamental logic that drives how it creates value:

BIO Bio-Organic & Perishable

IND Heavy Industrial & Extraction

FLO Trade, Logistics & Flow

UTL Utility, Grid & Network

DIG Digital, IP & Knowledge

FIN Financial & Asset Holding

SVC Human Service & Hospitality

Learn About Archetypes

Data Sources

Industry profiles are compiled from multiple authoritative sources:

ISIC Rev. 4, CPC Ver. 2.1, HS codes for standardized industry and product classification.

UN Comtrade, World Bank, IMF, WTO for trade flows, economic indicators, and market structure.

ISO, Codex Alimentarius, national regulatory agencies for compliance and standards requirements.

Academic literature, industry associations, and specialized research reports.

Validation Process

All industry profiles undergo a multi-stage validation process:

1

Structural Validation

Automated checks ensure all required attributes are scored, values are within valid ranges, and metadata is complete.

2

Cross-Reference Verification

Scores are compared against related industries and known benchmarks to identify anomalies.

3

Source Citation Review

Key assertions are traced to authoritative sources and citations are verified.

Limitations

Users should be aware of the following limitations:

Point-in-Time Analysis

Scores reflect conditions at the time of analysis and may not capture recent developments.

Global Averages

Unless specified, scores represent global industry averages and may vary significantly by region or market segment.

AI-Assisted Generation

While AI accelerates analysis, it may introduce errors or biases that human review aims to catch.

Subjective Elements

Some attributes involve qualitative judgment. We aim for consistency but acknowledge inherent subjectivity.