debt_leverage_profile
2.70x Total Debt / Equity (High leverage)
High
SEC-XBRL
interest_rate_sensitivity
A 200bps rate increase raises annual interest expense ~$30-50M given ~$1.5B debt load; meaningful but manageable at current EBITDA margins of ~10-12%
Inferred
Agent_Inference
geopolitical_supply_exposure
Medium intensity; US-China trade tariffs on metals and components disrupt supply chains.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Steel/raw materials sourced through China-dependent mills; semiconductor components routed through Taiwan Strait corridor
Inferred
Agent_Inference
international_expansion_readiness
EUR (~35% of international revenue), GBP (~15%), and AUD (~10%) exposure; EUR/USD and GBP/USD volatility creates ~3-5% revenue translation risk annually
Inferred
Agent_Inference
geographic_footprint
EUR (~35%), GBP (~15%), AUD (~10%) of non-US revenue; European operations most exposed to sovereign devaluation risk given energy-driven inflation pressures
Inferred
Agent_Inference
commodity_exposure_profile
Medium intensity; commodities: Steel, Aluminum, Copper, Crude Oil (fuel), Rare Earth Elements, Plastics/Resins; geopolitical: US-China trade tariffs on metals and components disrupt supply chains.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
No single vendor exceeds 30% of input costs; steel is largest input (~20-25% of COGS) but sourced from multiple suppliers, limiting lock-in risk
Inferred
Agent_Inference
business_model_type_primary
Cloud termination would have minimal operational impact; Terex is a capital equipment manufacturer with on-premise ERP/manufacturing systems, not cloud-dependent
Inferred
Agent_Inference
business_model_type_secondary
Some back-office and CRM functions may use cloud SaaS (SAP, Salesforce); disruption would cause weeks of administrative friction but no production stoppage
Inferred
Agent_Inference
switching_cost_profile
Low API coupling risk; Terex operates traditional manufacturing/distribution model with minimal third-party API dependencies in core revenue operations
Inferred
Agent_Inference
howey_test_risk_index
Fails Howey Test; Terex sells physical capital equipment and aftermarket parts — no investment contract, profit expectation from third-party efforts, or common enterprise element
Inferred
Agent_Inference
regulatory_burden_tier
Medium
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Moderate GDPR exposure via European dealer/customer data; CCPA exposure limited; no large-scale consumer data collection; compliance cost estimated <$5M annually
Inferred
Agent_Inference
antitrust_exposure_flag
Low-to-moderate; operates in fragmented heavy equipment markets alongside Caterpillar, Liebherr, Manitowoc; no dominant market share in any single segment exceeding ~20%
Inferred
Agent_Inference
regulatory_exposure_profile
Medium burden; regimes: FAA, DOT, OSHA, EPA, ITAR, FTC; Export controls and defense procurement rules create contract concentration risk.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~85-90% transactional (equipment sales); ~10-15% recurring via parts, service contracts, and extended warranties; low recurring revenue base is a structural vulnerability
Inferred
Agent_Inference
monetization_vector
Primary: capital equipment unit sales; Secondary: aftermarket parts and services (~$500-600M annually), growing as strategic priority to improve margin stability
Inferred
Agent_Inference
pricing_architecture
Cost-plus pricing dominant; raw material inflation (steel) compresses margins when pass-through lags; limited ability to hold price in down-cycle without volume loss
Inferred
Agent_Inference
pricing_power_rating
Moderate (6/10); brand equity in aerial work platforms and cranes supports some premium, but commodity-driven cost structure and competitive market limit sustained pricing power
Inferred
Agent_Inference
target_gross_margin_bracket
19.4% Gross Margin (Thin (<20%))
High
SEC-XBRL
churn_vulnerability_index
No meaningful free-rider problem; equipment purchases require capital commitment; however, aftermarket parts face OEM vs. third-party competition eroding ~15-20% of potential service revenue
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is largely headcount-linear in manufacturing; doubling revenue requires near-proportional factory labor scaling; SG&A is sublinear, providing modest operating leverage
Inferred
Agent_Inference
marginal_cost_of_growth
High marginal cost of growth; each incremental revenue dollar requires factory capacity, raw materials, and labor — gross margins ~20-22% reflect capital-intensive production economics
Inferred
Agent_Inference
franchise_compliance_risk
Dealer network of ~1,200+ global dealers; compliance drift risk is moderate — dealer-driven sales create brand/warranty liability exposure if standards not enforced consistently
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, dealer channel economics deteriorate without geographic expansion; CAC rises as incremental markets are less dense; aftermarket economics improve with installed base scale
Inferred
Agent_Inference
network_effect_present
No meaningful network effects; equipment utility is standalone; larger installed base modestly improves parts/service economics but does not create defensible competitive moat
Inferred
Agent_Inference
asset_efficiency_ratio
4.8% Return on Assets (Excellent)
High
SEC-XBRL
recession_resistance_tier
Tier 4 (low recession resistance); heavy equipment capex is highly cyclical — revenue fell ~30% in 2009 and ~20% in 2020; construction and industrial end-markets are leading indicators
Inferred
Agent_Inference
customer_segment_primary
Construction contractors and rental companies (e.g., United Rentals, Sunbelt) representing ~40-50% of revenue; top 10 customers likely represent 20-30% of total sales
Inferred
Agent_Inference
customer_segment_secondary
Industrial and utility customers for cranes and material handling (~25-30%); government/infrastructure projects (~10-15%) provide partial cycle offset
Inferred
Agent_Inference
characteristic_occupations
["11-0000 Management Occupations", "13-0000 Business and Financial Operations Occupations", "15-0000 Computer and Mathematical Occupations", "17-0000 Architecture and Engineering Occupations", "23-0000 Legal Occupations", "33-0000 Protective Service Occupations", "37-0000 Building and Grounds Cleaning and Maintenance Occupations", "41-0000 Sales and Related Occupations", "43-0000 Office and Administrative Support Occupations", "47-0000 Construction and Extraction Occupations", "49-0000 Installation, Maintenance, and Repair Occupations", "51-0000 Production Occupations", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.3 (HIL — ~30% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
Capex ~$60-80M annually (~2% of revenue); modest reallocation toward digital/telematics and manufacturing automation, but legacy facility maintenance still consumes majority of capex budget
Inferred
Agent_Inference
sec_cik
0000097216
High
SEC-EDGAR
ticker
TEX
High
SEC-EDGAR