debt_leverage_profile
High leverage ~5-6x Debt/EBITDA typical for MLP; 20% rate rise adds ~$15-20M annual interest expense given ~$500M floating-rate debt exposure
Inferred
Agent_Inference
interest_rate_sensitivity
Significant sensitivity; ~60-70% of debt estimated floating-rate; 20% rate increase (~100-120bps) compresses distributable cash flow by roughly 8-12%
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Strait of Hormuz (distillate imports) and Saint Lawrence Seaway / Northeast US port infrastructure for Canadian crude and refined product flows
Inferred
Agent_Inference
international_expansion_readiness
Minimal sovereign currency devaluation exposure; operations concentrated in USD-denominated US Northeast and Canadian markets with limited direct FX revenue risk
Inferred
Agent_Inference
geographic_footprint
Primarily US Northeast and Eastern Canada; CAD/USD cross-rate is primary FX risk, representing an estimated 10-15% of throughput volume
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Crude Oil, Natural Gas, Coal, Refined Petroleum Products, Uranium, Steel (equipment); geopolitical: OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
Dependency on major refined product suppliers (ExxonMobil, Citgo, Irving Oil) with no single vendor confirmed >30%, but regional terminal access creates moderate substitution risk
Inferred
Agent_Inference
business_model_type_primary
Physical asset-based petroleum distributor and terminaling MLP; minimal cloud infrastructure dependency; operations run on industrial SCADA/ERP, not hyperscaler-dependent
Inferred
Agent_Inference
business_model_type_secondary
Secondary logistics and supply chain coordination; cloud termination would disrupt back-office ERP/TMS but core terminal operations would continue within 30 days
Inferred
Agent_Inference
switching_cost_profile
Low API coupling risk; uses standard ERP (SAP/Oracle-type) and logistics software; no proprietary API ecosystem lock-in identified
Inferred
Agent_Inference
howey_test_risk_index
Low Howey risk; LP units are securities but revenue model is fee-for-service terminaling and commodity distribution, not an investment contract in regulatory gray zone
Inferred
Agent_Inference
regulatory_burden_tier
High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Low GDPR/CCPA exposure; B2B petroleum distribution with minimal consumer PII; Canadian operations carry modest PIPEDA compliance obligations
Inferred
Agent_Inference
antitrust_exposure_flag
Moderate; regional terminal dominance in select Northeast US markets could attract scrutiny, but national market share too small for federal antitrust action
Inferred
Agent_Inference
regulatory_exposure_profile
High burden; regimes: EPA, FERC, DOE, CFTC, OSHA, SEC; Accelerating emissions mandates and methane rules threaten capex economics.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
Predominantly transactional (~80%) via commodity sales and throughput fees; multi-year storage and terminaling contracts provide ~15-20% recurring base
Inferred
Agent_Inference
monetization_vector
Primary vectors: refined product resale margin, terminaling/throughput fees, and storage rental; fee-based segment growing as % of EBITDA mix
Inferred
Agent_Inference
pricing_architecture
Cost-plus and market-indexed pricing on commodity sales; terminaling fees set by contract; vulnerable to margin compression if crack spreads narrow or competition increases
Inferred
Agent_Inference
pricing_power_rating
Moderate-low; commodity resale margins are thin and market-driven; terminaling fees offer modest pricing power in captive regional markets
Inferred
Agent_Inference
target_gross_margin_bracket
Gross margins thin on product sales (~2-4%); terminaling/fee segment margins ~30-40%; blended EBITDA margin approximately 3-5% of total revenue
Inferred
Agent_Inference
churn_vulnerability_index
Low free-rider leakage; physical infrastructure access is gated; customers must contract for terminal capacity; no meaningful free-rider dynamic
Inferred
Agent_Inference
headcount_cost_structure
Largely headcount-linear for terminal operations; growth via acquisition of physical assets requires proportional operational staff; limited software-driven scalability
Inferred
Agent_Inference
marginal_cost_of_growth
High marginal cost; growth requires physical terminal acquisition or construction; not sublinear — each new asset adds capex, opex, and proportional headcount
Inferred
Agent_Inference
franchise_compliance_risk
Not applicable; Sprague operates as an MLP with company-owned terminals, not a franchise model
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, CAC remains asset-acquisition driven; unit economics depend on terminal utilization rates (target >85%) and throughput fee spread over fixed asset costs
Inferred
Agent_Inference
network_effect_present
Weak network effects; geographic terminal density creates modest route optimization advantages, but no demand-side network effect typical of platforms
Inferred
Agent_Inference
asset_efficiency_ratio
Low AI displacement risk; core value is physical terminal infrastructure and logistics execution; AI can optimize dispatch/routing but cannot replace asset base
Inferred
Agent_Inference
recession_resistance_tier
Moderate-high resilience; heating oil and distillates are essential energy products; volume declines ~5-10% in recession but demand floor remains high in Northeast US
Inferred
Agent_Inference
customer_segment_primary
Commercial and industrial energy buyers (manufacturers, municipalities, utilities) in US Northeast comprising estimated 50-60% of volume
Inferred
Agent_Inference
customer_segment_secondary
Wholesale distributors, home heating oil dealers, and marine/bunker fuel customers; concentration risk if top 10 customers represent >40% of revenue
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", "19-0000 Life, Physical, and Social Science Occupations", "23-0000 Legal 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", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.33 (HIL — ~33% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
Capital predominantly maintenance and bolt-on terminal acquisition; limited reallocation toward future-state infrastructure; legacy asset-heavy model persists
Inferred
Agent_Inference
sec_cik
1572032
Inferred
Agent_Inference
ticker
SRLP
Inferred
Agent_Inference