Charterers frequently make claims to LNG ship owners regarding issues like fuel oil consumption, performance guarantees, and other operational deviations. These claims can be complex, involving the validation of fuel data, checking against charter party agreement terms, and verifying the quality and reliability of performance data submitted by ship owners.
Common Guarantee and Claims Types
- Fuel Oil Consumption Claims: Charterers may allege that the ship consumed more fuel than allowed under guaranteed rates, demanding compensation for excess usage or underperformance.
- Performance Claims: These include speed, itinerary adherence, and delivery on contractual milestones. Underperformance or delays may trigger claims for penalties or additional costs.
- Operational Deviation: Claims concerning deviations from agreed cargo or voyage plans, or inefficient routing decisions impacting fuel usage or timelines.
How AI Can Help Manage and Validate Claims
- Automated Data Extraction and Normalization: AI can extract structured information from performance logs, statement of facts, and voyage reports, creating a standardized view for both charterers and owners.
- Performance Data Validation: AI models trained on ship performance benchmarks and weather/ocean data compare submitted voyage data against historical and expected norms, highlighting discrepancies, such as suspiciously high fuel consumption under certain conditions.
- Predictive Analysis: Advanced vessel performance models can forecast expected fuel usage on specific routes under given conditions, helping parties validate whether reported numbers are plausible or if a claim is likely valid.
- Contract Clause Simulation: By digitizing charter party clauses, AI allows simulation of “what if” scenarios and calculation of laytime or demurrage, enabling rapid validation of entitlement and claim size.
- Workflow Automation: Systems integrate with email and operational databases to assemble claim files, auto-calculate standard metrics, and prioritize urgent cases—freeing staff to focus on dispute resolution rather than manual data checks.
- Anomaly and Pattern Detection: Machine learning can detect patterns of recurring issues, such as systematic overstatement of fuel use or hidden operational inefficiencies, helping prevent future claims and improve contract negotiation.
Practical Impact
- Faster, Standardized Processing: Reduces time, costs, and manual intervention in claims reviews, ensuring better accuracy and consistency.
- Defensible Claims: Standardized, AI-driven documentation makes claims and responses easier to defend, reducing disputes and time to resolution.
- Strategic Insights: By providing aggregated views of claims and their causes, AI helps both charterers and shipowners optimize performance and risk management for future contracts.
AI adoption is accelerating the move from reactive claims management to proactive contract and performance optimization in LNG and broader shipping operations.