Context

LNG voyage planning requires tight coordination between cargo availability, vessel readiness, routing constraints, weather conditions, and port traffic. When this information sits across multiple systems, planners must manually compare routes and timelines, slowing decisions and increasing the chances of delays, inefficiencies, or missed windows. By applying AI to interpret schedules, assess route options, and identify risk signals early, planning becomes faster, more predictable, and more accurate.

Challenges

LNG logistics teams often face fragmented cargo, vessel, and routing data, making it difficult to build a complete picture before choosing a route. Manual analysis of distance, weather, congestion, and vessel limitations leads to inconsistent decisions and planning delays. Traditional planning tools struggle to adapt to real‑time changes at ports or throughout a voyage. Approvals and reviews frequently spread across email threads or disconnected spreadsheets, limiting transparency. Monitoring for deviations or delays requires constant manual effort, making proactive risk management difficult.

Solution

The LNG Routing Optimization Copilot, built with Microsoft Planner Designer, enables fully AI‑driven voyage planning using structured workflows and consolidated data. Copilot reviews cargo readiness and available routing windows, then evaluates each route based on distance, weather patterns, port activity, and vessel constraints. It ranks options clearly, making it easier for planners to select the best pathway.

Supervisors can review and approve route recommendations through a streamlined workflow, ensuring alignment and traceability. Automated monitoring detects delays, risk signals, or route deviations and prompts re‑evaluation when required. This creates a fast, transparent, and continuously improving LNG planning process that reduces manual workload and increases operational confidence.