RuAviation

St. Petersburg-based JSC RIVC-Pulkovo has introduced Rivc.FES, a software platform designed to enhance airline fuel management and operational performance. The system targets fuel burn optimization and provides management with actionable, data-driven insights into flight operations.

Rivc.FES benchmarks planned flight parameters against actual operational data and generates objective performance assessments. According to the developer, the platform consolidates experience gained from working with major Russian carriers and focuses on optimizing one of the largest airline cost drivers—jet fuel.

Core Capabilities

End-to-End Fuel Audit
The platform analyzes fuel consumption across all flight phases, from taxi-out to post-landing operations. It identifies deviations from planned values and flags areas of avoidable fuel loss.

Fuel–Commercial Performance Correlation
Algorithms correlate fuel burn with payload factors, route network structure, and aircraft type. This enables airlines to evaluate route profitability and optimize fleet assignment.

Flight Operations Assessment
The system ranks crew performance while normalizing for operational variables such as METAR/TAF weather data, route complexity, time of day, and delays. The methodology supports transparent KPI frameworks and performance-based incentives.

Historical Trend Analytics
Users can compare seasonal, quarterly, and annual data sets while filtering statistical noise. This allows airlines to quantify the impact of implemented operational changes.

Architecture and Deployment

Rivc.FES runs on a PostgreSQL-based architecture to ensure scalability, high-throughput data processing, and cybersecurity compliance. The web interface supports cross-platform deployment and integration into existing airline IT environments.

JSC RIVC-Pulkovo is listed in Russia’s official domestic software registry and provides IT solutions to major airport hubs and airlines. Rivc.FES converts operational flight data into decision-support intelligence, including optimized cruise level selection and fuel uplift planning based on predictive models.