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Automated Data Catcher Use Case #1 - Fuel Consumption Analytics and Optimization from Unstructured Data

  • efil52
  • 3 days ago
  • 3 min read

Introduction


This document outlines the Fuel Consumption Analytics and Optimization solution developed by Gelectric. The primary objective of this solution is to bridge the gap between unstructured vessel reporting and actionable fuel intelligence. By automating the extraction of fuel data from Noon Reports and technical logs, we enable ship owners and charterers to monitor performance in real-time, optimize bunker usage, and ensure environmental compliance.


Background


Fuel oil consumption represents the largest operational expense for marine vessels, often accounting for 50–60% of total operating costs. Traditionally, this data is trapped in manual "Noon Reports," handwritten logs, or disparate Excel files. These manual processes are not only labor-intensive but are also prone to reporting lags and human error, making it difficult for the head office to gain a transparent view of fleet-wide energy efficiency.


Problem Statement


The primary challenges identified in current fuel monitoring processes include:


  • Data Silos: Fuel data is locked in unstructured formats (PDFs, images of logs, manual emails).

  • Inaccuracy: Manual data entry leads to "fat-finger" errors, resulting in incorrect efficiency calculations.

  • Reporting Lag: Insights are often generated weeks after a voyage, preventing real-time course or speed corrections.

  • Compliance Pressure: New regulations like EU ETS and FuelEU Maritime require high-fidelity, auditable data that manual systems struggle to provide.


Objectives


The primary objectives of this solution:

  • To automate the extraction of fuel and operational data from any unstructured source using the Automated Data Catcher.

  • To provide real-time visibility into Specific Fuel Oil Consumption (SFOC).

  • To enable precise consumer based energy breakdown.

  • To streamline data submission for MRV/DCS and EU ETS verification.

  • To identify underperforming vessels within a fleet for targeted technical intervention.

 

Solution


Automated Data Catcher for Fuel Analytics


To address the identified problems, Gelectric integrates an automated data pipeline that involves:

  • Multi-Source Ingestion: Automatically "reading" Noon Reports (PDF/Word), Bunker Delivery Notes (BDNs), and Engine Room Logs.

  • Digital Structuring: Converting unstructured text and tables into a standardized SQL/JSON format.

  • Logic Validation: Cross-referencing fuel flow meter readings against tank sounding reports to flag discrepancies.

  • Cloud Dashboarding: Visualizing consumption trends against weather conditions and vessel speed.


Key Data Points Captured


  1. Main Engine & Auxiliary Consumption Extracting per-engine consumption allows for the calculation of load-specific efficiency. Source: Noon Reports & Engine Technical Logs.

  2. Bunker Delivery Validation Automatically comparing BDN figures against actual quantity received (ROB) to identify bunkering shortages. Source: Scanned BDN PDFs.

  3. Reefer Load Impact Calculating the specific fuel "penalty" of carrying refrigerated containers by extracting reefer plug-in counts and power draw. Source: Deck Logs & Reefer Manifests.

  4. Operational Context (Weather & Speed) Correlating fuel burn with Beaufort scale data and Speed Over Ground (SOG) to normalize performance data. Source: Noon Reports & AIS data.

 

Data Collection and Analysis


Following the activation of the Data Catcher, the system provides a continuous stream of structured intelligence. Authorized personnel can access:


  • Vessel Performance Benchmarking: Comparing sister ships to find the "Golden Vessel" efficiency standard.

  • EU ETS Financial Forecasting: Real-time calculation of carbon costs based on actual fuel burnt.

  • Anomalous Consumption Alerts: Automated notifications when fuel consumption deviates from the sea-trial curve by more than 5%.

  • Audit-Ready Trails: A digital "paper trail" from the original PDF report to the final emissions filing, ensuring 100% transparency for verifiers.


Conclusion


The Gelectric Automated Data Catcher transforms fuel monitoring from a clerical burden into a strategic advantage. By unlocking the data already present in vessel reports, we provide the transparency needed to reduce fuel costs, ensure regulatory compliance, and lower the carbon footprint of the fleet. The transition from reactive data entry to proactive fuel management is no longer an option but a necessity in the modern maritime landscape.


To learn more or request demo:



 
 
 
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