Driving $1.2M+ in
Annual Savings.
Specializing in industrial efficiency by optimizing fuel prediction engines (98.5% accuracy) and architecting vessel performance dashboards for narrative strategy.
System Overview
& Methodology
Data Modeling & Optimization: Engineered an advanced fuel prediction framework by analyzing 6 months of discrete vessel telemetry, replacing legacy static models with dynamic, density-based interpolation.
Algorithmic Tuning: Applied Root Mean Square (RMS) error minimization to historical scatter data (Speed vs. GPH), recalculating variable fuel constants against fixed speed coordinates to optimally fit the data density.
Commercial Impact: Skyrocketed prediction accuracy from 80% to 98.5%, directly driving a ~15% per-vessel efficiency gain, conserving 200k+ gallons of fuel, and unlocking $1.2M in annual cost savings.
Proof of Work:
Fuel Optimization
This time-series analysis demonstrates the 98.5% prediction accuracy achieved through rigorous algorithmic tuning and advanced data modeling, comparing actual consumption against projected telemetry.
Result:
Actual vs Predicted Vessel Fuel Consumption Accuracy
Stats
Model Accuracy Rate %
The $1.2M dollars of fuel savings is achieved by optimizing 8 such vessels which resulted in annual fuel savings of around 200,000+ gallons — a major commercial win.
Access Technical Documentation
Review my GitHub repositories for core analytics frameworks and models.