This project examines how retail fuel station performance drives daily liquidity within the treasury function of a Nigerian downstream oil and gas company. Using station-level sales data for October–December 2025, the analysis applies exploratory data analysis, visualisation, hypothesis testing, correlation, and regression to identify the true drivers of cash inflows. The study finds significant revenue concentration risk among a few stations, shows that revenue is primarily volume-driven rather than price-driven, and confirms seasonal growth in diesel (AGO) demand. A regression model highlights PMS throughput as the strongest predictor of revenue and identifies stations that over- or under-perform relative to expectations, providing practical insights for treasury forecasting and funding decisions.

This analysis was carried out in fufilment of the requirement of my DA course.