Data-driven jet fuel demand forecasting: A case study of Copenhagen Airport

TL;DR

This study evaluates various data-driven machine learning models for forecasting jet fuel demand at Copenhagen Airport, demonstrating their effectiveness and the benefits of including additional variables for improved accuracy.

Contribution

It provides a comprehensive case study comparing traditional and machine learning models for jet fuel demand forecasting using real industry data.

Findings

Data-driven models outperform traditional time series methods.

Incorporating additional variables improves forecast accuracy.

Hybrid models show promising results in demand prediction.

Abstract

Accurate forecasting of jet fuel demand is crucial for optimizing supply chain operations in the aviation market. Fuel distributors specifically require precise estimates to avoid inventory shortages or excesses. However, there is a lack of studies that analyze the jet fuel demand forecasting problem using machine learning models. Instead, many industry practitioners rely on deterministic or expertise-based models. In this research, we evaluate the performance of data-driven approaches using a substantial amount of data obtained from a major aviation fuel distributor in the Danish market. Our analysis compares the predictive capabilities of traditional time series models, Prophet, LSTM sequence-to-sequence neural networks, and hybrid models. A key challenge in developing these models is the required forecasting horizon, as fuel demand needs to be predicted for the next 30 days to optimize sourcing strategies. To ensure the reliability of the data-driven approaches and provide valuable insights to practitioners, we analyze three different datasets. The primary objective of this study is to present a comprehensive case study on jet fuel demand forecasting, demonstrating the advantages of employing data-driven models and highlighting the impact of incorporating additional variables in the predictive models.