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Modelling the impact of disease outbreaks on the international crude oil supply chain using Random Forest regression

Ganisha N.P. Athaudage (Department of Transport and Logistics Management, University of Moratuwa, Moratuwa, Sri Lanka and University Canada West, Vancouver, Canada)
H. Niles Perera (Professor H.Y. Ranjit Perera Institute for Applied Research, Nugegogda, Sri Lanka and Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Moratuwa, Sri Lanka)
P.T. Ranil S. Sugathadasa (Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Moratuwa, Sri Lanka)
M. Mavin De Silva (Department of Transport and Logistics Management, University of Moratuwa, Moratuwa, Sri Lanka and Extreme Energy-Density Research Institute, Nagaoka University of Technology, Nagaoka, Japan)
Oshadhi K. Herath (Department of Transport and Logistics Management, University of Moratuwa, Moratuwa, Sri Lanka and Extreme Energy-Density Research Institute, Nagaoka University of Technology, Nagaoka, Japan)

International Journal of Energy Sector Management

ISSN: 1750-6220

Article publication date: 20 December 2022

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Abstract

Purpose

The crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression.

Design/methodology/approach

This study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression.

Findings

The findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further.

Originality/value

This is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

Keywords

Acknowledgements

The authors wish to recognize Grant ID HYRP/2021/002 of the Professor H.Y. Ranjit Perera Institute for Applied Research for supporting the improvement of this manuscript.

Citation

Athaudage, G.N.P., Perera, H.N., Sugathadasa, P.T.R.S., De Silva, M.M. and Herath, O.K. (2022), "Modelling the impact of disease outbreaks on the international crude oil supply chain using Random Forest regression", International Journal of Energy Sector Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJESM-11-2021-0019

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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