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

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.

Passenger car units for different midblock sections in Sri Lanka under mixed traffic condition

“…Thus, the impact of traffic variables such as traffic speed, volume, composition, and geometric road features such as road width, gradient, and horizontal curvature has been considered [16]. However, the fact that the outcomes of one study contradict the other makes it difficult to accept specific relationships for these parameters with PCUs [3].…”

“…In such a case, PCU estimation should be done more precisely, considering all the factors impacting its accuracy. Furthermore, PCU values of a vehicle type depend not only on the vehicle's characteristics but also on factors such as road type, vehicle mix, nature of the traffic, and environmental conditions [3]. PCU values should be different for road section type and road type.…”

“…Most road networks in developing countries carry mixed traffic with poor lane discipline which is also referred to as non-homogeneous traffic. While PCU estimation for homogeneous traffic conditions can be performed using a single parameter, multiple parameters are required in non-homogeneous [3]. Parameters such as speed [4], vehicle composition [5], headway [6], and area of the vehicles [7] have been used in studies to develop methods for PCU estimation in non-homogeneous traffic.…”

“…Parameters such as speed [4], vehicle composition [5], headway [6], and area of the vehicles [7] have been used in studies to develop methods for PCU estimation in non-homogeneous traffic. Mainly five methods were used in previous studies for PCU estimation in urban midblock sections, such as Chandra's method, space occupancy, multiple linear regression, headway, and simulation methods [3]. Chandra's method, which utilizes the vehicles' speeds and area as the main two parameters, was used to estimate the PCU values in this study.…”

Sustainable/Robust Supplier Selection in the Post-Pandemic Era: Using Data Envelopment Analysis

“…Tang (2006) defined SCM as "the management of material, information, and financial flows through a network of organizations (i.e., suppliers, manufacturers, logistics providers, wholesalers/distributors, retailers) that aims to produce and deliver products or services for the consumers". Summing up the definitions given in the previous literature, a supply chain can be defined as an integrated network of resources, processes, and stakeholders who collaboratively manage the movement of materials, information, and money from the raw material acquisition, transforming materials to work-in-processed and finished goods to distributing finished products to the final customer to satisfy customer demand (Athaudage et al, 2022;Tang, 2006).…”