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.

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Adverse effects of inaccurate demand forecasts; stockouts, overstocks, customer loss have led academia and the business world towards accurate demand forecasting methods. Artificial Neural Network (ANN) is capable of highly accurate forecasts integrated with many variables. The use of Price and Promotion variables have increased the accuracy while the addition of other relevant variables would decrease the occurrences of errors. The use of the Federal Funds Rate as an additional macroeconomic variable to ANN forecasting models has been discussed in this research by the means of the accuracy measuring method: Average Relative Mean Absolute Error.

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The accurate and timely decision making of drivers is vital to ensuring public safety and reaching the destination in time. This paper presents a detailed comparison of the expectation of regulatory bodies in implementing flashing amber lights and explores the actual driver responses to flashing amber lights at a signalised Tintersection by taking speed variation as a proxy for their decision making.A survey was conducted at a signalised T-intersection during the operation of flashing amber lights to measure the speed variation. Time-distance and speed gun techniques were used to collect speed data of motor cars.Results reflect those drivers show only a marginal response to flashing amber lights. Therefore, the intention of regulatory bodies that vehicles maintaining lower speed at a T-intersection with flashing amber lights was not satisfactorily fulfilled. The marginal response of drivers to flashing amber lights can be a leading cause of accidents occurring in signalised T-intersections at odd (night) hours.