Prospective scenarios analysis: Impact on demand for oil and its derivatives after the COVID-19 pandemic

Abstract

Goal: This paper aims to illustrate one of the strategies found in the literature to deal with uncertainties: Prospecting scenarios in the demand for oil derivatives after the COVID-19 pandemic (identifying the desirable scenario, the undesirable and the trend, at a national level in the Brazilian market) and proposing a corresponding strategy plan for each prospection in order to minimize the resumption impacts. Design / Methodology / Approach: This study was carried out with relevant indicators of the oil and gas industry (downstream). Commercialization data (from 2017 to 2020) were compared in order to verify the pandemic impacts. After collecting the data, we used the Momentum approach to prospect the scenarios, suggesting a strategic action plan for each prospection. Results: The results reveal that, regardless the post-pandemic scenario that we will encounter, the industries belonging to the oil and gas sector will have to define strategies and implement solid action plans to remain competitive in the market. It was also possible to note that the action plan for the desirable and trend scenario has synergy. Limitations of the investigation: The results obtained in this research refer only to the Brazilian scenario, more specifically to the downstream sector of the oil and gas industry and may differ from other sectors or regions. Practical implications: This research provides ways for oil and gas companies to be prepared for the post-pandemic future, thus remaining competitive in the market. In addition, as a result of applying the Momentum method, it is possible to see how the sector's indicators relate to each other through the cross-impact matrix. Originality / Value: This is an unprecedented study that allows the downstream oil and gas companies to be better prepared to the post-COVID-19 scenario. It adds value not only to the oil and gas industry but also to other industries, since it is possible to replicate the method presented here in other contexts with different databases. © 2021, Associacao Brasileira de Engenharia de Producao. All rights reserved.