A0226
Title: Modeling and forecasting of sales in fuel retail market: The factors that boost a fuel station's sales
Authors: Marco Costa - University of Aveiro (Portugal) [presenting]
Daniel Magueta - University of Aveiro (Portugal)
Stephanie Espadilha - University of Aveiro (Portugal)
Abstract: Sales of a fuel company with a dense fuel station network were analyzed to identify and characterize potential variables with predictive capacity for sales of new fuel stations. The database consists of a set of context variables with predictive potential for sales of fuel stations and monthly sales in terms of fuel volume. The research methodology focused on multivariate statistical methods combining cluster analysis, regression models and forecasting models. The fuel station context variables tend to characterize the socio-economic conditions, such as population density variable, others related to the similar existing supply of both the company itself and the competing companies, and others related to geographical location and accessibility. The data analysis allowed us to identify clusters in the time series of sales, indicating that the investigation of factors must be segmented. Homogeneous groups of fuel stations were identified through a hierarchical agglomerative clustering procedure. For each of the groups identified, multiple linear regression models were adjusted considering the fuel sales in the first years of operation of the stations as dependent variables. It is possible to conclude that the average daily traffic is the variable with a higher predicted capacity for most of the groups of fuel stations analyzed.
