| Summary: | A great challenge in the fuel retailing is to identify the appropriate mix of factors, specific in each region, that affect fuel sales. In this project these factors-drivers are examined at individual and network level in two cities, Milan and Amsterdam, in order to understand how well a petrol station performs using the resources available and what is the impact of the competition and the surrounding environment, in the motor fuel sales. Two different
techniques are used as the most appropriate to identify the performance of retail stations and explore the network effect, the multiple regression analysis and DEA. The multiple regression
models provide useful information about the impact of location, traffic, facility and operations in the motor fuel volume of a petrol station. On the other hand DEA identifies the best performers in terms of specific criteria chosen. The network analysis explores the effects of closing a station or aggregating two stations into one, in the other stations located in the same city. The main findings are that manned hours, effective positions, brand preference have a positive relationship with volume (in Milan). In Amsterdam shop sales is the most significant variable. Moreover two type of sites Hypermarket followed by service area have very good motor fuel sales in both cities. The effect of closing a station is the redistribution of the
volumes of the stations, which is beneficial for some of them and negative for others. Finally the combination of the characteristics of two stations, results to an improved station, which can be more efficient than the individual stations.
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