The goals of the final degree project are, on the one hand, monitor and analyse the fuel data of Vueling Airlines and on the other hand suggest new KPI and measures that should be able to reduce the fuel consumption. In the first part of the project an introduction has been presented regarding the importance of the fuel in the aviation industry in all of their actors. Later on it has been described the analysis process of the fuel data from a descriptive point of view, also describing the environmental impact that the fuel has on aviation and the emissions monitoring plan of the company as well. The next step has been the deep analysis of the spreadsheets used by Vueling, in which a first study of the data has been carried on to check that a vast quantity of it is wrong and that it exists a correlation between the wrong data and the aircraft registration, although it has been dismissed the hypothesis that suggested that the oldest aircraft were the ones with wrong data. It has been also detected that there is a correlation between the wrong data and the airports flown by the company and also another correlation regarding the wrong data and the pilots. Once this first analysis has been conducted, it has been created a template so that to enhance the current system of data adjustment done prior to sending the information to Aeroconseil, a company that Vueling hired to carry on a flight data analysis. The template has been created to enhance the structure of the data and also to adjust the parameters calculated, defining control values for each parameter to help in the interpretation process of the wrong ones. It has been included 4 KPI in the template, two of which were previously computed and two new ones that it is believed that will contribute to reduce the fuel consumption, as it has been demonstrated through the study of the month carried on. The last part of the data analysis was focused on checking that the remaining KPI were well computed with the data that Aeroconseil sends back each month. Once it has been realized that there were some assumptions which we think are wrong such as the monthly graphs, which are computed with data from the last six months, and mistakes regarding the application rates of some indicators, it was decided to create a new template to well compute the average of the KPI and also to summarise the information in just one spreadsheet and not many as before, easing a great deal the work. Once the creation of the templates was done, a test was carried on with a technician of the department, who simulated all the monthly process related with the fuel analysis with the new method suggested and with the previous one, getting a clear reduction in terms of time spent in the computation and a high rate of satisfaction from his part. To sum up, two more initiatives, which are thought to be important in the future in terms of fuel consumption, were released. The first one was the creation of a new indicator that was able to show the rate of data used to create the KPI compared with the total amount of data of the month. This measure could increase the data efficiency if annual goals are set. The last one was the measurement of the ratio of flights that perform the holding optimization technique, a simple manoeuvre that let the company save a lot of fuel consumption, and whose related savings were well proven with the data computed.