Until now, airlines have faced many challenges, such as low-cost carrier competition and pilot shortage. They are constantly subject to user evaluation on airfare and in-flight services. However, now the imminent ranking of scheduled flight rates and a growing list of user evaluation criteria further increase competition, to say nothing of the increasing number of aircraft operations worldwide. Environmental concerns about CO2 emissions offer yet another challenge: the International Civil Aviation Organization aims to hold CO2 emissions steady after 2020 by imposing fuel efficiency improvements of 2% per year until 2050. The major factors to meeting these challenges are improving scheduled flight rates and reducing flight costs. In this study, to improve the quality of operations, discuss concurrent environmental solutions, and clarify the factors that affect aircraft operation, we first perform a basic analysis using actual flight data. We then calculate the relationship between two factors: the correlation coefficient, and confirm the relation of flight data. Next, we perform multiple regression analysis to calculate which factors affect the flight time and fuel usage. We analyze this separately in terms of the aircraft climbing, cruising, and descending. The significant factors for each are calculated, and we then obtain the results of the multiple regression analysis. Using these results, we perform optimization in each section using the regression equation obtained, including parameters. At cruising, considering the relationship between climbing and descending distance, cruising distance is calculated and optimization is performed. We found that pilot decisions can make an aircraft operate more efficiently in terms of flight time and fuel usage when factors such as cruising altitude, climbing, cruise and descent speeds are considered. Utilizing this results, pilots obtain the indicator to operate according to schedule as well as to save fuel. These indicators can enhance pilot education and become a dashboard for operational efficiency.