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论文编号：	14224
作者编号：	2320213512
上传时间：	2023/12/8 21:13:59
中文题目：	基于舱位存量控制的G航空公司收益管理提升策略研究
英文题目：	Research on Revenue Management Promotion Strategy of G Airline Based on Capacity Control Method
指导老师：	徐曼
中文关键字：	民航业; 收益管理; 舱位存量控制; EMSR模型
英文关键字：	Civil aviation industry; Revenue management; Capacity control; EMSR
中文摘要：	近年来，民航业数字化转型不断推进，伴随着疫情后民航市场的恢复，提升航空公司的收益管理水平不仅有利于航空公司的稳定经营，更成为了落实国家关于“智慧民航”相关政策的必然要求。 本研究采用了数学模型法以及案例分析法对航空公司收益管理的舱位存量控制进行研究。在梳理了案例企业G公司所处的环境以及民航业的发展情况之后，本研究运用SWOT分析阐释了公司内部的优劣势情况。随后研究将舱位存量控制的期望边际座位收益模型（EMSR模型）的相关理论研究作为基础，利用该模型的EMSR-a、EMSR-b两种算法，对G航空公司的天津进出港的10条航线在2019、2021、2022三年的销售数据进行了计算，对比分析了算法预估下的航线期望收入与实际销售收入数据。 研究表明，与原有的利用专家经验进行收益管理的舱位存量控制相比，利用模型辅助的预估期望收入比实际收入高10%-15%左右。同时在舱位销售梯度上，EMSR-b算法要比EMSR-a算法带来更为稳定的舱位销售梯度，更不容易在销售中出现价格大起大落的现象。不仅如此，该算法对于淡季航班的销售预估更为准确，预估结果与实际结果相差5%-10%左右。在以上结论的基础上，本研究也针对G公司的收益管理存量控制工作提出了相关提升策略：一是要转变淡旺季舱位存量控制思路；二是借用研究结论修改现有的工作评判标准；三是将现有收益管控系统增加存量控制辅助功能，在工作中不断做到专家经验与数据模型相结合。
英文摘要：	In recent years, the digital transformation of the civil aviation industry has been continuously promoted, along with the recovery of the civil aviation market after the end of the COVID-19 epidemic. In this context, improving the revenue management level of airlines is not only conducive to the operation of airlines, but also becomes an inevitable requirement for the implementation of civil aviation intelligence. This thesis uses mathematical model method and case analysis method to improve the cabin capacity control of revenue management. After sorting out the environment and industry of the case company G, SWOT analysis is used to analyze the internal advantages and disadvantages of Company G. Then, based on the relevant theoretical research of the expected marginal seat revenue (EMSR) of cabin capacity control, the sales data of 10 routes of G Airlines in 2019, 2021 and 2022 were calculated and analyzed using the EMSR-a and EMSR-b method. The expected revenue of the route predicted by the algorithm and the actual sales revenue data are compared and analyzed. The results show that the model-assisted estimated expected revenue is about 10%-15% higher than the actual revenue, compared with the original data based on expert experience. At the same time, in terms of cabin sales gradient, EMSR-b brings more stable cabin sales gradient than EMSR-a, and it is less likely to have large price fluctuations in sales. In addition, the simulation is more accurate in predicting the sales of off-season flights, and the estimated result is about 5%-10% different from the actual result. At the same time, on the basis of this conclusion, this thesis also puts forward relevant improvement strategies for G company's revenue management capacity control: First, it is necessary to change the train of thought of capacity control in off-peak season; The second is to use the existing conclusions to modify the existing work evaluation criteria; The third is to increase the auxiliary function of capacity control in the existing income control system, constantly combine expert experience with data model, and constantly make scientific decisions.
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