Estimating True Demand in Airline’s Revenue Management Systems using Observed Sales

Forecasting accuracy is very important in revenue management. Improved forecast accuracy, improves the decision made about inventory and this lead to a greater revenue. In the airline’s revenue management systems, the inventory is controlled by changing the product availability. As a consequence of changing availability, the recorded sales become a censored observation of underlying demand, so could not depict the true demand, and the accuracy of forecasting is affected by this censored data. This paper proposed a method to estimate true demand from censored data. In the literature, this process is referred to as unconstraining or uncensoring. Multinomial Logit model is used to model the customer choice behaviour. A simple algorithm is proposed to estimate the parameters (customers’ preference) of the model by using historical sales data, product availability info and the market share. The proposed method is evaluated using different simulated datasets and the results are compared with three benchmark models that are used commonly in airline revenue management practice. The experiments show that proposed method outperforms the others in terms of execution time and accuracy. A 47.64% improvement is reported in root mean square error between simulated and estimated demand in contrast to the benchmark models.