Forecasting according to seasons

In both civil and airline agencies, flight pcassenger forecasting is important. The procedure assists in making sure that the organization is running with high levels of effectiveness. Adapting to seasonal swings and changes should be a priority for flight companies. In order to predict potential outcomes, including those that could affect profitability, forecasting has proven extremely important. The purpose of the study is to assess the significance of airline seasonal forecasting and the consequences of the industry's finite capacity for trips and flights.

Seasonal forecasting is crucial.

According to research by Bao, Xiong, and Hu (2012), is crucial for managing the income of these companies passenger airlines plays a pivotal role in managing the revenues of the organization. It does this by reducing the risks and uncertainties of the company by evaluating demands and the needs of the business. Also, seasonal forecasting of air passenger airline helps in making planning decisions in the air transportation networks. Seasonal forecasting helps the airline companies to have a schedule for renovating and maintaining the crafts for better service provision to the customer. Seasonal forecasting has been assisting the flight industry in determining when to give offs to the employees on either voluntary or compulsory basis, therefore, managing the sector efficiently. According to Bao et al., (2012), seasonal forecasting has been playing a significant role in determining the number of flights per day by deciding whether to increase or reduce based on the number of customers. Forecasting has been helping the passenger flight companies to come up with a trend that helps them in making the appropriate decisions and help in coming up with strategies for managing changes in the number of the passengers.

How reducing capacity affects the demand and supply for passengers looking for business travel tickets

Limiting capacity regarding the available flights has been affecting the supply and demand for individuals looking for tickets in diverse ways. With the aim of reducing costs and maintaining high profits, the airline companies tend to increase fare during the low seasons. High transportation cost has been reducing the likelihood of individuals to purchase the tickets. Ghobbar and Friend (2003) argue that reducing the number of flights have been creating changes to the travel timetables of the companies. This has been affecting the demand and supply of passengers. Nam and Schaefe (2005) state that changing the travel schedule creates greater challenges, especially when they are made close to the departing time, therefore, affecting the demand of customers. However, reducing flights despite an increase in fare has been increasing demand level for individuals purchasing tickets. The demand level is in most cases determined by combining the market size and the individuals likely to travel. Reducing the number of flights minimizes the market size while the number of customers travelling may not reduce. This creates traffic in the industry. Therefore, reducing the number of flights can either reduce, maintain or increase demand, therefore, affecting differently the demand for the airline services by the customers. Reducing the number of flights in most cases results to increase in the fare charged to the passengers. Supply increases with the increase in price, and therefore, supply market is likely to increase with the reduction in a number of flights.

Conclusion

Seasonal forecasting in the airline industry has been helping organizations to reduce costs, maintain profits and managing the firms effectively. Seasonal forecasting has therefore been effective in maintaining the success of flight companies. Reducing capacity has in most cases been resulting in increasing in fare therefore reducing demand. On the other side, supply increases with increase in price and thus, when the price goes up the supply is also likely to increase.

Reference

Bao, Y., Xiong, T., & Hu, Z. (2012). Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method. Discrete Dynamics in Nature and Society, 2012.

Ghobbar, A. A., & Friend, C. H. (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Computers & Operations Research, 30(14), 2097-2114.

Nam, K., & Schaefer, T. (2005). Forecasting international airline passenger traffic using neural networks. Logistics and Transportation Review, 31(3), 239.