In the present scenario, it has become difficult for the business firms to survive and maintain leading position in the industry. On every condition, they have to manage their revenue up to a certain level. In this regard, firms can use varied forecast methods and by considering obtained results prediction can be made for the future time period. On the basis of results of the methods like time series analysis one can make better business decisions and can increase revenue or maintain stability in the same up to a certain level (Jerath, Netessine and Veeraraghavan, 2010). Thus, there is importance of the forecast methods for the firms which are operating in the aviation industry of UK.
Airline industry is one of the fastest growing industries in the UK. There is tough competition among the business firms and due to this reason it is significant to make prudent decisions with changes that come in the business environment. Most airline firms are currently focusing on the revenue management of their business. In order to manage revenue business, firms are focusing on various forecast methods. One of the most important forecast methods is time series analysis. By using time series analysis method, predictions are made in a proper manner. Ryanair needs to forecast its investment cash flows because by doing so it can identify the amount of revenue which can be earned on the investment that will be made on the specific project. Before making investment it is important to make prediction about the cash flows that will be earned because investment is made in the specific project (Vulcano, Van Ryzin and Chaar, 2010). If it is identified on the basis results that are obtained by applying specific forecast method on the data set that low amount of revenue can be earned in the business then Ryanair will abstain from making investment and relevant amount will be spend elsewhere in the business to maximize the profitability. In this way, forecast method will help Ryanair in managing revenue in its business. It can be said that there is a great importance of the forecast method for the business firms. Demand forecasting is done by Ryanair by using forecast method like time series analysis. The results that are obtained by using time series analysis help in ascertaining the number of customers which probably can be made by the firm in the current fiscal year. Ryanair make its entire efforts to achieve target which is reflected by the output of time series analysis (Meissner and Strauss, 2012). In this way, manage its revenue and ensure consistent elevation in the same. In order to ensure that all expense will be made in the specific range budget is prepared by the Ryanair. The results which are produced by the time series method are used to make projections in the budget. The standards that are determined in the budget are used to curb elevation in the expenses. This inclines the revenue upward in the business. From this it is clear that forecast methods such as time series analysis help the firm in managing its business revenue.
There are varied sort of plans which are prepared by the Ryanair in its business namely tactical and strategic planning. Tactical planning supports the implementation of the strategic planning at the workplace. In case of change in the business environment, it is the tactical plan which ensures that in every condition strategic plan will be implemented in the business. Strategic plan is one that is prepared to ascertain that specific objective will be achieved in the business. It can be said that there is a great importance of strategic and tactical plan. The strategic plan is successfully prepared by using the results of time series analysis (Perakis and Roels, 2010). In the time series analysis method, a chart is generated in the software. In these chart, different patterns related to the specific variable like cyclical, seasonal and decomposition are revealed clearly. Thus, trend in respect to specific variable in different conditions are identified by using time series analysis method. By using mentioned technique, past behavior of the variable in different situations is tracked and business strategy in respect to managing and increasing revenue in the business is prepared. Same method is used to prepare tactical plan. It can be said that forecast method is highly important in respect to revenue management.
Forecasting in respect to revenue management done by the Ryanair may be qualitative and quantitative in nature. There is a vast difference between both forecast methods. Quantitative forecasting is one in which data set that is related to the firm or industry is taken in to consideration to forecast future trends. In these methods, it is assumed that past repeat itself. It states that whatever happens in the past it will again occur in future time period. Thus, by using quantitative forecast method, firm predict whether in upcoming time period number of customers in the business will increase or decrease. According to the trend, business strategy is formulated to improve firm performance in comparison to previous year (Wright, Groenevelt and Shumsky, 2010). In this way, growth in revenue is managed. Other method of forecast that can be used to make revenue management better is qualitative research method. Under this method market research and historical analogy is included. Under market research, firm collects t data from people and tries to identify their perception in respect to the organization. By formulating suitable plan which is related to the firm, weak point internally is made stronger than before which help in serving the customers in a better way. This elevates revenue in the business and in this way revenue management is done by the business firm.
There are some characteristics of quantitative and qualitative methods. In the time series analysis method which is part of quantitative forecasting, values are compared with past year numbers in sequence which reveal the past year trends and the same is considered to make prediction for future time period. Other characteristic of the quantitative method is the assumption that the trends that were in respect to revenue which was earned by the Ryanair in its business will remain continue in future time period. Contrary to this, in case of qualitative method such kind of assumptions are not made in the quantitative methods (Wang, 2012). In case of relevant approach, current time period is evaluated and on the basis of judgment of the changes that may be observed in the business environment decisions must be made in respect to revenue management. Hence, it can be said that characteristics of the quantitative and qualitative research method vary from each other.
Time series can be used for the revenue management only for short term. This is because in long term internal policy of the Ryanair gets changed. Moreover, pressure from the external business environment may also increase above the expectation. Hence, due to this reason the prediction that is made in respect to long term period is not highly reliable if time series method is applied on the data that is related to the long time period. It is very hard for the management team of Ryanair to make accurate prediction about changes which have been observed in the business environment (Thompson, 2010). It is not necessary that whatever happens in the business environment in past years (long duration) will again occur in the future time period. Hence, Ryanair use time series analysis method in respect to revenue management only for short duration.
There are varied methods by using which time series method can be applied in the past year data. These two methods are adaptive and decomposed time series models. In case of the adaptive time series moving average and exponential smoothing methods are used. It is very easy to apply both methods as anyone can apply both on the data set. Other method of time series is decomposed model (Huang and Chang, 2010). In this model, varied elements such as trend and seasonality are separated from each other. Thus, varied trends which were related to the variable in respect to past time period are revealed separately by the chart that is produced under the decomposed time series model. Better forecast in respect to revenue management is made by the decomposed time series models. Hence, managers of the most business firm use mentioned model to make short term revenue management decisions for the business firm.
Time series is the one of the most important tool that is adopted the firm to make prediction (Revenue management, 2016). Decisions for the small time period in respect to revenue management can be easily and reliably made by the business firm by using time series method. Thus, it is clear that there is a great use of the time series method for the business firm in respect to making decision about the revenue management.
Huang, K. and Chang, K.C., 2010. An approximate algorithm for the two-dimensional air cargo revenue management problem. Transportation Research Part E: Logistics and Transportation Review. 46(3). pp.426-435.
Jerath, K., Netessine, S. and Veeraraghavan, S.K., 2010. Revenue management with strategic customers: Last-minute selling and opaque selling. Management Science. 56(3). pp.430-448.
Meissner, J. and Strauss, A., 2012. Network revenue management with inventory-sensitive bid prices and customer choice. European Journal of Operational Research. 216(2). pp.459-468.
Perakis, G. and Roels, G., 2010. Robust controls for network revenue management. Manufacturing & Service Operations Management. 12(1). pp.56-76.
Thompson, G.M., 2010. Restaurant profitability management the evolution of restaurant revenue management. Cornell Hospitality Quarterly. 51(3). pp.308-322.
Vulcano, G., Van Ryzin, G. and Chaar, W., 2010. Om practice-choice-based revenue management: An empirical study of estimation and optimization. Manufacturing & Service Operations Management. 12(3). pp.371-392.
Wang, X.L., 2012. Relationship or revenue: Potential management conflicts between customer relationship management and hotel revenue management. International Journal of Hospitality Management. 31(3). pp.864-874.
Wright, C.P., Groenevelt, H. and Shumsky, R.A., 2010. Dynamic revenue management in airline alliances. Transportation Science. 44(1), pp.15-37.
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