CHAPTER 1 INTRODUCTION 1

CHAPTER 1

INTRODUCTION

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1.1 Background of Study

Time series is a series of measurement over time, usually obtained at the same manner spaced intervals. Meanwhile, time series analysis is a statistical technique that deals with time series data, or trend analysis (Brockwell and Davis, 2001). Furthermore, time series forecasting is a techniques for the prediction of events through a sequence of time. By analysing the time series, it is used to describe the fundamental structure and the phenomenon as represent by the sequence of observations in the series. Forecasting can be used in variety of studies such as airline industry. Nowadays, airlines has become one of the necessity in people lives, it also helps to improve the national economic and tourism (Min, Kung and Liu, 2010). Moreover, forecasting can be considered as one of the tool for a better airline management and planning (Ming et al, 2014). According to Aderamo (2010), any airline organisations need to have estimation of expected future demands in order to improve their airline service.
Forecasting has many benefit towards the development of airline and it is depends on number of passenger on that time period (Andreoni & Postorino, 2006). Despite experienced a challenging moments such as political issues, economic issues and many more, the airline industry demand continuously rising. Even though it is keep rising, it has slightly give an impacted towards airline markets. In response to this issue, airlines are constantly improve their service structures in order to eliminate lose and to have continuous profit. Airlines’ passengers may have interest in the demand modelling and simulation, normally when there is a competition among airlines’ market and they need to choose which service they should take (Andreoni & Postorino, 2006).
In Malaysia, the list of airlines’ company are AirAsia, AirAsia X, Firefly, Malindo, Malaysian Airlines and others. The passenger traffic growth in 2017 is expected to overtake growth rates in 2016. In airline companies as well as for all types of companies, demand forecasting is a very significance issue. The success of the managers and companies are much related with suitable strategies which are composed with accurate future forecast. Demand forecasting for available seats in airlines is important to maximize the expected revenue by setting the appropriate fare levels for those seats. The accuracy of the forecast is the most significant tool of the revenue management systems, (MAVCOM).
In this study, we focus on Air Asia airlines as our case study. We would like to forecast the number of Air Asia passengers in Malaysia. In airline industry, it consists of two types of operations namely Full cost carriers (FCC) and low cost carriers (LCC) and Air Asia is one of the low cost carriers type. There are also several LCC such as Air Asia X, Firefly, Berjaya Air, and Sabah Air Aviation (David, 2011). Air Asia is the first low cost carriers company in Malaysia. Moreover, Air Asia also known as the largest and the best low fare in Asia. Air Asia continuously expand with their efficient services, passion toward business and has made a revolution in airline industry. Thus, more people are choosing Air Asia as their choice of airlines.

1.2 Problem Statement

As we know, the airline transport has become a demand nowadays. Based on the previous study (Asrah et al 2018) is a case study about airlines in Malaysia which is AirAsia and Malaysian Airlines. In this study, they compare the distributional behaviour data from the number of Air Asia and Malaysian Airline passenger. As MAS passenger airlines data set are not govern by geometric Brownian motion (GBM), they forecast the number of MAS airline passenger by using Box Jenkins method. Asrah et al, 2013 they forecast the number of Air Asia passenger by using geometric Brownian motion (GBM). As for this research, we use Box Jenkins method to forecast the number of airline passenger of Air Asia. By using forecasting method, it can help in terms of upgrading and improving an airline sector.

1.3 Objective

i. To study the behaviour of the Air Asia passenger data
ii. To find the best model for Air Asia passenger in Malaysia by using Box Jenkins method
iii. To forecast the number of Air Asia passenger by using the best model

1.4 Scope of Study

In this study, the data have been obtained from Malaysia Airport Holdings Berhad (MAHB). The data obtained were about the total number of passengers that arrived to Kuala Lumpur International Airport (KLIA). These set of time series data are for the number of Air Asia passengers from January 2009 until August 2012.

1.5 Significant of Study

This study has contribute to forecast the number of passenger in Air Asia airline. Besides that, this study also contribute to compare method used to forecast the number of airline passenger which is Air Asia as from previous study they used geometric Brownian motion (GBM) to forecast. This study also helps to enhance better understanding and knowledge about airline industry in Malaysia. This study also will helps the airline industry with the result obtain for them to set appropriate policy for a better management. Moreover, this study also give an overview about the relevant literature in order to explain and develop a better understanding about the airline industry and also the method used. Even though the objective of this study is to forecast the number of Air Asia passenger by using the best model, it is also give a knowledge about other method that has been used by the previous researcher.