Abstract
In this paper, we focus on improving the customer’s flow by harnessing the power of analytics and focusing on the arrival process of passengers at one of the busiest airports in Asia. As there is a recent growth in travelers, the airport is undergoing expansion and is thus under tremendous pressure to utilise its resources effectively and efficiently. We first leverage the historical data of the arrival flights, passenger load, and on-time performance flag indicator in order to predict the arriving passenger’ load for the immigration counters and taxi queues. We then build a decision support system using simulation to estimate the optimal number of immigration counter requirements so as to minimize the waiting time at the queues. This is also done to predict the number of taxis required to meet the service level agreement and to ensure the seamless flow of customers at various touch points to improve customer’ satisfaction. The tool developed has benefited the manager in his daily operations, and advanced his decision making process supported by data rather than personal experience or “gut” feeling.
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Ma, N.L., Choy, M. (2019). Improving Customer’s Flow Through Data Analytics. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_25
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DOI: https://doi.org/10.1007/978-3-030-22999-3_25
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