Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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UNSUPERVISED LEARNING ANALYSIS FOR OPERATIONAL EFFICIENCY IN AIRLINE INDUSTRY

Authors

  • Olumide S. Adesina
  • Adedayo F. Adedotun
  • Dorcas M. Okewole
  • J. A. Adeyiga
  • Hilary I. Okagbue
  • Imaga F. Ogbu

Keywords:

airline industry, cross-validation, machine learning, operations, principal component analysis, stake holders, Nigeria

DOI:

https://doi.org/10.17654/0972361724034

Abstract

The airline industry in Nigeria is faced with various challenges most of which are centered on operations. A cross-sectional survey was conducted to determine the operational efficiencies of the aviation industry in Nigeria. A sample of one hundred and fifteen was obtained with airline stakeholders as the target participants. Principal component analysis (PCA) and principal component regression (PCR) were conducted using leave-one-out cross validation for the training set based on machine learning procedures. The study shows that there is a need to improve airline operations in Nigeria. This study recommends that stakeholders should diligently consider measures to enable the airlines to have better operations.

Received: September 12, 2023
Accepted: December 4, 2023

References

C. R. de Almeida, V. Costa and J. Abrantes, Airline Business Models and Tourism Sector, Strategic Business Models to Support Demand, Supply, and Destination Management in the Tourism and Hospitality Industry, IGI Global, Abingdon, UK, 2020, pp. 216-239.

International Air Transport Association, Strong airline profitability continues in 2018, Press Release No. 70, 2017.

Retrieved from https://www.iata.org/pressroom/pr/Pages/2017-12-05-01.aspx.

E. Cronrath, The Airline Profit Cycle: A System Analysis of Airline Industry Dynamics, London: Taylor and Francis, 2017.

https://doi.org/10.4324/9781315188720

G. Lohmann and C. Vianna, Air route suspension: The role of stakeholder engagement and aviation and non-aviation factors, Journal of Air Transport Management 53 (2016), 199-210.

E. A. Ejem, C. U. Ekeugo, D. N. Dike, O. Chukwu, C. C. Igboanusi and O. Erumaka, Analysis of determinants of airlines productivity in Nigeria domestic network, Global Journal of Engineering Science and Research Management 4(12) (2017), 7-13.

R. Kivits and S. Sawang, Communicative planning, The Dynamism of Stakeholder Engagement, Cham: Springer, 2021, pp. 19-27.

https://doi.org/10.1007/978-3-030-70428-5_3

S. Theodoridis and K. Koutroumbas, Pattern recognition and neural networks, G. Paliouras, V. Karkaletsis, C. D. Spyropoulos, (eds.), Machine Learning and Its Applications, Berlin: Springer, 2001, pp. 169-195.

A. Jain and D. Zongker, Feature selection: evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2) (1997), 153-158. https://doi.org/10.1109/34.5747975201

K. Pearson, On lines and planes of closest fit to systems of points in space, Philosophical Magazine Series 6, 2 (1901), 559-572.

H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24 (1933), 417-441, 498-520.

J. Shlens, A tutorial on principal component analysis, arXiv: 1404.1100.

N. Salem and S. Hussein, Data dimensional reduction and principal component analysis, Procedia Computer Science 163 (2019), 292-299.

https://doi.org/10.1016/j.procs.2019.12.111

S. Chahboun and M. Maaroufi, Principal component analysis and machine learning approaches for photovoltaic power prediction: a comparative study, Applied Sciences 11(17) (2021), 7943. https://doi.org/10.3390/app11177943

R. Babikian, S. P. Lukachko and I. A. Waitz, The historical fuel efficiency characteristics of regional aircraft from technological, operational, and cost perspectives, Journal of Air Transport Management 8(6) (2002), 389-400.

https://doi.org/10.1016/S0969-6997(02)00020-0

K. Chaharbaghi, The problematic of strategy: A way of seeing is also a way of not seeing, Management Decision 45(3) (2007), 327-339.

https://doi.org/10.1108/00251740710744981.

S. Dozic and M. Kalic, Three-stage airline fleet planning model, Journal of Air Transport Management 46 (2015), 30-39.

https://doi.org/10.1016/j.jairtraman.2015.03.011.

K. Choi, Multi-period efficiency and productivity changes in US domestic airlines, Journal of Air Transport Management 59 (2017), 18-25.

http://doi.org/10.1016/j.jairtraman.2016.11.007.

J. Kumar, Case analysis I: How Icarus paradox Doomed Kingfisher Airlines, Vision: The Journal of Business Perspective 24(1) (2020), 125-127.

https://doi.org/10.1177/0972262920903901

A. T. Kottas and M. A. Madas, Comparative efficiency analysis of major international airlines using Data Envelopment Analysis: Exploring effects of alliance membership and other operational efficiency determinants, Journal of Air Transport Management 70 (2018), 1-17.

https://doi.org/10.1016/j.jairtraman.2018.04.014

J. Claussen, C. Essling and C. Peukert, Demand variation, strategic flexibility and market entry: Evidence from the U.S., airline industry, Strategic Management Journal 39(11) (2018), 2877-2898. https://doi.org/10.1002/smj.2940

F. Azadian and B. Vasigh, The blaring lines between full-service network carriers and low-cost carriers: A financial perspective on business model convergence, Transport Policy 75 (2019), 19-26. https://doi.org/10.1016/j.tranpol.2018.12.012

H. Martin, Demand for Air Travel is Strong, but Higher Fuel Costs are Cutting into Airline Profits, Los Angeles, CA: Tribune Interactive, LLC, 2018.

T. Hastie, H. Tibshirani and J. H. Friedman, eds., The Elements of Statistical Learning, Springer, New York, 2001.

G. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013, pp. 180-183.

K. Liland, B. Mevik and R. Wehrens, pls: Partial Least Squares and Principal Component Regression_. R package version 2.8-2, 2023.

https://CRAN.R-project.org/package=pls.

B. Mevik and H. R. Cederkvist, Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR), Journal of Chemometrics 18(9) (2004), 422-229.

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Published

05-04-2024

Issue

Vol. 91 No. 5 (2024)

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Articles

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How to Cite

UNSUPERVISED LEARNING ANALYSIS FOR OPERATIONAL EFFICIENCY IN AIRLINE INDUSTRY. (2024). Advances and Applications in Statistics , 91(5), 635-655. https://doi.org/10.17654/0972361724034

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